Intelligent management method and system for node units

Abstract

The embodiment of the invention relates to an intelligent management method and system for a node unit, and the method comprises the steps: obtaining exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of an exploration region, and the position information of a detection point; based on the number of the intelligent robots, the performance parameters and the topographic data of the exploration area, a clustering algorithm is adopted to divide the position of the detection point into a plurality of management areas, one intelligent robot is distributed to each management area, and in the division process of the clustering algorithm, the topographic weighted distance and the load rate of the intelligent robot serve as joint optimization factors; the task instruction is issued to each intelligent robot, the task instruction comprises at least one of a node unit arrangement instruction, a node unit recovery instruction and a process management instruction used for data recovery, normal form transformation from extensive manual division to automatic and fine distribution is achieved, and the overall operation efficiency of node unit management is improved.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to an intelligent management method and system for node units. Background Technology

[0002] As seismic exploration operations continue to extend into complex surface areas, nodal units, with their autonomous acquisition and cableless transmission capabilities, are playing an increasingly important role in seismic acquisition projects. However, in stark contrast, the field production management model for nodal units remains traditional, severely hindering the improvement of exploration efficiency and quality.

[0003] Currently, in onshore seismic exploration operations, the deployment, retrieval, and daily management of nodal units rely entirely on manual labor. Workers must carry heavy nodal equipment and perform high-intensity work in complex field environments. This operational mode has several significant drawbacks: First, manual operation is extremely inefficient, with a limited daily deployment capacity per person, making it difficult to meet the demands of large-scale, high-efficiency exploration; second, the quality of manual deployment is inconsistent, easily leading to problems such as nodal placement deviations and poor coupling with the ground surface, directly affecting the quality of seismic data acquisition.

[0004] Even with attempts to introduce automation into production management, the core task planning and allocation still heavily relies on the subjective experience of managers. This extensive management approach based on human judgment lacks objective and unified optimization standards, making it difficult to guarantee the rationality and scientific nature of task allocation when facing large-scale construction projects and complex and diverse surface environments. The direct consequence is that the cluster advantages of automated equipment cannot be fully utilized, overall operational efficiency is only slightly improved, resource utilization is low, and the increasing demands for operational efficiency and data quality from large-scale, high-density seismic exploration cannot be met.

[0005] Therefore, under the current technological conditions, the management of node units mainly faces the following technical bottlenecks: the planning and allocation of work tasks are crude and the level of intelligence is low, resulting in low exploration efficiency. Summary of the Invention

[0006] This invention provides an intelligent management method and system for node units to solve the technical problem of low exploration efficiency caused by the extensive planning and allocation of work tasks in seismic exploration node units and the low level of intelligence.

[0007] In a first aspect, the present invention provides an intelligent management method for node units, applied to a central control system, the method comprising: The exploration task parameters are obtained, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the receiver point locations into multiple management areas, and an intelligent robot is assigned to each management area. In the process of dividing the area, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors. Task instructions are issued to each of the intelligent robots, and the task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval.

[0008] In some embodiments, the clustering algorithm is implemented through the following steps: The cluster number K is determined based on the number of intelligent robots; Based on the node carrying capacity of each intelligent robot, all detector points are initially allocated to K clusters in proportion, and the initial cluster center of each cluster is determined. The following steps are executed iteratively until the preset convergence condition is met, and the final management region is output: Calculate the weighted distance between each receiver point and the current cluster center of each cluster. The weighted distance is a weighted sum of the terrain-weighted distance and the load rate. The terrain-weighted distance is determined based on the terrain data of the exploration area. The load rate is the ratio of the number of currently assigned receiver points of the corresponding cluster to the node carrying capacity of the intelligent robot responsible for the corresponding cluster. Based on the principle of minimum weighted distance, the detector points are reassigned to the cluster with the smallest weighted distance; Based on the redistribution results, the current cluster center of each cluster is recalculated.

[0009] In some embodiments, the instructions for deploying node units and the instructions for retrieving node units each include the management area they are responsible for, the number of node units being transported, the target station number, and the route planning. The process management instructions include quality control data retrieval instructions and seismic data retrieval instructions; Both the quality control data retrieval instruction and the seismic data retrieval instruction include a time range for data retrieval and may optionally include a specified intelligent robot identifier and node unit serial number to achieve full or partial data retrieval, respectively.

[0010] In some embodiments, the method further includes: Receive satellite timing signal quality information from node units reported by intelligent robots; If the timing signal quality information indicates that the signal difference is due to the deployment location, a re-deployment instruction is issued to the corresponding intelligent robot to deploy the node unit to a new location where the timing signal meets the requirements; if the timing signal quality information indicates that the signal difference is due to terrain, the corresponding intelligent robot is instructed to move to a relay location to serve as a relay station for the satellite timing signal.

[0011] Secondly, the present invention provides an intelligent management method for node units, applied to intelligent robots, the method comprising: Receive task instructions issued by the central control system, the task instructions including at least one of node unit deployment instructions, node unit recovery instructions, and process management instructions for data recovery; Execute the node unit operation corresponding to the task instruction; Among them, the intelligent robot that receives the task instruction is responsible for the corresponding management area. The management area is one of the multiple management areas divided by the central control system by acquiring exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the receiver point locations. In the process of dividing, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robots as joint optimization factors.

[0012] In some embodiments, when the task instruction is a node unit deployment instruction or a node unit retrieval instruction, the execution of the node unit operation corresponding to the task instruction includes: According to the travel route plan in the deployment or retrieval instructions for node units, move to the target station number; Perform the deployment or recycling of node units.

[0013] In some embodiments, the process management instructions include quality control data retrieval instructions and seismic data retrieval instructions, wherein the quality control data includes deployment and daily inspection quality control data and operational status quality control data; when the task instruction is a quality control data retrieval instruction, the execution of the node unit operation corresponding to the task instruction includes: Collect operational status quality control data from the managed node units, and send all or part of the operational status quality control data to the central control system according to the quality control data retrieval instruction; When the task instruction is a seismic data retrieval instruction, the execution of the node unit operation corresponding to the task instruction includes: Seismic data is collected from the managed node units, and all or part of the seismic data is sent to the central control system in accordance with the seismic data retrieval instruction.

