Pattern recognition system for cargo statistics

Abstract

The application relates to the technical field of pattern classification, and discloses a pattern recognition system for cargo statistics, which comprises the following steps: collecting inherent mechanical vibration spectrum signals and initial weight signals through a mechanical vibration monitoring unit and a dynamic weighing unit; extracting a cargo semantic feature weight value tensor by using an image acquisition unit; controlling a manifold decoupling operator to switch to a high-dimensional positive flow space to transform the weight value tensor according to a noise reduction time sequence weight change rate derived from the initial weight signal by a feature decoupling unit; and finally combining a time sequence sliding window integral arbitration output counting instruction by a statistical arbitration unit. The application eliminates the semantic interweaving of the overlapped cargo and reconstructs the classification discrimination boundary, in-situ cancels the periodic mechanical resonance interference, avoids the random divergence of the weight tensor, and improves the global accuracy of the cargo sorting statistics.

Description

Technical Field

[0001] This invention belongs to the field of pattern classification technology, and in particular relates to a pattern recognition system for cargo statistics. Background Technology

[0002] Currently, in automated warehousing sorting and conveying logistics systems, image sensors are used to capture the apparent edge contours and surface texture features of continuous goods on the conveyor line. A high-dimensional feature mapping space is then constructed within the information processing components to determine the category attributes of objects and superimpose statistical quantities. This is a common technical solution used by current logistics hubs to maintain high throughput. However, as the concurrent density of goods on the conveyor line increases, the dynamic sorting environment of densely clustered goods generates large-area physical overlap and occlusion phenomena, causing confusion of local contour features in continuous image signals within the mapping space. At the same time, the alternating resonance impact energy from the operation of the conveyor mechanical drive components introduces complex dynamic interference into the symmetrical components. This causes the feature extraction layer of the controller to encounter topological decoupling obstacles in the feature distribution space when receiving heterogeneous data streams containing high-frequency overshoot spikes. This leads to nonlinear distortion of feature discrimination boundaries and reduces the accuracy of global goods classification. Conventional linear hardware capability expansion schemes are difficult to achieve deep feature space property stripping when facing disordered overlap of irregular goods, and static filtering mechanisms suffer from slow temporal response when facing high-speed transient impact interference.

[0003] Not only are there objective limitations to the hardware structure expansion scheme, but the control methods and algorithm architectures also have shortcomings. For example, Chinese invention patent application CN113393411A discloses a package counting method and device, server, and computer-readable storage medium. It obtains prior boxes through a two-dimensional image target detection algorithm and uses a convolutional neural network to output the predicted number of bounding boxes. The scheme relies heavily on 2D pixel domain geometric boundary information and implicitly presupposes that packages have distinguishable independent edges. However, under high-density disordered stacking and strong mechanical resonance conditions, the apparent edge interweaving caused by dense clustering and transient physical impacts lead to nonlinear topological distortion of the 2D feature space. This makes it impossible to decouple the filtering mechanism that relies solely on image domain boundary regression and probability elimination, resulting in continuous missed detections and failing to meet the industrial requirements for continuous and stable operation of the production line.

[0004] Therefore, how to construct a pattern recognition architecture that can adaptively adjust the dimension of the feature manifold space projection matrix according to the transient load time change rate, and integrate the mechanical vibration reference hedging noise reduction unit and the time series sliding window integral arbitration mechanism to maintain the boundary integrity of the classification feature space under strong resonance noise and high-density adhesion occlusion conditions, has become a key issue that needs to be solved in this field. Summary of the Invention

[0005] This invention aims to solve the problems of classification and discrimination boundary drift under conditions of strong mechanical noise and high-density adhesion and occlusion, as well as the lack of spatiotemporal continuity verification of single-frame mode classification results.

[0006] A pattern recognition system for cargo statistics, comprising: The mechanical vibration monitoring unit collects the inherent mechanical vibration spectrum signal of the conveyor frame when it is unloaded, and the dynamic weighing unit collects the initial weight signal of the goods when they pass through the weighing area. The image acquisition unit receives the two-dimensional pixel feature matrix of the cargo and extracts the semantic feature weight tensor of the cargo. The feature decoupling unit calculates the noise reduction time-series weights based on the inherent mechanical vibration spectrum signal and the initial weight signal. When the time change rate of the noise reduction time-series weights is greater than 4.2 kg / s, the projection matrix dimension of the manifold decoupling operator is increased to a 128-dimensional orthogonal manifold space, and the cargo semantic feature weight tensor is transformed into mutually orthogonal independent physical property feature tensors. The statistical arbitration unit constructs a sliding time window on the time axis, calculates the time series integral of the local pattern recognition probability values ​​of multiple frames of digital images within the sliding time window, and calculates the global comprehensive statistical confidence index in conjunction with the quantitative benchmark derived from the noise reduction time series weight. When the global comprehensive statistical confidence index crosses the preset discrimination limit and converges monotonically for multiple consecutive frames, it outputs a count increment instruction.

