Campus safety risk identification method and system based on multi-algorithm nested cooperation, and medium

Abstract

The application discloses a campus safety risk identification method and system based on multi-algorithm nested cooperation and a medium, relates to the technical field of campus risk identification, and constructs a dynamic rule library, matches all dynamic rule graphs corresponding to initial event signals from the dynamic rule library, and creates dynamic rule graph instances; each node is activated according to a topological order and an activation condition, the comprehensive confidence and the quasi-truth degree of a potential risk hypothesis are calculated at a convergence node based on DS evidence theory to perform risk early warning; multi-dimensional information is deeply associated and comprehensively judged, and the problems of information silos and low-level misjudgment are solved; a school calendar rhythm is converted into a time weight factor and embedded into a DS evidence fusion process, time background can be perceived, the contribution degree of different types of evidence and the sensitivity of risk decision-making can be automatically adjusted, false positives caused by neglecting context are reduced, and early and accurate early warning can be ensured in a high-risk period.

Description

Technical Field

[0001] This invention relates to the field of campus risk identification technology, specifically to a campus security risk identification method, system, and medium based on multi-algorithm nesting and collaboration. Background Technology

[0002] In the field of campus security, utilizing artificial intelligence (AI) technology, especially computer vision algorithms, to automate the analysis of surveillance videos has become an important means of improving security efficiency. Existing technical solutions generally employ detection models based on single or a few specific algorithms to identify and trigger alarms for specific security events.

[0003] The typical technical implementation currently involves deploying cameras with video analytics capabilities in key areas of the campus, or deploying analytics servers in the backend, to run independent algorithm models such as area intrusion detection, crowd gathering detection, running and falling detection, and smoke and fire recognition. When a target or behavior matching the algorithm's preset characteristics appears in the monitored footage, such as someone entering the warning area, the system will trigger an alarm event directly corresponding to that algorithm. For example, a system may simultaneously run algorithm A (detecting area intrusion) and algorithm B (detecting people running), working in parallel and each outputting corresponding alarm information.

[0004] However, this single-event-triggered technical architecture has significant limitations and cannot meet the security risk prevention and control needs of the complex and dynamic environment of a campus. Its shortcomings are mainly reflected in the following three aspects:

[0005] The problems of algorithmic silos and information fragmentation: Various AI algorithms operate as independent perceptual islands, outputting only their own independent perceptual results. These outputs lack correlation, fusion, and collaborative analysis; the system cannot organically integrate multi-dimensional information such as identity, high-risk location, specific behavior, time series, and psychological background. For example, the system may separately detect someone entering the rooftop and identify a person, but it cannot automatically link the two and further combine them with the person's personal profile to determine that this is a high-risk event.

[0006] Risk misjudgment and underreporting: Single algorithms rely on limited visual features, making them prone to high false alarm rates in complex campus environments with varying lighting, occlusion, and interference from similar behaviors. For example, they might misjudge playful roughhousing as fighting. More importantly, many significant security risks are not directly manifested by a single abnormal behavior, but rather by a series of seemingly ordinary but highly correlated behaviors and states within a specific context. Current technologies lack the ability to perform correlation analysis on comprehensive behavioral patterns across time, space, and attributes, resulting in weak identification capabilities for these highly concealed and complex risks.

[0007] Issues include delayed response and insufficient decision support: Existing systems generate alarm information that is coarse-grained and lacks operability. Security personnel receive a large number of isolated alarms and must rely on personal experience for secondary screening, correlation analysis, and prioritization—a time-consuming, labor-intensive, and error-prone process. In emergency situations, there is a significant time delay between receiving a simple alarm and manually assessing the risk and initiating the correct response, potentially missing the optimal intervention window.

[0008] Therefore, in the specific field of campus security, there is an urgent need for a new technological solution to break down algorithmic silos, achieve intelligent collaboration and in-depth analysis of multi-dimensional perception information, and thus truly avoid campus security risks. Summary of the Invention

[0009] To address the shortcomings of traditional campus security systems that rely on single algorithms (such as facial recognition or area intrusion detection alone), which are unable to handle complex and multifaceted security incidents, suffer from high false alarm rates and delayed warnings due to independent algorithm operation, and are unable to perform cross-scenario risk correlation analysis, this invention aims to provide a campus security risk identification method, system, and medium based on multi-algorithm nesting and collaboration. By constructing a dynamic rule graph, multi-source perception algorithm data and business data are logically and temporally arranged into an executable collaborative reasoning network. This network can automatically perform deep correlation and comprehensive analysis of multi-dimensional information, and automatically connect and identify multiple isolated pieces of evidence as composite evidence, thus solving the problems of information silos and low-level false alarms.

[0010] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0011] This solution provides a campus security risk identification method based on multi-algorithm nesting and collaboration, the method including:

[0012] Construct a dynamic rule graph library: Define a directed acyclic graph containing data nodes, algorithm nodes, and logic nodes. The data nodes are used to represent input data or evidence, the algorithm nodes are used to associate preset AI algorithms, and the logic nodes are used to perform logical operations or time-series operations. Each node is associated with one or more activation conditions, and the nodes are connected by directed edges to represent dependencies, which include data flow relationships or control flow relationships.

