CN118884420B - A campus personnel gathering detection system based on radar wave technology - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/003—Transmission of data between radar, sonar or lidar systems and remote stations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
本发明公开了一种基于雷达波技术的校区人员聚集检测系统，涉及校园安全管理技术领域，该系统包括以下组成部分：雷达波探测模块：采用高精度、多频段的雷达波传感器，对校园内各区域进行不间断扫描，实时获取人员分布、移动速度和方向数据，本发明通过智能分析处理单元在触发预警时自动生成最优的疏散路径和方案，疏散指导信息通过校园广播系统、手机APP、电子显示屏多种渠道进行发布，为师生提供了清晰、直观的疏散指引，在疏散过程中，系统还能根据实时的人员流动情况和现场反馈，动态调整疏散指导信息，确保疏散过程的安全有序，这种智能化的疏散指导机制，不仅提高了疏散效率，还降低了因恐慌或混乱而导致的安全事故风险。
The present invention discloses a campus personnel gathering detection system based on radar wave technology, which relates to the field of campus safety management technology. The system includes the following components: a radar wave detection module: a high-precision, multi-band radar wave sensor is used to continuously scan various areas in the campus, and real-time data on personnel distribution, movement speed and direction are obtained. The present invention automatically generates the optimal evacuation path and plan through an intelligent analysis and processing unit when an early warning is triggered. The evacuation guidance information is released through multiple channels such as the campus broadcasting system, mobile phone APP, and electronic display screens, providing teachers and students with clear and intuitive evacuation guidance. During the evacuation process, the system can also dynamically adjust the evacuation guidance information according to real-time personnel flow and on-site feedback to ensure the safety and order of the evacuation process. This intelligent evacuation guidance mechanism not only improves the evacuation efficiency, but also reduces the risk of safety accidents caused by panic or chaos.
Description
Technical Field
The invention relates to the technical field of campus security management, in particular to a school zone personnel gathering detection system based on radar wave technology.
Background
In the current society, along with the vigorous development of education industry and the improvement of higher education popularity, the campus scale is continuously enlarged, the number of teachers and students is rapidly increased, the trend promotes academic communication and knowledge transmission, but simultaneously brings unprecedented challenges to campus safety management, particularly in holidays, large-scale activities or emergency situations, the phenomenon of personnel aggregation in the campus is particularly prominent, and once the management is improper, the trampling event, the congestion problem and other potential safety hazards are extremely easy to cause, so that the life and property safety of teachers and students is seriously threatened.
The traditional personnel gathering monitoring means mainly comprises a video monitoring system and a manual patrol, wherein the video monitoring system can intuitively display the site situation, but has a plurality of defects that firstly, video data is easily influenced by light change, shielding objects and bad weather factors, so that the monitoring effect is greatly reduced, secondly, the video monitoring relates to the collection and processing of a large amount of personal information, the risk of privacy leakage is high, the dissatisfaction and legal disputes are easily caused, and finally, the video monitoring system depends on the manual monitoring and judgment, so that the efficiency is low, and early warning opportunities are easily missed due to the manual negligence.
Based on the above problems, it is necessary to optimize the existing personnel aggregation monitoring means, realize real-time monitoring of personnel aggregation conditions in a campus by using a radar wave detection technology, further perform deep analysis and processing on collected data by integrating an advanced artificial intelligence algorithm, automatically identify and evaluate personnel aggregation density, and automatically trigger an early warning mechanism and an evacuation guidance information release platform when necessary, so that development of a personnel aggregation detection system for a school district based on the radar wave technology, which can comprehensively realize the above characteristics, has important significance.
Disclosure of Invention
The invention aims to make up the defects of the prior art, and provides a school zone personnel gathering detection system based on a radar wave technology, which can realize real-time monitoring and analysis of personnel gathering conditions in a campus by combining a high-precision multi-frequency-band radar wave sensor and an advanced artificial intelligence algorithm, automatically trigger an early warning mechanism and provide efficient and accurate evacuation guidance so as to prevent trampling events and other potential safety hazards.
