An intelligent terminal sensor control method and system based on an ant colony algorithm

Abstract

The application provides an intelligent terminal sensor control method and system based on an ant colony algorithm, which comprises the following steps: determining scene nodes and scene features in a route and task priorities; determining a current scene node; controlling each intelligent terminal to periodically broadcast through a low-power communication module; evaluating an efficiency-energy consumption ratio and mapping the result as pheromone concentration in the ant colony algorithm; selecting an intelligent terminal as a detection node; and controlling the detection node to generate a sensor scheme and detecting according to the sensor scheme. Through the cooperation of scene adaptation, dynamic evaluation, intelligent point selection and fine control, the application realizes scene, fine and self-adaptation of sensor control, so that the sensor control strategy is accurately matched with the environmental demand, the dynamic balance of energy consumption and precision and the optimal configuration of sensor resources are ensured, and the precision, adaptability and energy economy of the intelligent terminal sensor control are comprehensively improved.

Description

[0001] This application is a divisional application of Chinese invention patent application filed on December 19, 2025, with application number 2025119253397 and invention title "A Smart Terminal Sensor Control Method and System Based on Ant Colony Algorithm". Technical Field

[0002] This application relates to the field of smart terminals, and more specifically, to a smart terminal sensor control method and system based on ant colony algorithm. Background Technology

[0003] In complex environments such as emergency rescue, field operations, and geological exploration, intelligent terminals such as NBC (nuclear-proof, dustproof, and shockproof) equipment need to collect environmental data and equipment status information in real time through various sensors to provide key information for team decision-making and safety assurance.

[0004] Existing sensor control solutions for smart terminals have significant limitations. Most solutions employ fixed control strategies, such as pre-setting uniform sampling frequencies, detection accuracies, and operating modes, without dynamically adjusting them to suit the actual scenario. For example, in complex mountainous environments where environmental parameters fluctuate drastically, fixed low-frequency sampling struggles to capture critical changes, resulting in insufficient detection accuracy. Conversely, in stable environments like open plains, high-frequency sampling leads to unnecessary energy waste and, without adapting to the sensor's own characteristics, further exacerbates data errors.

[0005] Therefore, existing sensor control schemes for smart terminals have significant shortcomings and are difficult to meet the high requirements for sensor data reliability in complex scenarios. There is an urgent need for a technical solution that can dynamically adjust the sensor control strategy according to scenario characteristics and device status. Summary of the Invention

[0006] To address the problems existing in current technologies, this application provides a smart terminal sensor control method and system based on ant colony optimization. The specific solution is as follows: A smart terminal sensor control method based on ant colony algorithm, comprising: Based on the team's travel plan and geographic information system, multiple scene nodes and scene characteristics of the scene nodes in the route are identified, and the task priority of each scene node is determined based on the scene characteristics. Each intelligent terminal is controlled to acquire scene feature parameters through low-power scanning, and the scene feature parameters are matched with parameters in the scene feature library to determine the current scene node. Each intelligent terminal is controlled to periodically broadcast pre-stored hardware feature parameters and collected key status parameters to neighboring nodes at a dynamically adjusted frequency via a low-power communication module. The efficiency and energy consumption ratio are evaluated based on task priority, hardware characteristic parameters of neighboring nodes, and key state parameters, and the evaluation results are mapped to the pheromone concentration in the ant colony algorithm. Determine the pheromone gradient of the smart terminal, and combine it with the scene characteristics of the current scene nodes. Then, use a pre-set ant colony algorithm to distribute and select one or more smart terminals as detection nodes. The control detection node combines the scene characteristics of the current scene node with its own hardware characteristic parameters and key state parameters to generate a sensor scheme, and performs detection according to the sensor scheme.

[0007] In some specific embodiments, the process of determining the scene nodes specifically includes: Extract route information, including the starting point, ending point, key waypoints, and expected stop areas, from the team's travel plan, and combine it with geographic information system to obtain the route's terrain type, elevation change rate, vegetation coverage, and electromagnetic environment zoning data. The route was divided into multiple continuous scene nodes based on the threshold of terrain type change, abrupt change point of altitude gradient, jump interval of vegetation coverage, and boundary of electromagnetic environment. For each scene node, historical environmental monitoring data is retrieved through a geographic information system to determine the scene characteristics of that scene node. The scene characteristics include the typical range of environmental parameters and the signal transmission loss coefficient.

[0008] In some specific embodiments, the process of determining the current scene node includes: Each intelligent terminal is controlled to activate a low-power sensor module to perform scanning. The low-power sensor module includes a low-power temperature and humidity sensor, a signal strength detector, and an accelerometer. The smart terminal is divided into at least two scanning batches that scan alternately at intervals. At least one batch of devices prioritizes scanning environmental feature parameters, and at least one batch of devices prioritizes scanning signal feature parameters. The real-time scene feature parameters are obtained by combining them. Calculate the feature similarity between the real-time scene feature parameters and the parameters of each scene node in the scene feature library; when the feature similarity of a certain scene node exceeds the preset matching threshold, and the condition is met in 3 consecutive scans, the current scene node is determined to be that node.

[0009] In some specific embodiments, the hardware characteristic parameters are pre-stored in the device's non-volatile memory, including sensor type, theoretical accuracy level of the sensor, maximum transmission distance of the communication module, hardware anti-interference level, and rated operating current of each module; The key status parameters include the remaining battery power percentage, real-time satellite signal reception strength, ground communication signal RSSI value, number of currently active sensors, and current CPU operating frequency. The low-power communication module includes a BLE module or a LoRaWAN module.

[0010] In some specific embodiments, the task priority includes navigation priority, communication priority, and detection priority; When the signal stability index corresponding to the signal feature parameter in the scene features has the highest weight influencing the feasibility of the navigation task, and when the index deviates from the preset stability threshold, it will cause the navigation task to fail to achieve basic positioning accuracy, then the navigation task is determined to be prioritized. When the environmental complexity index corresponding to the environmental feature parameter in the scene features has the highest weight influencing the feasibility of the detection task, and when this index exceeds the preset conventional threshold, it will cause the detection task to fail to obtain effective environmental data, then the detection task is determined to be prioritized. When the signal transmission capability index corresponding to the signal feature parameter in the scene features has the highest weight in influencing the feasibility of the communication task, and when this index is lower than the preset transmission threshold, the communication task will be unable to complete basic data interaction, then the communication task is determined to be prioritized.

