CN-122020970-A - Two-dimensional environment field modeling method and related equipment for under-forest multi-parameter cooperation

CN122020970ACN 122020970 ACN122020970 ACN 122020970ACN-122020970-A

Abstract

The invention discloses a two-dimensional environmental field modeling method and related equipment of under-forest multi-parameter cooperation, which relate to the field of environmental field modeling, the method synchronously collects wind field related sensing data, environmental temperature, environmental humidity and harmful gas concentration data through each detection node and acquires a real-time geographic position, then a remote monitoring platform performs time synchronization and spatial position alignment on the multi-parameter environmental data based on a sampling time stamp and the geographic position, the multi-parameter environment data set with the unified space-time reference is formed, and a two-dimensional environment field covering the monitoring area is further constructed based on the data set and the geographic positions of all detection nodes, so that the transformation from discrete perception to a space continuous field model of the multi-parameter environment information under the forest is realized, and the problems that the monitoring data is isolated and a global environment situation cannot be formed in the prior art are effectively solved.

Inventors

ZHANG YINDONG
LUO ZHENZHONG
ZHANG YUKAI
CHEN KE
TAO XUEWEN
JING JING

Assignees

浙江省应急管理科学研究院(浙江省安全生产技术检测检验中心、浙江省危险化学品登记中心)

Dates

Publication Date: 20260512
Application Date: 20251229

Claims (10)

1. A method for modeling a two-dimensional environment field by under-forest multi-parameter cooperation is characterized by being applied to a mobile node ad hoc network system, wherein the system comprises a plurality of mobile deployed detection nodes, at least one LoRa gateway and a remote monitoring platform, each detection node is integrated with a wind parameter sensing unit, a temperature and humidity sensing unit, a harmful gas sensing unit and a positioning module and is internally provided with a LoRa communication module, each detection node self-organizes and constructs a dynamic wireless sensing network through the built-in LoRa communication module, collected data of each detection node is transmitted to the LoRa gateway through the dynamic wireless sensing network and is forwarded to the remote monitoring platform through the LoRa gateway, and the method comprises the following steps: s1, synchronously acquiring multi-parameter environmental data and acquiring a real-time geographic position of the multi-parameter environmental data through each detection node, wherein the multi-parameter environmental data comprises wind field related sensing data, environmental temperature data, environmental humidity data and harmful gas concentration data; s2, transmitting the multi-parameter environment data to a remote monitoring platform through a LoRa gateway through the dynamic wireless sensor network; s3, filtering and denoising the multi-parameter environmental data acquired by each detection node through a remote monitoring platform, and based on the sampling time stamp and the real-time geographic position of each detection node, performing time synchronization and spatial position alignment on the filtered data to obtain a multi-parameter environmental data set corresponding to each detection node one by one; and S4, constructing a two-dimensional environment field based on the multi-parameter environment data set and the geographic position of each detection node.
2. The method for modeling an under-forest multi-parameter collaborative two-dimensional environmental field according to claim 1, wherein the wind parameter sensing unit comprises four ultrasonic transducers arranged in two pairs to respectively form two groups of orthogonal ultrasonic transceiving paths along the geographic east-west direction and the geographic north-south direction; The two ultrasonic transducers in each group of paths are alternately used as a transmitting end and a receiving end to acquire ultrasonic propagation time along two opposite directions of the paths; the wind field related sensing data are four paths of ultrasonic wave propagation time measured by the two groups of orthogonal ultrasonic wave receiving and transmitting paths, and the four paths of ultrasonic wave propagation time comprise downwind propagation time and upwind propagation time on each group of paths.
3. The method for modeling a two-dimensional environmental field under a forest multi-parameter collaboration according to claim 2, wherein the step S3 specifically comprises: Carrying out denoising treatment on the environmental temperature data, the environmental humidity data and the harmful gas concentration data uploaded by each detection node by adopting moving average filtering, median filtering or first-order low-pass digital filtering to obtain target environmental temperature, target environmental humidity and target harmful gas concentration corresponding to each detection node; And processing the ultrasonic propagation time data uploaded by each detection node in any one of the following modes to obtain a target wind speed value of each detection node: (1) Adopting an extended Kalman filtering method to process, estimating east wind speed components and north wind speed components at the current moment of each detection node to obtain estimated wind speed values of each detection node, and carrying out temperature compensation on the corresponding estimated wind speed values based on the target environment temperature of each detection node at the corresponding moment to obtain target wind speed values corresponding to each detection node; (2) Based on the forward wind and the reverse wind direction propagation time on the two groups of orthogonal ultrasonic wave receiving and transmitting paths, directly calculating east wind velocity components and north wind velocity components by utilizing the physical relationship between wind speed and propagation time to obtain a wind speed calculation value, and performing temperature compensation on the wind speed calculation value based on the target environment temperature at the corresponding moment of each detection node to obtain a target wind speed value; And (3) performing cross-node time synchronization and spatial position alignment according to the sampling time stamp and the real-time geographic position information of each node to form a multi-parameter environment data set which corresponds to each detection node one by one and has a unified space-time reference.
4. The method for modeling a two-dimensional environmental field under a forest multi-parameter coordination according to claim 3, wherein the obtaining of the estimated wind speed value of the detection node specifically comprises: Defining a wind speed state quantity to be estimated, wherein the wind speed state quantity comprises an east wind speed component and a north wind speed component at the position of a detection node at the current moment; Based on the wind speed state quantity and process noise representing turbulence disturbance under forests, establishing a state equation describing a time evolution rule of the wind speed state quantity; acquiring four paths of ultrasonic wave propagation time output by the wind parameter sensing unit to form an observation data vector, wherein the four paths of ultrasonic wave propagation time respectively correspond to the downwind and upwind direction propagation time of two groups of orthogonal paths in the east-west direction and the north-south direction; Based on the physical association relation between ultrasonic propagation time, wind speed and sound velocity, determining the current sound velocity by using synchronously acquired ambient temperature, combining projection components of wind speed state quantity in two groups of orthogonal ultrasonic receiving and transmitting paths, constructing an observation equation for describing a nonlinear mapping relation between an observation data vector and the wind speed state quantity, and introducing the sound velocity into the observation equation as a known parameter, wherein the observation equation comprises observation noise caused by measurement uncertainty of a wind parameter sensing unit; Using the wind speed initial value obtained by preliminary calculation based on the observation data vector as an initial estimated value of a wind speed state quantity, and executing state prediction by combining the state equation to obtain a priori wind speed state estimation; Performing first-order linearization processing on the observation equation at the prior wind speed state estimation position to obtain an observation jacobian matrix; Based on the observation data vector, the observation jacobian matrix and preset noise parameters representing the measurement precision of the wind parameter sensing unit, calculating Kalman gain, and updating the wind speed state quantity by combining the prior wind speed state estimation to obtain posterior wind speed state estimation; And converting the posterior wind speed state estimation into wind power and wind direction angle to obtain an estimated wind speed value.
5. The method for modeling a two-dimensional environmental field under a forest multi-parameter cooperation according to claim 1, wherein the construction of the two-dimensional environmental field based on the multi-parameter environmental data set and the geographic position of each detection node is specifically as follows: Dividing the monitoring area into a plurality of grid points, wherein each grid point has a preset coordinate position; For each grid point, based on each target environment parameter in the multi-parameter environment data set corresponding to each detection node, estimating interpolation data corresponding to the grid point by adopting any one of the following modes: (1) Estimating by adopting an inverse distance weighted interpolation algorithm according to the plane distance between the grid point and each detection node; (2) Estimating by adopting a weighted average method in combination with the plane distance between the grid point and each detection node, the wireless communication link quality of each detection node and the measurement error of a sensing unit, performing confidence assessment on the estimation result of each grid node, and determining interpolation data of the grid point according to the assessment result; based on interpolation data of all grid points obtained in any mode, a two-dimensional wind field, a two-dimensional temperature field, a two-dimensional humidity field and a two-dimensional harmful gas concentration field which cover a monitoring area are constructed, and a multi-parameter collaborative two-dimensional environment field model is formed.
6. The method for modeling a two-dimensional environmental field under a forest multi-parameter cooperation according to claim 5, wherein the plane distance between the grid point and each detection node, the wireless communication link quality of each detection node and the measurement error of the sensing unit are combined, and a weighted average method is adopted for estimation, specifically: Aiming at each target environment parameter, calculating the fusion weight of each detection node to the target environment parameter based on the plane distance from each detection node to the grid point, the wireless communication link quality of each detection node and the measurement error of a sensing unit corresponding to the parameter; and carrying out weighted average on the values of the project label environmental parameters on each detection node by utilizing the fusion weight of the project label environmental parameters to obtain an estimation result of the project label environmental parameters at the grid point.
7. The method for modeling a two-dimensional environmental field under a forest multi-parameter collaboration according to claim 6, wherein the confidence evaluation is performed on the estimation result of each grid node, specifically: For each target environment parameter for each grid point: Calculating the maximum value and the minimum value in the target environment parameter values corresponding to the detection nodes; Calculating the deviation between the target environment parameter value corresponding to each detection node and the estimation result of the target environment parameter at the grid point to obtain a plurality of deviation values; Calculating a deviation statistic based on the plurality of deviation values; determining the confidence of the target environment parameter of the grid point based on the deviation statistic, the maximum value and the minimum value; and if the confidence coefficient is lower than a preset threshold value, judging that the estimation result is invalid, and executing data rejection and re-estimation operation.
8. An electronic device comprising a processor and a memory storing a program, characterized in that the program comprises instructions that when executed by the processor cause the processor to perform the two-dimensional ambient field modeling method according to any of claims 1 to 7.
9. A non-transitory machine readable medium storing computer instructions for causing the computer to perform the two-dimensional ambient field modeling method according to any one of claims 1 to 7.
10. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the two-dimensional ambient field modeling method of any of claims 1 to 7.