[0013] Event Triggering and Legend Creation: Receive the initial event signal from the sensing device, match all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and create a dynamic rule graph instance for each dynamic rule graph;

[0014] Forward reasoning and evidence generation: In the dynamic rule graph instance, each node is activated according to the topological order and activation conditions. When the current node is an algorithm node, the associated preset AI algorithm is called to process the input data of the current node to generate evidence. When the current node is a logic node, logical operations or temporal operations are performed on the evidence input to the current node to generate composite evidence.

[0015] Evidence fusion: When reasoning reaches the convergence node representing the potential risk hypothesis, all target evidence pointing to the potential risk hypothesis is collected, and the basic probability assignments of each target evidence are fused based on DS evidence theory and calendar rhythm to calculate the comprehensive reliability and similarity of the potential risk hypothesis; the calendar rhythm affects the contribution of each target evidence fusion process.

[0016] Risk warning: The overall reliability and likelihood of each potential risk hypothesis are compared with a preset threshold to generate a risk warning signal of the corresponding level.

[0017] A further optimization scheme is that the logic node includes at least one of the following: an AND node for performing logical AND operations, an OR node for performing logical OR operations, and a timing logic node for determining whether the input evidence occurred within a specific time window.

[0018] A further optimization is to configure contextual information related to the school calendar rhythm as an activation condition for the aggregation node representing potential risk assumptions when constructing the dynamic rule library; the contextual information includes daily rhythm periods, weekly rhythm periods, and semester rhythm periods.

[0019] The circadian rhythm period is represented by multiple daily periods divided according to the daily learning routine, with each daily period assigning different weights to different potential risk assumption types;

[0020] The weekly rhythm period is represented by the week period on which day of the week; each week period is assigned a different weight to different potential risk assumption types;

[0021] The semester rhythm period is represented by multiple semester stages that divide a semester according to the learning stage, with each semester stage assigning different weights to different potential risk assumption types.

[0022] A further optimization scheme is that the dynamic rule graph instance includes an interface node for accessing external business data. The interface node is used to obtain psychological profiles, attendance records or leave information related to the target personnel in real time during the forward reasoning process, and input them as evidence into subsequent nodes.

[0023] A further optimization scheme is that the calculation method for the comprehensive reliability and likelihood of the potential risk assumptions includes:

[0024] Define the identification framework of the DS evidence theory: Θ={H k ,¬H k}, where H k Indicates the potential risk assumption r k Established; ¬H k Indicates the potential risk assumption r k This is not valid; assign a basic probability assignment to each piece of target evidence, where the basic probability assignment for target evidence i supporting potential risk hypothesis k is m. ik ;

[0025] The time weighting factor is calculated based on the type of potential risk assumption and contextual information, and then the time weighting factor is normalized.

[0026] The ratio of the historical accuracy of each target's evidence to the overall average accuracy under the same time conditions is calculated as the time sensitivity coefficient.

[0027] By combining the time sensitivity coefficient, the time weight factor is used as a reliability discount to correct the basic probability allocation of the target evidence, and then the comprehensive reliability and likelihood of the potential risk hypothesis are fused together; or by combining the time sensitivity coefficient, the time weight factor is transformed into a combined weight of the target evidence, and then the comprehensive reliability and likelihood of the potential risk hypothesis are fused together.

[0028] A further optimization scheme is that the calculation method for the time weighting factor includes:

[0029] The time weighting factor T of the potential risk assumption k k For: the weight w assigned to the potential risk assumption k for each day period dk The weights w assigned to the potential risk assumption k over a given period of time. wk The weights w assigned to the potential risk assumption k during the semester phase sk The product of.

[0030] A further optimization scheme is as follows: the method of correcting the basic probability allocation of target evidence by using the time weighting factor as a reliability discount for target evidence includes:

[0031] Calculate the reliability discount factor B for the evidence i supporting the potential risk hypothesis k. ik :

[0032] B ik =min(1,T) k1 ×α ik / T1);

[0033] Among them, T k1 α represents the normalized time weighting factor. ikT1 represents the time sensitivity coefficient of target evidence i to potential risk hypothesis k; T1 represents the normalized baseline value.

[0034] Based on reliability discount factor B ik The base probability assignment for revising target evidence i to support potential risk hypothesis k is m. ik get:

[0035] ;

[0036] in, Let A represent the basic probability assignment that the modified target evidence i supports the potential risk hypothesis k; A represents any subset of the identification framework Θ. Indicate that A is a proper subset of Θ; This indicates that the target evidence i supports the basic probability assignment of the potential risk hypothesis k as A; This indicates that the target evidence i supports the potential risk hypothesis k, which is an uncertain basic probability assignment.

[0037] A further optimized solution is that the method for converting the time weight factor into a combined weight of the target evidence includes:

[0038] When weighting the support for potential risk hypothesis k from each piece of target evidence, the weight of the combination of support for potential risk hypothesis k from target evidence i is W. ik :

[0039] W ik = ;

[0040] Where exp() represents the exponential function; Indicates temperature parameter, ; denoted as the time sensitivity coefficient of target evidence j to potential risk hypothesis k; N represents the total number of target evidences pointing to potential risk hypothesis k.

[0041] This solution also provides a campus security risk identification system based on multi-algorithm nesting and collaboration, used to implement the aforementioned campus security risk identification method based on multi-algorithm nesting and collaboration. The system includes:

[0042] The dynamic rule graph library construction module is used to define a directed acyclic graph containing data nodes, algorithm nodes, and logic nodes. The data nodes are used to represent input data or evidence, the algorithm nodes are used to associate preset AI algorithms, and the logic nodes are used to perform logical operations or time-series operations. Each node is associated with one or more activation conditions, and the nodes are connected by directed edges to represent dependencies, including data flow relationships or control flow relationships.

[0043] The event triggering and legend creation module is used to receive the initial event signal from the sensing device, match all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and create a dynamic rule graph instance for each dynamic rule graph.

[0044] The forward reasoning and evidence generation module is used to activate each node in the dynamic rule graph instance according to the topological order and activation conditions. When the current node is an algorithm node, it calls the associated preset AI algorithm to process the input data of the current node to generate evidence; when the current node is a logic node, it performs logical operations or temporal operations on the evidence input to the current node to generate composite evidence.

[0045] The evidence fusion module is used to collect all target evidence pointing to the potential risk hypothesis when reasoning reaches the convergence node representing the potential risk hypothesis, and to fuse the basic probability assignments of each target evidence based on DS evidence theory and calendar rhythm to calculate the comprehensive reliability and similarity of the potential risk hypothesis; the calendar rhythm affects the contribution of each target evidence fusion process.

[0046] The risk warning module is used to compare the overall reliability and likelihood of each potential risk hypothesis with a preset threshold and generate a risk warning signal of the corresponding level.

[0047] This solution also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, can implement the campus security risk identification method based on multi-algorithm nesting and collaboration as described above.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This invention provides a campus security risk identification method, system, and medium based on multi-algorithm nesting and collaboration. By constructing a dynamic rule graph, it arranges multi-source perception algorithm data and business data into an executable collaborative reasoning network according to logical and temporal relationships; it can automatically perform deep correlation and comprehensive judgment of multi-dimensional information; it can automatically link and identify multiple isolated pieces of evidence as composite evidence, thus solving the problems of information silos and low-level misjudgments.

[0050] 2. This invention provides a campus safety risk identification method, system, and medium based on multi-algorithm nesting and collaboration. It transforms the school calendar rhythm (such as exam cycles, semester beginnings and endings, and day and night periods) into calculable time weight factors and embeds them into the DS evidence fusion process. This enables dynamic perception of the current time context and automatic adjustment of the contribution of different types of evidence and the sensitivity of risk decision-making, greatly reducing false alarms caused by ignoring context and ensuring earlier and more accurate early warnings during high-risk periods.

[0051] 3. This invention provides a campus security risk identification method, system, and medium based on multi-algorithm nesting and collaboration. The dynamic rule graph used provides an intuitive expression of risk identification logic. Security administrators can quickly adapt to new risk scenarios or optimize existing strategies by adding, deleting, or modifying nodes and logical relationships in the dynamic rule graph (such as adjusting AND conditions or modifying time windows) without reprogramming, which greatly enhances the system's flexibility and operability.

[0052] 4. This invention provides a campus safety risk identification method, system, and medium based on multi-algorithm nesting and collaboration. The entire reasoning process is based on the fusion calculation of rules and traceable evidence (reliability and similarity), making the decision basis for each warning clearly visible. This solves the trust problem caused by the black box decision-making of traditional AI models. At the same time, the rule parameters, time weights, and fusion thresholds can be continuously optimized by recording the handling feedback, so as to achieve iterative evolution of performance. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0054] Figure 1 This is a flowchart illustrating a campus security risk identification method based on multi-algorithm nesting and collaboration.

[0055] Figure 2 This is a schematic diagram of a dynamic rule library example;

[0056] Figure 3 This is a schematic diagram of a campus security risk identification system based on multi-algorithm nesting and collaboration. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0058] Traditional campus security systems employ single algorithms (such as facial recognition or area intrusion detection alone), which cannot handle complex and multifaceted security incidents. Independent operation of these algorithms leads to high false alarm rates, delayed warnings, and an inability to perform cross-scenario risk correlation analysis. Therefore, this solution provides the following embodiments to address the aforementioned technical problems:

[0059] Example 1

[0060] This embodiment provides a campus security risk identification method based on multi-algorithm nesting and collaboration, such as Figure 1 As shown, the method includes:

[0061] Step 1, Construct a dynamic rule graph library: Define a directed acyclic graph containing data nodes, algorithm nodes, and logic nodes. The data nodes are used to represent input data or evidence, the algorithm nodes are used to associate preset AI algorithms, and the logic nodes are used to perform logical operations or time-series operations. Each node is associated with one or more activation conditions, and the nodes are connected by directed edges to represent dependencies, including data flow relationships or control flow relationships.

[0062] The logical node includes at least one of the following: an AND node for performing logical AND operations, an OR node for performing logical OR operations, and a timing logic node for determining whether input evidence occurred within a specific time window.

[0063] Specifically, a directed acyclic graph is represented as G=(V,E,Λ,Γ); where V represents the set of nodes, divided into three categories: data nodes, algorithm nodes, and logic nodes; data nodes represent the original data input or algorithm output, i.e., evidence nodes; the input directed edges of algorithm nodes come from data nodes or other algorithm nodes, and the output directed edges point to data nodes; the input directed edges of logic nodes come from data nodes or logic nodes, and the output directed edges point to data nodes or trigger actions (representing the convergence node of potential risk assumptions); the set of directed edges is represented by E; Λ represents the set of activation condition functions on the nodes; Γ represents the set of action functions on the nodes (mainly the convergence node of potential risk assumptions, and the convergence node is mainly a logic node), which is the action to be executed when the current node output is true (such as generating an early warning, calling a new rule graph, etc.). The set of potential risk assumptions is R={r1,r2,…r i , …,r K}; where: potential risk assumption r1 is personal injury / violent conflict (including bullying, fighting, etc.); potential risk assumption r2 is psychological crisis / self-harm risk (including depression, anxiety, self-harm tendency); potential risk assumption r3 is accident (including falls, sports injuries, laboratory accidents); potential risk assumption r4 is property theft / damage; potential risk assumption r i For cybersecurity incidents (including cyberbullying and privacy breaches); potential risk assumption r K For public health emergencies (including infectious diseases, food poisoning, etc.).

[0064] When constructing the dynamic rule library, for the aggregation node representing potential risk assumptions, contextual information related to the school calendar rhythm is configured as an activation condition; the contextual information includes daily rhythm periods, weekly rhythm periods, and semester rhythm periods;

[0065] The circadian rhythm period is represented by multiple daily periods divided according to the daily learning routine, with each daily period assigning different weights to different potential risk assumption types;

[0066] This embodiment divides a day into: a quiet nighttime period (00:00-06:00), during which students should be in their dormitories and the campus should be quiet; a morning wake-up period (06:00-07:30), during which students wake up, do morning exercises, and have breakfast; a morning teaching period (07:30-12:00), during which students attend classes, conduct experiments, and participate in group teaching; a lunch break period (12:00-14:00), during which students have lunch, rest, and enjoy free time; and an afternoon teaching period (14:00-17:30), during which students attend classes, participate in club activities, etc. Sports; Evening activity days, from 17:30 to 19:00, include student dinner, free time, and social activities; Evening self-study days, from 19:00 to 22:00, include student self-study and library study; Nighttime bedtime days, from 22:00 to 24:00, include student preparation for bed and dormitory management; Based on educational psychology and safety statistics, different weights are assigned to different potential risk assumptions for each time period. For example, the psychological crisis risk r2 has the highest weight on quiet nighttime days because of the strongest sense of loneliness; the accident risk r3 is higher on early morning wake-up days and afternoon teaching days because of frequent activities; the property theft risk r4 has a high weight on quiet nighttime days because of weaker supervision.

[0067] The weekly rhythm period is represented by the week period on which day of the week; each week period is assigned a different weight to different potential risk assumption types; for example, the psychological crisis risk r2 is set highest on Sunday because of pre-school anxiety; the personal injury risk r1 is higher on Friday and Saturday because of more social activities.

[0068] The semester rhythm period is represented by multiple semester stages that divide a semester according to the learning stage, with each semester stage assigning different weights to different potential risk assumption types.

[0069] For example: the school adjustment period, the regular teaching period, exam week, and the period before and after holidays; this embodiment divides a semester into: the initial adjustment period, in the first 1-2 weeks of the semester, characterized by students adapting to the new environment and establishing rules; the regular teaching period, in the 3rd-10th weeks of the semester, characterized by students maintaining a stable teaching pace; the mid-term exam period, in the 11th-12th weeks of the semester, characterized by students experiencing exam pressure and anxiety about grades; the later teaching period, in the 13th-16th weeks of the semester, characterized by students facing heavy workloads and accumulating fatigue; the final exam period, in the 17th-18th weeks of the semester, characterized by students experiencing high pressure and peak anxiety; and the final closing period, in the 19th-20th weeks of the semester, characterized by students preparing for the release of grades and leaving school; different weights are assigned to different potential risk assumptions for each semester stage, among which, the psychological crisis risk r2 The final exam period has the highest weighting and the greatest pressure; the risk of accidents (r3) is higher at the beginning of the semester due to unfamiliarity with the environment; the risk of theft (r4) is higher at the beginning and end of the semester because valuables are often carried during this period.

[0070] Step 2, Event Triggering and Legend Creation: Receive the initial event signal from the sensing device, match all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and create a dynamic rule graph instance for each dynamic rule graph;

[0071] The dynamic rule graph instance includes interface nodes for accessing external business data. These interface nodes are used to obtain psychological profiles, attendance records, or leave information related to the target personnel in real time during the forward reasoning process and input them as evidence into subsequent nodes.

[0072] Specifically, the initial event signal is equivalent to a trigger seed evidence. The system searches for all dynamic rule graphs in the dynamic rule graph library that use the trigger seed evidence as an entry point, and creates an instance for each matching dynamic rule graph.

[0073] Step 3, Forward Reasoning and Evidence Generation: In the dynamic rule graph instance, each node is activated according to the topological order and activation conditions. When the current node is an algorithm node, the associated preset AI algorithm is called to process the input data of the current node to generate evidence. When the current node is a logic node, logical operations or temporal operations are performed on the evidence input to the current node to generate composite evidence.

[0074] Specifically, for an algorithm node, data from all its input directed edges is collected, the corresponding algorithm is called to perform calculations, and the calculated evidence results are passed to the output directed edges. The evidence output by the algorithm node is structured evidence with confidence, represented as a triple X{evidence type, evidence value, confidence}. The confidence is the probability value or score of the AI ​​algorithm, and the confidence value is in the range [0, 1].

[0075] For logical nodes, the evidence of the input directed edges is aggregated according to their logical type. Taking the AND node as an example, its output is the logical AND operation result of all evidence.

[0076] For sequential logic nodes, it is necessary to check whether the timestamp of the evidence for the input directed edge is within the preset time window Δt.

[0077] Step 4, Evidence Fusion: When reasoning reaches the convergence node representing the potential risk hypothesis, all target evidence pointing to the potential risk hypothesis is collected, and the basic probability allocation of each target evidence is fused based on DS evidence theory and calendar rhythm to calculate the comprehensive reliability and similarity of the potential risk hypothesis; the calendar rhythm affects the contribution of each target evidence fusion process.

[0078] The methods for calculating the overall reliability and likelihood of the potential risk assumptions include:

[0079] S41, Define the identification framework of the DS evidence theory: Θ={H k ,¬H k}, where H k Indicates the potential risk assumption r k Established; ¬H k Indicates the potential risk assumption r k This is not valid; assign a basic probability assignment to each piece of target evidence, where the basic probability assignment for target evidence i supporting potential risk hypothesis k is m. ik ;

[0080] Specifically, for each piece of target evidence, based on its evidence type and value, a predefined basic probability allocation function is used to generate a basic probability allocation for the potential risk hypothesis. For example, the basic probability allocation for target evidence i (emotional sadness) to potential risk hypothesis k might be: the probability allocation m for potential risk hypothesis k to be true. ik ({H}) is: m ik ({H})=0.4; Probability assignment m of potential risk assumption k not being true ik ({¬H}) is: m ik ({¬H})=0.1; Probability assignment m of the potential risk assumption k being uncertain. ik ({¬H}) is: m ik (Θ)=0.5.

[0081] S42, calculate the time weight factor based on the type and context information of the potential risk assumption, and normalize the time weight factor.

[0082] Since the time weight factor may have a large value, it needs to be mapped to a reasonable range of evidence weights. Therefore, this scheme normalizes the time weight factor.

[0083] The calculation method for the time weighting factor includes:

[0084] The time weighting factor T of the potential risk assumption k k For: the weight w assigned to the potential risk assumption k for each day period dk The weights w assigned to the potential risk assumption k over a given period of time. wk The weights w assigned to the potential risk assumption k during the semester phase sk The product of.

[0085] Taking the psychological crisis warning on Sunday evening during exam week as an example, the current time is 23:00, which is the nighttime bedtime period, with a weight of w. dk The weekly time period weight w is 1.9, corresponding to Sunday. wk The weight w corresponding to the final exam stage is 1.5. sk The value is 1.8, and the calculated time weighting factor is 1.9 × 1.5 × 1.8 = 5.13.

[0086] S43, calculate the ratio of the historical accuracy of each target's evidence to the overall average accuracy under the same time conditions for the potential risk assumption as the time sensitivity coefficient.

[0087] S44, Combining the time sensitivity coefficient, the time weight factor is used as a reliability discount of the target evidence to correct the basic probability allocation of the target evidence, and then the comprehensive reliability and likelihood of the potential risk hypothesis are fused; or Combining the time sensitivity coefficient, the time weight factor is transformed into a combined weight of the target evidence, and then the comprehensive reliability and likelihood of the potential risk hypothesis are fused.

[0088] In step S44, the basic probability allocation of the target evidence is corrected by using the time weighting factor as a reliability discount for the target evidence, including the following methods:

[0089] S441, Calculate the reliability discount factor B for target evidence i supporting potential risk assumption k. ik :

[0090] B ik =min(1,T) k1 ×α ik / T1);

[0091] Among them, T k1 α represents the normalized time weighting factor. ik T1 represents the time sensitivity coefficient of target evidence i to potential risk hypothesis k; T1 represents the normalized baseline value.

[0092] S442, based on reliability discount factor B ik The base probability assignment for revising target evidence i to support potential risk hypothesis k is m. ik get:

[0093] ;

[0094] in, Let A represent the basic probability assignment that the modified target evidence i supports the potential risk hypothesis k; A represents any subset of the identification framework Θ (the subsets of the identification framework Θ include: the empty set ∅, {H}, {¬H}, {H, ¬H}). Indicate that A is a proper subset of Θ; This indicates that the target evidence i supports the basic probability assignment of the potential risk hypothesis k as A; This indicates that the target evidence i supports the potential risk hypothesis k, which is an uncertain basic probability assignment.