[0011] In some specific embodiments, the evaluation weights of hardware feature parameters and key state parameters are assigned according to task priority. By fusing the weighted hardware feature parameters and key state parameters, the efficiency energy consumption ratio of neighboring nodes for different roles is calculated. This efficiency energy consumption ratio characterizes the role efficiency that a node can achieve per unit of energy consumption under the current task priority. Pheromones of different types are assigned to roles with different task priorities. The concentration of each type of pheromone is correlated with the performance-energy consumption ratio of the corresponding role, and the sensitivity of the correlation is positively correlated with the task priority of the role.

[0012] In some specific embodiments, the selection of the detection nodes includes: Each smart terminal first receives the detected pheromone concentration from neighboring nodes, and then forwards its own and neighboring nodes' pheromone data to adjacent devices, thus completing the distributed sharing of global pheromone data. The pheromone gradient is calculated based on the global pheromone concentration distribution, and the gradient direction points to the nodes where the pheromone concentration increases globally. Based on the environmental complexity and task priority of the current scene nodes, the selection conditions for detection nodes are set, including the minimum pheromone concentration threshold and the selection quantity range. The preset ant colony algorithm uses local collaborative computing on each device to sort the global smart terminals according to the pheromone gradient and select nodes that meet the selection criteria as detection nodes. During the selection process, a load balancing mechanism is retained. Nodes that have already assumed navigation or communication roles are given lower priority when selected as detection nodes to avoid overloading of a single device with multiple roles.

[0013] In some specific embodiments, the process of generating the sensor scheme includes: The control detection node selects suitable sensors from the sensor types included in its own hardware characteristic parameters based on the typical range of environmental parameters and signal transmission loss coefficient of the current scene node. Based on the sensor's theoretical accuracy level from its own hardware characteristic parameters and key state parameters such as the remaining battery power percentage and the current CPU operating frequency, the sensor's detection frequency and detection accuracy are determined, thus obtaining the sensor solution.

[0014] In some specific embodiments, during the detection process according to the generated sensor scheme, the changes in the current scene feature parameters and the fluctuations in its own key state parameters are monitored in real time, and the sensor type combination and operating parameters are dynamically updated to maintain a balance between detection efficiency and energy consumption.

[0015] A smart terminal sensor control system based on ant colony algorithm, comprising: The scene segmentation unit is used to determine multiple scene nodes and scene characteristics of scene nodes in the route based on the team's travel plan and geographic information system, and to determine the task priority of each scene node based on the scene characteristics. The scene determination unit is used to control each smart terminal to obtain scene feature parameters through low-power scanning, match the scene feature parameters with parameters in the scene feature library, and determine the scene node where it is currently located. The parameter broadcasting unit is used to control each smart terminal to periodically broadcast pre-stored hardware feature parameters and collected key status parameters to neighboring nodes at a dynamically adjusted frequency through a low-power communication module. The concentration mapping unit is used to evaluate the efficiency and energy consumption ratio based on task priority, hardware feature parameters of neighboring nodes and key state parameters, and to map the evaluation results to the pheromone concentration in the ant colony algorithm. The node selection unit is used to determine the pheromone gradient of the smart terminal and, in combination with the scene characteristics of the current scene nodes, to select one or more smart terminals as detection nodes in a distributed manner using a preset ant colony algorithm. The node detection unit is used to control the detection node to generate a sensor scheme by combining the scene characteristics of the current scene node with its own hardware feature parameters and key state parameters, and to perform detection according to the sensor scheme.

[0016] Beneficial Effects: This application proposes a smart terminal sensor control method and system based on ant colony algorithm. Through the synergy of scenario adaptation, dynamic evaluation, intelligent point selection and fine control, it realizes the transformation of sensor control from fixed and coarse control to scenario-based, fine and adaptive control. This enables the sensor control strategy to be accurately matched with environmental requirements, ensures the dynamic balance between energy consumption and accuracy and the optimal allocation of sensor resources, and comprehensively improves the accuracy, adaptability and energy economy of smart terminal sensor control. It fundamentally solves the problems of insufficient control accuracy and adaptability defects in existing solutions.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the intelligent terminal sensor control method of this application; Figure 2 This is a schematic diagram illustrating the process for determining the task priority in this application; Figure 3 This is a schematic diagram of the broadcast process of this application; Figure 4 This is a schematic diagram of the selection process for the detection nodes in this application; Figure 5 This is a schematic diagram of the intelligent terminal sensor control system module of this application.

[0020] Attached figure labels: 1-Scene division unit; 2-Scene determination unit; 3-Parameter broadcasting unit; 4-Concentration mapping unit; 5-Node selection unit; 6-Node detection unit. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0022] This application proposes a smart terminal sensor control method based on ant colony optimization. Through a collaborative mechanism encompassing dynamic scene node partitioning, task priority adaptation, real-time device parameter interaction, intelligent point selection using ant colony optimization, and dynamic sensor scheme generation, it achieves a transformation of sensor control from a fixed, extensive approach to a scenario-based, refined, and adaptive one. A flowchart of the smart terminal sensor control method is attached. Figure 1 As shown, the specific solution is as follows: A smart terminal sensor control method based on ant colony algorithm, comprising: 101. Based on the team's travel plan and geographic information system, identify multiple scene nodes in the route and the scene characteristics of the scene nodes, and determine the task priority of each scene node based on the scene characteristics. 102. Control each intelligent terminal to obtain scene feature parameters through low-power scanning, match the scene feature parameters with the parameters in the scene feature library, and determine the current scene node; 103. Control each intelligent terminal to periodically broadcast the pre-stored hardware feature parameters and collected key status parameters to the surrounding neighbor nodes at a dynamically adjusted frequency through the low-power communication module. 104. Efficiency and energy consumption ratio are evaluated based on task priority, hardware characteristic parameters of neighboring nodes, and key state parameters, and the evaluation results are mapped to pheromone concentration in ant colony algorithm. 105. Determine the pheromone gradient of the smart terminal, and combine it with the scene characteristics of the current scene nodes to select one or more smart terminals as detection nodes in a distributed manner using a preset ant colony algorithm. 106. The control detection node combines the scene characteristics of the current scene node with its own hardware characteristic parameters and key state parameters to generate a sensor scheme, and performs detection according to the sensor scheme.

[0023] The preferred smart terminal in this application is a rugged device, such as a rugged mobile phone or a rugged watch.

