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

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent terminals, in particular, to an intelligent terminal sensor control method and system based on an ant colony algorithm. BACKGROUND

[0002] In complex environments such as emergency rescue, field work, and geological exploration, intelligent terminals such as three-proofing equipment (specialized equipment with waterproof, dustproof, and impact-proof performance) need to collect environmental data and equipment status information in real time through various sensors to provide key basis for team decision-making and safety assurance.

[0003] The existing sensor control scheme of intelligent terminals has obvious limitations. Most schemes use fixed control strategies, such as presetting uniform sampling frequency, detection accuracy, and running mode, without dynamic adjustment in combination with actual scenarios. For example, in complex mountainous environments, environmental parameters fluctuate dramatically, and fixed low-frequency sampling cannot capture key changes, resulting in insufficient detection accuracy; while in stable environments such as open plains, high-frequency sampling causes unnecessary energy waste, and the control is not adapted to the characteristics of the sensors, further exacerbating data errors.

[0004] Therefore, the existing sensor control scheme of intelligent terminals has significant shortcomings, and it is difficult to meet the high requirements for sensor data reliability in complex scenarios, and there is an urgent need for a technical solution that can dynamically adjust the sensor control strategy according to the scene characteristics and equipment status. SUMMARY

[0005] Based on the problems existing in the prior art, the present application provides an intelligent terminal sensor control method and system based on an ant colony algorithm. The specific scheme is as follows:

[0006] An intelligent terminal sensor control method based on an ant colony algorithm, comprising:

[0007] Based on the team travel plan and geographic information system, determine 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;

[0008] Control each intelligent terminal to obtain scene characteristic parameters through low-power scanning, match the scene characteristic parameters with the parameters in the scene characteristic library, and determine the scene node currently located in;

[0009] Control each intelligent terminal to periodically broadcast the pre-stored hardware characteristic parameters and the collected key state parameters to the surrounding neighbor nodes at a dynamically adjusted frequency through a low-power communication module;

[0010] Based on the task priority, the hardware characteristic parameters and the key state parameters of the neighbor nodes, perform efficiency-energy consumption ratio evaluation, and map the evaluation results to the pheromone concentration in the ant colony algorithm;

[0011] determine pheromone gradient of the intelligent terminal, combine scene features of the current scene node, and select one or more intelligent terminals as detection nodes through a preset ant colony algorithm in a distributed manner;

[0012] The control detection node combines scene features of the current scene node and hardware feature parameters and key state parameters of the detection node to generate a sensor scheme, and performs detection according to the sensor scheme.

[0013] In some specific embodiments, the determination process of the scene node specifically includes:

[0014] Extract route information including a route starting point, an ending point, a key passing point, and a predicted stay area from the team travel plan, and combine geographic information system to obtain terrain type, altitude change rate, vegetation coverage rate, and electromagnetic environment partition data of the route;

[0015] Divide the travel route into a plurality of continuous scene nodes by taking terrain type change threshold, altitude gradient mutation point, vegetation coverage rate jump interval, and electromagnetic environment partition boundary as division bases.

[0016] For each scene node, determine scene features of the scene node by calling historical environment monitoring data through the geographic information system, and the scene features include a typical value range of an environmental parameter and a signal transmission loss coefficient.

[0017] In some specific embodiments, the process of determining the current scene node specifically includes:

[0018] Control the intelligent terminals to activate low-power sensor modules to perform scanning, and the low-power sensor modules include a low-power temperature and humidity sensor, a signal strength detector, and an accelerometer.

[0019] Divide the intelligent terminals into at least two scanning batches that alternately scan, at least one batch of devices preferentially scans environmental feature type parameters, at least one batch of devices preferentially scans signal feature type parameters, and real-time scene feature parameters are obtained by integration.

[0020] Calculate feature similarity of the real-time scene feature parameters and scene node parameters in the scene feature library; when the feature similarity of a scene node exceeds a preset matching threshold, and the condition is met for three consecutive scans, the current scene node is determined to be the scene node.

[0021] In some specific embodiments, the hardware feature parameters are pre-stored in a non-volatile memory of the device, and include a sensor type, a sensor theoretical accuracy level, a maximum transmission distance of a communication module, a hardware anti-interference level, and a rated working current of each module.

[0022] The key state parameters include a battery remaining percentage, a real-time satellite signal receiving strength, a ground communication signal RSSI value, a number of currently activated sensors, and a current CPU working frequency.

[0023] The low-power communication module includes a BLE module or a LoRaWAN module.

[0024] In some specific embodiments, the task priority includes navigation priority, communication priority, and detection priority.

[0025] When the signal stability index corresponding to the signal feature parameter in the scene feature has the highest weight proportion of influence on the feasibility of the navigation task, and the index deviates from the preset stability threshold, which leads to the navigation task failing to achieve basic positioning accuracy, it is determined that the navigation task is prioritized.

[0026] When the environmental complexity index corresponding to the environmental feature parameter in the scene feature has the highest weight proportion of influence on the feasibility of the detection task, and the index exceeds the preset regular threshold, which leads to the detection task failing to obtain effective environmental data, it is determined that the detection task is prioritized.

[0027] When the signal transmission capability index corresponding to the signal feature parameter in the scene feature has the highest weight proportion of influence on the feasibility of the communication task, and the index is lower than the preset transmission threshold, which leads to the communication task failing to complete basic data interaction, it is determined that the communication task is prioritized.