[0007] Preferably, in the feature decoupling unit, when calculating the time change rate, the following steps are introduced: calculate the discrete difference value between the noise reduction time sequence weight in the current control cycle and the adjacent preceding cycle, and divide the discrete difference value by the sampling time difference to determine the time change rate; when the time change rate is greater than 4.2 kg / s, the dimension of the projection matrix of the control manifold decoupling operator is increased to a 128-dimensional orthogonal alternating current space, and the dynamic window of the manifold decoupling operator is locked.

[0008] Preferably, in the feature decoupling unit, when transforming the semantic feature weight tensor of the goods, the overlapping mixed feature probability distribution is transformed into mutually orthogonal independent property feature tensors in the 128-dimensional orthogonal lattice space, thereby reducing feature interaction interference and reconstructing the target classification and discrimination boundary.

[0009] Preferably, in the statistical arbitration unit, the statistical arbitration unit calculates the time series integral of the local pattern recognition probability value of each frame of digital image output within the sliding time window on the time axis, and filters out the occasional noise introduced by instantaneous occlusion in single-frame pattern classification.

[0010] Preferably, in the statistical arbitration unit, when the global comprehensive statistical confidence index crosses the preset discrimination limit and converges monotonically for multiple consecutive frames, the statistical arbitration unit determines that the target count is incremented and outputs a count increment instruction.

[0011] Preferably, when the vibration amplitude of the inherent mechanical vibration spectrum signal exceeds the preset vibration threshold and cannot be filtered out, the statistical arbitration unit shuts down the continuous nonlinear mapping of the 128-dimensional positive AC shape space and retreats to the output of the count increment instruction based on the weight discrete step boundary and the image area pixel comparison judgment.

[0012] Preferably, the two-dimensional pixel feature matrix received by the image acquisition unit is captured online in real time by a high-speed image sensing component installed above the transport channel. The two-dimensional pixel feature matrix is ​​used to carry the pixel texture feature distribution pattern of high-density goods under overlapping and occlusion conditions.

[0013] Preferably, in the statistical arbitration unit, the quantity benchmark is calculated by dividing the noise reduction time series weight by the nominal average weight of a single item, and is used to provide quantity mapping input for the calculation of the global comprehensive statistical confidence index.

[0014] Preferably, in the statistical arbitration unit, the statistical arbitration unit adjusts the time span of the sliding time window within a preset range that adaptively matches the real-time speed feedback of the weighing area, based on the real-time speed feedback of the weighing area, so as to keep the total number of feature samples within the sliding time window synchronized with the dynamic time taken for the goods to pass through the weighing area.

[0015] Compared with existing technologies, the pattern recognition system for cargo statistics of this invention has the following advantages: 1. In pattern recognition for cargo statistics, the apparent features in the image stream are extracted by the feature extraction unit and semantic feature weight tensors are generated. Combined with the feature decoupling unit and the weight signal from the dynamic weighing unit, the projection matrix dimension of the manifold decoupling operator is adjusted according to the change in the weight over time. When dense physical occlusion causes the two-dimensional pixel edges to intertwine, the decoupling operator is driven to switch to a high-dimensional orthogonal manifold space. The orthogonal projection matrix is ​​used to strip away the common edge coupling features caused by adjacent objects, and the overlapping mixed probability distributions in the high-dimensional space are converted into mutually orthogonal independent physical property feature tensors. This not only eliminates the semantic intertwining in the image feature space, but also achieves stable reconstruction of the classification and discrimination boundary under high-density adhesion conditions.

[0016] 2. The mechanical vibration monitoring unit monitors the inherent mechanical vibration amplitude of the conveyor frame under no-load conditions in real time and outputs the reference vibration characteristic spectrum signal. The statistical arbitration unit simultaneously receives the initial weight signal containing periodic parasitic interference output by the dynamic weighing unit. The reference vibration characteristic spectrum signal and the mechanical damping transfer coefficient are used to perform differential noise reduction on the initial weight signal. In the digital domain, the periodic high-frequency mechanical vibration caused by motor operation and the dynamic overshoot load caused by cargo bouncing are canceled. This not only prevents high-frequency mechanical noise from penetrating into the weight matrix of the statistical arbitration unit, but also ensures the convergence of the classification and discrimination boundary judgment matrix in the subsequent heterogeneous feature fusion process.