[0014] In some embodiments, the method further includes at least one of the following: The deployment and daily inspection quality control data of the node units are automatically sent to the central control system. When the quality control data of the operating status of the node unit exceeds the corresponding first preset threshold, a warning message is automatically sent to the central control system. The system monitors the quality of the communication link between the managed node units and the central control system in real time. When the quality of the communication link is lower than a second preset threshold, the system autonomously moves to a location with better signal. The system monitors the working status of the built-in battery of the managed node unit in real time, and when it determines that the node unit has a risk of spontaneous combustion based on the working status of the built-in battery, it controls the built-in robotic arm to perform emergency handling operations, including covering the node unit with soil. When recovering a node unit, the temperature and / or shell integrity of the node unit are detected, and if the risk value determined based on the detection results exceeds a third preset threshold, the node unit is placed in a dedicated protective container for storage and transportation.

[0015] Thirdly, the present invention provides an intelligent management method for node units, comprising: The central control system acquires exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the geophone points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, the central control system uses a clustering algorithm to divide the geophone point locations into multiple management areas and assigns an intelligent robot to each management area. In the process of dividing the area, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors. The central control system issues task instructions to each of the intelligent robots. The task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval. The intelligent robot executes node unit operations corresponding to the task instructions.

[0016] Fourthly, the present invention provides an intelligent management system for node units, including a central control system and an intelligent robot; The central control system is used to execute the intelligent management method of the node unit as described in any of the first aspects, and the intelligent robot is used to execute the intelligent management method of the node unit as described in any of the second aspects.

[0017] The intelligent management method and system for node units provided by this invention first systematically acquires exploration task parameters, laying a comprehensive and objective data foundation for decision-making and replacing subjective human experience. Next, based on these parameters, the system automatically divides the management area using a clustering algorithm. This algorithm quantifies the impact of terrain complexity on operational costs (e.g., slope, surface cover) by introducing terrain-weighted distance, while dynamically balancing the actual workload of each robot through load rate, ensuring both efficiency and fairness in allocation. Finally, the system issues precise task instructions (including management area, payload quantity, target station list, and route planning) to each robot, enabling the entire robot cluster to work collaboratively in an orderly and efficient manner. In summary, this invention achieves a paradigm shift from extensive manual division to automated and refined allocation. This not only significantly improves the scientific and intelligent level of task planning itself but also fundamentally enhances the overall operational efficiency of node unit management by optimizing resource allocation and action paths, overcoming key obstacles restricting the improvement of exploration efficiency. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present invention; Figure 2 A flowchart illustrating an intelligent management method for node units provided in an embodiment of the present invention; Figure 3 A flowchart illustrating another intelligent management method for node units provided in an embodiment of the present invention; Figure 4 A flowchart illustrating another intelligent management method for node units provided in an embodiment of the present invention; Figure 5 A schematic diagram of the structure of a central control system provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an intelligent robot provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present invention, such as... Figure 1 As shown, this application scenario includes a central control system (also known as a central command system), multiple intelligent robots (also known as intelligent management devices), and a large number of node units. The intelligent robots communicate with the central control system via 4G / 5G or satellite networks and manage the node units within their jurisdiction via short-range wireless communication (such as LoRa and ZigBee).

[0023] The intelligent robot includes the following main modules: Task instruction receiving module: Its core is the communication unit, which is responsible for establishing a stable remote communication link (such as 4G / 5G, satellite communication) with the central control system, and receiving and parsing task instructions including management area, number of vehicles, target station list and route.

[0024] Node Unit Management Module: Responsible for real-time management of information such as the working status of node units, quality control (QC) data, and seismic data collected on-site.

[0025] Automatic Navigation Module: This module integrates the autonomous decision-making capabilities of the navigation and positioning, vision, and control modules. Based on the issued route planning, it utilizes a high-precision positioning system and vision sensors to achieve autonomous movement. Through intelligent environmental recognition technology, it perceives the surrounding environment in real time, dynamically avoids obstacles, and ultimately accurately reaches the target station.

[0026] Control node module: This module is the core of the robot's physical operations. It integrates the functions of node unit deployment and retrieval unit, data charging and download unit, and transportation unit.

[0027] The node unit deployment and retrieval unit includes mechanical components such as drilling, node switching, coupling detection (e.g., capturing images of the burial status and measuring tilt), deployment, and retrieval, which complete the precise placement and retrieval of the node at the receiver point.

[0028] Data charging and downloading unit: responsible for charging the carried node units, downloading data, and storing it locally.

[0029] Transport Unit: Based on the physical characteristics of the node units, the warehouse structure is scientifically designed to rationally plan and safely transport a specified number of node units.

[0030] In addition, there is a control module, which acts as the "brain" of the robot. It runs through the operation of all the above modules, coordinates the work of each module, and makes real-time decisions based on environmental perception information in autonomous control mode.

[0031] It should be noted that the central control system and the intelligent robot can execute the following embodiments separately, or they can work together to execute the following embodiments.

[0032] Figure 2 This is a flowchart illustrating an intelligent management method for node units provided in an embodiment of the present invention, applicable to, for example... Figure 1 The central control system shown. (As shown) Figure 2 As shown, the method includes: Step S201: Obtain exploration task parameters, which include the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points.

[0033] Specifically, the central control system first needs to collect and define the key parameters of this exploration mission. These parameters include, but are not limited to: the total number of intelligent robots available for scheduling, the performance indicators of each robot (such as maximum node carrying capacity, endurance, etc.), detailed terrain data of the work area (such as digital elevation models to reflect surface undulations, slopes, etc.), and the theoretical location information of all receiver points that need to be deployed. This step aims to transform the physical world's mission requirements and environmental constraints into data that the system can recognize and process, providing a basis for subsequent intelligent decision-making.

[0034] Step S202: Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the location of the receiver point into multiple management areas, and an intelligent robot is assigned to each management area. In the process of division, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors.

[0035] Specifically, after acquiring all task parameters, the central control system does not perform simple or manual area division. Instead, it employs an improved clustering algorithm. This algorithm, when dividing receiver points, does not only consider geographical distance but also uses two key factors—"terrain-weighted distance" (the actual distance taking into account terrain travel costs) and "robot load rate" (the ratio of the robot's currently assigned tasks to its maximum carrying capacity)—as optimization objectives. In this way, the algorithm automatically generates a partitioning scheme that ensures each robot's managed area is not only spatially compact to reduce movement costs but also that the task load among robots is balanced, thereby significantly improving overall operational efficiency and resource utilization.