[0028] In some specific embodiments, the evaluation weights of the hardware feature parameters and the key state parameters are allocated according to the task priority, and the efficiency-energy consumption ratio of the neighbor node for different roles is calculated by fusing the weighted hardware feature parameters and key state parameters, which represents the role efficiency per unit energy consumption of the node under the current task priority.

[0029] Different types of pheromones are set for different roles corresponding to different task priorities, and the concentration of each type of pheromone is associated with the efficiency-energy consumption ratio of the corresponding role, and the sensitivity of the association is positively correlated with the task priority of the role.

[0030] In some specific embodiments, the selection of the detection node includes:

[0031] Each intelligent terminal first receives the detection pheromone concentration of the neighbor node, and then forwards the pheromone data of itself and the neighbor node to the adjacent device to complete the distributed sharing of global pheromone data.

[0032] The calculation of the pheromone gradient is based on the global pheromone concentration distribution, and the gradient direction points to the node with increasing detection pheromone concentration in the global range.

[0033] The selection conditions of the detection node include a minimum pheromone concentration threshold and a selected number interval, which are set according to the environmental complexity of the current scene node and the task priority;

[0034] The preset ant colony algorithm sorts global intelligent terminals according to pheromone gradients through local collaborative calculation of each device, and selects nodes meeting the selection conditions as detection nodes;

[0035] In the selection process, a load balancing mechanism is maintained, and the selection priority of a node that has assumed a navigation or communication role as a detection node is reduced to avoid overloading of a single device with multiple roles.

[0036] In some specific embodiments, the generation process of the sensor scheme includes:

[0037] The detection node selects an adaptive sensor from the sensor types included in its own hardware characteristic parameters according to the typical value range of the environmental parameters of the current scene node and the signal transmission loss coefficient;

[0038] Based on the sensor theoretical accuracy level in the own hardware characteristic parameters and the battery remaining capacity percentage and CPU current working frequency in the key state parameters, the detection frequency and detection accuracy of the sensor are determined, and a sensor scheme is obtained.

[0039] In some specific embodiments, during the detection process performed according to the generated sensor scheme, the changes in the current scene characteristic parameters and the fluctuations in the own key state parameters are monitored in real time, and the type combination and operating parameters of the sensor are dynamically updated to maintain the balance between detection efficiency and energy consumption.

[0040] An intelligent terminal sensor control system based on an ant colony algorithm, comprising:

[0041] A scene division unit is configured to determine a plurality of scene nodes in a route and scene characteristics of the scene nodes based on a team travel plan and a geographic information system, and determine a task priority of each scene node based on the scene characteristics;

[0042] A scene determination unit is configured to control each intelligent terminal to obtain scene characteristic parameters through low-power scanning, match the scene characteristic parameters with parameters in a scene characteristic library, and determine a current scene node;

[0043] A parameter broadcast unit is configured to control each intelligent terminal to periodically broadcast pre-stored hardware characteristic parameters and collected key state parameters to surrounding neighbor nodes at a dynamically adjusted frequency through a low-power communication module;

[0044] A concentration mapping unit is configured to perform efficiency-energy consumption ratio evaluation based on the task priority, the hardware characteristic parameters and the key state parameters of the neighbor nodes, and map the evaluation results as pheromone concentrations in the ant colony algorithm.

[0045] a node selection unit configured to determine a pheromone gradient of the intelligent terminal, combine a scene feature of a current scene node, and select one or more intelligent terminals as detection nodes by using a preset ant colony algorithm in a distributed manner;

[0046] a node detection unit configured to control the detection nodes to generate a sensor scheme in combination with the scene feature of the current scene node and hardware feature parameters and key state parameters of the detection nodes, and perform detection according to the sensor scheme.

[0047] Beneficial effects: The application provides an intelligent terminal sensor control method and system based on an ant colony algorithm. Through the cooperation of scene adaptation, dynamic evaluation, intelligent point selection, and fine control, the sensor control is changed from fixed and extensive control to scene-based, fine, and self-adaptive control. The sensor control strategy is accurately matched with the environmental demand, the dynamic balance of energy consumption and precision is ensured, and the optimal configuration of sensor resources is ensured. The precision, adaptability, and energy economy of the intelligent terminal sensor control are comprehensively improved, and the control precision and adaptability defects of the existing scheme are fundamentally solved.

[0048] To make the above objectives, features and advantages of the application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0049] To more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be considered as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 is a flowchart of the intelligent terminal sensor control method of the application;

[0051] Figure 2 is a flowchart of the determination of the task priority of the application;

[0052] Figure 3 is a flowchart of the broadcast process of the application;

[0053] Figure 4 is a flowchart of the selection of the detection node of the application;

[0054] Figure 5 is a schematic diagram of the intelligent terminal sensor control system module of the application.

[0055] 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

[0056] 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.

[0057] 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:

[0058] A smart terminal sensor control method based on ant colony algorithm, comprising:

[0059] 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.

[0060] 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;

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] The intelligent terminal of the present application is preferably a three-proofing device, such as a three-proofing mobile phone, a three-proofing watch, etc.