The invention provides a system for detecting personnel gathering in a school zone based on a radar wave technology, which comprises the following components:
the radar wave detection module adopts a high-precision multi-band radar wave sensor to continuously scan each area in the campus, and acquires personnel distribution, moving speed and direction data in real time;
The intelligent analysis processing unit integrates an artificial intelligent algorithm, including but not limited to deep learning, machine learning and data mining technologies, processes and analyzes the data collected by the radar wave detection module in real time, automatically identifies and evaluates the personnel gathering density, and judges whether potential safety hazards exist or not;
The early warning triggering module is used for displaying that the personnel aggregation density of a certain area exceeds a preset safety threshold value according to an analysis result and automatically triggering early warning signals including but not limited to audible and visual alarm, short message notification and email alarm, so that relevant management personnel and security personnel can obtain warning information at the first time;
the evacuation guidance information release platform is used for automatically generating and releasing detailed evacuation guidance information, including evacuation routes, safe exit positions and notes, by combining with various channels of a campus broadcasting system, a mobile phone APP and an electronic display screen according to the specific conditions of an early warning area, and guiding teachers and students to withdraw orderly so as to avoid the occurrence of trampling safety accidents;
The self-learning and optimizing module is provided with self-learning capability, can continuously accumulate historical data, optimize an algorithm model, improve early warning accuracy and evacuation guidance effectiveness, support remote configuration and update, and facilitate flexible adjustment according to the actual situation of a campus.
Further, the radar wave detection module deploys a high-precision multi-band radar wave sensor in a critical area of the campus, performs initialization configuration on the radar sensor, comprises setting scanning frequency, detection range and data processing parameters, and adjusts according to specific environments and requirements of the campus, the radar sensor continuously scans a designated area, collects raw data of personnel distribution, moving speed and direction, the collected data is transmitted to an intelligent analysis processing unit in a wireless or wired mode, and the intelligent analysis processing unit performs preprocessing on the collected raw data, including denoising, filtering and calibration steps, so that the quality and accuracy of the data are improved.
Furthermore, the intelligent analysis processing unit receives the data from the radar wave detection module, performs preliminary data check and synchronous processing, extracts the characteristic information related to personnel aggregation from the preprocessed data by utilizing an image processing or signal processing algorithm, wherein the characteristic information comprises personnel quantity and density, the integrated artificial intelligent algorithm performs deep analysis on the extracted characteristic information, evaluates the severity degree and potential safety risk of personnel aggregation, judges whether an early warning mechanism needs to be triggered according to an analysis result, and enters an early warning process when the personnel aggregation density of a certain area exceeds a preset safety threshold.
Furthermore, the intelligent analysis processing unit adopts a density estimation algorithm based on an image processing algorithm to extract characteristic information related to personnel aggregation from the preprocessed data, wherein an algorithm formula is DENSITY ESTIMATE =f (Extracted Features, model Parameters), f represents a density estimation Model, extracted Features represents characteristics extracted from the preprocessed data, and Model Parameters represent Parameters of the Model.
Furthermore, the intelligent analysis processing unit adopts a support vector machine algorithm to evaluate the severity of personnel aggregation and potential safety risk, and the calculation formula is as follows: Wherein N is the number of training samples, x i is the feature vector of the ith sample, y i is the label of the ith sample, K (x i,xj) is a kernel function for calculating the similarity between samples, C is a regularization parameter for controlling the overfitting, a i is the lagrangian multiplier, and the extracted feature information is classified or regressed in real time by using a trained support vector machine model to evaluate the current personnel aggregation density and security risk level.
Furthermore, the early warning triggering module judges that the early warning needs to be triggered, the intelligent analysis processing unit immediately generates early warning signals, including audible and visual alarm signals, short message notification and email alarm, sends the early warning signals to terminal equipment of relevant management personnel and security personnel, and public release platforms of campus broadcasting systems and electronic display screens, and personnel receiving the early warning signals need to immediately confirm and take corresponding countermeasures, including going to site to check and starting an emergency plan.
Furthermore, the evacuation guidance information issuing platform automatically generates an optimal evacuation path and scheme according to the specific conditions of the early warning area, issues evacuation guidance information through a campus broadcasting system, a mobile phone APP and an electronic display screen, and the information content comprises detailed guidance of evacuation routes, safe exit positions and notes.
