An intelligent sensor network regulation method based on optimal perception energy consumption fusion strategy

Abstract

The application discloses an intelligent sensing network regulation method based on an optimal perception energy consumption fusion strategy, and comprises the following steps: acquiring sensor parameters, including the highest acquisition frequency, the lowest acquisition frequency, the sensor perception radius, the current power of each sensor and the coordinates; using a perception-energy consumption function to describe the sensor activity; forming an activity distribution map according to the sensor distribution according to the sensor activity; obtaining the activity ranking of all sensors according to the activity distribution map, and constructing the distribution network of the activated sensor and the low-power and / or dormant sensor around the sensor from high to low by using the activity ranking; and updating the sensor network. The application aims to overcome the slow response speed, the limited monitoring range, the measurement failure of partial areas caused by low energy of individual sensors, and the inability to provide real-time early warning of the existing forest fire prevention system, and provides a multi-source sensing data fusion, intelligent sensor state analysis and intelligent forest fire prevention sensing network layout method of dynamic adjustment of the activated sensor network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent sensing, 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: an intelligent sensing network regulation method based on an optimal sensing energy consumption fusion strategy, comprising the steps of:

[0007] S10: Obtain 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, Y) of each sensor;i ,Y i The sensor activity Di is described for each sensor using a sensing-energy consumption function.

[0008] S20: Based on the activity levels of all the sensors obtained, form an activity distribution map according to the sensor distribution;

[0009] S30: Obtain the activity ranking of all sensors according to the activity distribution map, and then use the activity ranking from high to low to construct a distribution network of active sensors and low-power and / or dormant sensors around the active sensors.

[0010] S40: Perform sensor network updates.

[0011] Furthermore, the sensing-energy consumption function is: Di = Bi * R.

[0012] Furthermore, after obtaining the activity levels of all sensors, a sensor activity distribution map is constructed based on the sensor activity levels 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.

[0013] Furthermore, an activation sensor is constructed, the sensor is activated and an activation command 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.

[0014] Furthermore, the activity levels of all sensors are sorted, and then the network of currently active sensors is built starting from the sensor with the highest activity level, thus completing the sensor network update.

[0015] Two recording sequences are constructed: Sequence 1 is set as the tag sequence of the active state sensor; Sequence 2 is set as the sensor tag sequence of low power consumption within the tolerance sensing range of the active sensor.

[0016] Furthermore, the sensor network update includes the following steps:

[0017] Iterate through all sensors sorted by activity level from largest to smallest, and check if they are marked as Sequence 1 and Sequence 2. If so, check if the sequence sorted by activity level has been completely traversed. If so, the update ends; otherwise, continue.

[0018] If the sensor is not marked in sequence 1 and sequence 2, then obtain the sensors around the sensor node S, and obtain the sensors within a circle with a radius of 2R centered on the sensor S, and calculate the number N of sensors within this range;

[0019] If N is less than the quantity num, then find the sensor S1 in sequence 2 that has an activity level greater than S and a distance of less than 2R from S, set it to active, and set the sensors within a radius of 2R of S1 to low power / sleep, and mark them in sequence 2. The sensors that have already been marked in sequence 1 are not changed. If sensor S1 cannot be found in sequence 2, then revert to the adjustment method when N is greater than the quantity num.

[0020] If N is greater than the quantity num, then S is set to active and marked in sequence 1; and the sensors within a radius of 2R of S are marked as low power / sleep and recorded in sequence 2.

[0021] Repeat the above steps until all sensor nodes have been traversed.

[0022] The beneficial effects of adopting this technical solution are:

[0023] This invention addresses the common problems in intelligent forest fire prevention terminals, such as uneven solar charging due to varying sensor locations, inconsistent energy states among terminals, and the potential for individual terminal malfunctions or loss of sensing area when using a uniform data acquisition strategy. It proposes an intelligent forest fire prevention sensor network system based on an optimal perception-energy consumption fusion strategy. Some sensor terminals in forest areas upload collected environmental data and their own status to a cloud system for forest fire early warning. To ensure accurate and rapid data uploads from sensors at any time and location, it is necessary to control the sensor operation and overall measurement performance. This system, combining an optimal perception-energy consumption fusion strategy, constructs a sensor activation network to address the impact of different environments on sensor energy. This strategy ensures that at least one sensor operates efficiently within a certain range, preventing all sensors within a certain area from malfunctioning due to lack of energy, thus avoiding delays in rapid response to forest fires. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the process of an intelligent sensor network control method based on an optimal sensing energy consumption fusion strategy according to the present invention.