[0066] In the present application, in addition to the detection nodes, there are necessary navigation nodes, communication nodes, etc. The navigation nodes, communication nodes and detection nodes together constitute the collaborative role system of the intelligent terminal. The selection of the navigation nodes follows the pheromone driving logic of the ant colony algorithm. When the task priority is navigation priority, the weights of the sensor theoretical accuracy level accuracy, anti-interference level of the satellite positioning module in the hardware characteristic parameters and the satellite signal strength, battery endurance of the key state parameters are significantly improved, the navigation role energy consumption ratio is calculated and mapped as high sensitivity navigation pheromone, realizing global pheromone sharing and gradient calculation. The navigation nodes are screened and load balancing is used to avoid superposition with the communication / detection core roles. Its core function is to select and adapt the navigation module based on the scene signal characteristics, dynamically adjust the positioning frequency combined with its own state, and broadcast the positioning data through the low-power communication module to provide a location reference for the whole world. The selection logic of the communication nodes is consistent with that of the navigation nodes, only focusing on the maximum transmission distance, anti-interference level and communication signal RSSI value of the communication module, and the rated current of the communication module, which is mapped as medium sensitivity communication pheromone. When selecting, the number interval is determined by combining the scene electromagnetic environment and the signal loss coefficient. The core function is to select the BLE or LoRaWAN module according to the scene, dynamically adjust the communication parameters to ensure smooth data interaction, and at the same time bear the data relay role to realize global information sharing.

[0067] The three types of nodes follow the role exclusion and load balancing rules of "one master and multiple auxiliaries". The same device is prohibited from assuming two or more core roles at the same time. The priority of other roles of the core role nodes is reduced. When the scene characteristics change, the role is switched without awareness through dynamic updating of the pheromone concentration, and the running parameters are adapted to each other. The positioning frequency of the navigation node determines the broadcast frequency of the communication node, and the data volume of the detection node adapts to the transmission capacity of the communication node.

[0068] In step 101, the scene node refers to a continuous area divided according to the change of the terrain type of the route, the sudden change of the altitude gradient and other characteristics. Its role is to divide the complex route into multiple units with similar environmental characteristics, so as to facilitate the development of targeted sensor control strategies. The scene characteristics include the typical value range of the environmental parameters and the signal transmission loss coefficient. The former reflects the conventional fluctuation interval of the environmental parameters such as temperature, humidity and air pressure in this area, and the latter reflects the attenuation characteristics of the signal in this area. Both of them together provide environmental basis for subsequent task priority determination and sensor scheme generation. The detailed process of step 1 is shown in FIG. 1. Figure 2

[0069] ​The principle of determining the priority of the task is based on the factors in the scene features that have the greatest impact on the execution of the task. For example, if the signal transmission loss coefficient in the scene features shows that signal stability plays a dominant role in navigation positioning, it is determined that navigation is prioritized; if the environmental parameter fluctuation range is large and the environmental complexity has a more significant impact on the effectiveness of the detection data, it is determined that detection is prioritized. The implementation is to combine the task objectives in the team travel plan, quantitatively analyze the influence weight of each scene feature on the navigation, communication, and detection tasks through historical data retrieved by the geographic information system, and finally determine the priority task type of each scene node to provide a direction for subsequent effectiveness evaluation and node selection.

[0070] In step 102, low-power scanning refers to activating low-power sensor modules in the device, such as low-power temperature and humidity sensors, signal strength detectors, etc., to obtain parameters through batch alternation scanning. The role of this scanning method is to reduce overall energy consumption while ensuring the comprehensiveness of the parameters, avoiding rapid consumption of device power due to continuous high-frequency scanning. Environmental feature parameters include temperature and humidity, terrain relief, etc., and signal feature parameters include satellite signal strength, communication signal attenuation rate, etc., which can comprehensively reflect the actual state of the current environment.

[0071] The principle of scene matching is to calculate the similarity of the scene feature parameters obtained in real time and the parameters of each node in the scene feature library, and to ensure the accuracy of the matching result through continuous multiple verifications. In specific implementation, first, the algorithm quantifies the difference between real-time parameters and library parameters, and when the similarity of a certain scene node exceeds the preset threshold and satisfies the continuous three times of scanning, it is determined that the current is in this node. The role of this step is to accurately locate the environment unit where the device is located, to ensure that the subsequent sensor control strategy can accurately adapt to the current scene features, and to avoid insufficient control accuracy due to scene recognition errors.

[0072] In step 103, hardware feature parameters are inherent properties pre-stored when the device is shipped, including sensor type, theoretical accuracy level, maximum communication module transmission distance, etc., which serve to let surrounding devices understand the hardware capabilities of this device and provide basic data for effectiveness evaluation. Key state parameters are real-time dynamic information collected by the device, including battery remaining capacity, real-time signal strength, and the number of currently activated sensors, etc. These parameters reflect the current running state of the device and are an important basis for determining whether it is suitable for a specific role.

[0073] The dynamically adjusted frequency refers to the period of device broadcast parameters being flexibly changed according to actual conditions. The principle is to combine the current communication signal quality and device power state, for example, a lower frequency is used when the signal is good and the power is sufficient, reducing energy consumption; when the signal is weak or the power is low, the frequency is increased to ensure that the surrounding devices can obtain its state in time. The implementation is that the device monitors the communication signal attenuation rate and battery power in real time, and automatically adjusts the broadcast period when the parameters reach the preset threshold. The role of this step is to balance the communication energy consumption while ensuring the timeliness of information, and to ensure that devices can work efficiently without excessive resource consumption.

