阅读说明：本技术 一种基于群体智能优化的震动感知系统及方法 (Vibration sensing system and method based on group intelligent optimization ) 是由 周求湛 陈禹竺 胡继康 于 2021-08-02 设计创作，主要内容包括：本发明提供了一种基于群体智能优化的震动感知系统及方法,包括主节点和多个子节点；子节点的振动传感器感知目标在安防保护区域附近运动产生的震动信号,通过信号调理电路对感知到的浅地表震动信号进行放大和滤波；随后通过子节点数据采集处理单元中的同步AD模数转换模块对震动信号进行采集,通过子节点数据采集处理单元中的STM32处理模块对震动信号进行分析处理,并通过无线通信模块将入侵目标的相关信息上传至主节点；主节点的核心处理模块利用经群体智能优化方法优化的TDOA定位算法对入侵目标信号进行精确定位,能减小外界因素对定位算法造成的误差,提高定位结果的准确度。(The invention provides a vibration sensing system and method based on group intelligent optimization, which comprises a main node and a plurality of sub-nodes, wherein the main node comprises a plurality of sub-nodes; the vibration sensor of the sub-node senses vibration signals generated by the movement of a target near a security protection area, and amplifies and filters the sensed shallow surface vibration signals through a signal conditioning circuit; then, a synchronous AD conversion module in the sub-node data acquisition and processing unit is used for acquiring the vibration signal, an STM32 processing module in the sub-node data acquisition and processing unit is used for analyzing and processing the vibration signal, and the related information of the invasion target is uploaded to the main node through the wireless communication module; the core processing module of the main node accurately positions the intrusion target signal by using the TDOA positioning algorithm optimized by the swarm intelligence optimization method, so that the error of the positioning algorithm caused by external factors can be reduced, and the accuracy of the positioning result is improved.)

1. The utility model provides a vibrations perception system based on crowd's intelligence is optimized which characterized in that: the system comprises a main node and a plurality of sub-nodes; the main node comprises a core processing module and a wireless communication module; each subnode comprises a data acquisition and processing unit and a plurality of vibration sensing units which are dispersedly arranged;

the vibration sensing unit comprises a vibration sensor and a signal conditioning circuit; the vibration sensor is used for acquiring vibration signals generated by the moving target on the shallow ground surface in real time; the signal conditioning circuit is used for amplifying and filtering a vibration signal sensed by the vibration sensor;

the data acquisition processing unit comprises a synchronous AD (analog-to-digital) conversion module, an STM32 processing module and a wireless communication module; the synchronous AD conversion module is used for acquiring vibration signals sensed by the plurality of vibration sensing units and performing analog-to-digital conversion on the vibration signals; the STM32 processing module is used for analyzing and processing the collected vibration signals, detecting and classifying the invading targets according to the characteristics of the time domain, the frequency domain and the time-frequency domain of the signals, and uploading the related information of the invading targets to the main node through the wireless communication module;

and a core processing module of the main node is provided with a TDOA (time difference of arrival) positioning algorithm optimized based on a swarm intelligence optimization method, and the TDOA positioning algorithm is used for accurately positioning the intrusion target signal based on the signal arrival time difference.

2. The vibration perception system based on group intelligence optimization of claim 1, wherein: the vibration sensor is a moving-coil type vibration sensor.

3. The vibration perception system based on group intelligence optimization of claim 1, wherein: the signal conditioning circuit adopts a fourth-order Butterworth low-pass filter for amplification and filtering.

4. The vibration perception system based on group intelligence optimization of claim 1, wherein: the synchronous AD analog-to-digital conversion module adopts a synchronous acquisition AD chip AD7779 to perform analog-to-digital conversion.

5. The vibration perception system based on group intelligence optimization of claim 1, wherein: the core processing module adopts a low-power consumption singlechip of STM32 series STM32L496 model.

6. The vibration perception system based on group intelligence optimization of claim 1, wherein: the adopted group intelligent optimization method is a cat group algorithm.