[0036] In some embodiments, step S202 includes: determining the number of clusters K based on the number of intelligent robots; initially allocating all detector points to K clusters proportionally based on the node carrying capacity of each intelligent robot, and determining the initial cluster center of each cluster; iteratively executing the following steps until a preset convergence condition is met, and outputting the final management area: calculating the weighted distance between each detector point and the current cluster center of each cluster, wherein the weighted distance is a weighted sum of terrain weighted distance and load rate, wherein the terrain weighted distance is determined based on the terrain data of the exploration area, and the load rate is the ratio of the number of currently allocated detector points of the corresponding cluster to the node carrying capacity of the intelligent robot responsible for the corresponding cluster; reallocating the detector points to the cluster with the smallest weighted distance according to the minimum weighted distance principle; and recalculating the current cluster center of each cluster based on the reallocation result.

[0037] The specific implementation process of the clustering algorithm provided in this embodiment is as follows: First, the task framework is determined and intelligent initialization is performed. Based on the total number of intelligent robots to be executed, the number of regions to be divided, i.e., the number of clusters K, is determined, with each cluster corresponding to one robot.

[0038] Next, considering the actual working capacity of each robot, i.e., its node carrying capacity, we calculate the proportion of each robot's capacity to the total capacity, and then roughly allocate all the detector points to each robot according to this proportion, forming a preliminary, coarse task division. Within this batch of detector points assigned to each robot, we calculate their geographical center and use this center point as the initial core of the robot's corresponding management area, i.e., the initial cluster center. The purpose of this step is to provide a wise and balanced starting point for subsequent optimization, rather than completely random one, thereby accelerating the entire calculation process.

[0039] Secondly, the region partitioning is dynamically optimized through iterative calculations. This involves repeatedly performing the following three sub-steps until a preset convergence condition is met, such as the cluster center of each region remaining stable or the preset number of iterations is reached: Calculate the weighted distance: For each detector point, calculate a "comprehensive cost" from it to the current cluster center of all regions, i.e., the weighted distance, which consists of two parts: the first part is the terrain-weighted distance, which assesses the actual travel difficulty cost based on actual terrain data (such as slope and surface conditions), as shown in formula (1); the second part is the load rate, which reflects the current busyness of the robot responsible for the area, and is calculated by "the amount of tasks assigned to the area divided by the robot's maximum carrying capacity", as shown in formula (2); add these two costs according to preset weights to obtain a weighted distance, as shown in formula (3): (1) (2) (3) in, Indicates terrain-weighted distance. d Indicates the actual physical distance. The topographic complexity coefficient is determined by the slope and surface conditions in the topographic data of the exploration area. Indicates load rate, This indicates the number of detectors currently allocated to the corresponding cluster. This indicates the maximum node capacity of the intelligent robot responsible for this cluster; Indicates the weighted distance. , This indicates the preset weighting coefficients.

[0040] Reallocating receiver points: Based on the overall cost calculated in the previous step, the algorithm follows the "minimum cost principle," reallocating each receiver point to the region with the lowest overall cost. This means that where a task point ultimately ends up depends not only on which region's core it's closer to, but also on whether the robots in that region have "spare capacity." If a robot's tasks are nearly saturated, even if a point is very close to its region's core, it might be assigned to another relatively idle robot, albeit slightly further away, but with a better overall cost.

[0041] Update the regional cluster center: After all the geophones have been reassigned, the composition of each administrative region has changed. Therefore, the geographical core (cluster center) of the region is recalculated and updated based on the new locations of all existing geophones in the region, in preparation for the next iteration.

[0042] Finally, the final solution is output. When the above iterative process results in no significant changes in the core locations of all regions or reaches the maximum number of iterations, the clustering algorithm automatically stops and outputs the final optimized management area division result. This result is a task allocation scheme that achieves the best balance between "geographical compactness," "terrain cost," and "load balancing for each robot," and can be directly assigned to each intelligent robot for execution.

[0043] Step S203: Issue task instructions to each of the intelligent robots. The task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval.

[0044] Specifically, based on the optimal partitioning scheme calculated in step S202, the central control system sends task instructions to each intelligent robot, enabling the intelligent robot to perform corresponding operations on the node units within its managed area. These task instructions include instructions for deploying node units, retrieving node units, and process management instructions. The process management instructions are used for retrieving data such as quality control and seismic data.

[0045] In some embodiments, the node unit deployment instruction and the node unit retrieval instruction both include the management area they are responsible for, the number of node units being transported, the target station number, and the route planning; the process management instruction includes the quality control data retrieval instruction and the seismic data retrieval instruction; the quality control data retrieval instruction and the seismic data retrieval instruction both include the time range for data retrieval, and may optionally include a specified intelligent robot identifier and node unit serial number, so as to achieve full data retrieval or partial data retrieval respectively.

[0046] Specifically, both the node deployment and node retrieval instructions specify the boundaries of the management area the robot is responsible for, the number of node units to be carried or retrieved, all target station numbers to be visited (in list form), and the optimal travel route planned by the system for it.

[0047] When the intelligent robot receives the instruction to deploy node units, it will perform the following operations in sequence: First, based on the planned route in the instruction, it will autonomously move to the target station using the automatic navigation module; after arriving precisely, the node control module will be activated, and the drilling component will drill holes at the designated locations for burying the nodes; next, the deployment component will remove the node unit from the transport compartment and precisely place it in the hole, and the node switch component will activate the node unit to put it into working condition; after completing the deployment task at this point, the robot will automatically prepare to move to the next target station to continue the deployment operation.

[0048] When the intelligent robot receives the instruction to recycle the node unit, its operation process is as follows: The robot also navigates to the location of the station where the node to be recycled is located according to the planned route; then, the recycling component performs the operation to safely remove the node unit from the ground and store it in the transport compartment. At the same time, the node switch component will shut down the node to end its work; after the recycling task is completed here, the robot is ready to move to the next target station and continue the node recycling work.