[0074] In step 104, the efficiency-energy consumption ratio is an index for measuring the efficiency that a device can achieve per unit energy consumption in a specific task, and its calculation needs to combine the task priority allocation weight. For example, when the task priority is detection priority, the weight of the device's environmental sensor precision, response speed and other hardware and state parameters related to detection will be increased to highlight the device's adaptability in the detection task. The role of this index is to quantitatively evaluate the ability of each device to assume different roles, providing an objective basis for subsequent node selection.

[0075] The principle of pheromone concentration mapping is to convert the efficiency-energy consumption ratio into identifiable pheromones in the ant colony algorithm, and different task priorities correspond to different types of pheromones, such as navigation pheromones and detection pheromones. The pheromone concentration is positively correlated with the efficiency-energy consumption ratio, i.e. the higher the efficiency and the better the energy consumption, the higher the corresponding pheromone concentration. The implementation is to convert the numerical range of the efficiency-energy consumption ratio into the value interval of the pheromone concentration through a preset mapping rule, and dynamically adjust it according to the device state, such as reducing the corresponding pheromone concentration when the power is too low. The role of this step is to convert the actual ability of the device into a decision variable of the ant colony algorithm, providing a unified judgment standard for distributed node selection.

[0076] In step 105, the pheromone gradient refers to the trend of the change of the pheromone concentration in space, and its direction points to the device with increasing pheromone concentration. The principle is to calculate the concentration change rate around each device based on the global pheromone distribution. The role of the gradient is to guide the ant colony algorithm to tilt towards devices with higher efficiency, ensuring that the selected nodes have better performance in the overall network.

[0077] The implementation of detection node selection is to combine the environmental complexity of the current scene node and the task priority, and to sort all global devices according to the pheromone gradient through a preset ant colony algorithm. For example, high environmental complexity scenarios will increase the demand for the number of detection nodes, and tasks with detection priority will strictly select devices with pheromone concentration meeting the standard. At the same time, a load balancing mechanism is introduced, and the priority of devices that have assumed the role of navigation or communication is reduced to avoid excessive load on a single device. The role of this step is to select the most suitable detection node from the global range, ensuring that the detection task can meet the accuracy requirements while balancing the energy consumption and load of each device.

[0078] In step 106, the generation of the sensor scheme needs to integrate the current scene characteristics, device hardware characteristic parameters and key state parameters. For example, scenes with large fluctuations in environmental parameters will preferentially select multiple types of sensors for collaborative detection, devices with high hardware precision will be assigned higher detection precision tasks, and low power devices will reduce the detection frequency. The scheme content includes sensor type combination, detection frequency, precision threshold, etc., which functions to accurately match the running state of the sensor with the current environmental demand and device capability.

[0079] When detecting according to the scheme, the device will monitor the changes in scene characteristic parameters and the fluctuations in its own key state in real time. When the environmental parameters exceed the typical range or the power significantly decreases, the type combination and operating parameters of the sensor are dynamically updated. For example, when the environment changes suddenly, an adaptive sensor is added, and when the power is too low, it is switched to a low-power mode. The function of this step is to ensure that the sensor always operates in the optimal state through dynamic adjustment, while ensuring the effectiveness of the detection data, and maximizing the device's endurance time, solving the problem of the difficulty in balancing precision and energy consumption under the traditional fixed strategy.

[0080] In some specific embodiments, the determination process of the scene node specifically includes: extracting route information including route starting point, ending point, key passing point and expected stay area from the team travel plan, combining geographic information system to obtain terrain type, altitude change rate, vegetation coverage rate and electromagnetic environment partition data of the route; taking terrain type change threshold, altitude gradient mutation point, vegetation coverage rate jump interval and electromagnetic environment partition boundary as the basis for division, respectively, dividing the travel route into multiple continuous scene nodes; for each scene node, the geographic information system is called to determine the scene characteristics of the scene node, including the typical value range of the environmental parameters and the signal transmission loss coefficient. The division of the scene node and the determination process of the task priority are as shown in Figure 2 .

[0081] Extracting route information from the team travel plan is the basis for determining the scene node. The route starting point and ending point clearly define the overall range of travel, the key passing point is usually a location with a landmark significance in the route, and the expected stay area is an area where the team may stay for a long time. These information collectively outline the route framework that needs to be analyzed. Combined with the terrain type, altitude change rate, vegetation coverage rate and electromagnetic environment partition data obtained by the geographic information system, quantitative environmental characteristic basis is provided for subsequent node division. These data can intuitively reflect the environmental differences of different sections of the route, and are the core indicators for distinguishing scene nodes.

[0082] The core of dividing the scene node is to define the continuous area based on the mutation point of the environmental characteristics. The terrain type change threshold refers to the critical value of the significant change of the terrain type, when the route crosses the threshold, it is divided as a node; the altitude gradient mutation point is the position where the altitude change rate exceeds the normal range; the vegetation coverage jump interval refers to the change range of the vegetation coverage within a short distance exceeding the preset proportion; and the electromagnetic environment partition boundary is the boundary where the electromagnetic interference level changes. Through these bases, the travel route is divided into multiple continuous scene nodes, and the terrain, altitude, vegetation, and electromagnetic environment characteristics in each node remain relatively stable, providing a clear environmental unit for subsequent targeted control of the sensor.