[0025] Figure 2 This is a schematic diagram of the sensor network update process in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of sensor network updates in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings.

[0028] Because solar-powered sensor networks often suffer from uneven solar charging and inconsistent energy states due to varying sensor terminal locations, using a uniform data acquisition strategy can lead to individual terminal failures or even loss of partial sensing areas. This invention establishes an intelligent forest fire prevention sensor network system that dynamically adjusts sensor data acquisition frequency and sleep strategies to achieve an optimal sensing-energy consumption strategy, including the following steps:

[0029] S10: Obtain parameters for all sensors in the network, including: maximum sampling frequency Fmax, minimum sampling frequency Fmin, sensor sensing radius R, current battery level Bi and coordinates (X) of each sensor. i ,Y i The sensor activity Di is described for each sensor using a sensing-energy consumption function.

[0030] S20: Based on the activity levels of all the sensors obtained, form an activity distribution map according to the sensor distribution;

[0031] S30: Obtain the activity ranking of all sensors according to the activity distribution map, and then use the activity ranking from high to low to construct a distribution network of active sensors and low-power and / or dormant sensors around the active sensors.

[0032] S40: Perform sensor network updates.

[0033] As an optimization of the above embodiment, the sensing-energy consumption function is: Di=Bi*R.

[0034] Sensor data measurement and uploading: Locally, the sensor uploads its current battery level to the intelligent forest protection sensor network system, which records the battery levels of all sensor devices. Subsequently, the system backend uses a sensing-energy consumption function to calculate and record the current activity level of each sensor.

[0035] As an optimization of the above embodiment, after obtaining the activity levels of all sensors, a sensor activity distribution map is constructed based on the sensor activity levels 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.

[0036] As an optimization of the above embodiment, an activation sensor is constructed, the sensor is activated and an activation command 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.

[0037] As an optimization of the above embodiment, the activity of all sensors is sorted, and then the network of the currently active sensors is built starting from the sensor with the highest activity, thus completing the sensor network update;

[0038] Two recording sequences are constructed: Sequence 1 is a tag sequence set as an active sensor (high activity); Sequence 2 is a sensor tag sequence set as low power within the tolerable sensing range of the active sensor.

[0039] Specifically, the sensor network update includes the following steps:

[0040] Iterate through all sensors sorted by activity level from largest to smallest, and check if they are marked as Sequence 1 and Sequence 2. If so, check if the sequence sorted by activity level has been completely traversed. If so, the update ends; otherwise, continue.

[0041] If the sensor is not marked in Sequence 1 and Sequence 2, then obtain the sensors around the sensor node S. Take the sensor S as the center and obtain the sensors within a circle with a radius of 2R (twice the maximum sensing radius R, where the sensing ranges of the two sensors are tangent). Calculate the number of sensors within this range (those not recorded in Sequence 2).

[0042] If N is less than the number num, then find sensor S1 (the sensor with the highest activity among those that meet the conditions) from sequence 2, which has an activity level greater than S and a distance of less than 2R from S, and set it to active. Also set all sensors within a radius of 2R of S1 (including S, but excluding those already set to active) to low power / sleep and mark them in sequence 2. If sensor S1 cannot be found in sequence 2, then revert to the adjustment method when N is greater than the number num, where num is a preset threshold for the number of unmarked sensors.

[0043] If N is greater than the quantity num, then S is set to active and marked in sequence 1; and the sensors within a radius of 2R of S (excluding the sensors that are already set to active) are marked as low power / sleep and recorded in sequence 2.

[0044] Repeat the above steps until all sensor nodes have been traversed. Because the radius of the low-power / sleep setting is 2R, which is exactly tangent to the maximum sensing area of ​​the two sensors, no unmarked sensors will appear at the end.

[0045] The latest sensor activation network is acquired, and the activated sensors and low-power / dormant sensors configured within this network are then used to monitor forest fire prevention. The monitored environmental data is uploaded to the system, which performs intelligent analysis to effectively monitor and issue early warnings for forest fires, thereby effectively protecting forest resources. After a certain monitoring period, the above process is repeated to update the sensor activation network again.

