A smart campus intelligent security early warning system and method

Abstract

The present invention discloses a smart campus intelligent security warning system and method, which relate to the field of campus security technology. The warning system includes a detection and startup module, a tag matching module, a monitoring and analysis module, and a warning adjustment module that run in sequence. The technical points are as follows: the warning system aims to monitor the pressure changes near the wall in real time through a pressure plate, and combines a camera to monitor and identify personnel, so as to realize intelligent campus security warning; the system can automatically judge the identity and behavior of personnel, and trigger different levels of warnings according to the situation, while intelligently dispatching security resources to ensure campus safety; this solution effectively solves the problems of slow response speed and unreasonable personnel scheduling in traditional campus security, and improves the efficiency and accuracy of emergency response; at the same time, through the application of prediction calculation model and fuzzy calculation model, the intelligent and refined dispatch of security personnel is realized, which not only ensures safety needs but also avoids waste of resources.

Description

Technical Field

[0001] The present invention relates to the field of campus security technology, and in particular to a smart campus intelligent security early warning system and method. Background Art

[0002] Campus security refers to a series of preventative measures and management systems implemented to ensure the safety of teachers and students and maintain order on campus. It encompasses multiple aspects, including but not limited to: access control: installing an intelligent access control system to control access to and from the campus, effectively preventing unauthorized entry and ensuring campus safety; video surveillance: installing high-definition cameras in key areas of the campus for 24 / 7, comprehensive monitoring, enabling timely detection and resolution of abnormal situations; alarm systems: installing emergency alarm devices, such as one-touch alarm phones and alarm posts, to ensure a rapid response and notification to relevant departments in emergencies; and patrol inspections: arranging security personnel to conduct regular patrols to check the safety of various areas of the campus and prevent potential safety hazards.

[0003] However, for security operations near the campus walls, traditional security measures only use cameras for 24-hour continuous monitoring, and issue an early warning when someone is detected approaching or climbing over the campus wall. The early warning method is usually to send a signal to the remote monitoring end, and the response action of the monitoring end is to dispatch on-duty personnel to stop it. There are many problems with this early warning method. For each camera, in order to ensure the clarity of the monitoring process, high-definition camera probes are selected, and they are turned on 24 hours a day, which will cause excessive energy consumption and thus lead to excessively high detection costs. In addition, for the behavior of climbing over the campus wall, if there are too many people engaging in this behavior, it is necessary to manually control the number of security personnel to be dispatched. If the scheduling time is too long, it will lead to slow response speed in campus security. At the same time, there will also be problems with unreasonable personnel scheduling. For example, if there are too many people climbing over the campus wall, only dispatching two security personnel will not be effective, resulting in poor overall security effect. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a smart campus intelligent security warning system and method. The system uses pressure plates to monitor pressure changes near the wall in real time, and combines cameras to monitor and identify personnel, thereby realizing intelligent campus security warning. The system can automatically determine the identity and behavior of personnel and trigger different levels of warnings according to the situation. At the same time, it intelligently dispatches security resources to ensure campus safety, solving the problems raised in the background technology.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A smart campus intelligent security early warning system, the system comprising:

[0009] The detection start module monitors the pressure output by the pre-installed pressure plates on the inner side of the target campus wall. When the instantaneous pressure value at any point exceeds the preset calibration threshold, the probe start signal is triggered;

[0010] When the probe at the corresponding position detects the target person, it will perform continuous monitoring action;

[0011] Otherwise, the probe at the corresponding position is turned off;

[0012] The tag matching module uses a deep learning algorithm to perform face recognition when a target person is detected by a probe at a corresponding position, and combines this with the campus database to determine whether the target person has been entered with a tag.

[0013] If it is determined that the target person has no marking information, a level one warning command will be issued;

[0014] Otherwise, the warning judgment mechanism is triggered;

[0015] The monitoring and analysis module implements the warning judgment mechanism and issues an audible and visual warning when it determines that the target person has marked information. This warning continues until the target person is no longer detected by the corresponding probe. While the audible and visual warning action is ongoing, the module executes the behavior recognition strategy and combines computer vision technology to analyze pre-recorded behaviors. When the target person is identified to have pre-recorded behavior, a secondary warning signal is issued and the target person's marking information is output simultaneously.

[0016] The early warning and adjustment module executes the first-level adjustment strategy upon receiving the first-level early warning instruction;

[0017] Upon receiving the second-level warning instruction, execute the second-level adjustment strategy:

[0018] Input and extract the target personnel's tag information, build an estimated calculation model, generate an estimated dispatch value for security personnel, and dispatch personnel in the duty room according to the estimated dispatch value; based on the warning valuation calculated in the estimated calculation model, combined with the distance value between the probe coordinates of the corresponding position and the duty room, build a fuzzy calculation model, generate the response time, and complete the dispatch action within the response time when dispatching personnel in the duty room according to the estimated dispatch value.