[0024] In this application, in addition to detection nodes, there are also necessary navigation nodes and communication nodes. Navigation nodes, communication nodes, and detection nodes together constitute the collaborative role system of the intelligent terminal. The selection of navigation nodes follows the pheromone-driven logic of the ant colony algorithm. When navigation is the priority of the task, the weights of the theoretical accuracy level and anti-interference level of the satellite positioning module's sensor, as well as the weights of satellite signal strength and battery life among the key state parameters, are significantly increased. The energy consumption ratio of the navigation role is calculated and mapped to a high-sensitivity navigation pheromone, achieving global pheromone sharing and gradient calculation. Navigation nodes are selected and load balancing is used to avoid overlap with the core communication / detection roles. Their core function is to select and adapt navigation modules based on scene signal characteristics, dynamically adjust the positioning frequency according to their own state, and broadcast positioning data through a low-power communication module to provide a global position reference. The selection logic for communication nodes is consistent with that for navigation nodes, focusing only on the maximum transmission distance of the communication module, the anti-interference level, the RSSI value of the communication signal, and the rated current of the communication module in terms of parameter weights. These are mapped to medium-sensitivity communication pheromones. The selection process combines the electromagnetic environment of the scene and the signal loss coefficient to determine the quantity range. The core function is to select BLE or LoRaWAN modules according to the scene, dynamically adjust communication parameters to ensure smooth data interaction, and at the same time assume the role of data relay to achieve global information sharing.

[0025] The three types of nodes follow the "one master and multiple assistants" role mutual exclusion and load balancing rules. The same device is prohibited from simultaneously undertaking two or more core roles. The selection priority of other roles of core role nodes is reduced. When changes in scene characteristics cause task priority to switch, the role is switched seamlessly through dynamic updates of pheromone concentration. The operating parameters are mutually adapted. The positioning frequency of the navigation node determines the broadcast frequency of the communication node, and the data volume of the detection node is adapted to the transmission capacity of the communication node.

[0026] In step 101, scene nodes refer to continuous regions defined based on features such as terrain changes and abrupt elevation gradients along the travel route. Their function is to decompose the complex travel route into multiple units with similar environmental characteristics, facilitating the development of targeted sensor control strategies. Scene characteristics include typical ranges of environmental parameters and signal transmission loss coefficients. The former reflects the typical fluctuation range of environmental parameters such as temperature, humidity, and air pressure in the area, while the latter reflects the attenuation characteristics of signals during transmission in that area. Both provide environmental basis for subsequent task priority determination and sensor scheme generation. The detailed process of step 1 is attached. Figure 2 As shown.

[0027] The principle behind determining task priorities is based on the factors that have the greatest impact on task execution within the scene's characteristics. For example, if the signal transmission loss coefficient in the scene characteristics indicates that signal stability plays a dominant role in navigation and positioning, then navigation is prioritized; if environmental parameters fluctuate significantly, and environmental complexity has a more significant impact on the effectiveness of detection data, then detection is prioritized. The implementation involves combining the task objectives in the team's travel plan with historical data retrieved from the Geographic Information System (GIS) to quantitatively analyze the impact weight of each scene characteristic on navigation, communication, and detection tasks. This ultimately determines the priority task type for each scene node, providing direction for subsequent performance evaluation and node selection.

[0028] In step 102, low-power scanning refers to activating low-power sensor modules in the device, such as low-power temperature and humidity sensors and signal strength detectors, and acquiring parameters through batch-by-batch alternating scanning. This scanning method aims to reduce overall energy consumption while ensuring comprehensive parameter coverage, avoiding rapid power depletion due to continuous high-frequency scanning. Environmental characteristic parameters include temperature and humidity, terrain undulation, etc., while signal characteristic parameters include satellite signal strength, communication signal attenuation rate, etc. Combining these two aspects provides a comprehensive reflection of the current environmental conditions.

[0029] The principle of scene matching is to calculate the similarity between the real-time acquired scene feature parameters and the parameters of each node in the scene feature library, and to ensure the accuracy of the matching results through multiple consecutive verifications. Specifically, the algorithm first quantifies the difference between the real-time parameters and the parameters in the library. When the similarity of a scene node exceeds a preset threshold and is satisfied in three consecutive scans, it is determined that the device is currently in that node. This step accurately locates the environmental unit in which the device is located, ensuring that subsequent sensor control strategies can accurately adapt to the current scene features and avoiding insufficient control precision due to scene recognition errors.

[0030] In step 103, hardware characteristic parameters are inherent attributes pre-stored in the device at the factory, including sensor type, theoretical accuracy level, and maximum transmission distance of the communication module. Their purpose is to allow surrounding devices to understand the device's hardware capabilities and provide basic data for performance evaluation. Key status parameters are dynamic information collected by the device in real time, including remaining battery power, real-time signal strength, and the number of currently active sensors. These parameters reflect the device's current operating status and are important criteria for determining whether it is suitable to assume a specific role.

[0031] Dynamically adjusted frequency refers to the flexible change of the broadcast parameter period of the device based on actual conditions. The principle is to combine the current communication signal quality and the device's battery status. For example, a lower frequency is used when the signal is good and the battery is sufficient to reduce energy consumption; the frequency is increased when the signal is weak or the battery is low to ensure that surrounding devices can obtain its status in a timely manner. This is achieved by the device monitoring the communication signal attenuation rate and battery level in real time, and automatically adjusting the broadcast period when the parameters reach a preset threshold. This step balances communication energy consumption while ensuring information timeliness, ensuring efficient collaboration between devices without excessive resource consumption.

[0032] In step 104, the efficiency-to-energy ratio is an indicator that measures the efficiency of a device per unit of energy consumption in a specific task. Its calculation requires weighting based on task priority. For example, when the task priority is detection first, the weights of hardware and status parameters related to detection, such as the accuracy and response speed of the device's environmental sensors, will be increased to highlight the device's adaptability to the detection task. The purpose of this indicator is to quantitatively evaluate the ability of each device to perform different roles, providing an objective basis for the selection of subsequent nodes.

[0033] The principle of pheromone concentration mapping is to convert the efficiency-to-energy ratio into identifiable pheromones in the ant colony algorithm. Different task priorities correspond to different types of pheromones, such as navigation pheromones and detection pheromones. Pheromone concentration is positively correlated with efficiency-to-energy ratio; that is, devices with higher efficiency and better energy consumption correspond to higher pheromone concentrations. This is achieved by using preset mapping rules to convert the numerical range of efficiency-to-energy ratio into a range of pheromone concentration values, and dynamically adjusting based on device status, such as reducing the corresponding pheromone concentration when the battery is too low. This step transforms the actual capability of the device into a decision variable for the ant colony algorithm, providing a unified criterion for selecting distributed nodes.

[0034] In step 105, the pheromone gradient refers to the spatial trend of pheromone concentration, with its direction pointing towards devices where pheromone concentration increases. The principle is based on calculating the concentration change rate around each device according to the global pheromone distribution. The gradient's role is to guide the ant colony algorithm towards devices with higher performance, ensuring that the selected nodes have better performance in the overall network.