[0046] like Figure 3 The diagram illustrates the sensor network update process of this invention. Pentagons represent sensors, and dashed circles represent the sensor's sensing radius R. Solid circles represent 2R. The blue-centered pentagon represents a currently active, highly active sensor. Other sensors within the solid circle are set to low-power / sleep mode. A red-centered pentagon marks the surrounding sensors. When this sensor is encountered, the system searches for a sensor S1 with higher activity within a 2R radius. If found, S1 is reactivated, and updates are performed around S1. If not found, it indicates that the current sensor has the highest activity among nearby sensors, and the current sensor is set to active.

[0047] To ensure rapid measurement and uploading of environmental data from sensors in any area at any time, a sensing-energy consumption function is used to calculate sensor activity. The sensor activation network is then reorganized based on the activity level of each sensor. This reorganized sensor network possesses the most sensitive sensing capabilities for all areas of the forest environment, enabling early warning and rapid response to forest fires. The sensor activation network design ensures that highly active sensors inevitably exist within a certain range. Specifically, the sensor's sensing radius is R, and multiple sensors are distributed within a circular area of ​​radius 2R (where the maximum sensing range of two adjacent sensors is tangent). Therefore, sensors with overlapping sensing areas will inevitably appear. If all sensors adopt high activity, energy waste will occur, and after a certain period, all sensors in the area may be unable to quickly and accurately collect and upload environmental data simultaneously. In actual environmental situations such as fires, low-activity sensors will also collect data, albeit at a relatively slower upload frequency. However, the smoke and high temperatures generated by combustion will gradually spread to the surrounding area. Nearby active (high-activity) sensors will immediately detect and upload data, and the system will immediately issue a warning upon receiving the data.

[0048] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1.A method for intelligent sensing network regulation based on optimal perception energy consumption fusion strategy, characterized in that, The method comprises the steps of: S10: Obtain all sensor parameters in the network, including: the highest collection frequency Fmax, the lowest collection frequency Fmin, the sensor sensing radius R, the current power Bi of each sensor and the coordinates (X i ,Y i ); describe the sensor activity Di of each sensor using a sensing-energy consumption function; the sensing-energy consumption function is: Di=Bi*R; S20: forming an activity distribution map according to the activity of all the sensors obtained, according to the sensor distribution; S30: obtaining the activity ranking of all the 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 active sensors from high to low according to the activity ranking; S40: updating the sensor network; constructing an active sensor, activating the sensor and issuing an activation instruction, setting the collection frequency to Bi*Fmax; obtaining low-power and / or dormant sensors around the active sensor, and setting the minimum collection frequency to Fmin; sorting the activity of all the sensors, and then constructing the network of the current active sensor from the sensor with the highest activity, and completing the update of the sensor network; constructing two record sequences, including: sequence 1 is set as the label sequence of the active sensor; and sequence 2 is set as the label sequence of the sensor set as low power within the tolerance sensing range of the active sensor; The sensor network update comprises the steps of: traversing all the sensors sorted by activity from high to low, whether they are marked to sequence 1 and sequence 2, if yes, whether the sequence after the activity sorting is traversed, if yes, the update is completed, if not, continue; if not marked to sequence 1 and sequence 2, obtaining the sensors around the sensor S node, obtaining the sensors within a range of 2R from the sensor S as the center, calculating the number N of the sensors within the range; if N is less than the number num, finding a sensor S1 from sequence 2, which has an activity greater than S and a distance less than 2R from S, setting S1 as active, setting the sensors within 2R from S1 as low power / dormant, and marking to sequence 2, wherein the sensors already marked to sequence 1 are not changed; if a sensor S1 cannot be found from sequence 2, returning to the adjustment mode when N is greater than the number num; if N is greater than the number num, setting S as active and marking to sequence 1; and setting the sensors within 2R from S as low power / dormant and recording to sequence 2; repeating the above sensor network update operation until all the sensor nodes are traversed. 2.The intelligent sensor network regulation method based on optimal perception energy consumption fusion strategy according to claim 1, characterized in that, After obtaining the activity of all the sensors, a sensor activity distribution map is constructed according to the deployment distribution map of the sensors on site and the activity of the sensors based on the relevant coordinate positions; the activity distribution map depicts the current state of all the sensors in the monitoring forest area.

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