[0019] Furthermore, the pre-laying position of the pressure plate is located on the inner side of the campus wall, and the distance between the pressure plate and the campus wall is S, and S>0.

[0020] Furthermore, the process of applying deep learning algorithms for face recognition is as follows:

[0021] Preprocessing: Preprocess the facial image of the monitored target person;

[0022] Among them, preprocessing includes cropping, scaling, grayscale and denoising;

[0023] Feature extraction: Use convolutional neural network (CNN) to extract features from preprocessed facial images;

[0024] Face recognition: The extracted feature vector is compared with the facial features entered in the campus database, and the similarity is calculated to determine whether they are the same person;

[0025] If the similarity exceeds the threshold, it is determined that the target person has the entered tag information;

[0026] If the similarity does not exceed the limit value, it is determined that the target person has no entered marking information.

[0027] Furthermore, the pre-recorded behavior at least includes wall climbing and scrambling; the process of analyzing the pre-recorded behavior is as follows:

[0028] Use computer vision technology to perform behavioral analysis on the real-time video stream captured by the corresponding position probe;

[0029] By comparing the target person's behavior in the real-time video stream with the pre-recorded behavior, it is possible to identify whether the target person has pre-recorded behavior.

[0030] Furthermore, the first-level adjustment strategy is: obtain the coordinate information of the corresponding position probe, immediately dispatch all personnel in the duty room, and simultaneously notify the inspection personnel.

[0031] Furthermore, the tag information includes at least name, student ID number and class. Before building the estimation calculation model, the maximum number of people and grade in the target personnel are extracted.

[0032] Furthermore, the process of building an estimated calculation model is as follows:

[0033] Based on the number of target personnel and the maximum grade, a warning valuation calculation formula is established:

[0034]

[0035] Where WV represents the warning valuation, N represents the number of target personnel, and M represents the maximum grade of target personnel; the grade value ranges from 0 to 12;

[0036] Then, based on the early warning valuation and the known number of basic dispatches, the estimated dispatch value calculation formula is established:

[0037]

[0038] In the formula, EDV represents the estimated dispatch value, k represents the adjustment coefficient, which ranges from 0 to 1, and B represents the basic number of dispatched personnel.

[0039] Furthermore, the process of building a fuzzy computing model is as follows:

[0040] Based on the warning estimate and the distance between the probe coordinates at the corresponding location and the duty room, a response time calculation formula is constructed:

[0041]

[0042] Modify formula (1) and add an upper limit constraint:

[0043]

[0044] In formulas (1) and (2), RT0 represents the initial set response time, and RT represents the final generated response time.

[0045] A smart campus intelligent security early warning method includes the following steps:

[0046] S1, monitoring the pressure output by the pre-laid pressure plates inside the target campus wall. When the instantaneous pressure value detected at any point exceeds the preset calibration threshold, the probe start signal is triggered;

[0047] When the probe at the corresponding position detects the target person, it will perform continuous monitoring action;

[0048] Otherwise, the probe at the corresponding position is turned off;

[0049] S2. When the target person is detected by the probe at the corresponding position, a deep learning algorithm is used for face recognition, combined with the campus database to determine whether the target person has been entered into the tag information;

[0050] If it is determined that the target person has no marking information, a level one warning command will be issued;

[0051] Otherwise, the warning judgment mechanism is triggered;

[0052] S3. Execute the warning judgment mechanism and issue an audible and visual warning when it is determined that the target person has marked information. This warning continues until the target person is no longer detected by the corresponding probe. While the audible and visual warning action is ongoing, the behavior recognition strategy is executed, combining computer vision technology to analyze pre-recorded behaviors. When the target person is identified to have pre-recorded behavior, a secondary warning signal is issued and the target person's marking information is output simultaneously.

[0053] S4. Upon receiving the first-level warning instruction, execute the first-level adjustment strategy;

[0054] Upon receiving the second-level warning instruction, execute the second-level adjustment strategy:

[0055] Input and extract the target personnel's tag information, build an estimated calculation model, generate an estimated dispatch value for security personnel, and dispatch personnel in the duty room according to the estimated dispatch value; based on the warning valuation calculated in the estimated calculation model, combined with the distance value between the probe coordinates of the corresponding position and the duty room, build a fuzzy calculation model, generate the response time, and complete the dispatch action within the response time when dispatching personnel in the duty room according to the estimated dispatch value.