[0083] When determining the scene characteristics for each scene node, the historical environmental monitoring data of the geographic information system is a key basis. These historical data include the statistical results of the long-term monitoring of environmental parameters such as temperature, humidity, air pressure, and wind speed in this area, and the typical value range of the environmental parameters can be determined by analyzing these data. That is, the conventional fluctuation interval of the environmental parameters in this node, such as the temperature of a certain mountainous node is usually 5-15℃, and the humidity is 60%-80%. The signal transmission loss coefficient is the attenuation characteristic of the signal in the node based on the historical data, to reflect the transmission ability of the signal in this environment, such as the radio signal in the forest node decays 20dB per kilometer, and the open land node decays 5dB per kilometer.

[0084] The determination of the scene characteristics provides a basic reference for the subsequent process. The typical value range of the environmental parameters determines the key parameters and precision requirements that the sensor needs to monitor; and the signal transmission loss coefficient affects the control strategy of the communication module and the positioning sensor. At the same time, these characteristics are also the core basis for determining the task priority, to ensure that the subsequent sensor control strategy can be accurately adapted to the node environment.

[0085] In some specific embodiments, the process of determining the current scene node includes: controlling each intelligent terminal to activate a 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 intelligent terminal into at least two scanning batches that alternately scan, at least one batch of devices preferentially scanning environmental characteristic parameters, at least one batch of devices preferentially scanning signal characteristic parameters, and comprehensively obtaining real-time scene characteristic parameters; calculating the feature similarity of the real-time scene characteristic parameters and the scene characteristic parameters in the scene characteristic library; when the feature similarity of a certain scene node exceeds a preset matching threshold, and the condition is met for three consecutive scans, the current scene node is determined to be the node.

[0086] Controlling each intelligent terminal to activate the low-power sensor module to perform scanning is the basis for obtaining real-time environmental information. The low-power temperature and humidity sensor is used to collect the temperature and humidity data of the current environment, which are the core components of the environmental characteristic parameters; the signal strength detector is responsible for monitoring the strength of satellite signals and ground communication signals, providing data support for signal characteristic parameters; the accelerometer can assist in judging the terrain undulation and other environmental characteristics by detecting the motion state of the device. The purpose of using low-power modules is to minimize energy consumption and prolong the battery life while ensuring data collection.

[0087] Dividing the intelligent terminal into at least two scanning batches and alternating scanning at intervals is a key design to balance 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 can avoid the concentration of energy consumption caused by simultaneous high-frequency scanning of all devices, while ensuring that both types of key parameters can be effectively collected. By integrating the scanning results of different batches, complete real-time scene characteristic parameters can be obtained, providing a comprehensive data basis for subsequent scene matching.

[0088] Calculating the feature similarity between real-time scene characteristic parameters and each node parameter in the scene characteristic library is a core step for scene matching. Feature similarity reflects the matching degree by quantifying the difference between real-time parameters and library parameters. The higher the value, the closer the characteristics of the current environment and the target scene node. The preset matching threshold is a critical standard for determining basic matching, ensuring that only scene nodes with similar characteristics are included in the candidate.

[0089] The condition that the similarity exceeds the preset threshold for three consecutive scans is to avoid false positives caused by accidental errors in a single scan. Environmental parameters and signal characteristics may fluctuate due to transient interference (such as sudden electromagnetic pulses, short-term airflow changes), and a single match may not reflect the true scene. By verifying for three consecutive times, the influence of transient interference can be filtered out, ensuring the stability and accuracy of the determination result. When this condition is met, the current scene node can be determined, providing an accurate environmental reference for the development of subsequent sensor control strategies.

[0090] In some embodiments, hardware characteristic parameters are pre-stored in the device's non-volatile memory, including sensor type, sensor theoretical accuracy level, communication module maximum transmission distance, hardware anti-interference level, and each module's rated working current; key state parameters include battery remaining capacity percentage, real-time satellite signal reception strength, ground communication signal RSSI value, the number of currently activated sensors, and the current CPU working frequency; low-power communication modules include BLE modules or LoRaWAN modules.

[0091] The hardware characteristic parameters are pre-stored in the non-volatile memory of the device, which ensures that the core hardware information can be retained after power-off or restart of the device, providing stable basic data for device initialization and cooperation with other nodes. The sensor type determines the type of detection task that the device can perform, such as temperature and humidity sensors, gas detectors, GPS modules, etc.; the sensor theoretical accuracy level quantifies the inherent detection capability of the hardware, which is the basis for evaluating the reliability of detection data; the maximum transmission distance of the communication module defines the communication coverage of the device, affecting the reachability of neighbor node identification and information exchange; the hardware anti-interference level reflects the stable operation capability of the device in complex environments such as electromagnetic interference and vibration; and the rated working current of each module provides key parameters for calculating device energy consumption and balancing task load. These parameters together constitute the basis for evaluating the hardware capability of the device, enabling other nodes to quickly determine whether it is suitable for the task requirements of the current scene.

[0092] The key state parameters are the dynamic reflection of the real-time running state of the device, continuously collected by sensors and internal monitoring modules, providing timely data for dynamic decision-making. The battery percentage directly relates to the endurance of the device, which is a core indicator for determining whether it can undertake high-energy-consumption tasks; the real-time satellite signal reception strength reflects the working quality of the positioning module, and when the strength is insufficient, the navigation strategy needs to be adjusted or the auxiliary positioning method needs to be switched; the ground communication signal RSSI value quantifies the communication link quality between the device and the neighbor nodes, and when the value is too low, the broadcast frequency needs to be optimized or the communication channel needs to be switched; the number of currently activated sensors directly reflects the real-time load of the device, and too many sensors may cause CPU overload or a sharp increase in energy consumption; the current CPU working frequency reflects the operation processing capability of the device, which can support complex data processing in high-frequency mode but consumes more energy. These dynamic parameters combined with hardware characteristic parameters form the basis for real-time capability evaluation of the device, ensuring that the task allocation matches the current state of the device and avoiding performance degradation due to overload or insufficient capability.

[0093] The selection of low-power communication modules is shown in FIG. 8. Figure 3 The low-power communication module is the physical carrier for parameter broadcasting between devices, and the selection of BLE module and LoRaWAN module takes into account communication efficiency and energy consumption control. The BLE module is suitable for short-distance, low-data-volume interaction scenarios, and its sleep-wakeup mechanism can greatly reduce energy consumption during idle time, making it suitable for near-distance cooperation in device-dense areas; the LoRaWAN module supports long-distance communication and has strong anti-interference capability, making it suitable for long-distance node interaction in open or complex terrain areas. Both types of modules have low-power characteristics, with energy consumption per broadcast being only 10%-30% of that of traditional communication modules, effectively extending the endurance of the device. Through these modules, the device can efficiently broadcast hardware characteristic parameters and key state parameters to neighbor nodes, providing real-time and accurate basic data for global performance evaluation and node selection, which is an important communication support for distributed collaborative control.

[0094] In some 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 feature has the highest weight proportion of influence on the feasibility of the navigation task, and the index deviates from the preset stability threshold, which will cause the navigation task to fail to achieve the basic positioning accuracy, it is determined that the navigation task is prioritized. When the environmental complexity index corresponding to the environmental feature parameter in the scene feature has the highest weight proportion of influence on the feasibility of the detection task, and the index exceeds the preset regular threshold, which will cause the detection task to fail to obtain effective environmental data, it is determined that the detection task is prioritized. When the signal transmission capability index corresponding to the signal feature parameter in the scene feature has the highest weight proportion of influence on the feasibility of the communication task, and the index is lower than the preset transmission threshold, which will cause the communication task to fail to complete the basic data interaction, it is determined that the communication task is prioritized.

[0095] The division of task priority is based on the influence degree of scene features on different tasks, and the priority is ensured to accurately match the scene demand through clear determination conditions. The three types of navigation priority, communication priority, and detection priority correspond to the environmental features that dominate navigation, communication, and detection tasks in the scene, and the core logic is to identify the scene parameters that have the greatest influence on task feasibility, and determine the priority in combination with the threshold condition, providing direction guidance for subsequent sensor control and node selection.

[0096] The determination of navigation priority focuses on the signal stability index corresponding to the signal feature parameter. The signal stability index is usually composed of signal transmission loss coefficient, signal fluctuation frequency, etc. in the scene feature, and directly affects the continuity and accuracy of navigation positioning. When the influence 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. The preset stability threshold is the critical value for guaranteeing the basic positioning accuracy. If the signal stability index deviates from this threshold, it will cause problems such as positioning drift and signal loss, making navigation unable to meet the basic needs of the team's progress. At this time, it is determined that navigation is prioritized, and resources can be prioritized to ensure the operation of navigation-related sensors and ensure reliable positioning.

[0097] The determination of detection priority takes the environmental complexity index corresponding to the environmental feature parameter as the core. The environmental complexity index is composed of the typical value range of environmental parameters, parameter fluctuation amplitude, etc., and reflects the complexity of environmental factors in the scene. When the influence weight of this index on the feasibility of the detection task is the highest, it means that environmental complexity is the key to determining the effectiveness of detection data. The preset regular threshold is the environmental range within which the detection device can normally obtain effective data. If the environmental complexity index exceeds this threshold, the detection data will be distorted or invalid. At this time, it is determined that detection is prioritized, and sensors that are adapted to complex environments can be activated first, and the detection frequency can be increased to ensure the reliability of environmental data.

[0098] The determination of communication priority depends on the signal transmission capability index corresponding to the signal characteristic parameter. The signal transmission capability index is composed of signal transmission loss coefficient, communication distance attenuation rate, etc., and determines the success rate and efficiency of data interaction between devices. When the index has the highest influence weight on the feasibility of the communication task, it indicates that the signal transmission capability is the core element to ensure device cooperation. The preset transmission threshold is the minimum signal requirement to realize basic data interaction. If the signal transmission capability index is lower than the threshold, it will lead to data packet loss, communication interruption, and failure to complete the basic cooperation between devices. At this time, it is determined that communication priority is given, and the communication module parameters can be adjusted in priority, and the energy consumption of the communication-related sensors can be ensured to ensure smooth information interaction between devices.

[0099] In some specific embodiments, the evaluation weights of the hardware characteristic parameters and the key state parameters are assigned according to the task priority, and the efficiency-energy consumption ratio of the neighbor node for different roles is calculated by fusing the weighted hardware characteristic parameters and key state parameters. The efficiency-energy consumption ratio represents the role efficiency that can be achieved per unit energy consumption of the node under the current task priority; different roles corresponding to different task priorities are set with different types of pheromones, and the concentration of each type of pheromone is associated with the efficiency-energy consumption ratio of the corresponding role, and the sensitivity of the association is positively correlated with the task priority of the role.