[0049] Process management instructions are used to manage and retrieve various types of data after node unit deployment, including quality control data retrieval instructions and seismic data retrieval instructions. Both types of instructions clearly specify the time range for the data to be retrieved. More importantly, the instructions can selectively include specific target parameters, such as the unique identifier of a smart robot or the serial number of a specific node unit. When the instruction contains these specific identifiers, the system performs "partial data retrieval," that is, precisely retrieving data from the specified device within a specific time period; conversely, if the instruction only includes a time range without specifying the specific device, the system performs "full data retrieval," that is, retrieving the target data from all relevant devices within that time period.

[0050] Preferably, the quality control data includes two categories: deployment and daily inspection quality control data, and operational status quality control data. The deployment and daily inspection quality control data is primarily used to monitor the physical deployment quality of the node units, including initial deployment data generated during node deployment (such as the unique serial number of the node unit, associated SPS line number and station number, burial status image, and deployment tilt), as well as daily inspection data after deployment. The operational status quality control data is primarily used to monitor the working status of the node units, such as real-time battery power, storage capacity, satellite status, and acquisition status.

[0051] Furthermore, in the deployment and daily inspection quality control data, the deployment tilt is a key indicator. After receiving this data, the central control system compares it with a preset safety threshold and automatically performs corresponding operations: if the tilt exceeds the safety threshold, the system immediately generates and issues a correction command, ordering the intelligent robot responsible for that node to return to the site to perform adjustments such as re-laying or straightening, to ensure data acquisition quality; if the tilt does not exceed the safety threshold, the system automatically determines that the node deployment is qualified and confirms that it has entered normal working status.

[0052] Preferably, after receiving quality control data, the central control system uses the data to construct and update a digital twin model of the exploration area. In this model, the real-time location of each intelligent robot, the exact station number and status (such as whether the inclination is acceptable) of each node unit it manages are presented intuitively and updated in real time. This constitutes a dynamic monitoring system covering the entire work area, providing core data support for achieving refined management.

[0053] In some embodiments, the method further includes: receiving satellite timing signal quality information of node units reported by intelligent robots; if the timing signal quality information indicates that the signal difference is caused by deployment factors, issuing a redeployment instruction to the corresponding intelligent robot, the redeployment instruction including new location information, to deploy the node unit to a new location where the timing signal meets the requirements; if the timing signal quality information indicates that the signal difference is caused by terrain, instructing the corresponding intelligent robot to act as a relay station for satellite timing signals.

[0054] Specifically, once a node unit begins operation, the quality of its satellite timing signal is crucial. The central control system continuously receives information from the intelligent robot regarding the timing signal status of the nodes it manages. When the system determines that poor signal quality is due to an undesirable current deployment location of the node (e.g., being obstructed by trees or rocks), it immediately issues a repositioning instruction to the intelligent robot responsible for that node, requiring it to move the node within a specific range and reposition it to a new location with an open environment and stable satellite signal reception.

[0055] However, if system analysis reveals that the signal problem does not originate from the node's own location, but rather from macroscopic terrain factors (such as the entire area being situated in a narrow valley), making it impossible for any location within the area to directly receive satellite signals, the system will activate an alternative solution. The system will instruct the intelligent robot to move to a relay location where it can receive a good satellite signal, acting as a temporary signal relay station. In this way, after receiving satellite timing information, the robot forwards it to node units within the area that cannot directly receive time signals, thereby ensuring that all nodes can indirectly synchronize their operating clocks and guaranteeing the normal operation of seismic data acquisition.

[0056] The intelligent management method for node units provided in this embodiment first systematically acquires exploration task parameters, laying a comprehensive and objective data foundation for decision-making and replacing subjective human experience. Next, based on these parameters, the system automatically divides the management area using a clustering algorithm. This algorithm quantifies the impact of terrain complexity on operational costs (e.g., slope, surface cover) by introducing terrain-weighted distance, while dynamically balancing the actual workload of each robot through load rate, ensuring both efficiency and fairness in allocation. Finally, the system issues precise task instructions (including management area, payload quantity, target station list, and route planning) to each robot, enabling the entire robot cluster to work collaboratively in an orderly and efficient manner. In summary, this invention achieves a paradigm shift from extensive manual division to automated and refined allocation. This not only significantly improves the scientific and intelligent level of task planning itself but also fundamentally enhances the overall operational efficiency of node unit management by optimizing resource allocation and action paths, overcoming key obstacles restricting the improvement of exploration efficiency.

[0057] Figure 3 A flowchart illustrating another intelligent management method for node units provided in an embodiment of the present invention, applied to, for example... Figure 1 The intelligent robot shown, such as Figure 3 As shown, the method includes: Step S301: Receive task instructions issued by the central control system. The task instructions include at least one of the following: node unit deployment instructions, node unit recovery instructions, and process management instructions for data recovery.

[0058] Specifically, the intelligent robot receives a task instruction from the central control system. This task instruction can be a node unit deployment instruction, a node unit retrieval instruction, a process management instruction, etc. The process management instruction includes a quality control data retrieval instruction, a seismic data retrieval instruction, etc.

[0059] Step S302: Execute the node unit operation corresponding to the task instruction.

[0060] Among them, the intelligent robot that receives the task instruction is responsible for the corresponding management area. The management area is one of the multiple management areas divided by the central control system by acquiring exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the receiver point locations. In the process of dividing, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robots as joint optimization factors.

[0061] Specifically, the intelligent robot performs corresponding on-site operations according to the type of task instruction received. For example, if the instruction is to deploy node units, the robot will accurately deploy the carried node units to the designated target station number within its management area; if the instruction is to retrieve node units, the robot will go to the corresponding target station number, safely retrieve the deployed node units to the transport warehouse, and transfer them to a centralized storage point or the next area to be deployed.

[0062] In some embodiments, when the task instruction is a node unit deployment instruction or a node unit retrieval instruction, step S302 includes: moving to the target station according to the travel route planning in the node unit deployment instruction or node unit retrieval instruction; and performing the node unit deployment or retrieval operation.