[0056] (3) Beneficial effects

[0057] The present invention provides a smart campus intelligent security early warning system and method, which has the following beneficial effects:

[0058] This solution uses pre-installed pressure plates to detect pressure in real time. When the instantaneous pressure exceeds a preset calibration threshold, the corresponding probe is precisely triggered to activate, effectively avoiding false alarms and energy waste. At the same time, combined with the conditional activation of high-definition camera probes, this not only ensures the clarity of the monitoring image, but also significantly reduces energy consumption, solving the high cost problem caused by traditional 24-hour probe operation.

[0059] This solution combines deep learning algorithms for facial recognition, accurately distinguishing between target individuals inside and outside the school, and conducting real-time monitoring and analysis of abnormal behavior by on-campus personnel. Once an intrusion by an outsider or abnormal behavior by an on-campus person is detected, the system can quickly issue a warning signal of the corresponding level and simultaneously output the target person's tag information so that timely countermeasures can be taken. This improves the intelligent and refined level of campus security management and provides strong support for the construction of smart campuses.

[0060] This solution automatically implements different adjustment strategies based on the warning level to ensure the rational allocation and efficient use of security resources;

[0061] In the case of a Level 1 warning, all on-duty personnel are immediately dispatched and patrol personnel are notified. The geographic information system is used to quickly locate and optimally dispatch security personnel to the scene of the incident. In the case of a Level 2 warning, an estimated calculation model is used to comprehensively consider the number and grade of the target personnel to generate a reasonable estimated dispatch value, and the dispatch is completed within the response time determined by the fuzzy calculation model.

[0062] This solution effectively solves problems such as slow response speed and unreasonable personnel scheduling in traditional campus security, and improves the efficiency and accuracy of emergency response. At the same time, through the application of predictive calculation models and fuzzy calculation models, it realizes the intelligent and refined dispatch of security personnel, which not only ensures safety needs but also avoids waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a modular schematic diagram of the early warning system of the present invention;

[0064] Figure 2 This is a schematic diagram of the actual configuration of the campus wall and the pressure plate in the present invention;

[0065] Figure 3 Schematic diagram of the overall process of the early warning method of the present invention. DETAILED DESCRIPTION

[0066] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] Example 1:

[0068] See also Figures 1 to 2 This embodiment provides a smart campus intelligent security warning system. An overview of this system is as follows: It aims to monitor pressure changes near the wall in real time through pressure plates, and combines this with cameras to monitor and identify people, thereby achieving intelligent campus security warnings. The system can automatically determine the identity and behavior of people, trigger different levels of warnings based on the situation, and intelligently dispatch security resources to ensure campus safety.

[0069] The early warning system includes various functional modules that operate in sequence: a detection and activation module, a tag matching module, a monitoring and analysis module, and an early warning and adjustment module. The entire early warning system intelligently monitors and warns of campus wall-climbing incidents, ensuring campus safety.

[0070] Functional modules and descriptions

[0071] The detection start module monitors the pressure value output by the pre-laid pressure plates inside the target campus wall. When the instantaneous pressure value detected at any point exceeds the preset calibration threshold, the probe start signal is triggered and the probe at the corresponding position starts working;

[0072] When the probe at the corresponding position detects the target person, it will perform continuous monitoring action;

[0073] When the probe at the corresponding position fails to detect the target person, it indicates a false alarm, and the probe at the corresponding position is turned off;

[0074] Among them, reference Figure 2 You can then see the location of the target campus's walls and its pressure plates;

[0075] The pre-installed location of the pressure plate is usually located inside the campus wall, and the distance from the campus wall is S. The value of S can be determined according to actual needs. For example, there is a drainage ditch close to the edge of the wall in the XX campus, so the pressure plate can be laid at intervals of the drainage ditch. In this case, the value of S is approximately equal to the maximum width of the drainage ditch.

[0076] For pressure plates:

[0077] They are usually part of security monitoring technology and are used to detect and alarm intruders or fleeing people near walls or borders; such devices are usually called "pressure sensors" or "pressure pads", which consist of a pad and several sensors installed under the pad, and can be integrated with security systems (such as alarm systems, monitoring systems or access control systems); the working principle of pressure sensors is based on the pressure change to detect whether there is an object (such as a person, animal or vehicle) exerting pressure on it; when a person or object steps on the pressure plate, it triggers a signal, which can be transmitted to the central control unit, thereby triggering an alarm or taking other safety measures;

[0078] Burying pressure plates within school walls can effectively enhance campus security, especially in areas requiring high-security monitoring, such as school boundaries, areas around important facilities, or unmanned areas at night. These pressure plates are part of a security system and are typically used in conjunction with other security equipment (such as cameras, lighting, and access control systems) to form a comprehensive and effective security monitoring system. Furthermore, the installation and maintenance of such equipment requires professional personnel to ensure its proper operation and reliability.