[0100] According to the task priority, the evaluation weight is assigned. The core is to let the parameters with high correlation degree to the priority task dominate in the evaluation, so as to realize the accurate matching of the evaluation result and the scene demand. For example, when the task priority is navigation priority, the weight proportion of the theoretical accuracy level and the anti-interference level of the positioning module in the hardware characteristic parameters, and the real-time satellite signal strength and the power supply stability of the navigation module in the key state parameters will be significantly improved; if it is detection priority, the detection accuracy and response speed of the environmental sensor and the running state and the remaining power of the currently activated sensor will be tilted. The principle of this weight distribution method is that the smooth execution of the priority task depends more on the specific hardware capability and real-time state. By increasing the weight of the associated parameters, the adaptability of the node to undertake the task can be more accurately evaluated.

[0101] The efficiency-energy consumption ratio is calculated by fusing the weighted hardware characteristic parameters and key state parameters, which is a quantitative evaluation of combining the capability and energy consumption of the node. Specifically, the hardware capability score and the state adaptation score are first obtained by weighted summation, and then the comprehensive score of the two is divided by the energy consumption per unit time to obtain the efficiency-energy consumption ratio. The higher the ratio, the stronger the role efficiency that can be achieved per unit energy consumption of the node under the current task priority. This index provides an objective quantitative basis for subsequent node selection, avoiding the one-sidedness of only looking at the capability or only looking at the energy consumption.

[0102] Setting a special pheromone type for different task priorities is the key design for the ant colony algorithm to identify the nodes for each role. For example, navigation priority corresponds to navigation pheromone, detection priority corresponds to detection pheromone, and communication priority corresponds to communication pheromone. Each type of pheromone independently reflects the performance level of the corresponding role. The concentration of each type of pheromone is positively correlated with the performance-energy consumption ratio of the corresponding role. That is, the higher the performance-energy consumption ratio of a node, the higher the concentration of the corresponding pheromone, which can attract more algorithm decisions to it.

[0103] The sensitivity of the associated sensitivity and the role task priority is positively correlated, which means that the higher the priority of the role, the more dramatic the change in the pheromone concentration with the performance-energy consumption ratio. For example, in the detection priority scene, the detection pheromone concentration is more sensitive to the change in the performance-energy consumption ratio than the navigation or communication pheromone. When the detection performance-energy consumption ratio of a node increases slightly, the detection pheromone concentration will increase significantly; on the contrary, if the performance ratio decreases, the concentration will also decrease rapidly. The principle of this design is to enable high-priority tasks to quickly lock the optimal node, ensure the priority allocation of resources for critical tasks, and improve the overall collaborative efficiency.

[0104] In some embodiments, the selection of the detection node includes: each intelligent terminal first receives the detection pheromone concentration of the neighbor node, and then transmits the pheromone data of itself and the neighbor node to the adjacent device to complete the distributed sharing of global pheromone data; the calculation of the pheromone gradient is based on the global pheromone concentration distribution, and the gradient direction points to the node with increasing detection pheromone concentration in the global range; the selection conditions of the detection node including the minimum pheromone concentration threshold and the selection number interval are set in combination with the environmental complexity of the current scene node and the task priority; the preset ant colony algorithm calculates locally by each device, sorts the global intelligent terminals according to the pheromone gradient, and selects the nodes meeting the selection conditions as the detection nodes; the load balancing mechanism is retained in the selection process, and the selection priority of the node that has assumed the navigation or communication role as a detection node is reduced to avoid the overload of a single device with multiple roles. The selection of the detection node is shown in FIG. 8. Figure 4

[0105] In some embodiments, the generation process of the sensor scheme includes: controlling the detection node to select the adaptive sensor from the sensor types contained in the hardware characteristic parameters of itself according to the typical value range of the environmental parameters of the current scene node and the signal transmission loss coefficient; determining the detection frequency and detection accuracy of the sensor based on the sensor theoretical accuracy level in the hardware characteristic parameters and the battery remaining percentage, the current CPU working frequency in the key state parameters, and obtaining the sensor scheme.

[0106] ​The process of controlling the detection node to screen the adaptive sensor is based on the accurate matching of scene characteristics and device hardware capabilities. The typical value range of the environmental parameters of the current scene node clearly defines the environmental interval that needs to be covered by the detection. The detection node will screen the sensors that can work stably in this interval from the sensor types contained in its own hardware characteristic parameters. The signal transmission loss coefficient reflects the signal attenuation characteristics in the scene. If the coefficient is high, sensors with anti-interference capability will be preferentially screened to avoid distortion of the detection data due to transmission loss. The core of this screening logic is to adapt the inherent performance of the sensor to the constraints of the scene environment to ensure the effectiveness of the detection data.