[0035] The selection of detection nodes is achieved by combining the environmental complexity and task priority of nodes in the current scene, and sorting all devices globally according to pheromone gradients using a pre-defined ant colony algorithm. For example, high environmental complexity scenarios will increase the requirement for the number of detection nodes, and tasks prioritizing detection will strictly select devices with pheromone concentrations that meet the standards. Simultaneously, a load balancing mechanism is introduced, lowering the priority of devices already performing navigation or communication roles to avoid excessive load on any single device. This step aims to select the most suitable detection nodes globally, ensuring that the detection task can meet accuracy requirements while balancing the energy consumption and load of each device.

[0036] In step 106, the generation of the sensor solution needs to comprehensively consider the characteristics of the current scene, the hardware characteristics of the device, and key state parameters. For example, in scenarios with large fluctuations in environmental parameters, multiple types of sensors will be selected for collaborative detection, devices with high hardware precision will be assigned higher detection precision tasks, and devices with low battery power will have their detection frequency reduced. The solution includes sensor type combinations, detection frequency, accuracy thresholds, etc., and its purpose is to ensure that the operating status of the sensors is accurately matched with the current environmental requirements and device capabilities.

[0037] During testing according to the plan, the device monitors changes in scene characteristic parameters and fluctuations in its own key states in real time. When environmental parameters exceed typical ranges or the battery level drops significantly, it dynamically updates the sensor type combination and operating parameters. For example, it adds an adaptive sensor when the environment changes abruptly, and switches to a low-power mode when the battery is too low. The purpose of this step is to ensure that the sensors always operate in optimal condition through dynamic adjustment, thereby maximizing the device's battery life while ensuring the validity of the detection data. This solves the problem of balancing accuracy and energy consumption under traditional fixed strategies.

[0038] In some specific embodiments, the process of determining scene nodes specifically includes: extracting route information, including the starting point, ending point, key waypoints, and expected stopping areas, from the team's travel plan; combining this information with a geographic information system (GIS) to obtain data on the route's terrain type, elevation change rate, vegetation coverage, and electromagnetic environment zoning; dividing the travel route into multiple consecutive scene nodes based on terrain type change thresholds, elevation gradient abrupt change points, vegetation coverage jump intervals, and electromagnetic environment zoning boundaries; and for each scene node, determining its scene characteristics by retrieving historical environmental monitoring data from the GIS. These scene characteristics include typical ranges of environmental parameters and signal transmission loss coefficients. The process for dividing scene nodes and determining task priorities is as follows: Figure 2 As shown.

[0039] Extracting route information from the team's travel plan is fundamental to determining scene nodes. The start and end points of the route define the overall scope of the journey, key waypoints are typically landmark locations along the route, and the expected stopping areas are areas where the team is likely to spend considerable time. This information collectively outlines the route framework to be analyzed. Combining terrain type, elevation change rate, vegetation cover, and electromagnetic environment zoning data obtained from a geographic information system provides a quantitative basis for subsequent node division. This data directly reflects the environmental differences between different sections of the route and is the core indicator for distinguishing scene nodes.

[0040] The core of scene node division is defining continuous areas based on abrupt changes in environmental characteristics. The terrain type change threshold refers to the critical value at which the terrain type undergoes a significant change; when the route crosses this threshold, it is designated as a node. Altitude gradient abrupt changes are locations where the rate of altitude change exceeds the normal range. Vegetation coverage jump intervals refer to vegetation coverage changes exceeding a preset proportion over a short distance. Electromagnetic environment zoning boundaries are the lines where electromagnetic interference levels change. Based on these criteria, the travel route is divided into multiple continuous scene nodes. Within each node, the terrain, altitude, vegetation, and electromagnetic environment characteristics remain relatively stable, providing a clear environmental unit for subsequent targeted sensor control.

[0041] When determining scene characteristics for each scene node, historical environmental monitoring data from the Geographic Information System (GIS) is crucial. This historical data includes statistical results of long-term monitoring of environmental parameters such as temperature, humidity, air pressure, and wind speed in the area. Analyzing this data allows us to determine the typical range of environmental parameters, i.e., the normal fluctuation range of environmental parameters within the node. For example, the temperature of a mountain node is typically 5-15℃, and the humidity is 60%-80%. The signal transmission loss coefficient is calculated based on historical data to reflect the signal attenuation characteristics during transmission at the node, reflecting the signal's transmission capability in that environment. For example, the radio signal attenuation is 20dB per kilometer at a forest node and 5dB per kilometer at an open area node.

[0042] Determining the scene characteristics provides a fundamental reference for subsequent processes. The typical range of environmental parameters determines the key parameters that sensors need to monitor and the accuracy requirements; the signal transmission loss coefficient affects the control strategies of the communication module and positioning sensors. Simultaneously, these characteristics are also the core basis for determining task priorities, ensuring that subsequent sensor control strategies are accurately adapted to the node environment.

[0043] In some specific embodiments, the process of determining the current scene node includes: controlling each smart terminal to activate the low-power sensor module to perform scanning, the low-power sensor module including a low-power temperature and humidity sensor, a signal strength detector and an accelerometer; dividing the smart terminals into at least two alternating scanning batches, with at least one batch of devices prioritizing the scanning of environmental feature parameters and at least one batch of devices prioritizing the scanning of signal feature parameters, and comprehensively obtaining real-time scene feature parameters; calculating the feature similarity between the real-time scene feature parameters and the feature parameters of each scene node in the scene feature library; when the feature similarity of a certain scene node exceeds a preset matching threshold, and the condition is met in three consecutive scans, the current scene node is determined to be that node.

[0044] Controlling the activation of low-power sensor modules on each smart terminal to perform scanning is fundamental to acquiring real-time environmental information. Low-power temperature and humidity sensors collect current environmental temperature and humidity data, which are core components of environmental characteristic parameters. Signal strength detectors monitor the strength of satellite and terrestrial communication signals, providing data support for signal characteristic parameters. Accelerometers can assist in determining environmental features such as terrain undulations by detecting the device's motion. The purpose of using low-power modules is to minimize device power consumption and extend battery life while ensuring data acquisition.

[0045] Dividing smart terminals into at least two scanning batches and alternating between them is a key design principle for balancing data comprehensiveness and energy consumption. At least one batch prioritizes scanning environmental characteristic parameters, and at least one batch prioritizes scanning signal characteristic parameters. This division of labor avoids concentrated energy consumption caused by all devices scanning at high frequencies simultaneously, while ensuring that both types of key parameters are effectively collected. By integrating the scanning results from different batches, complete real-time scene characteristic parameters can be obtained, providing a comprehensive data foundation for subsequent scene matching.

[0046] Calculating the feature similarity between real-time scene feature parameters and the parameters of each node in the scene feature library is the core step in achieving scene matching. Feature similarity reflects the degree of matching by quantifying the difference between real-time parameters and parameters in the library; a higher value indicates that the current environment is closer to the features of the target scene node. A preset matching threshold is the critical criterion for determining a basic match, ensuring that only scene nodes with sufficiently similar features are included as candidates.

[0047] Three consecutive scans must meet the similarity threshold to avoid misjudgments caused by accidental errors in a single scan. Environmental parameters and signal characteristics may fluctuate due to transient interference (such as sudden electromagnetic pulses or short-term airflow changes), and a single match may not reflect the real scene. Three consecutive verifications filter out the influence of transient interference, ensuring the stability and accuracy of the judgment results. When this condition is met, the current scene node can be determined, providing a precise environmental reference for the subsequent formulation of sensor control strategies.

[0048] In some specific embodiments, hardware characteristic parameters are pre-stored in the device's non-volatile memory, including sensor type, theoretical accuracy level of the sensor, maximum transmission distance of the communication module, hardware anti-interference level, and rated operating current of each module; key status parameters include remaining battery percentage, real-time satellite signal reception strength, RSSI value of ground communication signal, number of currently active sensors, and current CPU operating frequency; low-power communication modules include BLE modules or LoRaWAN modules.

[0049] Hardware characteristic parameters are pre-stored in the device's non-volatile memory. This design ensures that core hardware information is retained even after power failure or restart, providing stable foundational data for device initialization and collaboration with other nodes. Sensor types clearly define the detection components on the device, such as temperature and humidity sensors, gas detectors, and GPS modules, directly determining the types of detection tasks the device can perform. The theoretical accuracy level of the sensors quantifies the inherent detection capabilities of the hardware and serves as a benchmark for evaluating the reliability of detection data. The maximum transmission distance of the communication module defines the device's communication coverage, affecting the accessibility of neighboring node identification and information exchange. The hardware anti-interference level reflects the device's stable operation in complex environments such as electromagnetic interference and vibration. The rated operating current of each module provides key parameters for calculating device energy consumption and balancing task load. These parameters collectively constitute the basis for evaluating the device's hardware capabilities, enabling other nodes to quickly determine whether it is suitable for the task requirements of the current scenario.

[0050] Key status parameters dynamically reflect the real-time operating status of the equipment. These parameters are continuously collected by sensors and internal monitoring modules, providing timely data for dynamic decision-making. The remaining battery percentage directly relates to the equipment's endurance and is a core indicator of its ability to handle high-energy-consuming tasks. Real-time satellite signal reception strength reflects the working quality of the positioning module; insufficient strength necessitates adjusting the navigation strategy or switching the assisted positioning mode. The RSSI value of the ground communication signal quantifies the communication link quality between the equipment and neighboring nodes; a low value requires optimizing the broadcast frequency or switching the communication channel. The number of currently active sensors directly reflects the equipment's real-time load; an excessive number may lead to CPU overload or a surge in energy consumption. The current CPU operating frequency reflects the equipment's computing power; high-frequency mode can support complex data processing but consumes more energy. These dynamic parameters, combined with hardware characteristic parameters, form the basis for evaluating the equipment's real-time capabilities, ensuring that task allocation matches the equipment's current state and avoiding performance degradation due to overload or insufficient capacity.

[0051] The selection of low-power communication modules is shown in the attached figure. Figure 3 As shown, low-power communication modules serve as the physical carrier for parameter broadcasting between devices. The selection of BLE and LoRaWAN modules balances communication efficiency with energy consumption control. BLE modules are suitable for short-distance, low-data-volume interaction scenarios, and their sleep-wake mechanism can significantly reduce energy consumption during idle periods, making them suitable for close-range collaboration in densely populated areas. LoRaWAN modules, on the other hand, support long-distance communication and have strong anti-interference capabilities, making them suitable for long-distance node interaction in open or complex terrain areas. Both types of modules feature low power consumption, with the energy consumption of a single broadcast being only 10%-30% of that of traditional communication modules, effectively extending device battery life. Through these modules, devices can efficiently broadcast hardware characteristic parameters and key status parameters to neighboring nodes, providing real-time and accurate basic data for global performance evaluation and node selection, serving as crucial communication support for distributed collaborative control.

[0052] In some specific embodiments, task priorities include navigation priority, communication priority, and detection priority. When the signal stability index corresponding to the signal feature parameters in the scene features has the highest weight influencing the feasibility of the navigation task, and this index deviates from a preset stability threshold, causing the navigation task to fail to achieve basic positioning accuracy, then the navigation task is determined to have priority. When the environmental complexity index corresponding to the environmental feature parameters in the scene features has the highest weight influencing the feasibility of the detection task, and this index exceeds a preset normal threshold, causing the detection task to fail to acquire effective environmental data, then the detection task is determined to have priority. When the signal transmission capability index corresponding to the signal feature parameters in the scene features has the highest weight influencing the feasibility of the communication task, and this index is lower than a preset transmission threshold, causing the communication task to fail to complete basic data interaction, then the communication task is determined to have priority.

[0053] Task prioritization is based on the degree of influence of scene characteristics on different tasks. Clear judgment conditions ensure that priorities are accurately matched with scene requirements. The three types of priority—navigation priority, communication priority, and detection priority—correspond to the environmental characteristics that play a dominant role in navigation, communication, and detection tasks in the scene, respectively. The core logic is to identify the scene parameters that have the greatest impact on task feasibility and determine the priority in combination with threshold conditions, providing guidance for subsequent sensor control and node selection.

[0054] Navigation priority is determined by the signal stability index corresponding to the signal characteristic parameters. The signal stability index is typically composed of factors such as signal transmission loss coefficient and signal fluctuation frequency within the scene, directly affecting the continuity and accuracy of navigation and positioning. When the weight of this index on the feasibility of the navigation task exceeds that of other scene parameters, it means that signal stability is the core factor for the normal execution of navigation. A preset stability threshold is a critical value to ensure basic positioning accuracy. If the signal stability index deviates from this threshold, it will lead to problems such as positioning drift and signal loss, making navigation unable to meet the basic needs of the team's movement. In this case, navigation priority is determined, and resources can be prioritized to ensure the operation of navigation-related sensors, ensuring reliable positioning.

[0055] The determination of detection priority is based on the environmental complexity index corresponding to environmental characteristic parameters. The environmental complexity index consists of the typical value range and fluctuation range of environmental parameters, reflecting the complexity of environmental factors in the scene. When this index has the highest weight in influencing the feasibility of the detection task, it indicates that environmental complexity is the key factor determining the validity of the detection data. A preset conventional threshold represents the environmental range within which the detection equipment can normally acquire valid data. If the environmental complexity index exceeds this threshold, the detection data will be distorted or invalid. In this case, detection priority is determined, allowing for the activation of sensors adapted to complex environments and an increase in detection frequency to ensure the reliability of environmental data.

[0056] The determination of communication priority relies on the signal transmission capability index corresponding to the signal characteristic parameters. The signal transmission capability index, composed of factors such as signal transmission loss coefficient and communication distance attenuation rate, determines the success rate and efficiency of data interaction between devices. When this index has the highest weight in influencing the feasibility of the communication task, it indicates that signal transmission capability is a core element ensuring device collaboration. A preset transmission threshold is the minimum signal requirement for achieving basic data interaction. If the signal transmission capability index falls below this threshold, it will lead to data packet loss, communication interruption, and inability to complete basic collaboration between devices. In this case, communication priority is determined, and communication module parameters can be adjusted first, while ensuring the energy consumption of communication-related sensors to guarantee smooth information exchange between devices.

[0057] In some specific embodiments, the evaluation weights of hardware feature parameters and key state parameters are assigned according to task priority. By fusing the weighted hardware feature parameters and key state parameters, the efficiency energy consumption ratio of neighboring nodes for different roles is calculated. This efficiency energy consumption ratio characterizes the role efficiency that a node can achieve per unit of energy consumption under the current task priority. Pheromones are set for roles corresponding to different task priorities. The concentration of each type of pheromone is correlated with the efficiency energy consumption ratio of the corresponding role, and the sensitivity of the correlation is positively correlated with the task priority of the role.

[0058] The core principle of allocating evaluation weights based on task priority is to ensure that parameters highly correlated with priority tasks dominate the evaluation, achieving a precise match between the evaluation results and scenario requirements. For example, when navigation is the priority, the weight of hardware characteristic parameters such as the theoretical accuracy and anti-interference level of the positioning module, as well as key state parameters such as real-time satellite signal strength and battery power supply stability to the navigation module, will be significantly increased. If detection is the priority, the weight of hardware parameters such as the detection accuracy and response speed of environmental sensors, as well as state parameters such as the operating status of currently active sensors and the remaining battery power's support capacity for the detection module, will be tilted. The principle behind this weight allocation method is that the successful execution of priority tasks is more dependent on specific hardware capabilities and real-time status. By increasing the weight of related parameters, the suitability of a node for undertaking the task can be more accurately evaluated.

[0059] The performance-to-energy ratio (SPR) is calculated by combining weighted hardware characteristic parameters with key state parameters, providing a quantitative assessment that integrates node capabilities and energy consumption. Specifically, a weighted summation is used to obtain a hardware capability score and a state adaptation score. Then, the combined score is divided by the energy consumption per unit time; the resulting ratio is the SPR. A higher SPR indicates that the node achieves greater performance per unit of energy consumption for the current task priority. This metric provides an objective quantitative basis for subsequent node selection, avoiding the bias of considering only capabilities or only energy consumption.

[0060] Assigning specific pheromone types to roles corresponding to different task priorities is a key design feature that allows ant colony optimization (ACO) to specifically identify nodes suitable for each role. For example, navigation priority corresponds to navigation pheromones, detection priority to detection pheromones, and communication priority to communication pheromones. Each pheromone independently reflects the performance level of its corresponding role. The concentration of each type of pheromone is positively correlated with the performance-energy consumption ratio of the corresponding role; that is, nodes with higher performance-energy consumption ratios have higher pheromone concentrations, attracting more algorithmic decisions to favor them.

[0061] The sensitivity of the association is positively correlated with the role's task priority, meaning that the higher the priority of a role, the more drastic the change in its pheromone concentration with the efficiency-energy ratio. For example, in a detection-priority scenario, the detection pheromone concentration is more sensitive to changes in the efficiency-energy ratio than navigation or communication pheromones. When the detection efficiency-energy ratio of a node increases slightly, its detection pheromone concentration will increase significantly; conversely, if the efficiency ratio decreases, the concentration will also decrease rapidly. The principle behind this design is to enable high-priority tasks to lock onto the optimal node more quickly, ensuring priority resource allocation for critical tasks and improving overall collaboration efficiency.

[0062] In some specific embodiments, the selection of detection nodes includes: each smart terminal first receives the detection pheromone concentration of neighboring nodes, and then forwards its own and neighboring nodes' pheromone data to adjacent devices, completing the distributed sharing of global pheromone data; the calculation of the pheromone gradient is based on the global pheromone concentration distribution, with the gradient direction pointing towards nodes with increasing detection pheromone concentrations globally; selection conditions for detection nodes, including a minimum pheromone concentration threshold and a selection range, are set based on the environmental complexity and task priority of the nodes in the current scenario; a preset ant colony algorithm is used to sort the global smart terminals according to the pheromone gradient through local collaborative calculations of each device, and selects nodes that meet the selection conditions as detection nodes; a load balancing mechanism is maintained during the selection process, and the selection priority of nodes that have already assumed navigation or communication roles as detection nodes is reduced to avoid overload of a single device with multiple roles. The selection of detection nodes is shown in the attached figure. Figure 4 As shown.

[0063] In some specific embodiments, the sensor scheme generation process includes: controlling the detection node to select suitable sensors from the sensor types included in its own hardware feature parameters based on the typical range of environmental parameters and signal transmission loss coefficient of the current scene node; and determining the sensor detection frequency and detection accuracy based on the sensor's theoretical accuracy level in its own hardware feature parameters and the remaining battery power percentage and current CPU operating frequency in key state parameters, thereby obtaining the sensor scheme.

[0064] The process of controlling the detection node to select suitable sensors is based on a precise match between scene characteristics and device hardware capabilities. The typical range of environmental parameters for the current scene node clarifies the environmental range that the detection needs to cover. The detection node will select sensors that can operate stably within this range from the sensor types included in its own hardware characteristics. The signal transmission loss coefficient reflects the signal attenuation characteristics in the scene. If the coefficient is high, sensors with anti-interference capabilities will be prioritized to avoid data distortion due to transmission loss. The core of this selection logic is to ensure that the inherent performance of the sensor is compatible with the constraints of the scene environment, thus ensuring the validity of the detection data.