Furthermore, the evacuation guidance information distribution platform automatically generates an optimal evacuation path by adopting Dijkstra algorithm, and automatically generates the optimal evacuation path from the current position to the nearest safety exit according to campus map, real-time personnel distribution, safety exit position and obstacle distribution information, wherein the calculation formula is that the calculation of the evacuation path P can be expressed as the problem of finding the optimal path from the starting node S to the safety exit node T: Where P represents the optimal path to find, S and T are the start and end nodes of the path, respectively, all paths fromstot represents all possible paths from node S to node T, (e) represents the edges in the path, and Σ e∈p w (e) represents the sum of the weights of all the edges on the path P.
Furthermore, the self-learning and optimizing module continuously accumulates historical data through the system, including personnel gathering conditions, evacuation effects and response time of each early warning event, trains and optimizes the artificial intelligent algorithm model by utilizing the accumulated data, improves early warning accuracy and effectiveness of evacuation guidance, and comprises the steps of adjusting model parameters and improving algorithm structures, and simultaneously supports configuration and updating of the system remotely.
Furthermore, the self-learning and optimizing module optimizes the artificial intelligent algorithm model by Newton method, specifically, initializes the model parameter θ 0 and sets the parameters of the optimizing algorithm, and in each iteration, calculates the gradient of the objective function J (θ t) under the current model parameter θ t by using the data collected by the radar wave detection module as inputAnd a Hessian matrix H t, calculating an update direction according to Newton's method or its approximation algorithm, and updating model parameters θ t+1, namelyRepeating the iteration until reaching the convergence of the preset iteration times or objective function values, and automatically adjusting the parameters of the artificial intelligent algorithm model by the system through continuously accumulating historical data and utilizing the training and optimizing process.
Compared with the prior art, the school zone personnel gathering detection system based on the radar wave technology has the following beneficial effects:
1. According to the intelligent early warning system, the personnel gathering condition in the school zone is monitored in real time through the integrated radar wave technology, and the early warning signal is immediately triggered when abnormal gathering or potential danger is detected, so that the time from abnormal detection to early warning release is greatly shortened by the intelligent early warning mechanism, the response speed of early warning is improved, meanwhile, the necessity of early warning can be judged more accurately by the system based on big data analysis and optimization of an artificial intelligent algorithm model, false alarm and false alarm are reduced, and the accuracy and reliability of early warning information are ensured.
2. According to the intelligent evacuation guidance system, the intelligent analysis processing unit automatically generates the optimal evacuation path and the optimal evacuation proposal when the early warning is triggered, and the evacuation guidance information is issued through a campus broadcasting system, a mobile phone APP and an electronic display screen, so that clear and visual evacuation guidance is provided for teachers and students.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a system for detecting personnel accumulation in a school zone based on radar wave technology.
FIG. 2 is a flow chart of a system for detecting personnel gathering in a school zone based on radar wave technology.
Detailed Description
The following will clearly and completely describe the technical solutions in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The implementation process of the intelligent early warning function in the school district personnel gathering detection system based on the radar wave technology is elaborated, the real-time monitoring and evaluation of the personnel gathering condition of each area in the campus are realized by deploying the high-precision multi-band radar wave sensor and combining the artificial intelligent algorithm of the intelligent analysis processing unit, and the early warning signal is automatically triggered when the preset safety threshold value is exceeded, so that the campus safety is ensured.
The radar sensor is initialized and configured in critical areas in the campus, including teaching buildings, playgrounds and dining halls, and comprises scanning frequency, detection range and data processing parameters, so as to adapt to specific environments and requirements of the campus, ensure that the radar sensor can scan the designated areas uninterruptedly, collect raw data of personnel distribution, moving speed and direction, and transmit the raw data to the intelligent analysis processing unit in a wireless or wired mode.
The intelligent analysis processing unit receives data from the radar wave detection module, performs preliminary data check and synchronization processing, ensures the integrity and consistency of the data, performs pretreatment of denoising, filtering and calibration on the data by utilizing a signal processing algorithm to improve the quality and accuracy of the data, and extracts characteristic information related to personnel aggregation from the pretreated data by using a density estimation algorithm based on an image processing algorithm, wherein an algorithm formula is DENSITY ESTIMATE =f (Extracted Features, model Parameters), f represents a density estimation Model, extracted Features represents characteristics extracted from the pretreated data, model Parameters represent Parameters of the Model, and a support vector machine algorithm is adopted to evaluate the severity and potential safety risk of personnel aggregation, and the calculation formula is as follows: Wherein N is the number of training samples, x i is the feature vector of the ith sample, y i is the label of the ith sample, K (x i,xj) is a kernel function for calculating the similarity between samples, C is a regularization parameter for controlling the overfitting, a i is the lagrangian multiplier, and the extracted feature information is classified or regressed in real time by using a trained support vector machine model to evaluate the current personnel aggregation density and security risk level.