[0063] Specifically, after successfully receiving and parsing the instructions to deploy or retrieve node units, the intelligent robot enters the autonomous execution phase: relying on its built-in navigation and positioning system (such as GPS and BeiDou) and multimodal sensors (such as LiDAR and visual cameras), it strictly follows the issued route plan and begins moving towards the first location in the target station list. During this process, the intelligent robot can perceive the environment in real time, dynamically avoiding obstacles encountered along the way (such as ditches and trees), and ultimately accurately arrives at the designated target station. If an unexpected situation occurs during the journey, it will promptly send a takeover request to the central control system.

[0064] Upon successfully reaching the target mileage, the intelligent robot executes corresponding operations based on the type of task instruction. If the instruction is to deploy node units, the intelligent robot will activate its deployment module to complete a series of actions, including retrieving components from the transport compartment, drilling holes, placing the nodes, compacting, activating the equipment, and performing quality checks. If the instruction is to retrieve node units, the robot will operate its retrieval component to remove the designated node from the ground, perform basic appearance and condition checks, and then safely retrieve it back into the transport compartment.

[0065] In some embodiments, the process management instructions include quality control data retrieval instructions and seismic data retrieval instructions. The quality control data includes deployment and daily inspection quality control data and operational status quality control data. When the task instruction is a quality control data retrieval instruction, step S302 includes: collecting operational status quality control data from the managed node units and sending all or part of the operational status quality control data to the central control system according to the quality control data retrieval instruction. When the task instruction is a seismic data retrieval instruction, step S302 includes: collecting seismic data from the managed node units and sending all or part of the seismic data to the central control system according to the seismic data retrieval instruction.

[0066] In some embodiments, the method further includes at least one of the following: automatically sending the deployment and daily inspection quality control data of the node units to the central control system; and automatically sending a warning message to the central control system when the operating status quality control data of the node units exceeds the corresponding first preset threshold.

[0067] Specifically, after the node unit is deployed to the target station, the intelligent robot obtains its unique serial number through RFID reading or QR code scanning, accurately binding the physical device with its digital identity. Simultaneously, it automatically associates this serial number with the corresponding SPS line number and station, forming a crucial "device-location" mapping relationship. Subsequently, the robot uses a vision sensor to capture images of the node's on-site status after installation, visually recording the deployment effect; and uses a high-precision attitude sensor integrated into the robotic arm or body to measure the horizontal angle of the node's top cover, accurately calculating its deployment tilt. Finally, it collects the node unit's self-inspection information. The aforementioned serial number, location information, installation images, tilt, and self-inspection report together constitute the initial deployment data, stored locally and automatically reported to the central control system.

[0068] Subsequently, the intelligent robot reviews the physical deployment status of the node units daily, generates daily inspection data and stores it locally, while automatically reporting it to the central control system.

[0069] After the node units are deployed and enter normal working condition, the intelligent robot can send the requested operational status quality control data to the central control system according to the quality control data collection instruction issued by the central control system. Furthermore, as an optimization mechanism, the intelligent robot adopts a combined abnormal proactive and instruction-triggered mode for reporting operational status quality control data: if the data exceeds a first preset threshold, a warning message is immediately and proactively reported; if the data is normal, the data is only reported when a quality control data collection instruction is received from the central control system.

[0070] Similarly, during normal operation of the node unit, the intelligent robot continuously receives seismic data from the managed node unit and caches and manages it using local storage devices; after receiving a data retrieval instruction from the central control system, it transmits all or part of the seismic data back to the central control system as required by the instruction.

[0071] In some embodiments, the method further includes at least one of the following: real-time monitoring of the communication link quality between the managed node unit and the central control system; autonomously moving to a location with better signal when the communication link quality is lower than a second preset threshold; real-time monitoring of the working status of the built-in battery of the managed node unit; and controlling a built-in robotic arm to perform emergency handling operations when it is determined that the node unit has a risk of spontaneous combustion based on the working status of the built-in battery, the emergency handling operations including covering the node unit with soil; and detecting the temperature and / or shell integrity of the node unit during the recovery of the node unit, and placing the node unit in a dedicated protective container for storage and transportation when the risk value determined based on the detection results exceeds a third preset threshold.

[0072] Specifically, the intelligent robot will also continuously monitor whether the communication link between the managed node units and the central control system is unobstructed. Once it detects a decline in the quality of the communication signal with the node units or the central control system, it will immediately move autonomously to a location with a better signal.

[0073] The intelligent robot will also target the battery as a specific component. When it determines that a node unit has the risk of spontaneous combustion or overheating and catching fire, it will not only report the information, but will immediately initiate localized physical emergency operations, controlling the robotic arm to cover the node unit with soil to isolate it from the air and prevent the fire from spreading.

[0074] The intelligent robot also conducts a safety risk assessment of the reclaimed nodes by detecting their temperature and / or shell integrity during the node recycling process. High-risk nodes are physically isolated by placing them in specialized protective containers for storage and transportation, effectively managing potential safety risks during recycling and transfer and preventing secondary disasters.

[0075] The intelligent management method for node units provided in this embodiment firstly involves the system issuing task instructions to the intelligent robot that precisely include the management area, the quantity of vehicles to be transported, the list of target station numbers, and the travel route. This clarifies the responsibilities and movement path of each robot, preventing ambiguity and overlap in task allocation from the outset and laying the foundation for refined division of labor. Subsequently, based on pre-planned travel routes, the intelligent robot can autonomously and efficiently reach each target station number sequentially, significantly reducing time delays and resource waste caused by manual guidance or improper path selection, and improving operational continuity. Finally, upon reaching the target location, the robot automatically executes the corresponding node unit operation according to the instruction type, completing the closed-loop task flow from movement to execution. This continuous mechanism not only significantly reduces reliance on manual operation but also enables multiple robots to work in parallel and collaboratively under unified scheduling, thereby improving the overall efficiency, accuracy, and system controllability of node unit management.

[0076] Figure 4 This is a flowchart illustrating another intelligent management method for node units provided in an embodiment of the present invention, applied to, for example... Figure 1 The central control system and intelligent robot shown are as follows: Figure 4 As shown, the method includes: Step S401: The central control system acquires exploration task parameters, which include the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the geophone points.

[0077] Step S402: Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, the central control system uses a clustering algorithm to divide the location of the receiver point into multiple management areas, and assigns an intelligent robot to each management area. In the process of dividing the area, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors.

[0078] Step S403: The central control system issues task instructions to each of the intelligent robots. The task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval.