[0017] 3. When the statistical arbitration unit fuses the effective weight signal after noise reduction with the independent feature tensor output by the feature decoupling unit, it constructs a sliding time window containing multiple consecutive video frame sampling periods on the time axis. By calculating the time series integral of the local pattern recognition probability value output by each frame image within the current time window, and in conjunction with the quantitative benchmark derived from the weight signal, it calculates the global comprehensive statistical confidence index. When the index crosses the preset discrimination limit and maintains monotonically convergent for multiple consecutive frames, it outputs a control command to increment the count by one. This not only eliminates occasional misjudgments caused by instantaneous occlusion in single-frame pattern classification, but also ensures that the counting decision of the entire system has a temporally and spatially continuous consistency verification guarantee. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the pattern recognition system for cargo statistics according to the present invention; Figure 2 This is a processing diagram of the pattern recognition system for cargo statistics 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] A pattern recognition system for cargo statistics, comprising: The mechanical vibration monitoring unit collects the inherent mechanical vibration spectrum signal of the conveyor frame when it is unloaded, and the dynamic weighing unit collects the initial weight signal of the goods when they pass through the weighing area. The image acquisition unit receives the two-dimensional pixel feature matrix of the cargo and extracts the semantic feature weight tensor of the cargo. The feature decoupling unit calculates the noise reduction time-series weights based on the inherent mechanical vibration spectrum signal and the initial weight signal. When the time change rate of the noise reduction time-series weights is greater than 4.2 kg / s, the projection matrix dimension of the manifold decoupling operator is increased to a 128-dimensional orthogonal manifold space, and the cargo semantic feature weight tensor is transformed into mutually orthogonal independent physical property feature tensors. The statistical arbitration unit constructs a sliding time window on the time axis, calculates the time series integral of the local pattern recognition probability values ​​of multiple frames of digital images within the sliding time window, and calculates the global comprehensive statistical confidence index in conjunction with the quantitative benchmark derived from the noise reduction time series weight. When the global comprehensive statistical confidence index crosses the preset discrimination limit and converges monotonically for multiple consecutive frames, it outputs a count increment instruction.

[0021] Preferably, in the feature decoupling unit, when calculating the time change rate, the following steps are introduced: calculate the discrete difference value between the noise reduction time sequence weight in the current control cycle and the adjacent preceding cycle, and divide the discrete difference value by the sampling time difference to determine the time change rate; when the time change rate is greater than 4.2 kg / s, the dimension of the projection matrix of the control manifold decoupling operator is increased to a 128-dimensional orthogonal alternating current space, and the dynamic window of the manifold decoupling operator is locked.

[0022] Preferably, in the feature decoupling unit, when transforming the semantic feature weight tensor of the goods, the overlapping mixed feature probability distribution is transformed into mutually orthogonal independent property feature tensors in the 128-dimensional orthogonal lattice space, thereby reducing feature interaction interference and reconstructing the target classification and discrimination boundary.

[0023] Preferably, in the statistical arbitration unit, the statistical arbitration unit calculates the time series integral of the local pattern recognition probability value of each frame of digital image output within the sliding time window on the time axis, and filters out the occasional noise introduced by instantaneous occlusion in single-frame pattern classification.

[0024] Preferably, in the statistical arbitration unit, when the global comprehensive statistical confidence index crosses the preset discrimination limit and converges monotonically for multiple consecutive frames, the statistical arbitration unit determines that the target count is incremented and outputs a count increment instruction.

[0025] Preferably, when the vibration amplitude of the inherent mechanical vibration spectrum signal exceeds the preset vibration threshold and cannot be filtered out, the statistical arbitration unit shuts down the continuous nonlinear mapping of the 128-dimensional positive AC shape space and retreats to the output of the count increment instruction based on the weight discrete step boundary and the image area pixel comparison judgment.

[0026] Preferably, the two-dimensional pixel feature matrix received by the image acquisition unit is captured online in real time by a high-speed image sensing component installed above the transport channel. The two-dimensional pixel feature matrix is ​​used to carry the pixel texture feature distribution pattern of high-density goods under overlapping and occlusion conditions.

[0027] Preferably, in the statistical arbitration unit, the quantity benchmark is calculated by dividing the noise reduction time series weight by the nominal average weight of a single item, and is used to provide quantity mapping input for the calculation of the global comprehensive statistical confidence index.

[0028] Preferably, in the statistical arbitration unit, the statistical arbitration unit adjusts the time span of the sliding time window within a preset range that adaptively matches the real-time speed feedback of the weighing area, based on the real-time speed feedback of the weighing area, so as to keep the total number of feature samples within the sliding time window synchronized with the dynamic time taken for the goods to pass through the weighing area.