[0079] The length of the pressure plate is limited, usually based on the corresponding probe's shooting clarity limit. For example, a Class A camera can clearly capture the face of a person within 300 meters, so the pressure plate length is 300 meters. The Class A camera is flush with one end of the pressure plate, and the monitoring range covers the corresponding pressure plate and the campus wall. Each Class A camera corresponds to a pressure plate, and the position of the Class A camera is flush with the interval between adjacent pressure plates.

[0080] It should be noted that:

[0081] The preset calibration threshold is set manually, usually according to the minimum weight of students on campus. Some cats, dogs or other objects will not trigger the probe start signal, completing the targeted monitoring of students.

[0082] Each probe is not turned on 24 hours a day, but is turned on intermittently or under special circumstances according to the situation. In order to ensure the clarity of the monitoring process, the probes selected are high-definition camera probes. Continuous opening will consume too much energy, which will lead to excessively high detection costs. Therefore, in order to meet the detection effect and control the detection cost, conditional triggering is used to turn it on, which ensures the effectiveness of security work while solving the cost problem.

[0083] Specifically, the above solution achieves efficient and energy-saving security monitoring of the inside of the campus wall;

[0084] This technical solution uses pre-installed pressure plates to detect pressure values ​​in real time. When the instantaneous pressure value exceeds a preset calibration threshold, the corresponding probe is precisely triggered to activate, effectively avoiding false alarms and energy waste. At the same time, combined with the conditional activation of high-definition camera probes, this not only ensures the clarity of the monitoring image, but also significantly reduces energy consumption, solving the high cost problem caused by traditional 24-hour probe operation. In addition, the laying position and length of the pressure plates can be flexibly adjusted according to actual needs, and the coordination with the cameras has formed a comprehensive and effective security monitoring system, enhancing campus safety.

[0085] This technical solution not only improves the pertinence and effectiveness of security monitoring, but also fully considers economy and operability, effectively solves the technical difficulties in campus security monitoring, provides strong support for campus security prevention work, and ensures the harmony and stability of the campus environment and the safety of students.

[0086] Tag matching module:

[0087] When the target person is detected by the corresponding probe, a deep learning algorithm is used for face recognition, combined with the campus database to determine whether the target person has been recorded.

[0088] If the target person is determined to have no marked information, it means that the target person is an off-campus person and a level 1 warning instruction is issued;

[0089] If the target person is determined to have marked information, it means that the target person is an on-campus person, triggering the warning judgment mechanism;

[0090] The process of applying deep learning algorithms for face recognition is as follows:

[0091] Preprocessing: The facial image of the monitored target person is preprocessed, including cropping, scaling, grayscale conversion, and denoising, to improve recognition accuracy. Feature extraction: A convolutional neural network (CNN) is used to extract features from the preprocessed facial image to generate a unique feature vector representing the face. Face recognition: The extracted feature vector is compared with the facial features already entered in the campus database, and similarity is calculated to determine whether it is the same person.

[0092] If the similarity exceeds the threshold, it is determined to be the same person, indicating that the target person has entered tag information;

[0093] If the similarity does not exceed the threshold, it is determined that they are not the same person, indicating that the target person has no entered tag information;

[0094] It should be noted that for non-school personnel, Figure 2 As shown in the figure, outsiders may enter the campus from outside the campus wall, while insiders may leave the campus by climbing over the campus wall. This early warning system is designed to complete the security early warning operation of the campus in response to these two situations, realizing smart campus management.

[0095] For the target person, it is at least one person, usually several people;

[0096] Campus database comparison and judgment

[0097] Database construction: The campus database should contain the facial feature data of all campus personnel with entered tagged information, as well as related tagged information. The tagged information should at least include name, student ID, and class. Comparison and judgment: The captured facial features are compared one by one with the features in the database to find the record with the highest similarity. Judgment results: Without tagged information: If the similarity does not exceed the threshold, that is, no matching record is found, the target person is judged to be an off-campus person; With tagged information: If the similarity exceeds the threshold, that is, a matching record is found, the target person is judged to be an on-campus person.

[0098] In addition, it also includes ongoing operations, such as data updates: regularly updating the campus database to ensure that the facial feature data therein is consistent with the actual personnel; algorithm optimization: based on actual application results, continuously optimizing deep learning algorithms and facial recognition models to improve recognition accuracy and efficiency. These are not explained in detail here.

[0099] Monitoring and analysis module:

[0100] Execute the warning judgment mechanism and issue an audible and visual warning when it is determined that the target person has marked information. This will continue until the probe at the corresponding position no longer detects the target person. When the audible and visual warning action continues, the behavior recognition strategy will be executed.

[0101] Combined with computer vision technology to analyze pre-recorded behaviors, when the target person is identified to have pre-recorded behaviors, a secondary warning signal is issued and the target person's tag information is output simultaneously;

[0102] Among them, the sound and light warning method is carried out through the sound and light buzzer equipped on the corresponding probe, which plays a primary warning role; the purpose is to prevent people on campus from mistakenly entering the area near the campus wall and serve as a warning;

[0103] In actual applications, in the case of accidental entry, if the students have no intention of climbing over the wall, they will leave immediately after hearing the sound and light warning. At this time, the probe at the corresponding position does not detect the target person, and the sound and light warning is turned off. However, for some bold students, they may take advantage of campus sports events or when there is too much noise from outside interference to climb over the wall. In this case, the effect of the sound and light warning is limited, so subsequent implementation of relevant strategies or actions is required to achieve effective security.

[0104] The pre-recorded behavior includes at least wall climbing and scrambling;

[0105] The process for conducting pre-recorded behavior analysis is as follows:

[0106] Use computer vision technology to perform behavioral analysis on the real-time video stream captured by the corresponding position probe;

[0107] Pre-recorded behavior analysis: The system pre-defines several behaviors that may pose security risks, such as wall climbing and scrambling. By comparing the target person's behavior in the real-time video stream with pre-recorded behavior patterns, the system can identify whether the target person has any of these pre-recorded behaviors.

[0108] When the behavior recognition strategy confirms that the target person has engaged in pre-recorded behavior (such as climbing over a wall), a level 2 warning signal is immediately issued. Compared to the level 1 warning signal, there is no level distinction between the two, but rather a distinction in subsequent response and disposal actions. The corresponding execution actions of the warning signals of the same level are different.

[0109] Secondary warning signals and marking information can be sent simultaneously in a variety of ways;

[0110] It should include at least one or a combination of SMS, APP push, email notification and campus broadcasting system to ensure that relevant information can be quickly conveyed to relevant personnel and security departments.

[0111] Specifically, the above solution achieves efficient intelligent monitoring and early warning of the campus wall area;

[0112] This solution uses deep learning algorithms for facial recognition to accurately distinguish between target individuals inside and outside the school, and conducts real-time monitoring and analysis of abnormal behavior by those on campus (such as wall climbing). Once an intrusion by an outsider or abnormal behavior by an insider is detected, the system can quickly issue an appropriate level of warning signal and simultaneously output the target individual's tag information so that timely countermeasures can be taken.

[0113] This technical solution effectively solves the problems of high false alarm rate and slow response in traditional campus security systems, improves the intelligence and refinement of campus safety management, and provides strong support for the construction of smart campuses; at the same time, the combination of sound and light warnings and multiple warning signal sending methods ensures the timely transmission and effective response of warning information, further enhancing the campus's safety prevention capabilities.

[0114] Early warning adjustment module:

[0115] Upon receiving the first-level warning instruction, the first-level adjustment strategy is executed:

[0116] Obtain the coordinate information of the corresponding position probe, immediately dispatch all personnel in the duty room, and simultaneously notify the inspection personnel;

[0117] The Level 1 warning indicates that an outsider has invaded the campus. Therefore, the number of personnel to be dispatched is set to the maximum possible value, as the danger posed by outsiders is higher than that posed by those on campus. The response time reserved for personnel is also immediate, not a fixed timeframe, to avoid increased danger due to adjustments or adjustments.

[0118] To accurately obtain the geographic coordinates of the probe that triggered the Level 1 alert, this is usually achieved through the probe's built-in positioning function (using the built-in GPS) or a geographic information system (GIS) associated with the probe. The obtained coordinate information should be transmitted quickly and accurately to the responding personnel so that they can quickly locate the specific location of the incident.

[0119] For responders who need to go to the corresponding coordinates:

[0120] Typically, a geographic information system (GIS) and path planning algorithms are used to achieve optimal scheduling of security personnel. Common path planning algorithms used during the scheduling process include the Dijkstra algorithm and the A* algorithm. Path planning algorithms are used to search for the optimal path from the starting point to the end point within the campus road network. Various factors, such as shortest distance, shortest time, and avoiding congested roads, can also be considered to meet the needs of different scenarios. These factors are not discussed here. The generated path is presented to security personnel in the form of a map, along with detailed route information (such as distance traveled, estimated time, etc.). Paths are optimized and adjusted based on actual conditions and feedback from security personnel to ensure they can reach their designated locations smoothly.

[0121] Upon receiving the second-level warning instruction, execute the second-level adjustment strategy:

[0122] Input the target personnel's tag information, extract the maximum number of people and grade levels among the target personnel, build an estimated calculation model, generate an estimated dispatch value for security personnel, and dispatch personnel in the duty room according to the estimated dispatch value; based on the warning valuation calculated in the estimated calculation model, combined with the distance value between the probe coordinates of the corresponding position and the duty room, build a fuzzy calculation model, generate the response time, and when dispatching personnel in the duty room according to the estimated dispatch value, complete the dispatch action within the response time;

[0123] The marking information includes at least name, student number and class;

[0124] Extract the maximum number of people and grade of the target person in the marked information (usually this system is adapted to schools corresponding to junior high and high schools, so the grades range from 7 to 12). It should be noted that: 7th grade corresponds to the first grade of junior high school; 8th grade corresponds to the second grade of junior high school; 9th grade corresponds to the third grade of junior high school; 10th grade corresponds to the first grade of senior high school; 11th grade corresponds to the second grade of senior high school; 12th grade corresponds to the third grade of senior high school;

[0125] In addition, the target person's marking information can also be synchronously matched and sent to the head teacher of the corresponding grade. For example, when the target person is 1, the target person's name is XX, the student number is 32, and the class is Class 1, Grade 7, the marking information is sent to the academic affairs system for matching, and the head teacher of Class 1, Grade 7 is obtained as XX. The target person's marking information and the behavior of crossing the campus wall are packaged and sent to XX via SMS or other means, so that the head teacher can deal with the student in a timely manner and carry out subsequent ideological education or contact parents.

[0126] The process of building an estimated calculation model is as follows:

[0127] Based on the number of target personnel and the maximum grade, a warning valuation calculation formula is established:

[0128]

[0129] Where WV represents the warning value, N represents the number of target personnel, and M represents the maximum grade of the target personnel. Usually, the grade value ranges from 1 to 12, from elementary school to high school.

[0130] Logical explanation: The early warning valuation can be calculated based on the maximum number of people and grade level;

[0131] Taking into account the different effects of the number of people and the maximum grade level on security needs, we can take their product and add an adjustment term to reflect the additional impact of the maximum grade level on security needs; higher grades may mean more complex situations and higher risks, thus requiring more security personnel;

[0132] The N × M part takes into account the basic combined effects of number of people and grade maximum;

[0133] The M(M-1) / 2 part is an additional adjustment term based on the maximum grade. It is half of a quadratic term related to M and increases with the increase of M, reflecting the additional security requirements brought about by the increase in grade.

[0134] Then, based on the early warning valuation and the known number of basic dispatches, the estimated dispatch value calculation formula is established:

[0135]

[0136] Where, represents the rounding up of ., EDV represents the estimated dispatch value, k represents the adjustment coefficient, and its value range is 0 to 1. It can be adjusted according to the actual situation, such as 0.1, 0.2, etc., to reflect the conversion ratio from the early warning estimate to the actual number of dispatched personnel; B represents the basic number of dispatched personnel, which is usually set to 1;

[0137] Logic description: The first part multiplies the warning estimate by an adjustment factor k and then rounds up to ensure that the number of dispatched personnel is an integer. This adjustment factor can be set based on historical data or experience to reflect the relationship between the warning estimate and the actual dispatch demand. B is the base number of dispatched personnel, which ensures that a certain number of security personnel will be dispatched even if the warning estimate is very low.

[0138] In summary, the two calculation formulas in the prediction calculation model allow us to generate an estimated security personnel dispatch value based on the number of target personnel and the maximum grade level. The first formula calculates the early warning estimate, taking into account the impact of the number of personnel and the maximum grade level on security demand. The second formula calculates the final estimated dispatch value based on the early warning estimate and the base dispatch number, ensuring that the dispatch number is a positive integer and at least equal to the base dispatch number. This model not only considers the complexity of actual situations, but also ensures the rationality and operability of the dispatch number.

[0139] The process of building a fuzzy computing model is as follows:

[0140] Based on the warning estimate and the distance between the probe coordinates at the corresponding location and the duty room, a response time calculation formula is constructed:

[0141]

[0142] In order to ensure that the response time does not exceed 30s, Equation (1) is modified and an upper limit constraint is added:

[0143]

[0144] In formulas (1) and (2), RT0 represents the response time set at the primary level, and RT represents the final generated response time;

[0145] It should be noted that the distance between the probe coordinates and the duty room represents the navigation distance from the duty room to the probe coordinates, which is the actual distance rather than the straight-line distance;

[0146] Logic Explanation: A base response time of 10 seconds is set, considering that under normal circumstances, duty room personnel need a certain amount of time to react after receiving an alert. The warning estimate WV reflects the impact of the number of target personnel and the maximum grade on security needs. We divide WV by 10 to ensure that this contribution is not too large while also reflecting the positive impact of the warning estimate on the response time. The distance D between the probe coordinates and the duty room has a direct impact on the response time. We divide D by 100 to keep the impact of distance on the response time within a reasonable range. The min function ensures that the response time does not exceed 30 seconds. This is to ensure that the response time does not exceed the set upper limit and avoid subsequent problems caused by untimely response.

[0147] For example:

[0148] Assume that the warning estimate WV = 150, and the distance between the probe coordinates and the duty room D = 200 meters;

[0149] Then apply the upper limit constraint: RT = min(27, 30) = 27;

[0150] Therefore, in this case, the response time is 27 seconds; this formula takes into account both the impact of the warning valuation on the response time and the fine-tuning effect of the distance value on the response time, while ensuring that the response time is within a reasonable range.

[0151] Specifically, the above solution realizes the intelligent adjustment of campus safety warning and response;

[0152] The solution automatically implements different adjustment strategies based on the warning level (level one or level two) to ensure the rational allocation and efficient use of security resources. In the case of a level one warning, all on-duty personnel are immediately dispatched and patrol personnel are notified. The geographic information system is used to quickly locate and optimally dispatch security personnel to the scene of the incident. In the case of a level two warning, the estimated calculation model comprehensively considers the number and grade of the target personnel to generate a reasonable estimated dispatch value, and the dispatch is completed within the response time determined by the fuzzy calculation model, and the relevant class teacher is notified at the same time.

[0153] This solution effectively solves the problems of slow response speed and unreasonable personnel scheduling in traditional campus security, and improves the efficiency and accuracy of emergency response; at the same time, through the application of predictive calculation models and fuzzy calculation models, it realizes the intelligent and refined dispatch of security personnel, which not only ensures safety needs but also avoids waste of resources; in addition, the early warning information is sent to the class teacher at the same time, forming a safety prevention mechanism for joint education between home and school, and further improving the level and effectiveness of campus safety management.

[0154] Example 2:

[0155] See also Figure 3 Based on Example 1, this embodiment further provides a smart campus intelligent security early warning method, including the following specific steps:

[0156] S1, monitoring the pressure output by the pre-laid pressure plates inside the target campus wall. When the instantaneous pressure value detected at any point exceeds the preset calibration threshold, the probe start signal is triggered;

[0157] When the probe at the corresponding position detects the target person, it will perform continuous monitoring action;

[0158] Otherwise, the probe at the corresponding position is turned off;

[0159] S2. When the target person is detected by the probe at the corresponding position, a deep learning algorithm is used for face recognition, combined with the campus database to determine whether the target person has been entered into the tag information;

[0160] If it is determined that the target person has no marking information, a level one warning command will be issued;

[0161] Otherwise, the warning judgment mechanism is triggered;

[0162] S3. Execute the warning judgment mechanism and issue an audible and visual warning when it is determined that the target person has marked information. This warning continues until the target person is no longer detected by the corresponding probe. While the audible and visual warning action is ongoing, the behavior recognition strategy is executed, combining computer vision technology to analyze pre-recorded behaviors. When the target person is identified to have pre-recorded behavior, a secondary warning signal is issued and the target person's marking information is output simultaneously.

[0163] S4. Upon receiving the first-level warning instruction, execute the first-level adjustment strategy;

[0164] Upon receiving the second-level warning instruction, execute the second-level adjustment strategy:

[0165] Input and extract the target personnel's tag information, build an estimated calculation model, generate an estimated dispatch value for security personnel, and dispatch personnel in the duty room according to the estimated dispatch value; based on the warning valuation calculated in the estimated calculation model, combined with the distance value between the probe coordinates of the corresponding position and the duty room, build a fuzzy calculation model, generate the response time, and complete the dispatch action within the response time when dispatching personnel in the duty room according to the estimated dispatch value.

[0166] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. Before building the corresponding model for calculation, the required data will be dimensionlessly processed to ensure the smooth progress of the calculation process; when implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0167] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0168] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A smart campus intelligent security early warning system, characterized by: The system includes: The detection start module monitors the pressure output by the pre-installed pressure plates on the inner side of the target campus wall. When the instantaneous pressure value at any point exceeds the preset calibration threshold, the probe start signal is triggered; When the probe at the corresponding position detects the target person, it will perform continuous monitoring action; Otherwise, the probe at the corresponding position is turned off; The tag matching module uses a deep learning algorithm to perform face recognition when a target person is detected by a probe at a corresponding position, and combines this with the campus database to determine whether the target person has been entered with a tag. If it is determined that the target person has no marking information, a level one warning command will be issued; Otherwise, the warning judgment mechanism is triggered; The monitoring and analysis module implements the warning judgment mechanism and issues an audible and visual warning when it determines that the target person has marked information. This warning continues until the target person is no longer detected by the corresponding probe. While the audible and visual warning action is ongoing, the module executes the behavior recognition strategy and combines computer vision technology to analyze pre-recorded behaviors. When the target person is identified to have pre-recorded behavior, a secondary warning signal is issued and the target person's marking information is output simultaneously. The early warning and adjustment module executes the first-level adjustment strategy upon receiving the first-level early warning instruction; Upon receiving the second-level warning instruction, execute the second-level adjustment strategy: Input and extract the target personnel's tag information, build an estimated calculation model, generate an estimated dispatch value for security personnel, and dispatch personnel in the duty room according to the estimated dispatch value; based on the warning valuation calculated in the estimated calculation model, combined with the distance value between the probe coordinates of the corresponding position and the duty room, build a fuzzy calculation model to generate the response time. When dispatching personnel in the duty room according to the estimated dispatch value, the dispatch action is completed within the response time; the process of building the estimated calculation model is as follows: Based on the number of target personnel and the maximum grade, establish the warning valuation calculation formula: Where WV represents the warning valuation, N represents the number of target personnel, and M represents the maximum grade of target personnel; the grade value ranges from 0 to 12; Then, based on the early warning valuation and the known number of basic dispatches, the estimated dispatch value calculation formula is established: Where EDV represents the estimated dispatch value, k represents the adjustment coefficient, ranging from 0 to 1, and B represents the number of basic dispatch personnel. The process of building a fuzzy calculation model is as follows: Based on the warning estimate and the distance between the probe coordinates at the corresponding location and the duty room, a response time calculation formula is constructed: Modify formula (1) and add an upper limit constraint: In formulas (1) and (2), RT0 represents the initial set response time, and RT represents the final generated response time.

2. The smart campus intelligent security early warning system according to claim 1 is characterized by: The pre-laying position of the pressure plate is located on the inner side of the campus wall, and the distance between the pressure plate and the campus wall is S, and S>0.

3. The smart campus intelligent security early warning system according to claim 1 is characterized by: The process of applying deep learning algorithms for face recognition is as follows: Preprocessing: Preprocess the facial image of the monitored target person; Among them, preprocessing includes cropping, scaling, grayscale and denoising; Feature extraction: Use convolutional neural network (CNN) to extract features from preprocessed facial images; Face recognition: The extracted feature vector is compared with the facial features entered in the campus database, and the similarity is calculated to determine whether they are the same person; If the similarity exceeds the threshold, it is determined that the target person has the entered tag information; If the similarity does not exceed the limit value, it is determined that the target person has no entered marking information.

4. The smart campus intelligent security early warning system according to claim 1 is characterized by: The pre-recorded behaviors include at least wall climbing and scrambling. The process of analyzing the pre-recorded behaviors is as follows: Use computer vision technology to perform behavioral analysis on the real-time video stream captured by the corresponding position probe; By comparing the target person's behavior in the real-time video stream with the pre-recorded behavior, it is possible to identify whether the target person has pre-recorded behavior.

5. The smart campus intelligent security early warning system according to claim 1 is characterized by: The first-level adjustment strategy is: obtain the coordinate information of the corresponding position probe, immediately dispatch all personnel in the duty room, and simultaneously notify the inspection personnel.

6. The smart campus intelligent security early warning system according to claim 1 is characterized by: The tag information includes at least name, student ID number and class. Before building the estimation calculation model, the maximum number of target personnel and grade are extracted.

7. A smart campus intelligent security early warning method, using the system according to any one of claims 1 to 6, characterized in that: The steps include: S1, monitoring the pressure output by the pre-laid pressure plates inside the target campus wall. When the instantaneous pressure value detected at any point exceeds the preset calibration threshold, the probe start signal is triggered; When the probe at the corresponding position detects the target person, it will perform continuous monitoring action; Otherwise, the probe at the corresponding position is turned off; S2. When the target person is detected by the probe at the corresponding position, a deep learning algorithm is used for face recognition, combined with the campus database to determine whether the target person has been entered into the tag information; If it is determined that the target person has no marking information, a level one warning command will be issued; Otherwise, the warning judgment mechanism is triggered; S3. Execute the warning judgment mechanism and issue an audible and visual warning when it is determined that the target person has marked information. This warning continues until the target person is no longer detected by the corresponding probe. While the audible and visual warning action is ongoing, the behavior recognition strategy is executed, combining computer vision technology to analyze pre-recorded behaviors. When the target person is identified to have pre-recorded behavior, a secondary warning signal is issued and the target person's marking information is output simultaneously. S4. Upon receiving the first-level warning instruction, execute the first-level adjustment strategy; Upon receiving the second-level warning instruction, execute the second-level adjustment strategy: Input and extract the target personnel's tag information, build an estimated calculation model, generate the estimated dispatch value of security personnel, and dispatch personnel in the duty room according to the estimated dispatch value; Based on the warning valuation calculated in the estimated calculation model, combined with the distance value between the probe coordinates of the corresponding position and the duty room, a fuzzy calculation model is built to generate the response time. When the personnel in the duty room are dispatched according to the estimated dispatch value, the dispatch action is completed within the response time.

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