When the personnel aggregation density rho of a certain area exceeds a preset safety threshold rho threshold, the intelligent analysis processing unit immediately generates an early warning signal, wherein the early warning signal comprises an audible and visual alarm signal, a short message notification and an email alarm, and the early warning signal is issued through a campus broadcasting system and an electronic display screen in various channels, and the triggering logic of the early warning signal is expressed as follows: The terminal devices of the related manager and security personnel receive the early warning signals, and the corresponding countermeasures are required to be immediately confirmed and taken, including going to the site to check and start the emergency plan.
In summary, by implementing the embodiment, the personnel aggregation situation in the campus can be monitored in real time and effectively early-warned, so that the campus safety management level is remarkably improved, the potential safety hazards caused by personnel aggregation are reduced, the early-warning accuracy and the effectiveness of evacuation guidance are improved by continuously optimizing an algorithm model, and a powerful technical support is provided for campus safety.
Example 2
The implementation details of the evacuation guidance information release platform in the school district personnel gathering detection system based on the radar wave technology are elaborated in the embodiment, the platform aims at rapidly generating the optimal evacuation path and the optimal evacuation scheme after intelligent early warning triggering, and precisely conveying the evacuation guidance information through various channels so as to ensure that teachers and students can evacuate rapidly and orderly under emergency conditions, and effectively avoid trampling safety accidents.
For early warning triggering and response, setting D i as the personnel aggregation density of the area i, setting T as a preset safety threshold, triggering an early warning signal when the intelligent analysis processing unit detects that D i is more than T, and immediately starting an evacuation guidance information release flow.
For evacuation path and scheme generation, the generation of the evacuation path involves complex terrain and people stream analysis, and can be realized by adopting a shortest path algorithm in graph theory, namely Dijkstra algorithm and campus map data, G (V, E) is set as a graph representation of a campus map, wherein V is a node set (representing a building and a road intersection), E is an edge set (representing a path), w (E) is a weight of an edge E (representing distance, time or comprehensive risk index), and the calculation of the evacuation path P can be expressed as finding an optimal path problem from a starting node S to a safety exit node T: Where P represents the optimal path to find, S and T are the start and end nodes of the path, respectively, all paths fromstot represents all possible paths from node S to node T, (e) represents the edges in the path, and Σ e∈p w (e) represents the sum of the weights of all the edges on the path P.
The evacuation guidance information is issued through a campus broadcasting system, a mobile phone APP and an electronic display screen in multiple channels, wherein the issued contents comprise, but are not limited to, evacuation route description, a safe exit position and notice, in the evacuation process, the system dynamically adjusts the evacuation guidance information by utilizing real-time data, and if F (t) is real-time feedback data at time t, the dynamic adjustment can be expressed as follows: Wherein F (t, P) is an additional risk assessment of the path P based on real-time feedback data, lambda is an adjustment factor, management staff and security personnel rapidly act according to evacuation guiding information, meanwhile, the system collects real-time data and teacher and student feedback in the evacuation process, after the evacuation is finished, effect assessment is carried out, algorithms and parameters are optimized, system performance is improved, and assessment indexes comprise evacuation time, casualty rate and teacher and student satisfaction.
In summary, by implementing the embodiment, when facing the potential safety hazard caused by personnel aggregation, the campus can quickly generate and release evacuation guidance information based on the radar wave technology and the intelligent algorithm, so that the evacuation efficiency and the safety are remarkably improved, the risk of trampling a safety accident is reduced, and a powerful technical support is provided for campus emergency management.