[0095] Then, using Dempster's combination rule, the basic probability assignments are fused pairwise, and the final fusion result m is used. f Calculate the confidence level Bel(H) for the potential risk hypothesis k to be true: Bel(H) = m f ({H}) and similarity Pl(H) = 1 - m f ({H});

[0096] In step S44, the time weighting factor is converted into a combined weight of the target evidence, including the following methods:

[0097] When weighting the support for potential risk hypothesis k from each piece of target evidence, the weight of the combination of support for potential risk hypothesis k from target evidence i is W. ik :

[0098] W ik = ;

[0099] Where exp() represents the exponential function; Indicates temperature parameter, ; denoted as the time sensitivity coefficient of target evidence j to potential risk hypothesis k; N represents the total number of target evidences pointing to potential risk hypothesis k.

[0100] Based on the above formula, the weight vector W(t) of the N target pieces of evidence is determined as [W 1k W 2k , …,W ik …, W Nk ] T ;

[0101] Based on the weight vector W(t) and the basic probability assignments of each target piece of evidence supporting the potential risk hypothesis k, the weighted average basic probability assignment of all target pieces of evidence is calculated. This basic probability assignment is then self-combined N-1 times to obtain the final fusion result m. f According to the final fusion result m f Calculate the confidence level Bel(H) for the potential risk hypothesis k to be true: Bel(H) = m f ({H}) and similarity Pl(H) = 1 - m f ({H}).

[0102] Step 5, Risk Warning: Compare the overall reliability and likelihood of each potential risk hypothesis with a preset threshold to generate a risk warning signal of the corresponding level.

[0103] This embodiment transforms school calendar rhythms (such as exam cycles, semester start and end times, and day / night cycles) into calculable time weighting factors and embeds them into the DS evidence fusion process. This allows for dynamic perception of the current time context and automatic adjustment of the contribution of different types of evidence and the sensitivity of risk decisions. For example, during the late night of final exam week, the fusion weight of evidence related to psychological crisis risks (such as low mood or being alone) is significantly enhanced, and the warning threshold is correspondingly lowered, making the system more sensitive during this high-risk period. Conversely, during the afternoon of a regular teaching day, the weight of the same evidence is reduced, making the system more cautious. This greatly reduces false alarms caused by ignoring context and ensures that warnings are issued earlier and more accurately during high-risk periods.

[0104] This embodiment uses the prevention of falls from heights as a potential risk assumption, and its seed evidence e0 is "rooftop area intrusion," such as... Figure 2 As shown, the constructed dynamic rule graph example is as follows:

[0105] Entry node Vd1: Data node, type "Rooftop Area Intrusion", seed evidence e0 provided by the basic motion detection algorithm;

[0106] Algorithm node Va1: Associated with the face recognition algorithm; inputs an image region from seed evidence e0, outputs the person ID and emotional state as evidence e. id and evidence e emo ;

[0107] Data node Vd2: Combines with interface node Vj to query the psychological profile of the person's ID from the business database, and outputs the psychological risk level as evidence e. psy ;

[0108] Algorithm node Va2: Associates a fine-grained behavior recognition algorithm; continuously receives video streams, detects behaviors such as leaning against railings and leaning forward, and outputs behavior sequences as evidence. beh(t) ;

[0109] Logical node Vl1: A time-series logical node that checks whether the target remains within the region after being triggered by seed evidence e0, and outputs the check result as evidence e1.

[0110] Logical node Vl2: AND node; input is evidence e psy (High psychological risk), Evidence e emo (Negative emotions), Evidence e beh(t) (High-risk behavior detected) and evidence e1 (continued presence); activated when all inputs are true;

[0111] Convergence node Vs: Represents the assumption of isolation of fall risk; receives the output of logical node Vl2 and initiates evidence fusion.

[0112] Example 2

[0113] This embodiment provides a campus security risk identification system based on multi-algorithm nesting and collaboration, used to implement the campus security risk identification method based on multi-algorithm nesting and collaboration described in Embodiment 1, such as... Figure 3 As shown, the system includes:

[0114] The dynamic rule graph library construction module is used to define a directed acyclic graph containing data nodes, algorithm nodes, and logic nodes. The data nodes are used to represent input data or evidence, the algorithm nodes are used to associate preset AI algorithms, and the logic nodes are used to perform logical operations or time-series operations. Each node is associated with one or more activation conditions, and the nodes are connected by directed edges to represent dependencies, including data flow relationships or control flow relationships.

[0115] The event triggering and legend creation module is used to receive the initial event signal from the sensing device, match all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and create a dynamic rule graph instance for each dynamic rule graph.

[0116] The forward reasoning and evidence generation module is used to activate each node in the dynamic rule graph instance according to the topological order and activation conditions. When the current node is an algorithm node, it calls the associated preset AI algorithm to process the input data of the current node to generate evidence; when the current node is a logic node, it performs logical operations or temporal operations on the evidence input to the current node to generate composite evidence.

[0117] The evidence fusion module is used to collect all target evidence pointing to the potential risk hypothesis when reasoning reaches the convergence node representing the potential risk hypothesis, and to fuse the basic probability assignments of each target evidence based on DS evidence theory and calendar rhythm to calculate the comprehensive reliability and similarity of the potential risk hypothesis; the calendar rhythm affects the contribution of each target evidence fusion process.