[0079] Step S404: The intelligent robot executes the node unit operation corresponding to the task instruction.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process and corresponding beneficial effects of the node unit management method described above can be found in the corresponding process in the aforementioned method example, and will not be repeated here.

[0081] To further understand the complete process of the embodiments of the present invention, we will take a certain onshore seismic exploration project that uses 10,000 node units, is equipped with 10 intelligent robots (numbered R1-R10) and a central control system as an example for illustration.

[0082] Step 1: Intelligent Task Planning and Allocation The central control system acquires exploration task parameters, including the number of intelligent robots (10 units), performance parameters of each robot (such as node carrying capacity), topographic data of the exploration area (DEM digital elevation model), and location information of all geophone points (SPS station numbers). Based on these parameters, the central control system uses an improved clustering algorithm to automatically divide the geophone point locations into 10 management areas and assigns one intelligent robot to each area. The improvement in the clustering algorithm lies in using terrain-weighted distance and the load rate of the intelligent robot as joint optimization factors during the division process. Subsequently, the central control system issues node unit setting instructions to each intelligent robot, which include its managed area, the number of node units it carries, the target station number list, and the travel route plan.

[0083] Step 2: Autonomous Navigation and Deployment Operations After receiving the instruction to deploy node units, each intelligent robot (such as R1) autonomously moves to the target station location based on its route planning, combined with navigation, positioning, and multimodal sensor data, and automatically avoids obstacles. Upon reaching the target station, it performs the automatic deployment of node units.

[0084] Step 3: Deploy quality control and data reporting After the node units are deployed, the intelligent robot obtains the serial number of the node unit through radio frequency identification (RFID) or barcode scanning, and automatically associates and binds it with the SPS line number and station number. Simultaneously, the intelligent robot uses visual sensors to take photos of the node's installation status, collects environmental parameters (temperature, humidity, light intensity), retrieves the node unit's self-inspection report on its working status, and measures the horizontal angle of the node's top cover using an integrated attitude sensor to obtain the node's inclination index. The intelligent robot uses the serial number, SPS line number and station number, installation status images, environmental parameters, self-inspection report, and installation inclination as deployment quality control data, and sends them to the central control system in real time.

[0085] Step 4: Central Control System Monitoring and Model Update The central control system receives deployment quality control data sent by each intelligent robot and updates the digital twin model of the exploration area based on this data, thereby mapping the position and status of each intelligent robot and the node units it manages in real time and accurately.

[0086] Step 5: Correcting excessive tilt The central control system compares the received deployment tilt with the corresponding safety threshold. If the deployment tilt exceeds the threshold, a correction command is generated and sent to the corresponding intelligent robot, which then performs correction operations on the node unit accordingly. If the deployment tilt does not exceed the threshold, the deployment is confirmed to be qualified.

[0087] Step 6: Time synchronization signal guarantee When a node unit experiences poor satellite timing signal due to its deployment location, the intelligent robot reports the timing signal quality information to the central control system. If the signal deficiency is determined to be caused by the deployment location, the central control system issues a relocation command to the intelligent robot, placing it in a new location where the timing signal meets the requirements. If the signal deficiency is determined to be caused by terrain obstruction, the intelligent robot is instructed to move to a relay location capable of receiving satellite signals, acting as a relay station for satellite timing information to forward the timing information to the node unit.

[0088] Step 7: Dynamic Communication Maintenance The intelligent robot maintains constant heartbeat communication with the managed node units and the central control system to ensure uninterrupted communication and monitors the communication signal strength in real time. When the communication signal strength with a certain device falls below a second preset threshold, the intelligent robot autonomously moves to a location with a better signal to maintain and enhance the communication link quality.

[0089] Step 8: Seismic Data Acquisition and Transmission The central control system issues a seismic data acquisition start command to all intelligent robots. Upon receiving the command, each intelligent robot forwards it to the node units it manages. The node units then begin sending seismic data to the intelligent robots, which receive and store this data locally. Based on subsequent commands from the central control system, the intelligent robots transmit all or part of the stored seismic data back to the central control system. The intelligent robots can also re-arrange the seismic data to meet the central control system's requests for data at different time intervals.

[0090] Step 9: Node Status Monitoring and Emergency Handling The intelligent robot monitors the operating status parameters (such as temperature and voltage) of the managed node units in real time. When the operating status parameters indicate that a node unit is at risk of spontaneous combustion, the robot immediately controls its onboard robotic arm to perform emergency handling operations, including covering the node unit with soil to prevent a fire.

[0091] Step 10: End of Data Acquisition and Command to Stop When the data acquisition ends, the central control system sends a stop acquisition command to all or specific intelligent robots. Upon receiving the command, the intelligent robots cease collecting seismic data from the node units they manage.

[0092] Step 11: Node Recycling and Risk Assessment The central control system issues retrieval instructions to the intelligent robots that need to collect nodes. These instructions include the number of nodes to be collected, their location, target station number, and planned route. During node retrieval, the intelligent robots detect the temperature of the battery and / or the integrity of the casing of the node unit to be retrieved, and perform a risk assessment based on the detection results. If the risk exceeds a third preset threshold, the node unit is placed in a dedicated protective container for storage and transportation.

[0093] Step 12: State Update and Loop Execution The central control system automatically updates the status information of the intelligent robot and the node units it manages, including new locations, line numbers, and station numbers. The above process, from step two to step eleven, can be repeated cyclically according to the needs of the exploration task until the entire project is completed.

[0094] The core of this invention lies in constructing a complete hierarchical collaborative intelligent operation system. This system uses a central control system as the overall command center, responsible for strategic planning and resource allocation. Through intelligent algorithms, the exploration area is scientifically divided, and each intelligent robot is assigned a suitable management area and work task, achieving refined zoned and task-based management. At the field execution layer, the intelligent robot acts as the "field agent" for the node units it manages, fully responsible for the transportation, precise deployment, retrieval, and daily maintenance of the node units.

[0095] After deployment, the intelligent robot further assumes the function of a communication hub, establishing a stable and reliable connection channel between the node units and the central control system through real-time data transmission and heartbeat communication mechanisms. To ensure the continuous and stable operation of the system, the intelligent robot possesses autonomous optimization capabilities, dynamically adjusting its position based on communication quality to maintain optimal link status. In terms of data processing, the intelligent robot can both report node unit status information in real time and flexibly transmit all or part of the seismic data from the managed nodes according to instructions from the central control system.