Description

Two-dimensional environment field modeling method and related equipment for under-forest multi-parameter cooperation Technical Field The invention relates to the field of environmental field modeling, in particular to a two-dimensional environmental field modeling method and related equipment for under-forest multi-parameter cooperation. Background The occurrence and the spread of forest fires highly depend on the multi-parameter coupling action of the micro-environment under the forest, wherein key parameters such as wind field structure, temperature and humidity distribution, concentration of harmful gases (such as carbon monoxide) and the like jointly determine the development situation of fire and the evolution of dangerous areas, and meanwhile, the real-time position information of fire rescue workers is crucial to guaranteeing the operation safety of the fire rescue workers. However, the existing forest fire monitoring technology has the remarkable bottleneck that firstly, the multi-parameter integration level is low, most equipment can only singly acquire isolated information such as temperature, smoke or wind speed, the synchronous acquisition capability of wind, temperature and humidity, harmful gas and high-precision positioning data is lacked, comprehensive situation research and judgment of a fire scene are difficult to support, secondly, the adaptability of a wind parameter measurement scheme is poor, the traditional mechanical or simple ultrasonic anemometer is easily influenced by vegetation shielding and turbulence disturbance in a complex forest environment, the measurement precision and stability are insufficient, the networking mode is stiff, the existing system depends on fixed deployment nodes, flexible movement and dynamic ad hoc network of detection equipment cannot be realized, the monitoring requirement of rapid change of a fire scene is difficult to adapt, and finally, the effective space field modeling capability is lacked, even if discrete point data are obtained, the visual environment model such as a two-dimensional wind field and a temperature field which cover the whole field cannot be constructed, and accurate prediction and emergency decision support of the fire spreading trend are severely restricted. Therefore, an integrated solution integrating high-precision multi-parameter sensing, loRa mobile ad hoc network and intelligent two-dimensional environment field reconstruction capability is needed to realize comprehensive, dynamic and collaborative perception of fire scenes under forests. Disclosure of Invention The invention provides a two-dimensional environment field modeling method of under-forest multi-parameter cooperation, which is applied to a mobile node self-organizing network system and aims to solve the problems of low multi-parameter integration level, lack of wind field modeling capability, insufficient networking flexibility and the like in the existing under-forest fire monitoring, wherein the system comprises a plurality of mobile deployed detection nodes, at least one LoRa gateway and a remote monitoring platform, each detection node is integrated with a wind parameter sensing unit, a temperature and humidity sensing unit, a harmful gas sensing unit and a positioning module and is internally provided with a LoRa communication module, each detection node self-organizes and constructs a dynamic wireless sensing network through the built-in LoRa communication module, acquired data of each detection node is transmitted to the LoRa gateway through the dynamic wireless sensing network and is forwarded to the remote monitoring platform through the LoRa gateway, and the method comprises the following steps: s1, synchronously acquiring multi-parameter environmental data and acquiring a real-time geographic position of the multi-parameter environmental data through each detection node, wherein the multi-parameter environmental data comprises wind field related sensing data, environmental temperature data, environmental humidity data and harmful gas concentration data; s2, transmitting the multi-parameter environment data to a remote monitoring platform through a LoRa gateway through the dynamic wireless sensor network; s3, filtering and denoising the multi-parameter environmental data acquired by each detection node through a remote monitoring platform, and based on the sampling time stamp and the real-time geographic position of each detection node, performing time synchronization and spatial position alignment on the filtered data to obtain a multi-parameter environmental data set corresponding to each detection node one by one; and S4, constructing a two-dimensional environment field based on the multi-parameter environment data set and the geographic position of each detection node. Further, the wind parameter sensing unit comprises four ultrasonic transducers which are arranged in two pairs to respectively form two groups of orthogonal ultrasonic transceiving p