[0118] The risk warning module is used to compare the overall reliability and likelihood of each potential risk hypothesis with a preset threshold and generate a risk warning signal of the corresponding level.

[0119] Example 3

[0120] This embodiment provides a computer-readable medium storing a computer program. The computer program, when executed by a processor, can implement the campus security risk identification method based on multi-algorithm nesting and collaboration as described in Embodiment 1; specifically, it performs the following steps:

[0121] Step 1, Construct a dynamic rule graph library: Define a directed acyclic graph containing data nodes, algorithm nodes, and logic nodes. The data nodes are used to represent input data or evidence, the algorithm nodes are used to associate preset AI algorithms, and the logic nodes are used to perform logical operations or time-series operations. Each node is associated with one or more activation conditions, and the nodes are connected by directed edges to represent dependencies, including data flow relationships or control flow relationships.

[0122] Step 2, Event Triggering and Legend Creation: Receive the initial event signal from the sensing device, match all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and create a dynamic rule graph instance for each dynamic rule graph;

[0123] Step 3, Forward Reasoning and Evidence Generation: In the dynamic rule graph instance, each node is activated according to the topological order and activation conditions. When the current node is an algorithm node, the associated preset AI algorithm is called to process the input data of the current node to generate evidence. When the current node is a logic node, logical operations or temporal operations are performed on the evidence input to the current node to generate composite evidence.

[0124] Step 4, Evidence Fusion: When reasoning reaches the convergence node representing the potential risk hypothesis, all target evidence pointing to the potential risk hypothesis is collected, and the basic probability allocation of each target evidence is fused based on DS evidence theory and calendar rhythm to calculate the comprehensive reliability and similarity of the potential risk hypothesis; the calendar rhythm affects the contribution of each target evidence fusion process.

[0125] Step 5, Risk Warning: Compare the overall reliability and likelihood of each potential risk hypothesis with a preset threshold to generate a risk warning signal of the corresponding level.

[0126] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A campus security risk identification method based on multi-algorithm nested cooperation, characterized in that, The method comprises: Building a dynamic rule graph library: defining a directed acyclic graph comprising data nodes, algorithm nodes and logic nodes, the data nodes being used to represent input data or evidence, the algorithm nodes being used to associate preset AI algorithms; the logic nodes being used to perform logical operations or timing operations; each node being associated with one or more activation conditions, and the nodes being connected through directed edges to represent dependency relationships, the dependency relationships including data flow relationships or control flow relationships; Event triggering and graph creation: receiving an initial event signal from a sensing device, matching all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and creating a dynamic rule graph instance for each dynamic rule graph; Forward reasoning and evidence generation: in the dynamic rule graph instance, activating the nodes in topological order and according to the activation conditions, wherein, when the current node is an algorithm node, invoking the associated preset AI algorithm to process the input data of the current node to generate evidence; when the current node is a logic node, performing logical operations or timing operations on the evidence input into the current node to generate composite evidence; Evidence fusion: when reasoning to a convergence node representing a potential risk hypothesis, collecting all target evidence pointing to the potential risk hypothesis, and fusing the basic probability assignments of the target evidence based on DS evidence theory and a calendar rhythm to calculate the comprehensive belief and plausibility of the potential risk hypothesis; the calendar rhythm affects the contribution degree of the fusion process of each target evidence; Risk warning: comparing the comprehensive belief and plausibility of each potential risk hypothesis with a preset threshold to generate a risk warning signal of a corresponding level; When building the dynamic rule graph library, for the convergence node representing the potential risk hypothesis, context information related to the calendar rhythm is configured as an activation condition; the context information includes a daily rhythm period, a weekly rhythm period and a semester rhythm period; The daily rhythm period is represented by a plurality of daily periods divided according to the daily routine of learning, and each daily period assigns different weights to different potential risk hypothesis types; The weekly rhythm period is represented by a week period of the day of the week; each week period assigns different weights to different potential risk hypothesis types; The semester rhythm period is represented by a plurality of semester stages divided according to the learning stages of a semester, and each semester stage assigns different weights to different potential risk hypothesis types.

2. The campus security risk identification method based on multi-algorithm nested cooperation according to claim 1, characterized in that, The logic node comprises at least one of the following: an AND node for performing logical AND operation, an OR node for performing logical OR operation, and a timing logic node for judging whether the input evidence occurs within a specific time window.

3. The campus security risk identification method based on multi-algorithm nested cooperation according to claim 1, characterized in that, The dynamic rule graph instance comprises an interface node for accessing external business data, which is used to obtain psychological archives, attendance records or leave information related to the target person in real time during the forward reasoning process, and input them as evidence into the subsequent nodes.