[0096] This cluster operation mode, which integrates intelligent planning, precise execution, reliable communication and flexible data processing, realizes fully automated and unmanned management of node instruments from deployment, monitoring to retrieval, significantly improving the efficiency and data quality of seismic exploration operations.

[0097] Figure 5 A schematic diagram of the structure of a central control system provided in an embodiment of the present invention is shown below. Figure 5 As shown, the central control system includes: The exploration task acquisition module 501 is used to acquire exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points; the area division and allocation module 502 is used to divide the receiver point locations into multiple management areas based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, and to allocate an intelligent robot to each management area. In the process of division, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors. The task instruction issuing module 503 is used to issue task instructions to each of the intelligent robots. The task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval.

[0098] In some embodiments, the clustering algorithm is implemented through the following steps: The cluster number K is determined based on the number of intelligent robots; Based on the node carrying capacity of each intelligent robot, all detector points are initially allocated to K clusters in proportion, and the initial cluster center of each cluster is determined. The following steps are executed iteratively until the preset convergence condition is met, and the final management region is output: Calculate the weighted distance between each receiver point and the current cluster center of each cluster. The weighted distance is a weighted sum of the terrain-weighted distance and the load rate. The terrain-weighted distance is determined based on the terrain data of the exploration area. The load rate is the ratio of the number of currently assigned receiver points of the corresponding cluster to the node carrying capacity of the intelligent robot responsible for the corresponding cluster. Based on the principle of minimum weighted distance, the detector points are reassigned to the cluster with the smallest weighted distance; Based on the redistribution results, the current cluster center of each cluster is recalculated.

[0099] In some embodiments, the instructions for deploying node units and the instructions for retrieving node units each include the management area they are responsible for, the number of node units being transported, the target station number, and the route planning. The process management instructions include quality control data retrieval instructions and seismic data retrieval instructions; Both the quality control data retrieval instruction and the seismic data retrieval instruction include a time range for data retrieval and may optionally include a specified intelligent robot identifier and node unit serial number to achieve full or partial data retrieval, respectively.

[0100] In some embodiments, the central control system further includes a data receiving module 504; The data receiving module 504 is used to receive the satellite timing signal quality information of the node unit reported by the intelligent robot. The task instruction issuing module 503 is further configured to, if the timing signal quality information indicates that the signal difference is due to deployment factors, issue a redeployment instruction to the corresponding intelligent robot, the redeployment instruction including new location information, to deploy the node unit to a new location where the timing signal meets the requirements; if the timing signal quality information indicates that the signal difference is due to terrain, the instruction to the corresponding intelligent robot to act as a relay station for the satellite timing signal.

[0101] It should be noted that the data receiving module 504 is used to receive data information sent by the intelligent robot, including instruction receipt responses, quality control data, earthquake data, and alarm warning information.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process and corresponding beneficial effects of the central control system described above can be referred to the corresponding process in the aforementioned method examples, and will not be repeated here.

[0103] Figure 6 This is a structural schematic diagram of an intelligent robot provided in an embodiment of the present invention, such as... Figure 6 As shown, the intelligent robot includes: The task instruction receiving module 601 is used to receive task instructions issued by the central control system. The task instructions include at least one of the following: node unit deployment instructions, node unit recovery instructions, and process management instructions for data recovery. Task execution module 602 is used to execute node unit operations corresponding to the task instructions; Among them, the intelligent robot that receives the task instruction is responsible for the corresponding management area. The management area is one of the multiple management areas divided by the central control system by acquiring exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the receiver point locations. In the process of dividing, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robots as joint optimization factors.

[0104] In some embodiments, when the task instruction is a node unit deployment instruction or a node unit retrieval instruction, the task execution module 602 is specifically used for: According to the travel route plan in the deployment or retrieval instructions for node units, move to the target station number; Perform the deployment or recycling of node units.

[0105] In some embodiments, the process management instructions include quality control data retrieval instructions and seismic data retrieval instructions, wherein the quality control data includes deployment and daily inspection quality control data and operational status quality control data; when the task instruction is a quality control data retrieval instruction, the task execution module 602 is specifically used for: Collect operational status quality control data from the managed node units, and send all or part of the operational status quality control data to the central control system according to the quality control data retrieval instruction; When the task instruction is a seismic data retrieval instruction, the task execution module 602 is specifically used for: Seismic data is collected from the managed node units, and all or part of the seismic data is sent to the central control system in accordance with the seismic data retrieval instruction.

[0106] In some embodiments, the task execution module 602 is further configured to perform at least one of the following: The deployment and daily inspection quality control data of the node units are automatically sent to the central control system. When the quality control data of the operating status of the node unit exceeds the corresponding first preset threshold, a warning message is automatically sent to the central control system. The system monitors the quality of the communication link between the managed node units and the central control system in real time. When the quality of the communication link is lower than a second preset threshold, the system autonomously moves to a location with better signal. The system monitors the working status of the built-in battery of the managed node unit in real time, and when it determines that the node unit has a risk of spontaneous combustion based on the working status of the built-in battery, it controls the built-in robotic arm to perform emergency handling operations, including covering the node unit with soil. When recovering a node unit, the temperature and / or shell integrity of the node unit are detected, and if the risk value determined based on the detection results exceeds a third preset threshold, the node unit is placed in a dedicated protective container for storage and transportation.

[0107] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and corresponding beneficial effects of the intelligent robot described above can be found in the corresponding process in the aforementioned method examples, and will not be repeated here.

[0108] like Figure 7 As shown, this embodiment of the invention provides an electronic device, including a processor 701, a communication interface 702, a memory 703, and a communication bus 704, wherein the processor 701, the communication interface 702, and the memory 703 communicate with each other via the communication bus 704. Memory 703 is used to store computer programs; In one embodiment of the present invention, when the processor 701 executes the program stored in the memory 703, it implements the steps of the intelligent management method for node units provided in any of the foregoing method embodiments.

[0109] The electronic device provided in this embodiment of the invention has a similar implementation principle and technical effect to the above embodiments, and will not be described again here.

