Intelligent sensor network regulation and control method based on optimal sensing energy consumption fusion strategy

Abstract

The invention discloses an intelligent sensor network regulation and control method based on an optimal perception energy consumption fusion strategy. The method comprises the following steps: acquiring sensor parameters including the highest acquisition frequency, the lowest acquisition frequency, the sensor perception radius, and the current electric quantity and coordinates of each sensor; describing the activity of the sensor by using a perception-energy consumption function; according to the sensor activeness and sensor distribution, forming an activeness distribution diagram; obtaining an activeness sequence of all the sensors according to the activeness distribution diagram, and constructing a distribution network of the activated sensors and the low-power-consumption and / or dormant sensors around the activated sensors from high to low by utilizing the activeness sequence; and updating the sensor network. The invention aims to overcome the defects that an existing forest fire prevention system is low in response speed and limited in monitoring range, measurement of a part of areas fails due to low energy of individual sensors, and real-time early warning cannot be performed, and provides an intelligent forest fire prevention sensing network layout method which integrates multi-source sensing data, intelligently analyzes sensor states and dynamically adjusts a sensor activation network.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of intelligent sensing technology, and particularly relates to an intelligent sensing network regulation method based on an optimal sensing energy consumption fusion strategy. BACKGROUND

[0002] In areas with rich resources of ancient trees and famous trees, forest fires not only cause direct economic losses, but also lead to irreversible cultural and natural heritage losses. Such events highlight the inadequacy of existing forest fire prevention techniques, and there is an urgent need for a more efficient monitoring and emergency response system to protect forest resources.

[0003] Traditional forest fire prevention measures mainly include ground patrol, observation from watchtowers, and satellite remote sensing monitoring. However, these methods generally have slow response speed, limited monitoring range, and are unable to provide real-time early warning, making it difficult to effectively respond to sudden forest fires. With the development of information technology, especially the advancement of technologies such as the Internet of Things, big data analysis, and artificial intelligence, new solutions have been provided for forest fire prevention. By deploying multi-source sensing devices in forest areas, real-time collection of environmental data, and utilizing cloud computing platforms for data fusion and intelligent analysis, early warning and rapid response to forest fires can be achieved.

[0004] However, some existing forest fire prevention systems and sensors in the market still have some shortcomings. When sensors are distributed in the wild, solar energy combined with batteries can be used to provide energy for the sensors. However, the regions where fire prevention sensors are applied are mostly densely vegetated areas, and the light intensity received at different locations is also different. Therefore, the energy status of each terminal is not the same, which makes individual sensors inaccurate in detecting environmental values in the area, or even lose data signals. SUMMARY

[0005] To solve the above problems, the present application proposes an intelligent sensing network regulation method based on an optimal sensing energy consumption fusion strategy, aiming to overcome the slow response speed, limited monitoring range, and individual sensor low energy leading to partial area measurement failure of existing forest fire prevention systems, and to provide an intelligent forest fire prevention sensing network layout method that integrates multi-source sensing data fusion, intelligent analysis of sensor status, and dynamic adjustment of sensor activation network.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows: an intelligent sensing network regulation method based on an optimal sensing energy consumption fusion strategy, comprising the steps of: S10: obtaining all sensor parameters in the network, including: maximum collection frequency Fmax, minimum collection frequency Fmin, sensor sensing radius R, current power Bi of each sensor, and coordinates (X i ,Yi ); Use the perception-energy consumption function to describe the sensor activity Di for each sensor; S20: Based on the acquired activity of all sensors and the distribution of the sensors, an activity distribution map is formed; S30: Obtaining activity rankings of all sensors according to the activity distribution map, and then constructing a distribution network of active sensors and low-power and / or dormant sensors around the sensors from high to low using the activity rankings; S40: Perform sensor network update.

[0007] Furthermore, the perception-energy consumption function is: Di=Bi*R.

[0008] Furthermore, after obtaining the activity of all sensors, a sensor activity distribution map is constructed based on the sensor activity at relevant coordinate positions according to the on-site deployment distribution map of the sensors; the activity distribution map depicts the current status of all sensors in the monitored forest area.

[0009] Furthermore, an activation sensor is constructed, the sensor is activated and an activation instruction is issued, and the acquisition frequency is set to Bi*Fmax; low-power and / or dormant sensors around the activation sensor are acquired, and the minimum acquisition frequency is set to Fmin.

[0010] Furthermore, the activity of all sensors is sorted, and then the network of currently active sensors is constructed starting from the sensor with the highest activity, completing the sensor network update; Two record sequences are constructed, including: sequence 1 is set as a tag sequence of the activated sensor; sequence 2 is set as a sensor tag sequence set to low power consumption within the tolerance sensing range of the activated sensor.

[0011] Furthermore, the sensor network update comprises the steps of: Traverse all sensors sorted by activity from high to low to see if they are marked in sequence 1 and sequence 2. If so, check whether the activity-sorted sequences have been traversed. If so, the update ends; otherwise, continue; If it is not marked in sequence 1 and sequence 2, obtain the sensors around the sensor S node, obtain the sensors within the range circle with a radius of 2R with the sensor S as the center, and calculate the number N of sensors within the range; If N is less than the number num, find sensor S1 in sequence 2 whose activity is greater than S and whose distance from S is less than 2R and set it to active. Set all sensors within a radius of 2R from S1 to low power or sleep mode and mark them in sequence 2. Sensors already marked in sequence 1 remain unchanged. If sensor S1 cannot be found in sequence 2, revert to the adjustment method where N is greater than the number num. If N is greater than the number num, then set S to active and mark it to sequence 1; and mark the sensors within the radius of 2R of S as low power / sleep and record them to sequence 2; Repeat the above steps until all sensor nodes are traversed.

[0012] The beneficial effects of adopting this technical solution are: The present invention addresses the problem in intelligent forest fire prevention terminals that the widely existing sensor networks powered by solar energy have different distribution locations, which leads to uneven solar energy charging and inconsistent energy states of each terminal. Using a unified data acquisition strategy can cause individual terminals to fail or even lose part of the sensing area. The invention involves an intelligent forest fire prevention sensor network system based on an optimal perception-energy consumption fusion strategy. Some sensor terminals in the forest area upload the collected environmental data and their own status to the cloud system to achieve early warning of forest fires. In order to ensure that sensors can accurately and quickly upload data at any time and any place, it is necessary to control the working mode of the sensors and the overall measurement effect. The system combines the optimal perception-energy consumption fusion strategy and constructs a sensor activation network based on the impact of different environments on sensor energy. This strategy ensures that at least one sensor within a certain range is efficient. This avoids the situation where all sensors within a certain range fail due to lack of energy to support their work, which affects the rapid response to forest fires. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of an intelligent sensor network control method based on an optimal sensing energy consumption fusion strategy of the present invention; Figure 2 A schematic diagram of a sensor network update process according to an embodiment of the present invention; Figure 3 Schematic diagram of sensor network update in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.

[0015] Because solar-powered sensor networks often have uneven distribution of sensor terminals, resulting in uneven solar charging and inconsistent energy states for each terminal, using a unified data collection strategy can lead to individual terminal failures or even loss of some sensing areas. The present invention establishes an intelligent forest fire prevention sensor network system that can dynamically adjust sensor data collection frequency and sleep strategies to achieve an optimal sensing-energy consumption strategy, including the following steps: S10: Obtain all sensor parameters in the network, including: maximum acquisition frequency Fmax, minimum acquisition frequency Fmin, sensor perception radius R, current power Bi and coordinates (X i ,Y i ); Use the perception-energy consumption function to describe the sensor activity Di for each sensor; S20: Based on the acquired activity of all sensors and the distribution of the sensors, an activity distribution map is formed; S30: Obtaining activity rankings of all sensors according to the activity distribution map, and then constructing a distribution network of active sensors and low-power and / or dormant sensors around the sensors from high to low using the activity rankings; S40: Perform sensor network update.