7. The vibration perception system based on group intelligence optimization of claim 1, wherein: the wireless communication module adopts LoRa or big dipper short message wireless communication to accomplish data interaction.

8. A method for vibration sensing using a vibration sensing system based on swarm intelligence optimization according to any of claims 1-7, comprising the following steps:

s1, setting child nodes in each security protection area, and connecting each child node with the main node through a wireless communication module;

s2, sensing a vibration signal generated by the motion of the target near a security protection area by a vibration sensor of the child node in real time, and amplifying and filtering the shallow surface vibration signal sensed by the vibration sensor through a signal conditioning circuit to enhance the signal-to-noise ratio of the vibration signal generated by the motion of the target on the shallow surface; then, a synchronous AD (analog-to-digital) conversion module in the sub-node data acquisition and processing unit is used for acquiring vibration signals, an STM32 processing module in the sub-node data acquisition and processing unit is used for analyzing and processing the vibration signals, the time domain, the frequency domain and the time-frequency domain characteristics of the signals are compared, the intrusion targets are detected and classified, and the related information of the intrusion targets is uploaded to a main node through a wireless communication module;

s3, the core processing module of the main node accurately positions the intrusion target signal by using the TDOA positioning algorithm optimized by the swarm intelligence optimization method.

Technical Field

The invention relates to the field of perimeter security, in particular to a vibration sensing system and method based on group intelligent optimization.

Background

Currently, perimeter security systems are mainly classified into the following categories: an electronic fence type perimeter security system, an optical fiber sensor perimeter security system, a leaky cable perimeter security system, and a vibration sensing perimeter security system. The perimeter security system based on the vibration sensor detection means is commonly used for monitoring and identifying ground moving targets in recent years. When people or vehicles move on the ground, different vibration waveforms can be generated on the shallow ground surface, and the vibration waveform data of the shallow ground surface is collected through the vibration sensor and is processed and analyzed, so that whether intrusion behaviors exist or not, the type of an intrusion target, the position of the intrusion target and other related information can be obtained.

The existing vibration perception perimeter security system usually determines relevant information such as an intrusion target position by calculating the time of a seismic source signal reaching each sensor, the generation time of the seismic source signal needs to be determined in the solving process, the system is influenced by factors such as a layout environment and a layout mode, and the error of a finally calculated positioning result is large.

Disclosure of Invention

Aiming at the technical problems, the invention provides a vibration sensing system and method based on group intelligent optimization.

A vibration perception system based on swarm intelligence optimization comprises a main node and a plurality of sub-nodes; the main node comprises a core processing module and a wireless communication module; each subnode comprises a data acquisition and processing unit and a plurality of vibration sensing units which are dispersedly arranged;

the vibration sensing unit comprises a vibration sensor and a signal conditioning circuit; the vibration sensor is used for acquiring vibration signals generated by the moving target on the shallow ground surface in real time; the signal conditioning circuit is used for amplifying and filtering a vibration signal sensed by the vibration sensor;

the data acquisition processing unit comprises a synchronous AD (analog-to-digital) conversion module, an STM32 processing module and a wireless communication module; the synchronous AD conversion module is used for acquiring vibration signals sensed by the plurality of vibration sensing units and performing analog-to-digital conversion on the vibration signals; the STM32 processing module is used for analyzing and processing the collected vibration signals, detecting and classifying the invading targets according to the characteristics of the time domain, the frequency domain and the time-frequency domain of the signals, and uploading the related information of the invading targets to the main node through the wireless communication module;

and a core processing module of the main node is provided with a TDOA (time difference of arrival) positioning algorithm optimized based on a swarm intelligence optimization method, and the TDOA positioning algorithm is used for accurately positioning the intrusion target signal based on the signal arrival time difference.

Preferably, the vibration sensor is a moving coil type vibration sensor.

Preferably, the signal conditioning circuit performs amplification and filtering by using a fourth-order butterworth low-pass filter.

Preferably, the synchronous AD/a conversion module performs analog/digital conversion by using a synchronous acquisition AD chip AD 7779.

