Integrated supervision and comprehensive management platform for escort vehicle

Abstract

The invention relates to an integrated supervision and comprehensive management platform for an escort vehicle, relates to the technical field of vehicle safety monitoring, and aims to solve the problems that the prior art depends on static rules, early warning is not timely, diagnosis is inaccurate and intelligent decision support is lacked. The system comprises a data acquisition module; the state vector construction module is used for integrating multi-source data to construct a dynamic state vector; the state prediction module is used for predicting a theoretical normal state based on a historical state and a situation vector; the risk quantification module is used for calculating a deviation between a real state and a theoretical state and projecting the deviation to a threat space to generate a threat attribution vector and a predictive threat index; and the decision support module is used for performing simulation deduction on each intervention measure through anti-fact inference when the risk exceeds a threshold value, evaluating an expected future risk and recommending an optimal plan. According to the method, a technical closed loop from perception and cognition to decision making is constructed, so that the initiative of risk early warning, the accuracy of diagnosis and the intelligent level of emergency disposal are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of vehicle safety monitoring technology, and in particular to an integrated monitoring and management platform for escort vehicles. Background Technology

[0002] As special vehicles carrying high-value assets, the security monitoring of armored vehicles during transit has always been a top priority in the financial security field. Current technologies primarily rely on GPS for trajectory monitoring, combined with electronic geofencing, video surveillance, and simple sensors (such as door magnets and vibration sensors). However, these traditional monitoring methods are showing increasingly prominent limitations when facing increasingly complex, intelligent, and organized risk threats. The shortcomings of existing technologies are mainly reflected in the following aspects: First, existing monitoring platforms generally employ alarm mechanisms based on static rules and fixed thresholds. This means that an alarm is only triggered when a vehicle's behavior clearly violates a preset rule (e.g., speeding, deviating from the route by more than a fixed distance, or opening a door in an undesignated area). This "event-driven" model is essentially a passive and delayed response; it cannot identify subtle, gradual anomalies that deviate from the normal state without explicitly violating rules. More importantly, it lacks an understanding of the task context and cannot distinguish between "normal" and "abnormal" in different contexts. This leads to frequent false alarms and missed alarms in complex road conditions or special task scenarios, making it difficult to achieve truly predictive risk prevention.

[0003] Secondly, when existing technologies do detect anomalies and trigger alarms, the information they provide is often singular, discrete, and lacks in-depth diagnostic value. Supervisory personnel typically receive isolated event notifications such as "route deviation" or "prolonged stay," but cannot ascertain the underlying motives and risks behind such deviations. The system cannot assess the severity of the anomaly from an overall perspective, nor can it correlate it with other status indicators to determine the most likely type of threat (e.g., driver getting lost, vehicle malfunction, or potential hijacking or theft). This "knowing what, but not why" alarm pattern requires supervisory personnel to invest significant effort in manual verification and investigation for each alarm, greatly reducing the efficiency of emergency response.

[0004] Finally, in the emergency response phase after a risk occurs, existing technologies offer virtually no effective decision support. Upon receiving an alarm, command center personnel rely entirely on their professional experience and on-the-spot judgment to decide on intervention measures. The platform itself cannot conduct forward-looking effectiveness assessments of the various intervention methods in its toolkit (such as voice warnings, remote vehicle locking, and alarm linkage), nor can it inform operators which measure is most likely to resolve the crisis in the current situation. This entirely subjective decision-making model, under highly stressful emergency conditions, is not only inefficient but also prone to errors in judgment that could lead to missed opportunities for optimal response, and may even escalate the situation due to inappropriate intervention, making it difficult to ensure the optimal handling plan. Summary of the Invention

[0005] The purpose of this application is to provide an integrated monitoring and management platform for escort vehicles, which solves the problems of existing escort vehicle monitoring technologies relying on static rules for passive alarms, resulting in untimely risk warnings, inaccurate diagnosis, and a lack of intelligent decision support during emergency response.

[0006] Firstly, this application provides an integrated monitoring and management platform for escort vehicles, comprising: The data acquisition module is used to collect multi-source sensor data from the escort vehicle in real time; A state vector construction module, connected to the data acquisition module, is used to integrate multi-source sensor data and construct a dynamic state vector characterizing the current operating state of the escort vehicle. The state prediction module, connected to the state vector construction module, is used to predict the theoretical normal state vector at the next moment based on the historical dynamic state vector. The risk quantification module, connected to the state vector construction module and the state prediction module, is used to calculate the state deviation between the current real dynamic state vector and the theoretical normal state vector, and to generate a predictive threat index that characterizes the risk of the current task based on the state deviation.

[0007] Preferably, the dynamic state vector includes at least two sub-vectors selected from the following group: position state sub-vector, vehicle operating condition sub-vector, driver state sub-vector, cargo state sub-vector, and environment and planning sub-vector.

[0008] Preferably, the state prediction module is further configured to receive a context vector representing the macroscopic background of the task; The state prediction module predicts the theoretical normal state vector based on the historical dynamic state vector and the situation vector.

[0009] Preferably, the risk quantification module calculates the threat attribution vector T(τ) representing the degree of conformity to different threat modes using the following formula: T(τ)=W threat ·ΔS(τ); Where τ represents the next time step; T(τ) is the threat attribution vector at time τ; W threat Let be a pre-defined threat projection matrix composed of multiple threat basis vectors; ΔS(τ) is the state deviation at time τ; · represents the matrix-vector multiplication operation.

[0010] Preferably, the risk quantification module calculates the predictive threat index PTI(τ) based on the threat attribution vector using the following formula: PTI(τ) = ||T(τ)||2; Wherein, PTI(τ) is the predictive threat index at time τ; ||·||2 represents the L2 norm operation.

[0011] Preferred options also include: The decision support module is used to perform counterfactual inference on each intervention action based on a preset set of intervention actions when the predictive threat index exceeds a preset threshold, so as to generate the corresponding expected future risk.

[0012] Preferably, when the decision support module performs counterfactual inference, it constructs the hypothetical initial state S′ after the intervention action is applied to the current real dynamic state vector using the following formula. k (τ): S′ k (τ)=g k (S real (τ)); Among them, S′ k (τ) represents the hypothetical initial state constructed for the k-th intervention action at time τ; g k S is the effect model function corresponding to the k-th intervention action in the set of intervention actions; real (τ) is the true dynamic state vector at time τ; k is the index of the intervention action set.

[0013] Preferably, the decision support module is further configured to sort the set of intervention actions according to the expected future risks and recommend the optimal intervention plan.

[0014] Preferably, the risk quantification module is used to perform the following steps: Calculate the state deviation between the actual dynamic state vector at the current moment and the theoretical normal state vector; The state deviation is projected onto a preset threat space consisting of multiple threat basis vectors to generate a threat attribution vector, each component of which corresponds to a preset threat mode. The predictive threat index is calculated based on the threat attribution vector.

[0015] Secondly, this application provides an integrated supervision and management method for escort vehicles, which includes the following steps: S1. Real-time acquisition of multi-source sensor data from escort vehicles; S2. Integrate the multi-source sensor data to construct a dynamic state vector characterizing the current operating state of the escort vehicle; S3. Based on the historical dynamic state vector, predict the theoretical normal state vector for the next moment; S4. Calculate the state deviation between the current real dynamic state vector and the theoretical normal state vector, and generate a predictive threat index that characterizes the risk of the current task based on the state deviation.

[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. This application constructs a unified dynamic state vector encompassing multi-dimensional information such as people, vehicles, goods, environment, and task planning, and utilizes a deep learning model to perform temporal prediction on this vector, thereby achieving a dynamic and accurate characterization of the normal behavioral benchmark for escort missions. Compared to traditional methods that rely on fixed rules or thresholds, this invention can adaptively adjust the standard of "normal" according to different task scenarios, thus significantly improving the sensitivity and accuracy of identifying potential risks. It achieves a fundamental shift from "event-driven" passive alarms to "state-driven" proactive early warnings, effectively overcoming the problems of untimely warnings and high false alarm rates in traditional technologies when facing complex and dynamic risk scenarios. 2. This application, by projecting real-time state deviation vectors into a "threat space" learned from historical experience, can not only calculate a predictive threat index characterizing the overall risk level, but also attribute abstract abnormal states to specific, understandable threat patterns. This design transforms the monitoring platform from merely an "alarm device" into a "diagnostic instrument," revealing the nature and root causes of risks to command personnel, providing profound insights for subsequent decision-making, and greatly improving the depth and accuracy of risk assessment. 3. This application, through its decision support module, can automatically simulate and extrapolate multiple feasible intervention measures when a high-risk state is detected, predicting and quantifying the potential future risk evolution trajectory of each measure. By ranking and recommending the expected effects of different contingency plans, this invention transforms the emergency response process, which originally relied on personal experience and on-the-spot judgment, into a data-driven, quantitatively supported optimization selection process. This not only provides commanders with clear and reliable action guidelines but also significantly improves the decision-making efficiency and success rate of emergency response. Attached Figure Description

[0017] Figure 1 This is the system architecture diagram of this application; Figure 2 This is a flowchart of the method in this application. Detailed Implementation

[0018] The following is in conjunction with the appendix Figure 1 This application will be described in further detail below.

[0019] Example 1: An integrated monitoring and management platform for escort vehicles, comprising: The data acquisition module is used to collect multi-source sensor data from the escort vehicle in real time; In this embodiment, the platform's data acquisition module utilizes an onboard intelligent terminal deployed on the escort vehicle. Preferably, this terminal integrates multiple sensors and communication interfaces, enabling it to capture and upload full-dimensional data streams during task execution in real time and continuously. This process ensures the timeliness and accuracy of subsequent analysis.

[0020] Specifically, the data acquisition module is responsible for acquiring data from at least the following aspects. The selection of these data types is not arbitrary, but rather to comprehensively depict the complex escort system of "people-vehicle-goods-environment-task" from different dimensions: Regarding the vehicle's kinematics and operating conditions: This module collects high-precision positioning and motion information, such as longitude, latitude, altitude, ground velocity, acceleration, and heading angle obtained through the Global Navigation Satellite System. This information is the fundamental basis for determining whether the vehicle deviates from the preset route and whether there are abnormal parking or speeding behaviors.

[0021] Simultaneously, through its connection with the vehicle's onboard controller local area network, this module delves into the vehicle's underlying layers, decoding vehicle operating data such as engine speed, accelerator pedal opening, brake signals, gear information, and fuel level. This microscopic data can reveal the driver's driving intentions and the vehicle's health status, providing data support for identifying dangerous driving behaviors such as rapid acceleration and deceleration, or potential vehicle malfunctions.

[0022] Data regarding driver status and behavior: This module integrates the functions of a driver monitoring system, using driver-facing image sensors to analyze the driver's biometrics and behavioral characteristics in real time. This includes verifying the driver's identity through facial recognition technology to prevent unauthorized driving.

[0023] Furthermore, the system analyzes the driver's eye opening and closing, head posture, etc., to quantify their fatigue and distraction levels. This quantification allows the system to address risks arising from fatigue or distracted driving by considering human factors. Preferably, this module can also analyze the driver's facial micro-expressions to obtain a preliminary emotion index, providing clues for identifying abnormal emotional changes in the driver under duress or other extreme conditions.

[0024] Safety data regarding the cargo and its surrounding environment: To ensure the safety of the core assets being transported, this module monitors sensor data directly related to the cargo, such as the status of the magnetic door switches and the authorization and opening / closing records of the electronic locks. Preferably, vibration or acceleration sensors can also be integrated to detect abnormal bumps or impacts inside the cargo hold.

[0025] In addition, this module also acquires information about the vehicle's driving environment through the cameras of the advanced driver assistance system, such as the trigger status and confidence level of events like forward collision warning and lane departure warning. This information together constitutes the perception of the task execution environment.

[0026] Data regarding the compliance of task planning: This module synchronizes the planning information for this escort mission from the backend server, including the preset driving route (consisting of a series of geographic coordinates), the permitted geofenced areas, and the mission execution time window. During the vehicle's journey, this module calculates in real time the deviation between the vehicle's current location and the preset route, determines whether the vehicle is within a legal geofenced area, and assesses the alignment of the current time with the mission time window. This data is crucial for identifying early signs of theft, such as route deviation or illegal parking.

[0027] After completing the collection of the aforementioned multi-source data, this invention does not simply list the data. To ensure that this data, with its diverse dimensions and meanings, can be effectively processed by subsequent deep learning models, the platform's state vector construction module will perform crucial preprocessing and integration steps.

[0028] First, the purpose of this step is to eliminate the influence of different physical dimensions and prevent certain dimensions of data from becoming dominant in model training due to their large numerical range.

[0029] Subsequently, at each discrete sampling time t, all normalized data are combined into a high-dimensional dynamic state vector S(t) according to a predefined order that reflects the inherent logic. This vector can be mathematically expressed as: S(t) = [P(t); V(t); D(t); C(t); E(t)] T ; This formula is a holographic mathematical model of the escort mission status: P(t) is the position state sub-vector, representing the macroscopic kinematic information of the vehicle.

[0030] V(t) is the vehicle operating condition sub-vector, representing the vehicle's microscopic operating state.

[0031] D(t) is the driver's state subvector, representing the driver's biological and behavioral characteristics.

[0032] C(t) is the cargo state subvector, representing the safety state of the core assets being escorted.

[0033] E(t) is the environment and planning sub-vector, representing the compliance of task execution with the environmental context.

[0034] At this point, the data acquisition and processing flow has completed a closed loop. The final output, the dynamic state vector S(t), serves as a highly condensed and standardized information carrier, and is continuously and in real time transmitted to the subsequent state prediction module. The significance of this approach, which fuses multi-source information into a single vector, lies in enabling subsequent intelligent models to learn and discover complex, non-linear relationships between information of different dimensions within a unified feature space. For example, it reveals a potential correlation between a minor route deviation (reflected in E(t)) and abnormal fluctuations in the driver's emotions (reflected in D(t)), thus laying a solid data foundation for accurate and predictive risk identification.

[0035] The state vector construction module, connected to the data acquisition module, is used to integrate multi-source sensor data and construct a dynamic state vector representing the current operating state of the escort vehicle. This module is the core hub connecting raw data perception and advanced intelligent analysis. Its fundamental task is to transform the multi-source, heterogeneous, and unstructured data streams collected by the data acquisition module into a unified, regular, and efficient high-dimensional mathematical entity, namely a dynamic state vector, through a series of standardized processes and integrations.

[0036] The input to the state vector construction module is the real-time data stream from the data acquisition module. These data streams vary in physical dimensions, numerical ranges, and update frequencies; without processing, they cannot be used for effective joint analysis. Therefore, the first step in this module is data preprocessing.

[0037] Preferably, this module performs a normalization operation on all received numerical raw data. For example, the min-max normalization method is used to linearly map the value of each data feature to the interval [0,1]. The purpose of this step is not simply numerical compression, but to eliminate the scale differences caused by different physical units, ensuring that features such as engine speed in "revolutions per minute" and heading angle in "degrees" receive equal weight in subsequent model calculations, thereby avoiding bias in the model training process due to the excessively large numerical range of individual features.

[0038] After data preprocessing is completed, the core function of this module—vector construction and assembly—begins to execute. At each discrete sampling time t, this module integrates and arranges all normalized data points representing the current instantaneous state according to a predefined order that reflects the internal logic and system structure, ultimately constructing a high-dimensional dynamic state vector S(t).

[0039] The construction of this dynamic state vector S(t) is key to the holographic representation of complex escort tasks in this invention. Its mathematical form can be rigorously expressed as a column vector composed of multiple sub-vectors: S(t)=[P(t);V(t);D(t);C(t);E(t)] T ; Here, the formula is not a random accumulation of data, but a mathematical reorganization after a systematic deconstruction of the escort mission. Each sub-vector carries state information in a specific dimension, and its specific structure and design intent are as follows: Regarding the positional state subvector P(t): This subvector encapsulates the vehicle's macroscopic kinematic characteristics in physical space. Its components include, but are not limited to: normalized longitude, latitude, altitude, ground velocity, acceleration along the x and y axes of the vehicle coordinate system, and heading angle. The purpose of constructing this subvector is to provide the subsequent state prediction module with the fundamental kinematic reference for determining whether the vehicle exhibits abnormal trajectories (such as deviation from the preset route), abnormal motion states (such as prolonged stationary parking in areas where parking is prohibited), and dangerous driving behaviors (such as speeding).

[0040] Regarding the vehicle operating condition subvector V(t): This subvector aims to delve into the vehicle's internal workings, characterizing its microscopic mechanical and electronic system states. Its components include, but are not limited to: engine speed, accelerator pedal opening percentage, brake signal status (binary value), current gear, and event confidence levels for forward collision warnings and lane departure warnings output by advanced driver assistance systems (ADAS). The significance of introducing this subvector lies in its ability to reveal the driver's immediate driving intentions (such as rapid acceleration or deceleration) and reflect the vehicle's own health status. Simultaneously, the outputs of ADAS are incorporated into a unified state space as part of the vehicle's perception of its surroundings.

[0041] Regarding the driver state sub-vector D(t): This sub-vector is a concentrated manifestation of the core risk factor of "human" in this invention, aiming to quantify the driver's physiological and behavioral state. Its constituent elements include, but are not limited to: driver identity verification via facial recognition, the percentage of eye closure time (PERCLOS) value used to characterize fatigue levels, the distraction level obtained through head posture and gaze direction analysis, and detection markers indicating the presence of specific distracting behaviors such as phone calls or smoking. Preferably, it may also include a preliminary emotion index obtained by analyzing facial micro-expressions. The construction of this sub-vector enables the platform to make the driver's latent state explicit, thereby identifying and issuing warnings of potential risks caused by human factors (such as fatigue, distraction, or even abnormal emotions under duress).

[0042] Regarding the cargo state subvector C(t): This sub-vector is directly related to the core objective of the escort mission—the safety of the cargo. Its components include, but are not limited to: the magnetic switch status of the cargo door, the authorization and opening / closing records of the electronic lock, and the intensity readings of vibration sensors deployed inside the cargo hold. The construction of this sub-vector provides the system with a pair of "eyes" that directly monitor the cargo; any unauthorized door opening attempts or abnormal internal impacts will be directly reflected in the numerical changes of this sub-vector.

[0043] Regarding the environmental and planning subvector E(t): This subvector aims to measure the degree of conformity between the current task execution status and the preset plan. Its components include, but are not limited to: the shortest vertical distance from the vehicle's current location to the preset route (i.e., route deviation), a binary flag indicating whether the vehicle is within a preset geofence (such as a legal parking spot), and the degree of conformity between the current time and the task's specified time window. The establishment of this subvector places the vehicle's "behavior" within the framework of "rules," which is key to effectively identifying behaviors suspected of espionage, such as deliberate route deviation and illegal parking.

[0044] Ultimately, at each time step t, the state vector construction module outputs a structurally complete and meaningful dynamic state vector S(t). This vector, as a highly condensed information carrier, is continuously and in real time transmitted to the subsequent state prediction module. The essence of this construction process is to successfully transform a complex, multi-dimensional physical system problem into a standard mathematical problem suitable for processing by temporal deep learning models, thus laying a solid and reliable data foundation for subsequent accurate risk prediction and intelligent decision-making.

[0045] The state prediction module, connected to the state vector construction module, is used to predict the theoretical normal state vector at the next moment based on the historical dynamic state vector. This module plays a "prophetic" role in the entire regulatory system. Its core function is not to directly determine risk, but to provide a high-precision, dynamically changing "normal behavior benchmark" for subsequent risk quantification modules through deep learning of dynamic time-series data on task status.

[0046] Those skilled in the art will understand that the "normal state" of an escort mission is not static. For example, it is normal for a vehicle to travel at high speed on an urban expressway during the day, but it is highly likely to be abnormal to travel at the same speed on a country road late at night. Therefore, a fixed, static rule base cannot accurately define the dynamic boundaries of normalcy. The state prediction module of this invention is designed to solve this technical problem, and its goal is to build a dynamic prediction model that can understand and adapt to different external conditions.

[0047] The input to the state prediction module is the continuous dynamic state vector time series, ..., S(t-1), S(t), output by the aforementioned state vector construction module. More importantly, in order to achieve adaptive prediction for different scenarios, this module also introduces a key input: the context vector Context(t).

[0048] The context vector Context(t) is a mathematical description of the macroscopic background during the execution of the current task. Preferably, it consists of multiple discrete features representing the environment and background, such as: the current time period (e.g., daytime, dusk, nighttime), weather condition (e.g., sunny, rainy, foggy), road type (e.g., highway, urban arterial road, rural road), and static task identifiers (e.g., escort ID, vehicle ID). These discrete categorical features are transformed into a fixed-dimensional numerical vector through techniques such as one-hot encoding or word embedding to facilitate processing by the neural network model.

[0049] The fundamental purpose of introducing context vectors is to enable the prediction model to have "context awareness" capabilities, allowing it to understand and predict instantaneous vehicle states within a broader context.

[0050] In a preferred embodiment of the present invention, the core of the state prediction module is a conditional long short-term memory network. This model is chosen because it not only inherits the inherent advantages of standard LSTM networks in processing time-series data, effectively capturing the long-term dependencies of state vectors evolving over time, but more importantly, through improvements to the network structure, it can directly apply the context vector Context(t) as an additional condition to the network's gating units, thereby dynamically adjusting the model's memory and prediction behavior.

[0051] Specifically, the internal computation process of this conditional LSTM network is as follows, where the computation of each gate unit integrates information from three aspects: state input, historical memory, and current context: i t =σ(W si S(t)+W hi h t-1 +W ci Context(t)+b i ); if t =σ(W sf S(t)+W hf h t-1 +W cf Context(t)+b f ); ot=σ(W so S(t)+W ho h t-1 +W co Context(t)+b o ); h t =o t ⊙tanh(c t ); The symbols are defined as follows: S(t) is the dynamic state vector at time t, which serves as the sequence input at the current time.

[0052] Context(t) is the context vector at time t, which serves as the conditional input at the current time.

[0053] h t-1 and c t-1 These represent the hidden state and cell state at the previous time step t-1, respectively, and symbolize historical memory.

[0054] i t ,f t ,o tThese are the activation vectors for the input gate, forget gate, and output gate at time t, respectively.

[0055] and c t These represent the candidate cell state and the final cell state at time t, respectively.

[0056] h t The output hidden state at time t.

[0057] W and b represent the weight matrix and bias vector for different connections, respectively. For example, W ci This is the weight matrix from the context vector to the input gate. These parameters are learned during the model training phase.

[0058] σ represents the Sigmoid activation function, tanh represents the hyperbolic tangent activation function, and ⊙ represents the Hadamard product.

[0059] By adding W to the calculation of each gating unit c With the *Context(t) term, the model learns to dynamically adjust its information flow based on the context (t). For example, when the context vector indicates that the current time is "late at night," the forgetting gate f... t The weights may be adjusted so that the model is more inclined to "forget" the historical state of high-speed driving during the day, because it is no longer of much reference value for predicting normal behavior in the current situation.

[0060] To obtain the hidden state h at the current moment t Then, the module maps the vector to a space with the same dimension as the state vector through a fully connected output layer, thereby obtaining a predicted value of the theoretical normal state vector for the next time step τ (τ = t + 1).

[0061] The model parameters for this status prediction module are not updated online. Instead, they are obtained through offline supervised learning on massive amounts of "golden" historical escort data—data that has been manually screened or automatically labeled to ensure there are no security incidents—before platform deployment. The training objective is to minimize the model's predicted values. The mean square error between the observed value S and the true observed value S.

[0062] In summary, the final output of the state prediction module is a high-dimensional vector. It represents the invention's best mathematical estimate of "the most likely normal state of the escort mission in the next moment under the current historical context and situation." It does not directly assess risk itself, but rather provides a dynamic, precise, and context-aware "measuring stick" for subsequent risk quantification modules, enabling subsequent risk assessments to be built on a reliable and adaptive benchmark.

[0063] The risk quantification module, connected to the state vector construction module and the state prediction module, is used to calculate the state deviation between the current real dynamic state vector and the theoretical normal state vector, and to generate a predictive threat index that characterizes the risk of the current task based on the state deviation.

[0064] This module is the core of this invention, enabling a shift from passive perception to proactive cognition, and constitutes the intelligent analysis center of the entire system. Its fundamental purpose is to transform invisible and abstract task risks into measurable, attributable, and actionable quantitative indicators through a series of rigorous mathematical operations.

[0065] The operation of this module is based on the output of the aforementioned modules. Specifically, at each analysis time τ, it receives two crucial high-dimensional vector inputs: first, the real dynamic state vector Sreal(τ), provided by the state vector construction module, representing the current physical world's true state; and second, the predicted normal state vector, provided by the state prediction module, representing the theoretical normal behavior benchmark.

[0066] Upon receiving the above input, the risk quantification module will perform calculations, which will break down the abstract concept of risk into specific numerical values ​​layer by layer. First, the state deviation is calculated.

[0067] This is the first step in risk quantification, aiming to quantify the gap between "reality" and "expectation." The module obtains a state deviation vector ΔS(τ) by subtracting the two input vectors element by element: in, τ represents the current moment in the analysis; S real (τ) is the true dynamic state vector at time τ; Let be the predicted normal state vector at time τ; ΔS(τ) is the state deviation vector at time τ calculated from this.

[0068] The deviation vector ΔS(τ) is of great significance; it is no longer a single alarm signal, but a high-dimensional vector containing rich diagnostic information. The magnitude and sign of each component precisely indicate the degree and direction of deviation of a specific state characteristic (such as speed, driver's line of sight, cargo door status, etc.) from its normal expectation.

[0069] Secondly, a projection analysis of threat attribution is conducted.

[0070] After obtaining the state bias vector, this invention introduces a key innovative step: attributing this generalized bias to specific, predefined threat patterns through projection analysis. To this end, this module utilizes a pre-trained and loaded threat projection matrix W. threat .

[0071] The matrix W threat The construction of the system was completed offline before platform deployment. Preferably, technical personnel collected a large amount of historically confirmed real-world anomaly data (such as hijacking, embezzlement, and serious traffic accidents), and extracted the corresponding state deviation vector for each type of event. By applying dimensionality reduction and feature extraction techniques such as principal component analysis or linear discriminant analysis to these sets of deviation vectors of the same category, a "threat basis vector" that best represents the core anomaly characteristics of each threat pattern can be extracted. These threat basis vectors ultimately constitute the threat projection matrix W. threat Therefore, this matrix is ​​essentially a knowledge base containing the "mathematical fingerprints" of various known threats.

[0072] During real-time operation, the risk quantification module multiplies the state deviation vector ΔS(τ) on the left by the threat projection matrix to calculate the threat attribution vector T(τ): T(τ)=W threat ·ΔS(τ); in, T(τ) is the threat attribution vector calculated at time τ; W threat The preset threat projection matrix; ΔS(τ) is the state deviation vector calculated in the previous step; • Represents the multiplication operation between a matrix and a vector.

[0073] The essence of this step is to project the current high-dimensional state bias into a "threat space" defined by various threat patterns. The resulting threat attribution vector T(τ) has each component T... i The values ​​quantify the degree or magnitude of the current state deviation's conformity to the i-th threat mode. This enables the system to output diagnostic information such as "the current abnormal mode conforms to the 'hijacking' mode by 0.8 and to the 'fatigue driving' mode by 0.2," thereby achieving qualitative attribution of risk.

[0074] Finally, the comprehensive risk index is calculated. To obtain a single, intuitive, and easily threshold-based overall risk measure, the risk quantification module needs to further process the threat attribution vector. This module generates the final predictive threat index by calculating the L2 norm of the threat attribution vector T(τ): PTI(τ) = ||T(τ)||2; in, PTI(τ) is the predictive threat index finally calculated at time τ; T(τ) is the threat attribution vector obtained in the previous step; ||·||2 represents the L2 norm operation.

[0075] The PTI value, as a scalar, combines the magnitudes of the current state deviation across all known threat patterns. A significant deviation that closely aligns with one or more threat patterns will result in a significantly elevated PTI value. This index provides a clear and explicit trigger for subsequent decision-making processes.

[0076] In summary, the final output of the risk quantification module consists of two parts: a scalar PTI(τ) value used to trigger alarms and conduct macro-risk assessments, and a vector T(τ) used for in-depth diagnosis and causal analysis. These two outputs are then passed to the subsequent decision support module and user interface, thus completing the intelligent leap from "discovering the problem" to "understanding the problem," providing sufficient and necessary information support for accurate early warning and effective intervention.

[0077] The decision support module is used to generate the corresponding expected future risk by performing counterfactual inferences on each intervention action based on a preset set of intervention actions when the predictive threat index exceeds a preset threshold.

[0078] This module is the key difference between this invention and traditional alarm systems; it is the final executor of the closed loop from risk warning to intelligent intervention. Its design purpose is not simply to present risk alerts to users, but to proactively and forward-lookingly provide command personnel with the optimal response plan, based on quantitative assessment, when a risk occurs.

[0079] In emergency situations, human decision-making is often influenced by stress and a lack of information. The decision support module of this invention aims to leverage machine computing power and simulation to act as a calm and rational "intelligent advisor" for human commanders, thereby improving the efficiency and success rate of emergency response.

[0080] The decision support module does not run continuously; rather, it is triggered by the output of the aforementioned risk quantification module. Specifically, when the predictive threat index PTI(τ) calculated by the risk quantification module exceeds a pre-set security threshold PTI that can be dynamically adjusted by the user according to security policies, the decision support module will initiate a decision. threshold At that time, this decision support module is activated and begins to execute its core counterfactual inference process.

[0081] First, the intervention action set is initialized. At the moment the module is activated, it starts from a predefined intervention action set A = a1, a2, ..., a... N The system retrieves all currently feasible remote intervention measures. This action set is the platform's "toolbox," and its contents can be configured according to vehicle configuration and management strategies. Preferably, this set includes, but is not limited to: a1: initiating a voice intercom with the driver; a2: remotely activating the vehicle's audible and visual alarm; a3: remotely imposing a speed limit on the vehicle; a4: sending a coordination request to other security forces within the area.

[0082] Secondly, each feasible intervention action is simulated and analyzed independently. This is the most innovative part of this module. The module iterates through each action a in action set A. k And perform a separate, complete future simulation for it. This simulation process is further subdivided into: Construct a hypothetical initial state: The module needs to transform abstract "intervention actions" into concrete effects on the system state. To this end, this module defines each action a... k They are all associated with an effect model function g. k The function simulates action a. k The direct effect on the current real state at the moment of execution. The module uses the following formula, based on the current real dynamic state vector S. real (τ), constructing a hypothetical initial state S′ after the action is performed. k (τ): S′ k (τ)=g k (S real (τ)); in, S′ k (τ) represents the hypothetical initial state constructed at time τ for the k-th intervention action; g k Is with action a k The corresponding effect model function; S real (τ) is the actual dynamic state vector provided by the state vector construction module when the alarm is triggered; k is the index of the intervention action in the set. For example, if the risk attribution points to "driver fatigue," then the effect model function g1 for action a1 (voice intercom) might assign S... real The component value representing driver fatigue or distraction level in (τ) is reset to a lower "normal" value to simulate the state after the driver is woken up by the intercom. If the risk attribution points to "vehicle speeding", then for action a3 (mandatory speed limit) g3, S will be directly modified.real The component representing speed in (τ) is set to be equal to the preset safe speed limit.

[0083] Iteratively extrapolate the future state trajectory: After obtaining the hypothetical initial state S'_k(τ), this module uses it as new input and calls the aforementioned state prediction module. By using S′ k (τ) is fed into the prediction model and iterated over multiple steps (e.g., extrapolating forward M time steps), and the module can generate a line specific to action a. k Possible future state trajectory S′ k (τ+1),S′ k (τ+2),...,S′ k (τ+M). This trajectory represents the invention's response to "if action a is taken". k The mathematical prediction of how the system is most likely to evolve in the next M time steps.

[0084] Assess anticipated future risks: After obtaining the simulated future trajectory, the module needs to evaluate the "quality" of the trajectory, i.e., the expected effect of the intervention. Preferably, the module will evaluate each or the last state point S′ in the simulated trajectory. k (τ+M), the calculation logic of the risk quantification module is called again to obtain its corresponding predictive threat index PTI′. k (τ+m). By weighting these future PTI values ​​or directly taking the final value, we can ultimately determine the intervention action a. k Calculate a single expected future risk index PTI′ that represents the intervention's effect. k .

[0085] Finally, the intervention plans are prioritized and recommended. This module completes the review of all feasible intervention actions. k The simulation was conducted, and a set of corresponding expected future risk indices PTI′1, PTI′2, ..., PTI′ were obtained. N Then, it will process these actions according to their PTI′ k Sort the values ​​in ascending order.

[0086] The top of the sorting results, i.e., PTI′ k The action with the lowest value is the "optimal intervention plan" predicted by this invention, which can most effectively alleviate the current risk and restore the vehicle's status to normal.

[0087] Ultimately, the decision support module doesn't just output an isolated action name. It presents the decision results in an intuitive and information-rich way on the platform's monitoring and command interface. For example, it highlights the 1-3 optimal contingency plans and includes key decision-making evidence, such as: "Preferred suggestion: Initiate voice communication. Cause analysis: The current risk is mainly attributed to driver fatigue (compliance rate 75%). Expected effect: The risk index is expected to decrease to below the safety threshold within 1 minute." Through the above series of steps, this decision support module transforms a complex, dynamic, and high-risk emergency response problem into a clear, data-driven optimization problem with quantifiable expected results, thus completing a full closed loop from risk perception to intelligent decision-making.

[0088] Combined with appendix Figure 2 Another embodiment of this application provides an integrated supervision and management method for escort vehicles, including the following steps: S1. Real-time acquisition of multi-source sensor data from escort vehicles; S2. Integrate multi-source sensor data to construct a dynamic state vector representing the current operating state of the escort vehicle; S3. Based on the historical dynamic state vector, predict the theoretical normal state vector for the next moment; S4. Calculate the state deviation between the current real dynamic state vector and the theoretical normal state vector, and generate a predictive threat index that characterizes the current task risk based on the state deviation.

[0089] The method in this embodiment can be used to execute the above system embodiment, and its principle and technical effect are similar, so it will not be described again here.

[0090] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A comprehensive management platform for the integrated supervision of escort vehicles, characterized in that, include: The data acquisition module is used to collect multi-source sensor data from the escort vehicle in real time; A state vector construction module, connected to the data acquisition module, is used to integrate multi-source sensor data and construct a dynamic state vector characterizing the current operating state of the escort vehicle. The state prediction module, connected to the state vector construction module, is used to predict the theoretical normal state vector at the next moment based on the historical dynamic state vector. The risk quantification module, connected to the state vector construction module and the state prediction module, is used to calculate the state deviation between the current real dynamic state vector and the theoretical normal state vector, and to generate a predictive threat index that characterizes the risk of the current task based on the state deviation.

2. The integrated supervision and management platform for escort vehicles according to claim 1, characterized in that, The dynamic state vector includes at least two sub-vectors selected from the following group: position state sub-vector, vehicle condition sub-vector, driver state sub-vector, cargo state sub-vector, and environment and planning sub-vector.

3. The integrated supervision and management platform for escort vehicles according to claim 1, characterized in that, The state prediction module is also used to receive a context vector representing the macroscopic background of the task. The state prediction module predicts the theoretical normal state vector based on the historical dynamic state vector and the situation vector.

4. The integrated supervision and management platform for escort vehicles according to claim 1, characterized in that, The risk quantification module calculates the threat attribution vector T(τ) representing the degree of conformity to different threat modes using the following formula: T(τ)=W threat ·ΔS(τ); Where τ represents the next time step; T(τ) is the threat attribution vector at time τ; W threat Let be a pre-defined threat projection matrix composed of multiple threat basis vectors; ΔS(τ) is the state deviation at time τ; · represents the matrix-vector multiplication operation.

5. The integrated supervision and management platform for escort vehicles according to claim 4, characterized in that, The risk quantification module calculates the predictive threat index PTI(τ) based on the threat attribution vector using the following formula: PTI(τ) = ||T(τ)||2; Wherein, PTI(τ) is the predictive threat index at time τ; ||·||2 represents the L2 norm operation.

6. The integrated supervision and management platform for escort vehicles according to claim 1, characterized in that, Also includes: The decision support module is used to perform counterfactual inference on each intervention action based on a preset set of intervention actions when the predictive threat index exceeds a preset threshold, so as to generate the corresponding expected future risk.

7. The integrated supervision and management platform for escort vehicles according to claim 6, characterized in that, When the decision support module performs counterfactual inference, it constructs the hypothetical initial state S after the intervention action is applied to the current real dynamic state vector using the following formula. ′ k (τ): S ′ k (τ)=g k (S real (t)); Among them, S ′ k (τ) represents the hypothetical initial state constructed for the k-th intervention action at time τ; g k S is the effect model function corresponding to the k-th intervention action in the intervention action set; real (τ) is the true dynamic state vector at time τ; k is the index of the intervention action set.

8. The integrated supervision and management platform for escort vehicles according to claim 7, characterized in that, The decision support module is also used to sort the set of intervention actions according to the expected future risks and recommend the optimal intervention plan.

9. The integrated supervision and management platform for escort vehicles according to claim 1, characterized in that, The risk quantification module is used to perform the following steps: Calculate the state deviation between the actual dynamic state vector at the current moment and the theoretical normal state vector; The state deviation is projected onto a preset threat space consisting of multiple threat basis vectors to generate a threat attribution vector, each component of which corresponds to a preset threat mode. The predictive threat index is calculated based on the threat attribution vector.

10. A method for integrated supervision and management of escort vehicles, comprising an integrated supervision and management platform for escort vehicles according to any one of claims 1-9, characterized in that, Includes the following steps: S1. Real-time acquisition of multi-source sensor data from escort vehicles; S2. Integrate the multi-source sensor data to construct a dynamic state vector characterizing the current operating state of the escort vehicle; S3. Based on the historical dynamic state vector, predict the theoretical normal state vector for the next moment; S4. Calculate the state deviation between the current real dynamic state vector and the theoretical normal state vector, and generate a predictive threat index that characterizes the risk of the current task based on the state deviation.

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