[0029] Example 1: A pattern recognition system for cargo statistics is deployed on an automated sorting and conveyor line. The system includes an image acquisition unit, a dynamic weighing unit, a mechanical vibration monitoring unit, a feature extraction unit, a feature decoupling unit, and a statistical arbitration unit. The image acquisition unit is mounted directly above the weighing sampling area of ​​the conveyor line to acquire digital image stream signals of the cargo to be counted flowing through the weighing sampling area. The dynamic weighing unit is embedded below the weighing sampling area of ​​the conveyor line to collect the transient total weight of the cargo as it passes through the area in real time and output a dynamic weight scalar signal. The mechanical vibration monitoring unit is fixed on the non-weighing steel frame of the conveyor line to monitor the amplitude of the inherent mechanical vibration of the unloaded triaxial structure generated by the operation of the conveyor line in real time and output a reference vibration feature spectrum signal. The feature extraction unit is connected to the image acquisition unit, receives the digital image stream signal, performs matrix dot product operations through the edge contrast local texture extraction matrix to extract the local appearance features of the cargo, and outputs a semantic feature weight tensor with a dimension of 1×512. This weight tensor is stored in the random access memory of the feature extraction unit to quantitatively characterize the overlapping probability manifold state of different cargo categories in the image space.

[0030] The mechanical vibration monitoring unit continuously samples the physical vibration components of the symmetrical resampling area to capture high-frequency periodic mechanical noise. The discretized expression is a function of the noise vibration component changing with time. The statistical arbitration unit receives the initial weight signal containing high-frequency glitches and performs a differential noise reduction operator to filter out the physical vibration components from the initial weight signal. The effective transient weight after noise reduction is calculated. The calculation formula is that the effective transient weight equals the initial weight signal minus the product of the mechanical damping transfer coefficient and the noise vibration component. The mechanical damping transfer coefficient is set to 0.85. The dimension conversion formula is constructed based on Newton's second law of motion to convert the dynamic vibration acceleration of the elastic support frame into an equivalent disturbance mass. To ensure consistency of physical dimensions, the noise vibration component is converted into an equivalent weight value with mass dimensions before participating in the differential calculation. The calculation formula for the noise vibration component is as follows: , where variables For noise vibration components, parameters This is the physical dimension conversion factor, with a working range of 0.05 to 0.25, and the variable... The vertical axial vibration acceleration amplitude acquired by the triaxial accelerometer, within the operating range, parameters When the value is selected as 0.12, the variance of the weight signal after offsetting reaches a minimum value, thus determining the optimal value as 0.12. The acceleration signal is converted into a weight scalar using this conversion formula, eliminating dimensional differences. The statistical arbitration unit subtracts the product of the mechanical damping transfer coefficient and the equivalent weight value from the initial weight signal, achieving differential noise reduction of the same-dimensional signal. The feature decoupling unit receives the effective transient weight in real time and calculates the time change rate of the effective transient weight through discrete difference. This time change rate is equal to the effective transient weight of the current control cycle minus the effective transient weight of the adjacent preceding cycle. The difference is divided by the sampling time difference. When the time change rate exceeds 4.2 kg / s, the feature decoupling unit switches the projection matrix dimension of the control manifold decoupling operator from a low-dimensional space to 128. In a 128-dimensional orthogonal manifold space, the feature decoupling unit imports semantic feature weight tensors. Within this space, the manifold decoupling operator is invoked to perform orthogonal basis projection transformation on the semantic feature weight tensors. This process removes shared edge coupling features caused by the interweaving and occlusion of adjacent goods, decoupling and mapping the intertwined mixed probability distributions into a set of mutually orthogonal independent property feature tensors. The orthogonal basis projection transformation is constructed based on the orthogonal projection theory of linear algebraic matrices to eliminate linear correlations between multivariate high-dimensional feature vectors. The feature decoupling unit includes a feature processor and random access memory. The data flow processing path for converting the semantic feature weight tensors is as follows: a 1×512 one-dimensional floating-point array is read from the random access memory, which stores semantic features... The original values ​​of the weighting tensor are obtained; a pre-stored orthogonal basis projection matrix with a dimension of 512×128 is retrieved from read-only memory. This matrix is ​​obtained by processing the unloaded background physical property characteristics using the Schmidt orthogonalization method; the dot product of the one-dimensional floating-point array and the orthogonal basis projection matrix is ​​calculated to obtain an independent physical property feature array with a dimension of 1×128; this array is written as an independent physical property feature tensor into random access memory and provided to the statistical arbitration unit. It should be noted that in this embodiment, the core judgment parameters such as the time change rate threshold of 4.2 kg / s, the mechanical damping transmission coefficient of 0.85, the physical dimension conversion coefficient of 0.12, and the critical value of the global comprehensive statistical confidence index of 0.78 are all verified through extreme value boundary tests and error variance convergence tests in industrial settings. The calibration results show that the 4.2 kg / s time change rate threshold corresponds to the lower limit of the physical mass flow increment limit when two high-density goods are stuck together and cut into the weighing sampling area at a nominal flow rate of 1.5 m / s. If the threshold is lowered to 2.0 kg / s, it will cause false dimensional switching of ordinary single goods. If it is increased to 6.0 kg / s, the system will not be able to effectively drive dimensional switching for stuck two goods. The mechanical damping transmission coefficient of 0.85 is determined by the material attenuation constant of the elastic weighing frame. Within the experimental range of 0.80 to 0.90, selecting 0.85 can make the variance of the weight signal after offset and noise reduction reach the global minimum. The confidence index critical value of 0.78 is the optimal boundary value for classification decision to balance the false alarm of instantaneous occlusion in a single frame and the missed detection in multiple consecutive frames.