[0065] Determining the sensor's detection frequency and accuracy requires balancing hardware capabilities with real-time limitations. The sensor's theoretical accuracy level, a key hardware characteristic parameter, serves as the baseline for setting accuracy, but it needs to be dynamically adjusted based on critical state parameters: when the battery's remaining percentage is below 20%, the detection accuracy will be increased to 1.5 times the theoretical value to extend battery life; when the CPU's current operating frequency is below a preset threshold, the detection frequency will be reduced (e.g., from once per second to once every 3 seconds) due to limited data processing capabilities to avoid data backlog. Conversely, when the battery is fully charged and the CPU is running at a high frequency, parameters can be set according to the highest frequency required by the theoretical accuracy and the scenario. In this way, the final sensor solution can meet the detection needs of the current scenario while adapting to the real-time operating status of the equipment, achieving a balance between performance and energy consumption.

[0066] In some specific embodiments, during the detection process according to the generated sensor scheme, changes in the current scene's characteristic parameters and fluctuations in the device's own key state parameters are monitored in real time. The sensor type combination and operating parameters are dynamically updated to maintain a balance between detection efficiency and energy consumption. Real-time monitoring is a prerequisite for dynamic adjustment, and its core is the continuous tracking of changes in two types of key parameters. Changes in the current scene's characteristic parameters mainly include whether environmental parameters exceed typical value ranges and whether the signal transmission loss coefficient exhibits abnormal fluctuations. These changes directly reflect whether the environment deviates from the expected state. Fluctuations in the device's own key state parameters focus on the rate of decrease in the remaining battery power percentage, the stability of the CPU's current operating frequency, changes in real-time signal reception strength, and the operating temperature of activated sensors. These parameters reflect whether the device can continuously support the operation of the current sensor scheme. Through high-frequency sampling and analysis, subtle changes in the environment and device state can be captured in a timely manner, providing trigger signals for subsequent adjustments and preventing detection data failure or energy consumption runaway due to parameter mutations.

[0067] Dynamically updating the sensor type combination and operating parameters is a key step in maintaining balance, and its adjustment logic is closely linked to the monitored changes. When scene characteristic parameters change, it triggers an update to the sensor type combination, such as adding high-temperature resistant sensors to supplement data dimensions, or disabling sensors that cannot operate in extreme environments to reduce unnecessary energy consumption. When its own key state parameters fluctuate, the focus is on adjusting operating parameters: such as reducing the detection frequency to reduce energy consumption, and appropriately relaxing the detection accuracy to match the processing power of the CPU when running at low frequencies. If the signal transmission loss coefficient suddenly increases, sensors with high anti-interference levels will be prioritized, and the single detection time will be shortened to reduce data transmission volume. This dynamic adjustment mechanism ensures that the sensors always operate in the optimal mode adapted to the current environment and equipment status, avoiding the decline in detection performance caused by sudden environmental changes and preventing energy waste caused by equipment deterioration, ultimately achieving a dynamic balance between detection performance and energy consumption.

[0068] This application also proposes an intelligent terminal sensor control system based on ant colony algorithm, and the system module diagram is attached. Figure 5 As shown, the system includes: Scene segmentation unit 1 is used to determine multiple scene nodes and scene characteristics of scene nodes in the route based on the team's travel plan and geographic information system, and to determine the task priority of each scene node based on the scene characteristics. Scene determination unit 2 is used to control each smart terminal to obtain scene feature parameters through low-power scanning, match the scene feature parameters with the parameters in the scene feature library, and determine the current scene node. The parameter broadcasting unit 3 is used to control each smart terminal to periodically broadcast the pre-stored hardware feature parameters and the collected key status parameters to the surrounding neighboring nodes at a dynamically adjusted frequency through the low-power communication module. Concentration mapping unit 4 is used to evaluate the efficiency and energy consumption ratio based on task priority, hardware feature parameters of neighboring nodes and key state parameters, and to map the evaluation results to the pheromone concentration in the ant colony algorithm. The node selection unit 5 is used to determine the pheromone gradient of the smart terminal and, in combination with the scene characteristics of the current scene nodes, to select one or more smart terminals as detection nodes in a distributed manner using a preset ant colony algorithm. The node detection unit 6 is used to control the detection node to generate a sensor scheme by combining the scene characteristics of the current scene node with its own hardware feature parameters and key state parameters, and to perform detection according to the sensor scheme.

[0069] This application proposes a smart terminal sensor control method and system based on ant colony algorithm. Through the synergy of scenario adaptation, dynamic evaluation, intelligent point selection and fine control, it realizes the transformation of sensor control from fixed and coarse control to scenario-based, fine and adaptive control. This enables the sensor control strategy to be accurately matched with environmental requirements, ensures the dynamic balance between energy consumption and accuracy and the optimal allocation of sensor resources, and comprehensively improves the accuracy, adaptability and energy economy of smart terminal sensor control. It fundamentally solves the problems of insufficient control accuracy and adaptability defects in existing solutions.

[0070] Those skilled in the art will understand that the modules described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using computer-executable program code, allowing them to be stored in a storage system for execution by the computing system. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0071] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

[0072] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A smart terminal sensor control method based on ant colony algorithm, characterized in that, include: Based on the team's travel plan and geographic information system, multiple scene nodes and scene characteristics of the scene nodes are identified in the route, and the task priority of each scene node is determined based on the scene characteristics; the task priority includes navigation priority, communication priority, and detection priority. Each intelligent terminal is controlled to acquire scene feature parameters through low-power scanning, and the scene feature parameters are matched with parameters in the scene feature library to determine the current scene node. Each intelligent terminal is controlled to periodically broadcast pre-stored hardware characteristic parameters and collected key status parameters to neighboring nodes at a dynamically adjusted frequency via a low-power communication module. The hardware characteristic parameters are pre-stored in the device's non-volatile memory and include sensor type, sensor theoretical accuracy level, maximum transmission distance of communication module, hardware anti-interference level, and rated operating current of each module. The key status parameters include remaining battery percentage, real-time satellite signal reception strength, ground communication signal RSSI value, number of currently active sensors, and current CPU operating frequency. The efficiency and energy consumption ratio are evaluated based on task priority, hardware characteristic parameters of neighboring nodes, and key state parameters, and the evaluation results are mapped to the pheromone concentration in the ant colony algorithm. Determine the pheromone gradient of the smart terminal, and combine it with the scene characteristics of the current scene nodes. Then, use a pre-set ant colony algorithm to distribute and select one or more smart terminals as detection nodes. The control detection node combines the scene characteristics of the current scene node with its own hardware characteristic parameters and key state parameters to generate a sensor scheme, and performs detection according to the sensor scheme; Specifically, the evaluation weights of hardware feature parameters and key state parameters are assigned according to task priority. By fusing the weighted hardware feature parameters and key state parameters, the efficiency energy consumption ratio of neighboring nodes for different roles is calculated. This efficiency energy consumption ratio represents the role efficiency that a node can achieve per unit of energy consumption under the current task priority. Pheromones are set for roles corresponding to different task priorities. The concentration of each type of pheromone is correlated with the efficiency energy consumption ratio of the corresponding role, and the sensitivity of the correlation is positively correlated with the task priority of the role.

2. The intelligent terminal sensor control method according to claim 1, characterized in that, The process of determining the scene nodes specifically includes: Extract route information, including the starting point, ending point, key waypoints, and expected stop areas, from the team's travel plan, and combine it with geographic information system to obtain the route's terrain type, elevation change rate, vegetation coverage, and electromagnetic environment zoning data. The route was divided into multiple continuous scene nodes based on the threshold of terrain type change, abrupt change point of altitude gradient, jump interval of vegetation coverage, and boundary of electromagnetic environment. For each scene node, historical environmental monitoring data is retrieved through a geographic information system to determine the scene characteristics of that scene node. The scene characteristics include the typical range of environmental parameters and the signal transmission loss coefficient.

3. The intelligent terminal sensor control method according to claim 1, characterized in that, The process of determining the current scene node specifically includes: Each intelligent terminal is controlled to activate a low-power sensor module to perform scanning. The low-power sensor module includes a low-power temperature and humidity sensor, a signal strength detector, and an accelerometer. The smart terminal is divided into at least two scanning batches that scan alternately at intervals. At least one batch of devices prioritizes scanning environmental feature parameters, and at least one batch of devices prioritizes scanning signal feature parameters. The real-time scene feature parameters are obtained by combining them. Calculate the feature similarity between the real-time scene feature parameters and the parameters of each scene node in the scene feature library; when the feature similarity of a certain scene node exceeds the preset matching threshold, and the condition is met in 3 consecutive scans, the current scene node is determined to be that node.

4. The intelligent terminal sensor control method according to claim 1, characterized in that, The low-power communication module includes a BLE module or a LoRaWAN module.

5. The intelligent terminal sensor control method according to claim 1, characterized in that, The task priorities include navigation priority, communication priority, and detection priority. When the signal stability index corresponding to the signal feature parameter in the scene features has the highest weight influencing the feasibility of the navigation task, and when the index deviates from the preset stability threshold, it will cause the navigation task to fail to achieve basic positioning accuracy, then the navigation task is determined to be prioritized. When the environmental complexity index corresponding to the environmental feature parameter in the scene features has the highest weight influencing the feasibility of the detection task, and when this index exceeds the preset conventional threshold, it will cause the detection task to fail to obtain effective environmental data, then the detection task is determined to be prioritized. When the signal transmission capability index corresponding to the signal feature parameter in the scene features has the highest weight in influencing the feasibility of the communication task, and when this index is lower than the preset transmission threshold, the communication task will be unable to complete basic data interaction, then the communication task is determined to be prioritized.

6. The intelligent terminal sensor control method according to claim 1, characterized in that, The selection of the detection nodes includes: Each smart terminal first receives the detected pheromone concentration from neighboring nodes, and then forwards its own and neighboring nodes' pheromone data to adjacent devices, thus completing the distributed sharing of global pheromone data. The pheromone gradient is calculated based on the global pheromone concentration distribution, and the gradient direction points to the nodes where the pheromone concentration increases globally. Based on the environmental complexity and task priority of the current scene nodes, the selection conditions for detection nodes are set, including the minimum pheromone concentration threshold and the selection quantity range. The preset ant colony algorithm uses local collaborative computing on each device to sort the global smart terminals according to the pheromone gradient and select nodes that meet the selection criteria as detection nodes. During the selection process, a load balancing mechanism is retained. Nodes that have already assumed navigation or communication roles are given lower priority when selected as detection nodes to avoid overloading of a single device with multiple roles.

7. The intelligent terminal sensor control method according to claim 1, characterized in that, The process of generating the sensor scheme includes: The control detection node selects suitable sensors from the sensor types included in its own hardware characteristic parameters based on the typical range of environmental parameters and signal transmission loss coefficient of the current scene node. Based on the sensor's theoretical accuracy level from its own hardware characteristic parameters and key state parameters such as the remaining battery power percentage and the current CPU operating frequency, the sensor's detection frequency and detection accuracy are determined, thus obtaining the sensor solution.

8. The intelligent terminal sensor control method according to claim 1, characterized in that, During the detection process according to the generated sensor scheme, the changes in the current scene feature parameters and the fluctuations in its own key state parameters are monitored in real time, and the sensor type combination and operating parameters are dynamically updated to maintain a balance between detection efficiency and energy consumption.

9. A smart terminal sensor control system based on ant colony algorithm, characterized in that, include: The scene segmentation unit is used to determine multiple scene nodes and scene characteristics of the scene nodes in the route based on the team's travel plan and geographic information system, and to determine the task priority of each scene node based on the scene characteristics; the task priority includes navigation priority, communication priority, and detection priority. The scene determination unit is used to control each smart terminal to obtain scene feature parameters through low-power scanning, match the scene feature parameters with parameters in the scene feature library, and determine the scene node where it is currently located. The parameter broadcasting unit controls each intelligent terminal to periodically broadcast pre-stored hardware characteristic parameters and collected key status parameters to neighboring nodes at a dynamically adjusted frequency via a low-power communication module. The hardware characteristic parameters are pre-stored in the device's non-volatile memory and include sensor type, theoretical sensor accuracy level, maximum transmission distance of the communication module, hardware anti-interference level, and rated operating current of each module. The key status parameters include remaining battery percentage, real-time satellite signal reception strength, ground communication signal RSSI value, number of currently active sensors, and current CPU operating frequency. The concentration mapping unit is used to evaluate the efficiency and energy consumption ratio based on task priority, hardware feature parameters of neighboring nodes and key state parameters, and to map the evaluation results to the pheromone concentration in the ant colony algorithm. The node selection unit is used to determine the pheromone gradient of the smart terminal and, in combination with the scene characteristics of the current scene nodes, to select one or more smart terminals as detection nodes in a distributed manner using a preset ant colony algorithm. The node detection unit is used to control the detection node to generate a sensor scheme by combining the scene characteristics of the current scene node with its own hardware feature parameters and key state parameters, and to perform detection according to the sensor scheme. Specifically, the evaluation weights of hardware feature parameters and key state parameters are assigned according to task priority. By fusing the weighted hardware feature parameters and key state parameters, the efficiency energy consumption ratio of neighboring nodes for different roles is calculated. This efficiency energy consumption ratio represents the role efficiency that a node can achieve per unit of energy consumption under the current task priority. Pheromones are set for roles corresponding to different task priorities. The concentration of each type of pheromone is correlated with the efficiency energy consumption ratio of the corresponding role, and the sensitivity of the correlation is positively correlated with the task priority of the role.

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