Example 3
The steps of the system are based on examples 1 and 2:
(1) Installing a high-precision multi-band radar wave sensor in a selected key area in a campus, ensuring complete coverage and no dead angle, connecting a radar wave detection module to an intelligent analysis processing unit, initializing system hardware and software, configuring parameters of an early warning trigger mechanism, including a preset safety threshold, an alarm mode and a receiving personnel list, integrating a campus broadcasting system, a mobile phone APP and an electronic display screen evacuation guidance information release platform, and ensuring that information can be accurately and quickly transmitted to teachers and students;
(2) Starting a radar wave detection module, continuously scanning each area in the campus, acquiring personnel distribution, moving speed and direction data in real time, and transmitting the data to an intelligent analysis processing unit in a wired or wireless mode for preliminary processing and storage;
(3) The intelligent analysis processing unit utilizes an integrated artificial intelligent algorithm to process and analyze the data collected by the radar wave detection module in real time, automatically identifies and evaluates the personnel aggregation density of each area, compares the personnel aggregation density with a preset safety threshold, immediately triggers an early warning mechanism once the personnel aggregation density of a certain area is found to exceed the safety threshold, sends out an audible and visual alarm, and informs relevant management personnel and security personnel in a short message and electronic mail mode;
(4) According to the specific situation of the early warning area, the evacuation guiding information issuing platform automatically generates detailed evacuation guiding information, including evacuation routes, safe exit positions and notes, plays emergency evacuation broadcasting through a campus broadcasting system, and issues the evacuation guiding information on a mobile phone APP and an electronic display screen, so that teachers and students can acquire and understand evacuation instructions in time;
(5) The teachers and students withdraw to the safe area according to the evacuation guidance information in order, the system records the data of the evacuation process, including evacuation time and personnel flow condition, provides basis for subsequent analysis and optimization, and the management personnel and security personnel adjust the evacuation scheme according to the actual condition to ensure the safety and high efficiency of the evacuation process;
(6) The system continuously accumulates historical data, optimizes a model through a self-learning algorithm, improves early warning accuracy and evacuation guidance effectiveness, supports remote configuration and updating, flexibly adjusts according to campus actual conditions and seasonal changes, and ensures adaptability and reliability of the system;
(7) And (3) periodically maintaining and checking, namely periodically checking and maintaining the radar wave sensor, the intelligent analysis processing unit and the information release platform, ensuring the normal operation and data accuracy of the radar wave sensor, the intelligent analysis processing unit and the information release platform, evaluating the performance of the system, and correspondingly optimizing and upgrading the system according to the evaluation result.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. The system for detecting the personnel gathering in the school zone based on the radar wave technology is characterized by comprising the following components:
the radar wave detection module adopts a high-precision multi-band radar wave sensor to continuously scan each area in the campus, and acquires personnel distribution, moving speed and direction data in real time;
The intelligent analysis processing unit is used for integrating an artificial intelligence algorithm, comprising deep learning, machine learning and data mining technologies, receiving data from the radar wave detection module, performing preliminary data verification and synchronous processing, extracting characteristic information related to personnel aggregation from the preprocessed data, wherein the characteristic information comprises personnel quantity and density, and the integrated artificial intelligence algorithm is used for performing deep analysis on the extracted characteristic information to evaluate the severity and potential safety risk of the personnel aggregation, wherein the calculation formula is as follows: Wherein, the method comprises the steps of, wherein, Is the number of training samples to be used,Is the eigenvector of the i-th sample,Is the label of the i-th sample,Is a kernel function, used to calculate the similarity between samples,Is a regularization parameter, used to control the overfit,The method comprises the steps that Lagrange multipliers are utilized, the extracted characteristic information is classified or regressed in real time by utilizing a trained support vector machine model, so that the current personnel aggregation density and the safety risk level are estimated, whether an early warning mechanism needs to be triggered or not is judged by an intelligent analysis processing unit according to an analysis result, and an early warning process is started when the personnel aggregation density of a certain area exceeds a preset safety threshold;
The early warning triggering module is used for displaying that the personnel aggregation density of a certain area exceeds a preset safety threshold value according to an analysis result, and automatically triggering early warning signals, including audible and visual warning, short message notification and email warning, so that related management personnel and security personnel can obtain warning information at the first time;
the evacuation guidance information release platform is used for automatically generating and releasing detailed evacuation guidance information, including evacuation routes, safe exit positions and notes, by combining with various channels of a campus broadcasting system, a mobile phone APP and an electronic display screen according to the specific conditions of an early warning area, and guiding teachers and students to withdraw orderly so as to avoid the occurrence of trampling safety accidents;
The self-learning and optimizing module is provided with self-learning capability, can continuously accumulate historical data, optimize an algorithm model, improve early warning accuracy and evacuation guidance effectiveness, support remote configuration and update, and facilitate flexible adjustment according to the actual situation of a campus.