Preferably, the core processing module adopts a low-power consumption singlechip of STM32 series STM32L496 model.

Preferably, the adopted group intelligent optimization method is a cat group algorithm.

Preferably, the wireless communication module completes data interaction by adopting LoRa or Beidou short message wireless communication.

A method for vibration perception by adopting the vibration perception system mainly comprises the following steps:

s1, setting child nodes in each security protection area, and connecting each child node with the main node through a wireless communication module;

s2, sensing a vibration signal generated by the motion of the target near a security protection area by a vibration sensor of the child node in real time, and amplifying and filtering the shallow surface vibration signal sensed by the vibration sensor through a signal conditioning circuit to enhance the signal-to-noise ratio of the vibration signal generated by the motion of the target on the shallow surface; then, a synchronous AD (analog-to-digital) conversion module in the sub-node data acquisition and processing unit is used for acquiring vibration signals, an STM32 processing module in the sub-node data acquisition and processing unit is used for analyzing and processing the vibration signals, the time domain, the frequency domain and the time-frequency domain characteristics of the signals are compared, the intrusion targets are detected and classified, and the related information of the intrusion targets is uploaded to a main node through a wireless communication module;

s3, the core processing module of the main node accurately positions the intrusion target signal by using the TDOA positioning algorithm optimized by the swarm intelligence optimization method.

The invention has the beneficial effects that:

1. the synchronous AD conversion module in the sub-node data acquisition and processing unit is used for acquiring the vibration signals, so that the time reference of the target signals acquired by each vibration sensor is consistent; accurately positioning the invasion target signal based on the signal arrival time difference by using a TDOA positioning algorithm without determining the generation time of the seismic source signal; when the signal propagation speed is known, obtaining the time difference of the seismic source signal reaching the two vibration sensors means obtaining the distance difference of the seismic source signal reaching the two sensors, and obtaining a group of hyperbolas according to the distance difference of the seismic source signal reaching the two sensors.

2. A group intelligent optimization method is utilized to optimize the traditional TDOA positioning algorithm so as to reduce errors caused by external factors such as layout environment, layout mode and the like to the positioning algorithm, thereby further improving the accuracy of the positioning result.

3. The wireless communication module completes data interaction by adopting LoRa or Beidou short message wireless communication; the data transmission realized by the LoRa technology can reduce the error rate, and the transmission distance is 3-5 times higher than that of the common wireless transmission technology; the Beidou short message wireless communication can meet the remote communication requirement of the system in unmanned areas, and the integrity of a communication link is guaranteed without being affected.

Drawings

The invention will be further described with reference to the accompanying drawings.

FIG. 1 is an overall system architecture diagram of an embodiment of the present invention;

FIG. 2 is a schematic circuit diagram of an AD7779 chip for synchronous acquisition according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the connection between an STM32 processing module and an AD7779 in an embodiment of the invention;

FIG. 4 is a schematic circuit diagram of a core processing module according to an embodiment of the present invention;

FIG. 5 is a flow chart of a cat swarm algorithm in an embodiment of the present invention;

FIG. 6 is a schematic diagram illustrating preliminary results generated by a simulated TDOA location algorithm in an embodiment of the present invention;

FIG. 7 is a schematic diagram of a positioning result after optimization by a cat swarm algorithm in the embodiment of the present invention;

fig. 8 is a schematic diagram of the positioning result enlarged in fig. 7.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art without any creative work based on the embodiments of the present invention belong to the protection scope of the present invention.

As shown in FIG. 1, a vibration perception system based on swarm intelligence optimization comprises a main node and a plurality of sub-nodes. The main node comprises a core processing module and a wireless communication module. Each subnode comprises a data acquisition and processing unit and a plurality of vibration sensing units which are dispersedly arranged. The vibration sensing unit comprises a vibration sensor and a signal conditioning circuit. The data acquisition processing unit comprises a synchronous AD analog-to-digital conversion module, an STM32 processing module and a wireless communication module.