[0107] Determining the detection frequency and detection accuracy of the sensor needs to balance the limitations of hardware capabilities and real-time state. The theoretical accuracy level of the sensor in the hardware characteristic parameters is the basic reference for setting the accuracy, but it needs to be dynamically adjusted in combination with the key state parameters: when the battery percentage is lower than 20%, the detection accuracy is relaxed to 1.5 times the theoretical value to prolong the battery life; when the current CPU working frequency is lower than the preset threshold, the detection frequency is reduced (e.g., from 1 time per second to 1 time every 3 seconds) to avoid data backlog due to limited data processing capability. Conversely, when the battery is fully charged and the CPU is running at a high frequency, the parameters can be set according to the theoretical accuracy and the highest frequency required by the scene. In this way, the final sensor scheme can meet the detection needs of the current scene and adapt to the real-time running state of the device, achieving a balance between efficiency and energy consumption.

[0108] In some embodiments, during the detection process performed according to the generated sensor scheme, the changes in the current scene characteristic parameters and the fluctuations in the key state parameters of the device are monitored in real time, and the type combination and operating parameters of the sensor are dynamically updated to maintain the balance between detection efficiency and energy consumption. Real-time monitoring is the premise of dynamic adjustment, and its core is to continuously track the changes in the two types of key parameters. The changes in the current scene characteristic parameters mainly include whether the environmental parameters exceed the typical value range and whether the signal transmission loss coefficient abnormally fluctuates, which directly reflects whether the environment deviates from the expected state. The fluctuations in the key state parameters of the device focus on the decline rate of the battery percentage, the stability of the current CPU working frequency, the changes in the real-time signal reception strength, and the operating temperature of the activated sensors, etc. These parameters reflect whether the device can continue to 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 time to provide a trigger signal for subsequent adjustment, avoiding detection data invalidation or energy consumption out of control due to parameter mutations.

[0109] Dynamic updating of the combination of sensor types and operating parameters is a key action to maintain balance, and its adjustment logic is closely related to the changes monitored. When the scene characteristic parameters change, the sensor type combination will be updated, such as adding high-temperature-resistant sensors to supplement the data dimensions, or disabling sensors that cannot work in extreme environments to reduce invalid energy consumption. When the key state parameters fluctuate, the operating parameters are adjusted: such as reducing the detection frequency to reduce energy consumption, and moderately relaxing the detection accuracy to match the processing capacity of the CPU running at low frequency. If the signal transmission loss coefficient suddenly increases, sensors with high anti-interference level will be retained first, and the single detection time will be shortened to reduce data transmission. This dynamic adjustment mechanism ensures that the sensors are always running in the optimal mode that adapts to the current environment and device state, avoiding the decline in detection efficiency caused by environmental changes, and preventing energy waste caused by device state deterioration, ultimately achieving dynamic balance between detection efficiency and energy consumption.

[0110] The application also proposes an intelligent terminal sensor control system based on an ant colony algorithm. The system module diagram is shown in FIG. 1. Figure 5 The system includes:

[0111] A scene division unit 1 is used to determine multiple scene nodes in the route and the scene characteristics of the scene nodes based on the team travel plan and the geographic information system, and determine the task priority of each scene node based on the scene characteristics.

[0112] A scene determination unit 2 is used to control each intelligent terminal to obtain scene characteristic parameters through low-power scanning, match the scene characteristic parameters with the parameters in the scene characteristic library, and determine the current scene node.

[0113] A parameter broadcast unit 3 is used to control each intelligent terminal to periodically broadcast the pre-stored hardware characteristic parameters and the collected key state parameters to the surrounding neighbor nodes at a dynamically adjusted frequency through a low-power communication module.

[0114] A concentration mapping unit 4 is used to evaluate the efficiency-energy consumption ratio based on the task priority, the hardware characteristic parameters and the key state parameters of the neighbor nodes, and map the evaluation results as the pheromone concentration in the ant colony algorithm.

[0115] A node selection unit 5 is used to determine the pheromone gradient of the intelligent terminal, combine the scene characteristics of the current scene node, and select one or more intelligent terminals as detection nodes through a preset ant colony algorithm.

[0116] A node detection unit 6 is used to control the detection nodes to generate a sensor scheme combining the scene characteristics of the current scene node and the hardware characteristic parameters and the key state parameters of the detection nodes, and perform detection according to the sensor scheme.

[0117] The application provides an intelligent terminal sensor control method and system based on an ant colony algorithm, which realizes the transformation of sensor control from fixed and extensive control to scene-based, fine and self-adaptive control through the cooperation of scene-based adaptation, dynamic evaluation, intelligent point selection and fine 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, and the control precision and adaptability defects of the prior art are fundamentally solved.

[0118] Those skilled in the art should understand that the modules of the application described above can be realized by a general computing system, which can be concentrated on a single computing system or distributed on a network composed of multiple computing systems, and can be realized by computer system executable program codes, so that they can be stored in a storage system and executed by a computing system, or they can be respectively manufactured into integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the application is not limited to any specific combination of hardware and software.

[0119] Note that the above is only the preferred embodiment of the application and the technical principles applied. Those skilled in the art will understand that the application is not limited to the specific embodiments herein, and those skilled in the art can make various obvious changes, readjustments and substitutions without departing from the scope of the application. Therefore, although the application is described in detail through the above embodiments, the application is not limited to the above embodiments, and more other equivalent embodiments can be included without departing from the concept of the application, and the scope of the application is determined by the scope of the appended claims.

[0120] The above disclosure is only a few specific implementation scenarios of the application, but the application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the 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 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.

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 hardware characteristic parameters are pre-stored in the device's non-volatile memory, including 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 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.

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 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 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.

7. 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.

8. 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.

9. 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.

10. 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 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.

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