[0031] The statistical arbitration unit receives the independent physical property feature tensors produced by decoupling, synchronously imports the effective transient weight step increment value, and logically constructs a temporal sliding window containing five consecutive video frame sampling periods. Within the sliding window, a temporal integral is performed on the semantic confidence of each independent physical property feature tensor. Combined with the expected cargo quantity benchmark derived from the effective transient weight increment, a global comprehensive statistical confidence index is calculated. The global comprehensive statistical confidence index is equal to the sum of the time series integrals of the image weight coefficient and the local pattern recognition probability values ​​of each frame of digital image within the sliding window, plus the product of the weight weight coefficient and the function value of the effective transient weight step increment value. The image weight coefficient is set to 0.60, the weight weight coefficient is set to 0.40, and the function value of the denoised transient weight step increment value is constructed based on the scaling principle to normalize the physical weight increment into a dimensionless probability confidence. The function value calculation formula is as follows: , where variables The value is the function value, ranging from 0 to 3.0. (Variable) For the step increment of the transient weight during noise reduction, the parameters are... The nominal average weight of a single item depends on the specifications and category of the goods being statistically analyzed. The processor within the statistical arbitration unit reads the step increment value and the nominal average weight from internal memory, calculates their ratio to obtain the function value. To eliminate the influence of the cumulative dimension in the time domain caused by time series integration, the sum of the time series integrals of the local pattern recognition probability values ​​of multiple frames of digital images within the time-series sliding window, before multiplying by the image weight coefficient, needs to be divided by the total number of video frame feature samples within the current sliding time window. This performs time-dimensional normalization and smoothing, transforming the time-series integral value into a dimensionless average probability confidence level between 0 and 1.0, thus achieving its proportionality with the dimensionless value. Complete equivalence and unification in mathematical and physical dimensions ensure that the global comprehensive statistical confidence index satisfies the law of dimensional conservation during weighted superposition. When the global comprehensive statistical confidence index crosses 0.78 and maintains monotonically convergent for three consecutive frames, the statistical arbitration unit issues a global control command to increment the cargo statistical count by one. When the mechanical vibration amplitude exceeds the preset vibration threshold and cannot be eliminated by the differential operator, the statistical arbitration unit stops the continuous nonlinear mapping in the 128-dimensional orthogonal AC space and switches to a logic state machine judgment based on the weight discrete step threshold and the comparison of image area pixels. The system ensures basic statistical functions under high-frequency disturbance conditions, exchanging decoupled precision local adjustment for industrial-grade operational stability. Specifically, the implementation path for image area-to-pixel comparison is as follows: The image acquisition unit removes the background using a global fixed threshold segmentation algorithm, extracts the total number of dynamic pixels in the foreground area of ​​the goods in the current video frame, and divides it by the standard nominal pixel area of ​​a single item to calculate the dimensionless area-to-pixel ratio. The logic state machine synchronously monitors this area-to-pixel ratio and the weight step multiple obtained by dividing the transient weight increment by the nominal single item weight. When both values ​​are greater than or equal to the preset discrete step integer threshold of 1.0, the state machine determines that the current quantity of goods meets the increment condition and directly triggers a count increment instruction to the output, thereby maintaining the basic counting function under strong interference conditions.

[0032] Example 2: A pattern recognition system for cargo statistics is used. The following experimental design is employed for performance verification and stability evaluation. By comparing the performance trends of the control and test groups, the counting accuracy of the feature decoupling unit and the effectiveness of the boundary constraint mechanism in complex logistics environments are verified. The test platform is constructed using a 1.0m wide automated conveyor line. The weighing sampling area is equipped with a dynamic weighing unit composed of piezoelectric sensors. The sampling frequency is set to 1000Hz, the weighing range is 0.1kg to 50.0kg, and the static measurement accuracy is 0.01kg. The image acquisition unit uses a global shutter industrial camera to synchronously capture video streams directly above the conveyor line at a rate of 60 frames per second. Mechanical vibration monitoring is also included. The measurement unit uses a triaxial accelerometer with a measurement accuracy of 0.001g to collect real-time mechanical vibration data of the conveyor frame under different operating conditions. The test process includes a test sample group, control group A, and control group B. In the test sample group, the feature decoupling unit dynamically performs a 128-dimensional orthogonal AC spatial transformation based on the real-time received noise reduction time sequence weights, and adjusts the decoupling operator weights in real time according to the cargo adhesion state. Control group A disables the mechanical vibration monitoring unit and directly performs threshold judgment on the initial weight signal output by the dynamic weighing unit. Control group B sets the time change rate thresholds involved in the feature decoupling logic to 2.0kg / s and 6.0kg / s, respectively, to verify the engineering boundary of the defined 4.2kg / s value range.