The system selects a moving-coil vibration sensor as a sensing unit of a target signal, and collects vibration signals generated by a moving target on the shallow ground surface in real time. The moving-coil vibration sensor serves as a passive sensor, and can convert a vibration signal of the shallow ground surface into an electric signal in a mode that an internal coil cuts a magnetic induction line without additional power supply input. The signal conditioning circuit adopts a fourth-order Butterworth low-pass filter to amplify and filter the electric signals output by the moving-coil vibration sensor, the amplification factor is 900 times, and the cut-off frequency is 200 Hz.

In order to ensure that the time references of the target signals acquired by the vibration sensors are consistent, the synchronous AD analog-to-digital conversion module selects a synchronous acquisition AD chip AD7779 shown in fig. 2 to acquire vibration signals sensed by the plurality of vibration sensing units and performs analog-to-digital conversion on the vibration signals. The STM32 processing module selects an STM32 series STM32L496 model low-power consumption single chip microcomputer to complete logic control and data processing of each module (a connection schematic diagram of the STM32 processing module and the AD7779 is shown in FIG. 3). The wireless communication modules of the sub-nodes and the main node adopt LoRa or Beidou short message wireless communication to complete data interaction between each sub-node and the main node, so that the completeness of a communication link can be guaranteed, and the stability of the system in communication in remote unmanned areas is guaranteed.

The core processing module of the master node adopts a low-power consumption single chip microcomputer of STM32 series STM32L496 model shown in fig. 4, and carries a TDOA positioning algorithm optimized by using a group intelligence optimization method (in the embodiment, the cat swarm optimization algorithm shown in fig. 5 is adopted for optimization). The TDOA positioning algorithm accurately positions the intrusion target signal based on the time difference of the seismic source signal reaching each vibration sensor.

A method for vibration perception by adopting the vibration perception system mainly comprises the following steps:

s1, setting sub-nodes in each security protection area, and connecting each sub-node with the main node in a LoRa or Beidou short message wireless communication mode.

S2, sensing vibration signals generated by the movement of the target near a security protection area in real time through the moving-coil vibration sensor by the child node, amplifying and filtering the shallow ground surface vibration signals sensed by the vibration sensor through the signal conditioning circuit, and enhancing the signal-to-noise ratio of the vibration signals generated by the movement of the target on the shallow ground surface. The synchronous AD analog-to-digital conversion module in the data acquisition processing unit is used for acquiring vibration signals, the STM32 processing module in the data acquisition processing unit is used for analyzing and processing the vibration signals, signal time domain, frequency domain and time-frequency domain characteristics are compared, the intrusion target is detected and classified, and relevant information of the intrusion target is uploaded to the main node through LoRa or Beidou short message wireless communication.

S3, the core processing module of the main node accurately positions the intrusion target signal by using the TDOA positioning algorithm optimized by the cat swarm algorithm. The TDOA location algorithm locates a target signal based on the signal time difference of arrival. Because the system adopts the synchronous acquisition AD chip to acquire the vibration signal of the target, the signal of each channel has the same time reference. When an intrusion behavior occurs, the multiple acquisition channels simultaneously acquire vibration data and generate alarm signals, the system can acquire the number of the corresponding alarm channel and the time of a system real-time clock of the corresponding channel, the time difference of the arrival of the vibration waves at each vibration sensor can be obtained through calculation, a primary TDOA positioning result is obtained, and information such as the type and the position of an intrusion target is obtained.

Setting the SRD range of the change field to be 0.2, setting the c1 to be 1, setting the initial speed to be 0.03 and the iteration times to be 200, and writing a cat swarm algorithm program for verification. The preset intrusion target position is (7.5,2.5), and 48 points are randomly generated near the intrusion target position to be regarded as the initial result of the TDOA location algorithm, as shown in FIG. 6, the abscissa range is 7-8, and the ordinate range is 2-3. After the cat swarm optimization, the positioning result is shown in fig. 7 (fig. 8 is the positioning result amplified in fig. 7), so that the conventional TDOA positioning algorithm optimized by the cat swarm optimization has a small error and high accuracy.

Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention.