A border inspection wharf operation area risk identification and early warning method and system

Abstract

This invention relates to the field of electronic digital data processing technology and discloses a risk identification and early warning method and system for border inspection terminal operation areas. The method includes: acquiring the perception tags, spatiotemporal trajectories, and business execution stub data of controlled targets; extracting category identification identifiers and kinematic parameters; calculating feature distribution parameters using a kinematic constraint model and comparing them with a preset logical threshold; determining misidentified tags; triggering logical correction operations; resetting the category identification identifiers using task instructions to generate a smooth sequence; reconstructing the topology of the logical judgment area based on the operation's spatiotemporal density parameters; calculating displacement trend deviation values; and outputting early warning data. This invention eliminates the non-steady-state semantic jitter of the perception front end through causal arbitration in the digital logical space, ensuring that risk judgment is supported by physical conservation laws, achieving precise alignment between the perception space and the physical operation scenario, and improving the penetrating identification capability of abnormal behavior.

Description

Technical Field

[0001] This invention belongs to the field of electronic digital data processing technology, and in particular relates to a risk identification and early warning method and system for border inspection terminal operation areas. Background Technology

[0002] In the current process of port automation construction, the safety protection of the operation area usually adopts an electronic digital data processing system. This system uses edge-side acquisition devices to acquire monitoring images, and converts the images into motion feature vector sequences through feature extraction algorithms. The edge side then uploads the dimensionality-reduced feature vector sequences to the central processing cluster, where the central end performs risk identification and graded early warning according to preset logic. This edge-cloud collaborative architecture reduces communication bandwidth pressure while meeting the central end's concurrent demand for massive data processing. For example, Chinese invention patent CN114360117B discloses an unmanned smart border inspection booth device and its operation control method at a wharf. The solution relies on the front-end camera to capture facial features and compare them with the back-end document database using a single identity rule. This control logic based on discrete identity verification relies on the stability of the perception link. When faced with sudden changes in light and shadow at the border inspection wharf or obstruction by large metal equipment, the system's ability to identify semantic label jump noise is insufficient, resulting in decision oscillations. It lacks closed-loop verification of the physical rationality of the target's motion trajectory and is difficult to execute self-healing logic under the premise of pixel information distortion.

[0003] Affected by physical environmental factors such as sudden changes in light and shadow at the border inspection terminal, obstruction by large metal equipment, and tidal fluctuations, the semantic tags generated by the front-end acquisition equipment exhibit non-steady-state jitter. Specifically, the target category may experience attribute jumps within adjacent time frames or instantaneous displacements that exceed dynamic extremes. Since the central processing cluster receives digital features detached from pixel information, the system cannot perform visual verification of logical anomalies. This direct acceptance of perception results makes the early warning system prone to decision oscillations under extreme interference environments. Attempts to improve perception accuracy by increasing sampling frequency or expanding transmission bandwidth not only lead to an exponential increase in backbone network communication load but also fail to eliminate the mismatch between digital domain features and physical spatiotemporal logic. Furthermore, static operation area masks are difficult to adapt to the dynamically evolving safety boundaries during terminal production operations, resulting in a large number of invalid feature components occupying system computing resources and reducing the real-time performance of risk assessment.

[0004] Therefore, how to construct a digital domain logic self-examination mechanism based on physical consistency constraints and job instruction flow coupling, and achieve self-healing and accurate early warning of perception conclusions under the condition of missing pixel information, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a method for risk identification and early warning in border inspection terminal operation areas, comprising the following steps: Step S101: Obtain the heterogeneous data stream of the controlled target. The heterogeneous data stream includes perception tag data, spatiotemporal trajectory data, and business execution stub data. Step S102: Extract category identification identifiers from the perception tag data and extract kinematic parameters corresponding to the category identification identifiers from the spatiotemporal trajectory data. The kinematic parameters include acceleration and steering curvature. Step S103: Establish a kinematic constraint model, calculate the feature distribution parameters of the controlled target using kinematic parameters, and logically compare the feature distribution parameters with the preset kinematic logic threshold. Step S104: When the feature distribution parameter exceeds the kinematic logic threshold, the category identification label is determined to be a misidentified label, and a logic correction operation is triggered. The category identification label is reset using the task instructions in the business execution stub data to generate a smooth data sequence. Step S105: Extract the job spatiotemporal density parameters from the business execution stub data, and perform topology reconstruction on the preset logical judgment area based on the job spatiotemporal density parameters so that the topology of the logical judgment area is aligned with the real-time business distribution. Step S106: Calculate the displacement trend deviation value of the smoothed data sequence within the logical judgment region after topological reconstruction. When the displacement trend deviation value deviates from the preset behavior benchmark envelope, output early warning data indicating that the controlled target has abnormal behavior.

[0006] Preferably, step S104 specifically includes: identifying the category identification markers that have changed in the perception tag data, and retrieving the business records that match the time-series coordinates of the controlled target in the business execution stub data; obtaining the target attribute parameters defined in the business records, and using the target attribute parameters as logical benchmark values ​​to reset the category identification markers that have changed in the perception tag data in order to reconstruct a smooth data sequence.

[0007] Preferably, the kinematic logic threshold is preset based on the kinematic extreme values ​​of the controlled target. These kinematic extreme values ​​include acceleration extreme values ​​and steering curvature extreme values ​​corresponding to the kinematic parameters, wherein the unit of the acceleration extreme value is... The kinematic constraint model is used to limit the spatial displacement of the category identification marker within a unit of time to not exceed the preset kinematic displacement extreme value.

[0008] Preferably, the step of calculating the feature distribution parameter in step S103 includes: step S401, extracting the kinematic parameters within the sampling period and performing normalization processing; step S402, statistically analyzing the distribution frequency of the normalized kinematic parameters in the time domain interval to obtain the probability distribution value; step S403, calculating the feature distribution entropy of the controlled target using the following formula, and using the feature distribution entropy as the feature distribution parameter: ,in, The characteristic distribution entropy, in units of ; The first one obtained in step S402 The probability distribution values ​​of each sampling point; This represents the total number of samples taken within the preset time window.

[0009] Preferably, step S105 specifically includes: converting the spatiotemporal density parameters of the operation into a logic enable signal, using the logic enable signal to drive the processing module to adjust the calculation weight distribution of the logic judgment region; and removing invalid feature data in the non-operation state so that the sampling frequency is synchronized with the actual operation rhythm.

[0010] Preferably, it also includes: establishing coordinate transformation logic based on reference vectors, and performing dynamic linear translation and rotation correction on the logic judgment region by calculating the deviation vector of preset static reference points in the heterogeneous data stream in real time.

[0011] Preferably, the coordinate transformation logic specifically includes: acquiring environmental displacement data provided by an external positioning reference station, and the coordinate transformation logic is used to reconstruct the logical judgment area with the environmental displacement data in order to maintain the alignment of the sensing tag data with the coordinate system of the physical working space.

[0012] Preferably, step S106 specifically includes: performing algebraic operations on the temporal displacement data of the controlled target to determine the displacement topology deviation; when the displacement topology deviation exceeds a preset anomaly probability threshold, increasing the upload priority of the corresponding feature data in the perception tag data.

[0013] Preferably, after the early warning data is output, the correlation and deduction logic of the central processing cluster is triggered to perform topological matching between the heterogeneous data stream and the historical risk feature model in order to identify whether the controlled target conforms to a specific risk evolution trend. The business execution stub data comes from the production management database, and the task attribute parameters in the business execution stub data are used as the judgment weight for the logical correction operation.

[0014] A risk identification and early warning system for border inspection terminal operation areas includes a data acquisition module, a feature extraction module, a logical comparison module, a logical correction module, an area reconstruction module, and a risk early warning module. The data acquisition module is used to acquire heterogeneous data streams of the controlled target, including perception tag data, spatiotemporal trajectory data, and business execution stub data. The feature extraction module is used to extract category identification identifiers from the perception label data and extract kinematic parameters corresponding to the category identification identifiers from the spatiotemporal trajectory data. The logic comparison module is used to calculate the feature distribution parameters of the controlled target using kinematic parameters, and then logically compare the feature distribution parameters with the preset kinematic logic threshold. The logic correction module is used to reset the category identification label by using the task instructions in the business execution stub data when the feature distribution parameter exceeds the kinematic logic threshold, so as to generate a smooth data sequence. The region reconstruction module is used to reconstruct the topology of a preset logical judgment region based on the job spatiotemporal density parameters in the business execution stub data. The risk warning module is used to calculate the displacement trend deviation value of the smoothed data sequence within the logical judgment area after topological reconstruction, and output warning data indicating abnormal behavior of the controlled target.

[0015] Compared with existing technologies, the risk identification and early warning method for border inspection terminal operation areas of the present invention has the following advantages: 1. In risk identification in the border inspection terminal operation area, by establishing a physical consistency verification mechanism, the central processing cluster can perform logical arbitration using the kinematic properties of the target in the feature vector space detached from the original image sequence. When the jump trajectory of the semantic label breaks through the dynamic extreme value constraint of the corresponding object attribute, the system triggers state interpolation repair based on spatiotemporal correlation. Thus, without retrieving pixel information, the non-steady semantic jitter generated by the perception front end is eliminated through data processing, ensuring that the risk judgment conclusion is supported by physical conservation laws and reducing the probability of false warnings.

[0016] 2. By utilizing the business instruction flow-driven spatiotemporal mask dynamic shaping mechanism, the production instructions of the job management system are converted into control switches for the data processing flow. This enables the edge computing unit to dynamically change the activation range and weight distribution of the topology mask according to the spatiotemporal parameters of real-time business, thereby achieving precise alignment between sampling frequency and job rhythm, eliminating invalid feature data in non-job states, and improving the system's response sensitivity in large-scale concurrent measurement environment while ensuring backbone network communication bandwidth.