[0110] The aforementioned memory 703 can be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 703 has storage space for program code used to perform any of the method steps described above. For example, the storage space for program code may include individual program codes for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, optical discs (CDs), memory cards, or floppy disks. Such computer program products are typically portable or fixed storage units. The storage unit may have storage segments or storage spaces arranged similarly to memory 703 in the aforementioned electronic device. The program code may be compressed, for example, in a suitable form. Typically, the storage unit includes programs for performing the method steps according to embodiments of the invention, i.e., code that can be read by a processor such as 701, which, when run by the electronic device, causes the electronic device to perform the various steps in the methods described above.

[0111] Embodiments of the present invention also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent management method for node units as described above.

[0112] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of the present invention.

[0113] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0114] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0115] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for intelligent management of node units, characterized in that, Applied to a central control system, the method includes: The exploration task parameters are obtained, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the receiver point locations into multiple management areas, and an intelligent robot is assigned to each management area. In the process of dividing the area, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors. Task instructions are issued to each of the intelligent robots, and the task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval.

2. The method according to claim 1, characterized in that, The clustering algorithm is implemented through the following steps: The cluster number K is determined based on the number of intelligent robots; Based on the node carrying capacity of each intelligent robot, all detector points are initially allocated to K clusters in proportion, and the initial cluster center of each cluster is determined. The following steps are executed iteratively until the preset convergence condition is met, and the final management region is output: Calculate the weighted distance between each receiver point and the current cluster center of each cluster. The weighted distance is a weighted sum of the terrain-weighted distance and the load rate. The terrain-weighted distance is determined based on the terrain data of the exploration area. The load rate is the ratio of the number of currently assigned receiver points of the corresponding cluster to the node carrying capacity of the intelligent robot responsible for the corresponding cluster. Based on the principle of minimum weighted distance, the detector points are reassigned to the cluster with the smallest weighted distance; Based on the redistribution results, the current cluster center of each cluster is recalculated.

3. The method according to claim 1 or 2, characterized in that, The instructions for deploying and retrieving node units both include the management area they are responsible for, the number of node units being transported, the target station number, and the planned route. The process management instructions include quality control data retrieval instructions and seismic data retrieval instructions; Both the quality control data retrieval instruction and the seismic data retrieval instruction include a time range for data retrieval and may optionally include a specified intelligent robot identifier and node unit serial number to achieve full or partial data retrieval, respectively.

4. The method according to claim 1 or 2, characterized in that, The method further includes: Receive satellite timing signal quality information from node units reported by intelligent robots; If the timing signal quality information indicates that the signal difference is due to deployment factors, a re-deployment instruction is issued to the corresponding intelligent robot. The re-deployment instruction includes new location information to deploy the node unit to a new location where the timing signal meets the requirements. If the timing signal quality information indicates that the signal difference is due to terrain, the corresponding intelligent robot is instructed to act as a relay station for the satellite timing signal.

5. An intelligent management method for node units, characterized in that, Applied to intelligent robots, the method includes: Receive task instructions issued by the central control system, the task instructions including at least one of node unit deployment instructions, node unit recovery instructions, and process management instructions for data recovery; Execute the node unit operation corresponding to the task instruction; Among them, the intelligent robot that receives the task instruction is responsible for the corresponding management area. The management area is one of the multiple management areas divided by the central control system by acquiring exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the receiver points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, a clustering algorithm is used to divide the receiver point locations. In the process of dividing, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robots as joint optimization factors.

6. The method according to claim 5, characterized in that, When the task instruction is a node unit deployment instruction or a node unit retrieval instruction, the execution of the node unit operation corresponding to the task instruction includes: According to the travel route plan in the deployment or retrieval instructions for node units, move to the target station number; Perform the deployment or recycling of node units.

7. The method according to claim 5, characterized in that, The process management instructions include quality control data retrieval instructions and seismic data retrieval instructions. The quality control data includes deployment and daily inspection quality control data, and operational status quality control data. When the task instruction is a quality control data retrieval instruction, the execution of the node unit operation corresponding to the task instruction includes: Collect operational status quality control data from the managed node units, and send all or part of the operational status quality control data to the central control system according to the quality control data retrieval instruction; When the task instruction is a seismic data retrieval instruction, the execution of the node unit operation corresponding to the task instruction includes: Seismic data is collected from the managed node units, and all or part of the seismic data is sent to the central control system in accordance with the seismic data retrieval instruction.

8. The method according to claim 7, characterized in that, The method further includes at least one of the following: The deployment and daily inspection quality control data of the node units are automatically sent to the central control system. When the quality control data of the operating status of the node unit exceeds the corresponding first preset threshold, a warning message is automatically sent to the central control system. The system monitors the quality of the communication link between the managed node units and the central control system in real time. When the quality of the communication link is lower than a second preset threshold, the system autonomously moves to a location with better signal. The system monitors the working status of the built-in battery of the managed node unit in real time, and when it determines that the node unit has a risk of spontaneous combustion based on the working status of the built-in battery, it controls the built-in robotic arm to perform emergency handling operations, including covering the node unit with soil. When recovering a node unit, the temperature and / or shell integrity of the node unit are detected, and if the risk value determined based on the detection results exceeds a third preset threshold, the node unit is placed in a dedicated protective container for storage and transportation.

9. A method for intelligent management of node units, characterized in that, include: The central control system acquires exploration task parameters, including the number of intelligent robots, the performance parameters of each intelligent robot, the topographic data of the exploration area, and the location information of the geophone points. Based on the number of intelligent robots, performance parameters, and topographic data of the exploration area, the central control system uses a clustering algorithm to divide the geophone point locations into multiple management areas and assigns an intelligent robot to each management area. In the process of dividing the area, the clustering algorithm uses the terrain weighted distance and the load rate of the intelligent robot as joint optimization factors. The central control system issues task instructions to each of the intelligent robots. The task instructions include at least one of the following: node unit deployment instructions, node unit retrieval instructions, and process management instructions for data retrieval. The intelligent robot executes node unit operations corresponding to the task instructions.

10. An intelligent management system for node units, characterized in that, Including a central control system and intelligent robots; The central control system is used to execute the intelligent management method of the node unit as described in any one of claims 1-4, and the intelligent robot is used to execute the intelligent management method of the node unit as described in any one of claims 5-8.

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