[0033] In a single test cycle, the conveyor line transports goods at a speed of 1.5 m / s, including three forms: single piece, double pieces stuck together, and three pieces stacked at high density. The data acquisition process is as follows: First, the original vibration waveform and zero-point drift data of the weighing sensor under no-load conditions are collected to establish the background noise benchmark of the system operation; the goods to be tested are randomly inserted into the sampling area, and the initial weight signal output by the dynamic weighing unit is superimposed with the periodic high-frequency resonance noise caused by the conveyor motor; the statistical arbitration unit calls the differential noise reduction operator to perform cyclic smoothing filtering with a time step of 0.02 s to convert the initial weight signal into an effective transient weight, where the mechanical damping transmission coefficient is set to 0.85.

[0034] Experimental data show that, under the working conditions of the present invention's sample group, when the effective transient weight change rate reaches 4.5 kg / s due to cargo adhesion, the manifold decoupling operator smoothly switches to 128-dimensional space, the semantic confidence of the independent physical property feature tensor increases from 0.45 to 0.82, and the cargo counting accuracy reaches 99.6%. In control group A, due to the lack of counterbalancing of mechanical noise, the residual parasitic vibration component in the original weight signal causes a 0.5 kg / s pseudo-increment in the time change rate determination, leading to false triggering of the manifold decoupling operator and a drop in cargo counting accuracy to 88.3%. In control group B, when the time change rate threshold is set to 2.0 kg / s, the system performs better on ordinary single-piece cargo. The system experienced false triggering due to allergic reactions, causing frequent fluctuations in the classification boundary. When the threshold was set at 6.0 kg / s, the system could not effectively drive the dimensional transformation for tightly adhered pairs of goods, and the semantic confidence of the independent physical property feature tensor could only be maintained at 0.55, resulting in missed detections and a drop in accuracy to 79.1%. The above data indicates that 4.2 kg / s as the time change rate threshold is within the effective working window to ensure stable reconstruction of the classification boundary under high-speed adhesion conditions. Deviating from this threshold will lead to a rapid deterioration in the statistical performance of the system. This verifies the synergistic effect and boundary constraint effectiveness of the mechanical vibration hedging mechanism and dynamic manifold decoupling logic in the system architecture for the identification of high-density adhered goods.

[0035] Example 3: This example combines Figures 1 to 2 A description of a pattern recognition system used for cargo statistics, such as... Figure 1As shown, the system includes a dynamic weighing unit, a mechanical vibration monitoring unit, an image acquisition unit, a feature decoupling unit, and a statistical arbitration unit. The dynamic weighing unit acquires an initial weight signal and transmits it to the feature decoupling unit. The mechanical vibration monitoring unit acquires an inherent mechanical vibration spectrum signal and transmits it to the feature decoupling unit. The image acquisition unit extracts a semantic feature weight tensor of the cargo and inputs it to the feature decoupling unit. The feature decoupling unit receives the signal and transforms it into an independent physical property feature tensor, which is then transmitted to the statistical arbitration unit. Finally, the statistical arbitration unit outputs a count increment command. Figure 2 As shown, the overall workflow begins with receiving the two-dimensional pixel feature matrix of the cargo, then proceeds to extract the semantic feature weight tensor of the cargo. If the time change rate of the noise reduction temporal weight is greater than 4.2 kg / s, the transformation into mutually orthogonal independent physical property feature tensors is performed. It should be noted that during the extraction of the semantic feature weight tensor of the cargo or the transformation into mutually orthogonal independent physical property feature tensors, if the vibration amplitude exceeds the preset vibration threshold and cannot be filtered out, it will trigger and jump to turn off continuous nonlinear mapping. After the transformation into mutually orthogonal independent physical property feature tensors is completed normally, if it further meets the condition of crossing the preset discrimination boundary and monotonically converging for multiple consecutive frames, it will point to the output count increment instruction. In the case of turning off continuous nonlinear mapping, the judgment is based on the comparison between the discrete step boundary of weight and the pixel area of ​​the image, and the same flow will be to output the count increment instruction. At this point, all logical judgments are completed.

[0036] Example 4: A pattern recognition system for cargo statistics, which embeds negative event recording injection and abnormal pattern self-healing procedures, is deployed on an automated sorting and conveyor line. The system includes a visual semantic analysis unit, a dynamic weighing execution unit, an environmental vibration compensation unit, a feature dimensionality reduction projection unit, an implicit negative event recording library, and a statistical decision arbitration unit. The visual semantic analysis unit is used to collect image frame streams of the cargo to be counted, extract apparent features through local texture gradient operators, and output semantic feature weight tensors. The dynamic weighing execution unit collects the transient weight signals of the cargo in real time and stores the raw data in a high-speed buffer at a sampling frequency of 1000Hz. The environmental vibration compensation unit monitors the physical vibration of the conveyor line and generates compensation feature tensors to offset parasitic interference in real time, so as to ensure that the weight data entering the statistical arbitration unit has a constant signal-to-noise ratio.

[0037] The implicit negative event log is maintained by the statistical decision arbitration unit, storing various non-standard logistics status information that occurs during system operation. This includes zero-weight idle records when goods pass through the weighing area once, records of overlapping goods not fully triggering photoelectric sensors, and static load records caused by conveyor belt mechanical jamming. When executing statistical decisions for goods, the statistical decision arbitration unit automatically constructs a bias compensation operator for the current physical feature sequence based on the historical records in the implicit negative event log. When the weight step signal detected by the dynamic weighing execution unit exhibits characteristics that deviate from the known single-trigger patterns in the implicit negative event log, the statistical decision arbitration unit retrieves the corresponding bias compensation operator and applies it to the semantic features output by the image acquisition unit. The feature weight tensor undergoes inverse phase reshaping to offset semantic distribution distortion caused by the injection of negative event records, ensuring the integrity of the pattern classification and discrimination boundary. In practice, inverse phase reshaping is manifested at the algorithmic level as performing element-level direction correction and linear bias offset on the semantic feature weight tensor in a multi-dimensional vector space. Specifically, the statistical decision arbitration unit uses the bias compensation operator as a correction matrix and performs matrix subtraction or spatial rotation transformation with the semantic feature weight tensor in the form of a high-dimensional static real vector. By adjusting the numerical distribution weights of each feature channel in the tensor, the probabilistic geometric center offset caused by non-standard logistics conditions is inversely offset in the mathematical feature space, thereby completing the vector mapping reshaping at the pure data stream level without physical wave packet attributes.

[0038] When handling situations involving multiple adhered goods, the feature dimensionality reduction projection unit receives the semantic feature weight tensor and the bias compensation operator, and completes the reconstruction mapping of the feature space within a 128-dimensional orthogonal coordinate space. The statistical decision arbitration unit calculates a multi-dimensional feature fusion confidence index based on the reconstructed physical property feature tensor and the effective transient weight signal. This index is a linear weighted sum of the semantic confidence integral value and the weight change fluctuation amplitude, with weight coefficients set to 0.65 and 0.35. When the global statistical confidence index value crosses 0.82 and remains stable for two consecutive frames, the system issues a command to increment the goods count. If the current input data and the implicit negative... The similarity of historical high-frequency negative event patterns in the event log library spans 0.85. The statistical decision arbitration unit triggers self-healing logic, performs zero-value filtering on the feature tensor generated at that moment, and reports a status log containing timestamps, original sensor data fingerprints, and feature bias offsets to the management terminal. It records the data stream characteristics after desensitization. Through continuous injection and reverse phase reshaping of implicit negative event records, the system reduces cargo statistical deviation under high-density occlusion conditions without introducing additional sensor hardware, and achieves a closed-loop transformation from original discrete signals to stable statistical instructions through software-level status recording and adaptive compensation.

[0039] Example 5: A pattern recognition system for cargo statistics. Addressing transient load fluctuations and sensor aging drift in sorting environments, the system integrates an offline parameter calibration procedure and a sliding window adaptive compensation model within the statistical decision arbitration unit. This ensures the long-term stability of cargo statistics criteria. The offline parameter calibration procedure involves the mechanical vibration monitoring unit collecting raw vibration data from the conveyor line running unloaded for one hour before system deployment. The standard deviation distribution of this dataset across three axes is then statistically calculated and used as the baseline feature spectrum for inherent mechanical disturbances at each workstation. This spectrum is written into the read-only storage area of ​​the statistical decision arbitration unit. During the real-time operation of cargo passing through the weighing sampling area, the statistical decision arbitration unit synchronously subtracts the transient weight data output by the dynamic weighing execution unit from the corresponding baseline feature spectrum. This counteracts parasitic signals generated by thermal drift of physical components over time, ensuring that the weight data sequence input to the statistical arbitration logic contains only the physical mass component of the cargo.

[0040] To address image feature drift caused by differences in the reflectivity of cargo surface materials, the statistical decision arbitration unit establishes a time-series-based statistical calibration model. This model employs a sliding time window technique to monitor the mean distribution of semantic feature weight tensors output by the visual semantic analysis unit in real time over continuous sampling periods. When the drift rate of the mean distribution exceeds a preset threshold, the system determines that the shift is caused by sensor aging or slow changes in the lighting environment. The statistical decision arbitration unit automatically generates a reverse shift correction vector based on the direction of the mean distribution's movement, mapping the current semantic feature weight tensor to the initial calibration reference space for normalization. This normalization process... The processing includes the following steps: selecting the statistical mean of the set of semantic feature weight tensors within the most recent 100 sampling periods; comparing this statistical mean with the initial baseline distribution mean during system factory calibration and calculating the difference; using this difference as an offset correction vector and superimposing it onto the real-time generated semantic feature weight tensor to complete the drift compensation of the feature distribution. Through the linkage between the baseline calibration data and the sliding window statistical calibration model, the system constructs a self-healing framework for the dynamic evolution of cargo statistical criteria with the environment, avoiding the failure of classification and discrimination boundaries caused by long-term operation, and ensuring that the system maintains the stability of cargo counting in complex industrial sites for a long time.

[0041] 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 pattern recognition system for cargo statistics, characterized in that, include: The mechanical vibration monitoring unit collects the inherent mechanical vibration spectrum signal of the conveyor frame when it is unloaded, and the dynamic weighing unit collects the initial weight signal of the goods when they pass through the weighing area. The image acquisition unit receives the two-dimensional pixel feature matrix of the cargo and extracts the semantic feature weight tensor of the cargo. The feature decoupling unit calculates the noise reduction time-series weights based on the inherent mechanical vibration spectrum signal and the initial weight signal. When the time change rate of the noise reduction time-series weights is greater than 4.2 kg / s, the projection matrix dimension of the manifold decoupling operator is increased to a 128-dimensional orthogonal manifold space, and the cargo semantic feature weight tensor is transformed into mutually orthogonal independent physical property feature tensors. The statistical arbitration unit constructs a sliding time window on the time axis, calculates the time series integral of the local pattern recognition probability values ​​of multiple frames of digital images within the sliding time window, and calculates the global comprehensive statistical confidence index in conjunction with the quantitative benchmark derived from the noise reduction time series weight. When the global comprehensive statistical confidence index crosses the preset discrimination limit and converges monotonically for multiple consecutive frames, it outputs a count increment instruction.

2. The pattern recognition system for cargo statistics according to claim 1, characterized in that, In the characteristic decoupling unit, the following steps are introduced when calculating the rate of change over time: The discrete difference between the noise reduction time-series weights in the current control cycle and the adjacent preceding cycle is calculated, and the discrete difference is divided by the sampling time difference to determine the time change rate. When the time change rate is greater than 4.2 kg / s, the dimension of the projection matrix of the control manifold decoupling operator is increased to a 128-dimensional orthogonal AC shape space, and the dynamic window of the manifold decoupling operator is locked.

3. A pattern recognition system for cargo statistics according to claim 1, characterized in that, In the feature decoupling unit, when transforming the semantic feature weight tensor of the goods, the overlapping mixed feature probability distribution is transformed into mutually orthogonal independent property feature tensors in the 128-dimensional orthogonal space, reducing feature interaction interference and reconstructing the target classification and discrimination boundary.

4. A pattern recognition system for cargo statistics according to claim 1, characterized in that, In the statistical arbitration unit, the statistical arbitration unit calculates the time series integral of the local pattern recognition probability value of each frame of digital image output within the sliding time window on the time axis, and filters out the occasional noise introduced by instantaneous occlusion in single-frame pattern classification.

5. A pattern recognition system for cargo statistics according to claim 1, characterized in that, In the statistical arbitration unit, when the global comprehensive statistical confidence index crosses the preset discrimination limit and converges monotonically for multiple consecutive frames, the statistical arbitration unit determines that the target count is incremented and outputs a count increment instruction.

6. A pattern recognition system for cargo statistics according to claim 1, characterized in that, When the vibration amplitude of the inherent mechanical vibration spectrum signal exceeds the preset vibration threshold and cannot be filtered out, the statistical arbitration unit shuts down the continuous nonlinear mapping of the 128-dimensional positive AC shape space and retreats to the output of the count increment instruction based on the weight discrete step boundary and the image area pixel comparison judgment.

7. A pattern recognition system for cargo statistics according to claim 1, characterized in that, The two-dimensional pixel feature matrix received by the image acquisition unit is captured online in real time by a high-speed image sensing component installed above the transportation channel. The two-dimensional pixel feature matrix is ​​used to carry the pixel texture feature distribution pattern of high-density goods under overlapping and occlusion conditions.

8. A pattern recognition system for cargo statistics according to claim 1, characterized in that, In the statistical arbitration unit, the quantity benchmark is calculated by dividing the noise reduction time series weight by the nominal average weight of a single item, and is used to provide quantity mapping input for the calculation of the global comprehensive statistical confidence index.

9. A pattern recognition system for cargo statistics according to claim 1, characterized in that, In the statistical arbitration unit, the statistical arbitration unit adjusts the time span of the sliding time window within a preset range that adaptively matches the real-time speed feedback of the weighing area, based on the real-time speed feedback, so as to keep the total number of feature samples within the sliding time window synchronized with the dynamic time taken for the goods to pass through the weighing area.

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