[0017] 3. A coordinate field transformation mechanism based on quasi-static reference vectors is adopted. By calculating the deviation vector of the preset static anchor point in the feature vector sequence in real time, dynamic linear translation and rotation correction is performed on the preset spatial logic mask. This enables the digital early warning boundary to be reconstructed synchronously with environmental changes such as tides or ship drift. Under the condition of utilizing the existing sensing data stream, the long-term coordinate consistency between the sensing space and the physical operation scene is maintained, avoiding logic mismatch and filtering failure caused by environmental deformation. Attached Figure Description

[0018] Figure 1 This is a flowchart of a risk identification and early warning method that incorporates logic correction and topology reconstruction mechanisms according to the present invention. Figure 2 This is a schematic diagram of the architecture and interaction of a risk identification and early warning system based on heterogeneous data stream processing according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, low, lateral, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0022] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] This invention provides a risk identification and early warning method and system for border inspection terminal operation areas. It establishes an alignment mechanism between the sensing space and the physical operation scene through a processor, and uses kinematic constraints and business logic within the electronic digital space to correct non-steady semantic jitter generated by the sensing front end. The method includes: acquiring heterogeneous data streams of controlled targets, extracting category identification labels and kinematic parameters, calculating feature distribution parameters using a kinematic constraint model and performing logical comparison, triggering logical correction to generate a smooth data sequence, reconstructing the topology of the logical judgment area based on the spatiotemporal density parameters of the operation, calculating displacement trend deviation values, and outputting early warning data. In this embodiment, the acquisition method of the front-end sensing data has high flexibility and coverage. Specifically, sensing tag data and spatiotemporal trajectory data can be captured in real time by monitoring cameras fixed at key points on the terminal shore (such as around mooring bollards, the central control tower, or the operation gate). Simultaneously, to eliminate fixed viewing angles... To eliminate blind spots in monitoring, the system also supports autonomous inspection using unmanned robots. These robots utilize visual sensors or lidar to dynamically scan the work area, enabling comprehensive, blind-spot-free heterogeneous data collection of controlled targets. To address the technical challenge of abrupt changes in sensor tag attributes caused by sudden changes in light and shadow at border inspection docks and obstructions from large equipment, the processor acquires heterogeneous data streams from the controlled targets. These streams include sensor tag data, spatiotemporal trajectory data, and business execution stub data. The processor extracts category identification markers from the sensor tag data and kinematic parameters corresponding to these markers from the spatiotemporal trajectory data. These kinematic parameters include the target's acceleration and turning curvature. By introducing multi-source heterogeneous data into the electronic digital processing flow, the system establishes a feature basis for subsequent logical arbitration that deviates from the original pixel information. The data acquisition module aligns the time axis when receiving the sensor stream and business stream, allocating a fixed sampling step size in memory. The synchronization buffer uses the time of the business execution stub data production task instruction as the zero reference point to extract the time deviation within the synchronization buffer. Using the sensing tag data within nm and the spatiotemporal trajectory data as inputs to the logical comparison module, the digital features detached from pixel information and the specific business authorization logic are causally locked together on the physical spatiotemporal axis.

[0024] To address the issue of decision-making oscillations caused by instantaneous displacements exceeding dynamic extrema in front-end sensing results, the system establishes a kinematic constraint model through the following procedures: The processor calculates the feature distribution parameters of the controlled target using the extracted kinematic parameters and performs a logical comparison with a preset kinematic logic threshold; the specific calculation path is as follows: extracting kinematic parameters within the sampling period and performing normalization processing; statistically analyzing the distribution frequency of the normalized parameters in the time domain to determine the probability distribution value; and calculating the feature distribution entropy of the controlled target using a formula. And use this entropy value as a feature distribution parameter: ,in, The characteristic distribution entropy, in units of ; For the first The probability distribution values ​​of each sampling point; The total number of samples within a preset time window; the kinematic logic threshold is preset based on the kinematic extreme values ​​of the controlled target, and the acceleration extreme value of the container truck is set to... This procedure verifies the authenticity of perceived conclusions through physical constant constraints, resolving the problem of false alarms caused by sensor errors; kinematic logic threshold. Determination method: Select the historical displacement sequence of the controlled target under standard operating conditions as the calibration sample, discretize the calibration sample and extract the acceleration component. With turning curvature ;pass Calculate the feature distribution entropy of each sampling window Statistical calibration of sample feature distribution entropy Cumulative frequency distribution, to achieve cumulative frequency distribution The corresponding entropy value is determined as the kinematic logic threshold. Benchmark value.

[0025] After determining that a sensing tag is misidentified, the processor triggers a logic correction operation to maintain the continuity of the early warning logic. It identifies category identifiers that have changed in the sensing tag data and retrieves business records from the business execution stub data that match the time-series coordinates of the controlled target. The business execution stub data originates from the production management database and contains task attribute parameters. The processor uses the target attribute parameters defined in the business records as logical baseline values ​​and resets the category identifiers that have changed, reconstructing a smooth data sequence. When a sudden change in the target category from personnel to truck is detected and the displacement exceeds the physical extreme value, the target is reset to a valid asset attribute for that time period based on the operation identifier in the business instruction. This operation utilizes the causal relationship between sensing features and execution instructions. The link realizes logical self-healing of perception conclusions; to solve the data redundancy problem caused by the inability of static masks to adapt to dynamically evolving job safety boundaries, the system initiates regional topology reconstruction; it extracts job spatiotemporal density parameters from business execution stub data, and performs topology reconstruction on the preset logical judgment region based on these parameters, so that the structure of the logical judgment region is aligned with the real-time business distribution; it converts the job spatiotemporal density parameters into logical enable signals, drives the processing module to adjust the calculation weight distribution of the logical judgment region, and removes invalid feature data in non-job states, so that the sampling frequency is synchronized with the actual job rhythm; this mechanism transforms the production instructions of the business management system into control switches for data flow, improving the system's response efficiency in large-scale concurrent environments.

[0026] The processor adjusts the calculation weights of the logic decision region using a dynamic gain mapping mechanism based on job density, which incorporates job spatiotemporal density parameters. The pixel gain matrix is ​​mapped to the logical judgment mask. Pixels within a preset distance range on both sides of the operation centerline are weighted, and edge pixels exceeding the operation boundary are weighted according to the distance decay function. This allows the processing module to prioritize the feature distribution parameters of high-density operation areas. By performing spatial filtering on physical coordinate weights in the electrical digital domain, the system achieves physical shielding of invalid interference data while maintaining the same sampling step size. To address the mismatch between digital coordinates and physical boundaries caused by tidal fluctuations or ship drift, the system establishes coordinate transformation logic based on reference vectors. The deviation vector of preset static reference points in heterogeneous data streams is calculated in real time. The identification procedure for static reference points is as follows: the processing module retrieves the pixel coordinates of preset mooring bollards or quay bridge foundation corner markers from the sensing tag data as the current position value; it reads environmental displacement data provided by external positioning reference stations, calculates the arithmetic difference between the current position value and the standard latitude and longitude coordinates of the physical marker stored in the production management database, obtains the translation increment component, and uses this increment component to update the translation parameters in the coordinate transformation matrix in real time, performing dynamic linear translation and rotation correction on the logical judgment area.

[0027] The coordinate transformation logic is used to reconstruct the logical judgment area with environmental displacement data. Environmental displacement data provided by an external positioning reference station maintains the coordinate system synchronization between the sensing tag data and the physical operating space. By mining the static remainder in the data stream for self-calibration, the system ensures the long-term effectiveness of the warning boundary under physical deformation conditions. Finally, the processor calculates the displacement trend deviation value of the smoothed data sequence within the logical judgment area after topological reconstruction. When this displacement trend deviation value deviates from the preset behavioral reference envelope, warning data indicating abnormal behavior of the controlled target is output. Algebraic operations are performed on the target's temporal displacement data to determine the displacement topological deviation. If the deviation exceeds a preset abnormal probability threshold, the upload priority of this feature data is increased. After the warning data is output, the system triggers the correlation inference logic, performing topological matching between the heterogeneous data stream and the historical risk feature model to identify whether the controlled target conforms to a specific risk evolution trend. This procedure uses trajectory topological entropy measurement to capture risks not covered by the rule base, realizing a shift from simple rule matching to pre-emptive situational awareness.

[0028] The processor's execution of the correlation inference logic includes establishing the feature space projection relationship between trajectory features and the risk behavior database, converting smooth data sequences into multi-dimensional motion feature vectors, and calling a pre-set risk evolution situation model to perform spatiotemporal similarity measurement. By calculating the Euclidean distance between the current trajectory vector and historical violation behavior vectors, the abnormal probability score of the controlled target behavior is determined. When the abnormal probability score deviates from the preset behavior benchmark envelope, the processor outputs early warning data for a specific risk category, realizing a logical upgrade from single coordinate judgment to serialized situation recognition. The above method is implemented through a risk identification and early warning system for border inspection terminal operation areas, including a data acquisition module, a feature extraction module, a logical comparison module, a logical correction module, a region reconstruction module, and a risk early warning module. The data acquisition module is used to acquire heterogeneous data streams; the feature extraction module is used to extract identifiers and kinematic parameters; the logical comparison module is used to perform threshold comparison of feature distribution parameters; the logical correction module is used to generate smooth data sequences; the region reconstruction module is used to perform topology reshaping; and the risk early warning module is used to output early warning data. The modules work together to eliminate the non-steady-state jitter of the sensing front end at the electronic digital data processing level, ensuring the physical rationality of risk judgment.

[0029] Example 1: In a nighttime border inspection terminal operation scenario, when the fog droplet concentration exceeds... Furthermore, when high-power, high-frequency strobe spotlights create backlight shadow drift in the container yard, the front-end sensing equipment detects targets moving towards the berth warning line. Due to abrupt changes in light and shadow, the category identification markers in the sensing tag data exhibit high-frequency jumps between pedestrians and debris, and discontinuous displacement jumps appear in the spatiotemporal trajectory data between adjacent frames. In this nighttime scenario, the front-end sensing equipment consists of fixed-point monitoring cameras on the shore and multiple unmanned inspection robots. The fixed-point cameras are responsible for global monitoring of the large scene, while the unmanned robots perform autonomous inspection tasks in the container yard according to preset paths. When a target enters an obstructed area or an area with complex light and shadow, the system automatically retrieves the most recent patrol data. The robot's perception tag data is examined to compensate for perception gaps caused by fixed viewpoints. The processor acquires heterogeneous data streams from the controlled target, extracts kinematic parameters such as acceleration and turning curvature, and runs a kinematic constraint model to perform logical arbitration on pedestrian tags. The displacement vector distribution of the controlled target is obtained according to a preset sampling period of 100 milliseconds. The sliding window used to calculate the feature distribution entropy contains 20 consecutive sampling points, with a step increment of 10 sampling points. There is a 50% data overlap between adjacent windows. Smoothed feature distribution parameters are output every 1000 milliseconds, using the formula... Calculate the characteristic distribution entropy The entropy value remains constant in this specific scenario. The above-mentioned deviations from the physical reasonableness threshold range of pedestrian movement characteristics are used to determine that the current category identification label is a misidentified label.

[0030] The system triggers a logical correction operation, retrieving task records corresponding to the temporal coordinates of the controlled target from the business execution stub data. This reveals that the current berth is in the automated loading operation phase, and the target attribute parameter defined in the task record is a container truck. By establishing a causal mapping between the business logic baseline value and the perception tag data, the processor resets the pedestrian tag that has changed to a container truck and uses a linear interpolation algorithm in the electronic digital space to complete the missing trajectory coordinates, generating a smooth data sequence that conforms to the laws of physical conservation. Addressing the safety boundary ambiguity caused by occlusion during operations, the system extracts the spatiotemporal density parameters of the operation from the business execution stub data, shrinking the topology of the logical judgment region from a global static mask to a subset of operations below the currently operating crane, and then, based on the positioning baseline... The environmental displacement data provided by the quasi-station is subjected to linear translation correction based on the reference vector, so that the coordinate system of the logical judgment area is reconstructed synchronously with the ship's displacement, eliminating the logical mismatch caused by environmental deformation. Within the reconstructed logical judgment area, the processor calculates the displacement trend deviation value of the smoothed data sequence, detects that the displacement topology deviation of the target deviates from the trajectory centerline in the normal behavior envelope, determines that there is unauthorized boundary crossing behavior, and outputs early warning data indicating abnormal behavior of the controlled target. By performing algebraic operations on the discrete distribution of the controlled target's temporal displacement vector, it is found that in an environment where the perception front end continuously outputs false labels, the system filters light and shadow noise interference and identifies the risk evolution trend of the controlled target at the output end through the collaboration of physical constraints and business logic in the digital domain.

[0031] Example 2: In a test environment simulating automated border inspection terminal operations, the risk identification effectiveness of the method of the present invention was quantitatively verified using an edge computing testbed. A logic simulation system with electrical digital processing capabilities was adopted, and its data source consisted of a pre-set heterogeneous data sequence of terminal operations, including sensing tags, kinematic trajectories, and business execution stubs. Its processing core had floating-point operation capabilities and an interruption response cycle of no more than [missing information]. During the experimental design phase, the sampling period is set. As a key parameter, its value is set logically to balance the integrity of data feature extraction with the computational load of the processing unit. According to signal processing principles, when the upper limit of the controlled target's motion speed in the physical scene increases, the sampling period is adjusted to avoid displacement feature aliasing. The sampling period should be adjusted towards the lower limit of the displacement feature capture window, targeting the rate of change of the target displacement. Set as To ensure the accuracy of kinematic parameter capture when the target performs a turning maneuver; to verify the stability of the scheme under industrial electromagnetic and light interference environments, semantic jitter noise of different intensities was actively injected into the sensing tag data during the experiment, and the signal-to-noise ratio was used to measure the noise level. To characterize the noise intensity gradient, a control group, a partially missing group, and the sample group of this invention were compared and analyzed. The control group used a rule-matching-based identification method, the partially missing group used a kinematic constraint model with the business correction logic removed, and the sample group of this invention adopted the aforementioned technical solution, by changing... The numerical observation system's suppression rate of misidentified labels is recorded in Table 1.

[0032] Table 1: Comparison of Label Recognition Accuracy under Different Signal-to-Noise Ratio Gradients As shown in Table 1, when the intermediate feature values ​​produced by the kinematic constraint model synergistically interact with the business logic baseline values, the system corrects attribute jumps at the perception front end. Furthermore, because the business stubs provide target recognition support for kinematic trajectory completion, the performance of the sample group of this invention is superior to the sum of the effects of each individual feature, achieving a gain in recognition robustness. Analysis of Table 1... for Based on the operating conditions, the judgment function of the control group decreased to However, because the sample group of this invention establishes a causal link between physical conservation laws and business instructions, its recognition accuracy remains at [a certain level]. For the entropy threshold of feature distribution in the kinematic constraint model The rationality of the execution boundary test was carried out by observing the sensitivity and false alarm rate of the risk warning by setting multiple gradient thresholds, in order to find the working window of the system operation, and the characteristic distribution entropy in the experiment. The calculation formula is referenced. The experimental data are recorded in Table 2.

[0033] Table 2: Correlation between Feature Distribution Entropy Threshold and Early Warning Effectiveness Analysis of the trends in Table 2 shows that when the threshold Below At this time, the system misinterprets normal kinematic fluctuations, leading to an increase in the false alarm rate. When the threshold is reached... Exceed Subsequently, the system's sensitivity to detecting errors decreased, and the accuracy of its early warnings dropped to [a lower level]. .

[0034] Example 3: This example combines Figures 1 to 2 This document describes a method and system for risk identification and early warning in border inspection terminal operation areas. Figure 1As shown, step S101 acquires the heterogeneous data stream of the controlled target, which includes perception tag data, spatiotemporal trajectory data, and business execution stub data. Step S102 extracts the category identification identifier from the perception tag data and extracts kinematic parameters such as acceleration and turning curvature corresponding to the identifier from the spatiotemporal trajectory data. Then, in step S103, a kinematic constraint model is established, and the feature distribution parameters are calculated using the kinematic parameters. The feature distribution parameters are then logically compared with a preset kinematic logic threshold. When the feature distribution parameters exceed the logic threshold, step S104 is executed, which determines that it is a misidentification. The task instructions in the business execution stub data are used to trigger a logical correction operation and reset the identifier to generate a smooth data sequence. Then, step S105 is executed, which extracts the job spatiotemporal density parameter and performs topological reconstruction on the preset logical judgment region according to the parameter to align the topology with the real-time business distribution. Finally, in step S106, the displacement trend deviation value of the smooth data sequence in the logical judgment region after topological reconstruction is calculated. When it deviates from the preset behavior reference envelope, warning data indicating abnormal behavior is output.

[0035] like Figure 2 As shown, the data interaction of this system begins with the front-end sensing device and the production management database. The front-end sensing device inputs raw sensing signals to the heterogeneous data stream acquisition module, while the production management database simultaneously provides business stub data. The heterogeneous data stream acquisition module sends the data to the kinematic parameter extraction module and the feature distribution parameter and kinematic constraint model calculation module, respectively. During this process, the system uses the task instructions provided by the production management database to trigger logical correction operations. On the other hand, it drives the reconstruction logic judgment area module to run based on the operation density parameters provided by the production management database. At the same time, the external positioning reference station provides environmental displacement data to the system to support the dynamic coordinate transformation correction module in maintaining coordinate system alignment. Finally, the system generates decision data through the abnormal behavior early warning module. This data is sent to the monitoring center personnel on one side and triggers the associated situation inference module on the other side after the risk is confirmed.

[0036] Example 4: In a scenario involving operations beneath a quay crane at a large automated container berth, the processor acquires a heterogeneous data stream of the controlled target input from the edge gateway. This heterogeneous data stream includes a two-dimensional spatiotemporal coordinate sequence of the controlled target and a real-time task stream recorded in the business execution stub. At this time, the signal-to-noise ratio of the front-end sensors is affected by obstruction from the large metal spreader. The coordinate disturbance; the processor executes the following procedural procedure to determine the displacement trend deviation value. Extract the controlled target within the preset sampling window Displacement vector of each sampling point And retrieve the task centerline vector corresponding to the current job identifier from the historical risk feature model as the reference baseline vector. For each sampling point, the displacement components are algebraically subtracted, and the displacement trend deviation value is calculated using a formula. : ,in, This is the displacement trend deviation value, in units of ; The total number of samples; For the first The actual displacement vector of each sampling point; This serves as the reference vector.

[0037] The process of the processor constructing the behavioral baseline envelope follows statistical distribution laws. It retrieves the set of historical motion trajectories of the controlled target stored in the production management database and uses the maximum likelihood estimation algorithm to calculate the average displacement path and standard deviation of the controlled target within a specific work area. The behavioral baseline envelope is defined as a preset multiple of the standard deviation centered on the average displacement path. A continuous pipeline space with a radius; the processor performs radius correction based on the job spatiotemporal density parameters in the business execution stub data, when the job density exceeds 10,000 square meters. When processing one work unit, the processor will use the radius scaling factor. From initial value Downgraded to The specific scaling logic employs a linear mapping based on incremental step control: when the work density exceeds 10 units, for every 5 units increase in the work density parameter, the radius scaling factor is reduced by a fixed step of 0.3 from the current value, until the radius scaling factor reaches a preset minimum limit of 1.5. This tightens the physical boundary of the logic judgment area, improving the recognition accuracy of minute displacements; the behavior reference envelope radius scaling factor... The spatiotemporal density parameters of the operation are determined using a piecewise mapping function based on the operation intensity. less than the preset density threshold When, radius proportionality coefficient Take the first value; spatiotemporal density parameter of the operation Exceeding the preset density threshold At that time, calculate the spatiotemporal density parameters of the operation. The quotient of the increment and the first value determines the weight decay factor, which is then used to synchronously reduce the radius scaling factor. The value is set such that the behavioral baseline envelope tightens the physical warning boundary as the port's operational activity level increases.

[0038] The processor calibrates the anomaly probability threshold using an offline traversal procedure, with bandwidth not less than... On the computing platform, a set of test trajectories with known risk attributes is loaded and simulated recognition is performed by traversing... to Within the interval, candidate probability values ​​are calculated, and the false alarm rate and missed detection rate corresponding to each candidate value are recorded. This is specifically achieved through an accumulation comparison procedure: the processor calculates the difference between the actual displacement vector and the reference vector for each sampling point within the current sampling window, sums the absolute values ​​of all differences, and divides the sum by the total number of samples to obtain the arithmetic mean. This arithmetic mean is used as the displacement topology deviation. When the false alarm rate is within a certain range... The following and the false alarm rate is The processor determines the candidate probability value at this time as the anomaly probability threshold and writes it into the non-volatile memory. In the real-time identification process, the processor calculates the displacement topological deviation of the smoothed data sequence in the reconstructed logical judgment region and compares the deviation value with the anomaly probability threshold. When the deviation exceeds the anomaly probability threshold, a risk warning signal is triggered.

[0039] Example 5: In the newly deployed border inspection terminal environment, the processor executes a pre-calibration procedure based on physical dimension alignment, retrieving a total number of samples of no less than [number missing]. For a controlled truck motion sequence with a period of time, the root mean square (RMS) value of the acceleration of the motion sequence under the standard path is calculated, and this RMS value is defined as the physical reference component. By combining real-time acquired kinematic parameters with physical reference components Convert to feature vectors and perform spatial projection to calculate the kinematic logical threshold applicable to the physical site. The initial value is used, and the reference vector in the coordinate transformation logic is zeroed using the tidal displacement compensation value measured in the field. The quantitative alignment of the perception space and the physical dimension of the dock is completed before the real-time production business flow is connected.

[0040] When the system is in a state of heterogeneity in business execution stub data due to cross-regional operation instruction switching, the processor initiates the model data filling and weight synchronization procedure. In the algorithm logic for executing the reset operation, the physical attributes defined in the task instruction are used as hard constraint factors. If the category identification identifier of the perception tag data is consistent with the current task attribute, the judgment weight is set to 1.0; if the two are inconsistent and the feature distribution parameter exceeds the threshold, the correction judgment weight is increased to 1.0, the category identification identifier that has jumped is modified to the target attribute parameter defined in the business record, and the past data is extracted from the shore-based management system. The container handling instruction records within a calendar month are hash-mapped according to the identification identifiers of the controlled targets, and the coordinate time series is time-aligned. The information gain algorithm is used to identify the displacement distribution characteristics under each task path, and the calculated discrete distribution patterns are recorded in the index nodes of the historical risk feature model. A dynamic sampling step size is set for the reconstructed logical judgment region. In the operation density of 10,000 square meters The number of work units increased to During each work unit, the processor adjusts the sampling step size. Reduce feature distribution entropy The sliding window step count is adjusted to maintain logical consistency in the output of early warning data under increased data load conditions.

[0041] Example 6: In a border inspection terminal deployment scenario that includes elevated hoisting equipment and where the working plane is dynamically changing, the processor executes a physical mapping relationship calibration procedure based on homogeneous coordinate transformation, and sets the working area at the front of the terminal. For a physical marker with known geodetic coordinates, a front-end sensing device collects the pixel coordinate sequence of the corresponding physical marker and converts it into a digital matrix. The least squares fitting method is used to solve the mapping residual between the digital matrix and the geodetic coordinates, and the transformation matrix for performing coordinate field transformation is determined. The processor will acquire the controlled target pixel displacement vector and transformation matrix in real time. Perform algebraic operations to reconstruct the physical motion vector of the controlled target. And through the preset Trajectory closure error verification is performed under a meter-length baseline, limiting the geometric projection error to within... Within a meter, the linear transformation relationship between the sensing space and the physical operating space is established before the system is put into formal operation.

[0042] Under test conditions simulating continuous high-frequency semantic jitter, the processor executes a sliding window step optimization procedure based on a tradeoff in temporal resolution, within the feature distribution entropy. During the calculation process, the total number of samples within the sampling window is... Set as a variable and record it in Values ​​from Increment to During the process, the system filters out interference from category identification marker transitions and assesses the instruction delay time of warning signals. By comparing the distribution characteristics of the filtering rate and instruction delay time under different sampling step sizes, the processor identifies when... for The system is suppressing While controlling the signal-to-noise ratio disturbance above decibels, the response hysteresis is kept within [a certain value]. Within milliseconds, this value Recorded in the index node of the historical risk characteristic model, in conjunction with the spatiotemporal density parameters of the operation. Scaling adjustment is performed on the sampling step size to ensure that the risk warning logic maintains logical consistency in the output of warning data under increased business load.

[0043] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A method for risk identification and early warning in border inspection terminal operation areas, characterized in that, Includes the following steps: Step S101: Obtain the heterogeneous data stream of the controlled target. The heterogeneous data stream includes perception tag data, spatiotemporal trajectory data, and business execution stub data. Step S102: Extract category identification identifiers from the perception tag data and extract kinematic parameters corresponding to the category identification identifiers from the spatiotemporal trajectory data. The kinematic parameters include acceleration and steering curvature. Step S103: Establish a kinematic constraint model, calculate the feature distribution parameters of the controlled target using kinematic parameters, and logically compare the feature distribution parameters with the preset kinematic logic threshold. Step S104: When the feature distribution parameter exceeds the kinematic logic threshold, the category identification label is determined to be a misidentified label, and a logic correction operation is triggered. The category identification label is reset using the task instructions in the business execution stub data to generate a smooth data sequence. Step S105: Extract the job spatiotemporal density parameters from the business execution stub data, and perform topology reconstruction on the preset logical judgment area based on the job spatiotemporal density parameters so that the topology of the logical judgment area is aligned with the real-time business distribution. Step S106: Calculate the displacement trend deviation value of the smoothed data sequence within the logical judgment region after topological reconstruction. When the displacement trend deviation value deviates from the preset behavior benchmark envelope, output early warning data indicating that the controlled target has abnormal behavior.

2. The method for risk identification and early warning in border inspection terminal operation areas according to claim 1, characterized in that, Step S104 specifically includes: identifying the category identification markers that have changed in the perception tag data, and retrieving the business records that match the time-series coordinates of the controlled target in the business execution stub data; obtaining the target attribute parameters defined in the business records, using the target attribute parameters as logical benchmark values, and resetting the category identification markers that have changed in the perception tag data to reconstruct a smooth data sequence.

3. The method for risk identification and early warning in border inspection terminal operation areas according to claim 1, characterized in that, The kinematic logic threshold is preset based on the kinematic extremes of the controlled target. These extremes include acceleration and steering curvature extremes corresponding to kinematic parameters, where the unit of acceleration extremes is... The kinematic constraint model is used to limit the spatial displacement of the category identification marker within a unit of time to not exceed the preset kinematic displacement extreme value.

4. The method for risk identification and early warning in border inspection terminal operation areas according to claim 1, characterized in that, The steps in step S103 for calculating the feature distribution parameters include: Step S401, extracting the kinematic parameters within the sampling period and performing normalization; Step S402, statistically analyzing the distribution frequency of the normalized kinematic parameters within the time domain to obtain the probability distribution value; Step S403, calculating the feature distribution entropy of the controlled target using the following formula, and using the feature distribution entropy as the feature distribution parameter: ,in, The characteristic distribution entropy, in units of ; The first one obtained in step S402 The probability distribution values ​​of each sampling point; This represents the total number of samples taken within the preset time window.

5. The method for risk identification and early warning in border inspection terminal operation areas according to claim 1, characterized in that, Step S105 specifically includes: converting the spatiotemporal density parameters of the operation into a logic enable signal, using the logic enable signal to drive the processing module to adjust the calculation weight distribution of the logic judgment region; and removing invalid feature data in the non-operation state so that the sampling frequency is synchronized with the actual operation rhythm.

6. The method for risk identification and early warning in border inspection terminal operation areas according to claim 1, characterized in that, It also includes: establishing coordinate transformation logic based on reference vectors, and performing dynamic linear translation and rotation correction on the logic judgment area by calculating the deviation vector of preset static reference points in heterogeneous data streams in real time.

7. The method for risk identification and early warning in border inspection terminal operation areas according to claim 6, characterized in that, The coordinate transformation logic specifically includes: acquiring environmental displacement data provided by an external positioning reference station; the coordinate transformation logic is used to reconstruct the logical judgment area with the environmental displacement data in order to maintain the alignment of the sensing tag data with the coordinate system of the physical working space.

8. The method for risk identification and early warning in border inspection terminal operation areas according to claim 1, characterized in that, Step S106 specifically includes: performing algebraic operations on the temporal displacement data of the controlled target to determine the displacement topology deviation; when the displacement topology deviation exceeds a preset anomaly probability threshold, increasing the upload priority of the corresponding feature data in the perception tag data.

9. A risk identification and early warning method for border inspection terminal operation areas according to claim 1, characterized in that, After the early warning data is output, the correlation and inference logic of the central processing cluster is triggered to perform topological matching between the heterogeneous data stream and the historical risk feature model in order to identify whether the controlled target conforms to a specific risk evolution trend. The business execution stub data comes from the production management database, and the task attribute parameters in the business execution stub data are used as the judgment weight for the logical correction operation.

10. A risk identification and early warning system for border inspection terminal operation areas, used to execute the risk identification and early warning method for border inspection terminal operation areas as described in any one of claims 1-9, characterized in that, The system includes a data acquisition module, a feature extraction module, a logical comparison module, a logical correction module, a region reconstruction module, and a risk warning module. The data acquisition module is used to acquire heterogeneous data streams of the controlled target, including perception tag data, spatiotemporal trajectory data, and business execution stub data. The feature extraction module is used to extract category identification identifiers from the perception label data and extract kinematic parameters corresponding to the category identification identifiers from the spatiotemporal trajectory data. The logic comparison module is used to calculate the feature distribution parameters of the controlled target using kinematic parameters, and then logically compare the feature distribution parameters with the preset kinematic logic threshold. The logic correction module is used to reset the category identification label by using the task instructions in the business execution stub data when the feature distribution parameter exceeds the kinematic logic threshold, so as to generate a smooth data sequence. The region reconstruction module is used to reconstruct the topology of a preset logical judgment region based on the job spatiotemporal density parameters in the business execution stub data. The risk warning module is used to calculate the displacement trend deviation value of the smoothed data sequence within the logical judgment area after topological reconstruction, and output warning data indicating abnormal behavior of the controlled target.

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