4. The campus security risk identification method based on multi-algorithm nested cooperation according to claim 1, characterized in that, The method for calculating the comprehensive belief and plausibility of the potential risk hypothesis comprises: Define the recognition frame Θ = {H k ,¬H k} of the DS evidence theory, wherein H k represents that the potential risk assumption r k is true;¬H k represents that the potential risk assumption r k is not true; and assign a basic probability assignment to each target evidence, wherein the basic probability assignment of the target evidence i to the potential risk assumption k is m ik ; calculating a time weight factor based on the type of the potential risk hypothesis and the context information, and normalizing the time weight factor; The ratio of the historical accuracy of each target evidence to the potential risk hypothesis under the same time condition to the overall average accuracy is calculated as a time sensitivity coefficient; Based on the time sensitivity coefficient, a time weight factor is used as a reliability discount to modify the basic probability assignment of the target evidence, and then the comprehensive confidence and plausibility of the potential risk hypothesis are fused; or based on the time sensitivity coefficient, the time weight factor is converted into a combination weight of the target evidence, and then the comprehensive confidence and plausibility of the potential risk hypothesis are fused.

5. The campus security risk identification method based on multi-algorithm nested cooperation according to claim 4, characterized in that, The method for calculating the time weight factor includes: Time weight factor T for potential risk hypothesis k k is: the weight w assigned to potential risk hypothesis k for the day period dk , the weight w assigned to potential risk hypothesis k for the week period wk , and the weight w assigned to potential risk hypothesis k for the semester period sk , respectively.

6. The campus security risk identification method based on multi-algorithm nested cooperation according to claim 4, characterized in that, The method for using the time weight factor as a reliability discount to modify the basic probability assignment of the target evidence includes: A reliability discount factor B that a target evidence i supports a potential risk hypothesis k is calculated ik : B ik = min(1, T k1 x a ik / T1); wherein T k1 denotes the normalized time weight factor; a ik denotes the time sensitivity coefficient of the target evidence i to the potential risk hypothesis k; T1denotes the normalized reference value; Based on a reliability discount factor B ik The basic probability assignment m of the target evidence i supporting the potential risk hypothesis k is revised ik We get: ； wherein, represents the basic probability assignment of the target evidence i supporting the potential risk hypothesis k after revision; A represents an arbitrary subset of the identification framework Θ; represents that A is a true subset of Θ; represents the basic probability assignment of the target evidence i supporting the potential risk hypothesis k being A; represents the basic probability assignment of the target evidence i supporting the potential risk hypothesis k being uncertain.

7. The campus security risk identification method based on multi-algorithm nested cooperation according to claim 4, characterized in that, The method for converting the time weight factor into a combination weight of the target evidence includes: In the weighted combination of the target evidence supporting the potential risk hypothesis k, the combination weight of the target evidence i supporting the potential risk hypothesis k is W ik : W ik = ； where exp() denotes the exponential function; denotes the temperature parameter, ; denotes the time sensitivity coefficient of target evidence j to potential risk hypothesis k; N denotes the total number of target evidence pointing to potential risk hypothesis k.

8. A campus security risk identification system based on multi-algorithm nested cooperation, characterized in that, The system for implementing the campus safety risk identification method based on multi-algorithm nested cooperation according to any one of claims 1-7 includes: A dynamic rule graph library construction module is configured to define a directed acyclic graph including data nodes, algorithm nodes and logic nodes, the data nodes are used to represent input data or evidence, the algorithm nodes are used to associate preset AI algorithms, and the logic nodes are used to perform logical operations or time sequence operations; each node is associated with one or more activation conditions, and the nodes are connected through directed edges to represent a dependency relationship, the dependency relationship includes a data flow relationship or a control flow relationship; An event trigger and graph creation module is configured to receive an initial event signal from a sensing device, match all dynamic rule graphs corresponding to the initial event signal from the dynamic rule graph library, and create a dynamic rule graph instance for each dynamic rule graph; A forward reasoning and evidence generation module is configured to activate the nodes in the dynamic rule graph instance in topological order and according to the activation conditions, wherein when the current node is an algorithm node, a preset AI algorithm associated with the current node is called to process the input data of the current node to generate evidence; and when the current node is a logic node, a logical operation or a time sequence operation is performed on the evidence input into the current node to generate composite evidence; An evidence fusion module is configured to collect all target evidence pointing to a potential risk hypothesis when reasoning to a convergence node representing the potential risk hypothesis, fuse the basic probability assignments of the target evidence based on the DS evidence theory and a school calendar rhythm, and calculate the comprehensive confidence and plausibility of the potential risk hypothesis; the school calendar rhythm affects the contribution of the fusion process of the target evidence; A risk warning module is configured to compare the comprehensive confidence and plausibility of each potential risk hypothesis with a preset threshold to generate a risk warning signal of a corresponding level; When constructing the dynamic rule graph library, context information related to the school calendar rhythm is configured as an activation condition for the convergence node representing the potential risk hypothesis; the context information includes a daily rhythm period, a weekly rhythm period and a semester rhythm period; The daily rhythm period is represented by a plurality of daily periods divided according to the daily routine of learning, and each daily period assigns different weights to different potential risk hypothesis types; The weekly rhythm period is represented by a week period of a day of the week, and each week period assigns different weights to different potential risk hypothesis types; and The semester rhythm period is represented by a semester period of a semester, and each semester period assigns different weights to different potential risk hypothesis types. The semester rhythm period is represented by a plurality of semester stages divided from a semester according to learning stages, and each semester stage allocates different weights to different potential risk hypothesis types.

9. A computer readable medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the campus security risk identification method based on multi-algorithm nested cooperation as claimed in any one of claims 1-7.

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