2. The system for detecting personnel gathering in a school zone based on the radar wave technology according to claim 1, wherein the radar wave detection module deploys a high-precision multi-band radar wave sensor in a key area of a campus, performs initialization configuration on the radar sensor, comprises setting scanning frequency, detection range and data processing parameters, and adjusts according to specific environments and requirements of the campus, the radar sensor continuously scans a designated area, collects raw data of personnel distribution, moving speed and direction, the collected data is transmitted to an intelligent analysis processing unit in a wireless or wired mode, and the intelligent analysis processing unit performs preprocessing on the collected raw data, including denoising, filtering and calibration steps, so as to improve the quality and accuracy of the data.
3. The system for detecting personnel accumulation in a school district based on the radar wave technology according to claim 1, wherein the intelligent analysis processing unit adopts a density estimation algorithm based on an image processing algorithm to extract characteristic information related to personnel accumulation from the preprocessed data, and an algorithm formula is as follows: Wherein, the method comprises the steps of, wherein, Representing a model of the density estimation,Representing features extracted from the preprocessed data,Representing parameters of the model.
4. The system of claim 1, wherein the early warning triggering module determines that an early warning needs to be triggered, the intelligent analysis processing unit immediately generates an early warning signal, including an audible and visual alarm signal, a short message notification, an email alarm, sends the early warning signal to terminal devices of related management personnel and security personnel, and a public release platform of a campus broadcasting system and an electronic display screen, and the personnel receiving the early warning signal need to immediately confirm and take corresponding countermeasures, including going to a site to check and start an emergency plan.
5. The system for detecting personnel gathering in the school district based on the radar wave technology according to claim 1, wherein the evacuation guiding information issuing platform automatically generates an optimal evacuation path and scheme according to the specific condition of the early warning area, the evacuation guiding information is issued through a campus broadcasting system, a mobile phone APP and an electronic display screen, the information content comprises detailed guidance of evacuation route, safe exit position and notice, and during evacuation, the intelligent analysis processing unit can dynamically adjust the evacuation guiding information according to real-time personnel flowing condition and site feedback.
6. The system for detecting personnel accumulation in a school district based on the radar wave technology according to claim 5, wherein the evacuation guidance information distribution platform automatically generates an optimal evacuation path by Dijkstra algorithm, and automatically generates an optimal evacuation path from a current position to a nearest safety exit according to campus map, real-time personnel distribution, safety exit position and obstacle distribution information, wherein the calculation formula is as followsExpressed as finding an optimal path problem from the starting node S to the secure egress node T: Wherein, the method comprises the steps of, wherein, Representing the optimal path to be found, S and T are the start node and the end node of the path respectively,Representing all possible paths from node S to node T,Representing the edges in the path and,Representing a pathThe sum of the weights of all the edges.
7. The system for detecting personnel accumulation in a school district based on the radar wave technology according to claim 1, wherein the self-learning and optimizing module continuously accumulates historical data through the system, including personnel accumulation conditions, evacuation effects and response time of each early warning event, trains and optimizes an artificial intelligence algorithm model by utilizing the accumulated data, improves early warning accuracy and effectiveness of evacuation guidance, and comprises the steps of adjusting model parameters and improving algorithm structures, and simultaneously supports configuration and updating of the system remotely.
8. The system for detecting the concentration of staff in a school district based on the radar wave technology according to claim 1, wherein the self-learning and optimizing module optimizes the artificial intelligence algorithm model by newton's method, in particular, initializes model parametersSetting parameters of an optimization algorithm, and calculating current model parameters by using data collected by the radar wave detection module as input in each iterationObjective function belowGradient of (2)AndRepeating the iteration until reaching the convergence of the preset iteration times or objective function values, and automatically adjusting the parameters of the artificial intelligent algorithm model by the system through continuously accumulating historical data and utilizing the training and optimizing process.
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