[0016] As an optimization solution of the above embodiment, the perception-energy consumption function is: Di=Bi*R.

[0017] Sensor data is measured and uploaded. Locally, the sensor uploads the device's current battery level to the intelligent forest fire prevention sensor network system, which records the battery levels of all sensor devices. The system then calculates and records the current activity of each sensor using a perception-energy consumption function.

[0018] As an optimization solution for the above embodiment, after obtaining the activity of all sensors, a sensor activity distribution map is constructed based on the sensor activity at relevant coordinate positions according to the on-site deployment distribution map of the sensors; the activity distribution map depicts the current status of all sensors in the monitored forest area.

[0019] As an optimization solution for the above embodiment, an activation sensor is constructed, the sensor is activated and an activation instruction is issued, and the acquisition frequency is set to Bi*Fmax; low-power and / or dormant sensors around the activation sensor are acquired, and the minimum acquisition frequency is set to Fmin.

[0020] As an optimization solution of the above embodiment, the activity of all sensors is sorted, and then the network of currently activated sensors is constructed starting from the sensor with the highest activity, thereby completing the sensor network update; Two record sequences are constructed, including: sequence 1 is set as a tag sequence of activated sensors (high activity); sequence 2 is set as a sensor tag sequence set to low power consumption within the tolerance perception range of the activated sensors.

[0021] Specifically, the sensor network update includes the following steps: Traverse all sensors sorted by activity from high to low to see if they are marked in sequence 1 and sequence 2. If so, check whether the activity-sorted sequences have been traversed. If so, the update ends; otherwise, continue; If it is not marked in sequence 1 or sequence 2, obtain the sensors around the sensor node S. With sensor S as the center, obtain the sensors within a range circle with a radius of 2R (twice the maximum perception radius R, the perception ranges of the two sensors are tangent) and calculate the number of sensors within this range (not recorded in sequence 2). If N is less than the number num, find sensor S1 (the sensor with the highest activity in the conditions) in sequence 2 whose activity is greater than S and whose distance from S is less than 2R. Set it to active. Set all sensors within a radius of 2R from S1 (including S, excluding sensors already set to active) to low power / sleep mode and mark them in sequence 2. If sensor S1 cannot be found in sequence 2, revert to the adjustment method where N is greater than the number num. num is the preset threshold for the number of unmarked sensors. If N is greater than the number num, set S to active and mark it to sequence 1; and mark all sensors within a radius of 2R from S (excluding sensors that have been set to active) as low power / sleep and record them to sequence 2; Repeat the above steps until all sensor nodes are traversed. Because the radius set to low power / sleep is 2R, which is exactly the tangent of the maximum sensing area of ​​the two sensors, there will be no unmarked sensors in the end.

[0022] Obtain the latest sensor activation network and subsequently use the activated sensors and low-power / sleep sensors configured within this network to monitor forest fire prevention. This environmental data is uploaded to the system, which intelligently analyzes it to effectively monitor and warn of forest fires, thereby effectively protecting forest resources. Repeat this process after a certain monitoring period to update the sensor activation network.

[0023] like Figure 3 The figure shows a schematic diagram of the sensor network update of the present invention, in which the five-pointed star represents the sensor, and the dotted circle is the sensor's perception radius R. The solid circle is 2R. The five-pointed star with blue as the center is the high-activity sensor currently selected as the active state, and the remaining sensors within the solid circle will be set to low power / sleep mode. There is a five-pointed star with red as the center on the way, and the surrounding sensors have been marked. When traversing to this sensor, it will search for a sensor S1 with greater activity than itself within a radius of 2R. If there is one, S1 will be reactivated, and updates and adjustments will be made with S1 as the center. If it is not found, it means that its current activity is the highest among the nearby sensors, and it will set itself to the active state.

[0024] To ensure that sensors in all areas are capable of rapidly measuring and uploading environmental data at all times, a perception-energy consumption function is used to calculate sensor activity. The sensor activation network is reconfigured based on the activity of each sensor. This reconfigured sensor network provides the most sensitive perception of all areas of the forest environment, enabling early warning and rapid response to forest fires. The sensor activation network ensures that there are always highly active sensors within a certain range. Specifically, the sensor's perception radius is R. Within a circular area with a radius of 2R (the tangent of the maximum perception ranges of two adjacent sensors), multiple sensors are distributed. Therefore, there will inevitably be overlapping perception areas. If all sensors use high activity, energy will be wasted. Furthermore, after a certain period of time, all sensors in the area may not be able to quickly and accurately collect and upload environmental data simultaneously. In real-world situations such as fires, sensors with low activity levels will also collect data, albeit at a slower rate. However, smoke and high temperatures generated by the fire will gradually spread to the surrounding area. Nearby active (high activity) sensors will immediately detect the fire and upload data, and the system will issue an immediate warning upon receiving the data.

[0025] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent sensor network control method based on optimal sensing energy consumption fusion strategy, characterized in that: Including steps: S10: Obtain all sensor parameters in the network, including: maximum acquisition frequency Fmax, minimum acquisition frequency Fmin, sensor perception radius R, current power Bi and coordinates (X i ,Y i ); Use the perception-energy consumption function to describe the sensor activity Di for each sensor; S20: Based on the acquired activity of all sensors and the distribution of the sensors, an activity distribution map is formed; S30: Obtaining activity rankings of all sensors according to the activity distribution map, and then constructing a distribution network of active sensors and low-power and / or dormant sensors around the sensors from high to low using the activity rankings; S40: Perform sensor network update.

2. The intelligent sensor network control method based on the optimal sensing energy consumption fusion strategy according to claim 1 is characterized in that: The perception-energy consumption function is: Di=Bi*R.

3. The intelligent sensor network control method based on the optimal sensing energy consumption fusion strategy according to claim 1 is characterized in that: After obtaining the activity of all sensors, a sensor activity distribution map is constructed based on the sensor activity at relevant coordinate locations according to the on-site deployment distribution map of the sensors; the activity distribution map depicts the current status of all sensors in the monitored forest area.

4. The intelligent sensor network control method based on the optimal sensing energy consumption fusion strategy according to claim 1 is characterized in that: Build an activation sensor, activate the sensor and send an activation command, and set the acquisition frequency to Bi*Fmax; Acquire low-power and / or dormant sensors around active sensors, with the minimum acquisition frequency set to Fmin.

5. The intelligent sensor network control method based on the optimal sensing energy consumption fusion strategy according to claim 1 or 4 is characterized in that: Sort the activity of all sensors, then build the current active sensor network starting from the most active sensor to complete the sensor network update; Two record sequences are constructed, including: sequence 1 is set as the tag sequence of the activated state sensor; Sequence 2 sets the sequence of sensor tags that are set to low power consumption within the tolerance sensing range of the activated sensor.

6. The intelligent sensor network control method based on the optimal sensing energy consumption fusion strategy according to claim 5 is characterized in that: The sensor network update comprises the steps of: Traverse all sensors sorted by activity from high to low to see if they are marked in sequence 1 and sequence 2. If so, check whether the activity-sorted sequences have been traversed. If so, the update ends; otherwise, continue; If it is not marked in sequence 1 and sequence 2, obtain the sensors around the sensor S node, obtain the sensors within the range circle with a radius of 2R with the sensor S as the center, and calculate the number N of sensors within the range; If N is less than the number num, find sensor S1 in sequence 2 whose activity is greater than S and whose distance from S is less than 2R and set it to active. Set all sensors within a radius of 2R from S1 to low power or sleep mode and mark them in sequence 2. Sensors already marked in sequence 1 remain unchanged. If sensor S1 cannot be found in sequence 2, revert to the adjustment method where N is greater than the number num. If N is greater than the number num, set S to active and mark it to sequence 1; And mark the sensors within the radius of 2R as low power / sleep, and record it in sequence 2; Repeat the above steps until all sensor nodes are traversed.

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