Intelligent logistics distribution data management platform
By optimizing the logistics distribution data management platform through dual-channel feature encoding, hybrid memory network and spatiotemporal attention mechanism, the prediction bias problem of the hidden Markov model in scenarios driven by multiple factors is solved, and accurate matching of logistics needs and efficient allocation of resources are achieved.
Patent Information
- Application Number
- CN202510836319.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-21
- Publication Date
- 2025-10-03
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing hidden Markov model has a systematic deviation between the predicted value and the true value due to the fixed transition probability matrix in logistics demand forecasting. It is difficult to accurately match logistics demand, especially in non-stationary scenarios driven by multiple factors.
A dual-channel feature encoding network combined with contrast reinforcement learning is used for data governance, a hybrid memory network is used to model the evolution of demand, long-term and short-term memory are integrated with external event embedding through temporal self-attention, and resource matching is performed in combination with the spatiotemporal attention mechanism. The demand state transition probability matrix and order priority score are dynamically generated, and resource decisions are optimized through nonlinear cost proofreading.
It improves the accuracy of logistics demand forecasting and the efficiency of resource matching, breaks through the limitations of traditional hidden Markov models, and achieves more accurate order priority scoring and low-cost and efficient resource allocation.
Smart Images
Figure CN120746414A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of logistics distribution data management, and in particular to a smart logistics distribution data management platform. Background Art
[0002] Against the backdrop of the deep integration of the digital and real economies, building a smart logistics and distribution data management platform has become a key tool for addressing pain points in the traditional logistics industry and achieving cost reduction and efficiency gains across the entire supply chain. With e-commerce penetration exceeding 30%, the logistics industry faces a fundamental conflict between exponential order growth and limited distribution resources. By building a three-tiered architecture encompassing "data governance, demand forecasting, and intelligent matching," the Smart Logistics and Distribution Data Management Platform has achieved revolutionary breakthroughs in three dimensions. By empowering the industry with AI, it is driving the transformation of the logistics industry from a labor-intensive to a technology-intensive one. This platform will help manufacturers reduce inventory turnover days, increase user repurchase rates for e-commerce platforms, and alleviate traffic congestion in urban areas. Ultimately, it will help bring the proportion of total logistics costs to GDP closer to the 8% level seen in developed countries, creating significant economic and social value.
[0003] In the existing technology, HMM naturally adapts to the temporal characteristics of logistics demand through the mapping relationship between hidden states and observation sequences. However, in non-stationary scenarios where logistics demand is driven by multiple factors, the fixed transition probability matrix of the hidden Markov model (HMM) can lead to systematic deviations between the predicted values and the true values. Therefore, an intelligent logistics distribution data management platform is proposed. Summary of the Invention
[0004] The purpose of this invention is to solve the shortcomings of the existing technology and propose an intelligent logistics distribution data management platform.
[0005] In order to achieve the above object, the present invention adopts the following technical solutions:
[0006] A smart logistics distribution data management platform, including:
[0007] Data governance layer: Responsible for multimodal data fusion, feature decoupling and enhancement, and constructing feature vectors using a dual-channel feature encoding network combined with contrast enhancement learning. The dual-channel feature encoding network includes static feature encoding and dynamic feature encoding.
[0008] Dynamic demand forecasting layer: Based on a hybrid memory network, it models demand evolution patterns. Based on a hybrid memory state transition network, it uses short-term memory units to model short-term Markov state transitions. It fuses long-term historical memory with external event embedding through temporal self-attention, and uses SHAP values to quantify event impacts for gated fusion. It then dynamically generates a demand state transition probability matrix and order priority scores.
[0009] Intelligent resource matching layer: This layer achieves joint cost-timeliness optimization through the spatiotemporal attention mechanism. By combining the multi-head spatiotemporal attention mechanism with time-sensitive gating and a dynamic cost correction formula, it ultimately outputs a distribution resource decision that has both timeliness and cost advantages.
[0010] The above technical solution further includes:
[0011] Furthermore, the data governance layer uses a dual-channel feature encoding network to map the original order and resource data into structured feature vectors, decoupling static / dynamic attributes and enhancing temporal correlation, including the following steps:
[0012] Static feature encoding: Use the fully connected layer to perform full connection mapping and nonlinear activation on the structured static data to generate a low-dimensional static feature vector to decouple the core attributes, expressed as f static =ReLU(W1x static +b1),W1∈R 128×1024 ,b1∈R 128 ;
[0013] Dynamic feature encoding: Transformer is used to capture temporal dynamic data, and a multi-head self-attention mechanism is used to model temporal dependencies to generate dynamic feature vectors that include context awareness.
[0014] Resource feature encoding: CNN is used to extract the spatial grid features of the delivery resource route data, and combined with the resource type embedding vector to generate a resource feature vector. Subsequently, static and dynamic features are fused through layer normalization to form a unified order representation.
[0015] Furthermore, the data governance layer constructs positive and negative sample pairs on this dual-channel feature encoding network, applies a contrastive loss function to increase the feature distance between matched order-resource pairs and randomly mismatched pairs, forcing the model to learn more discriminative feature representations, and ultimately outputs standardized order and resource feature vectors to the dynamic demand forecasting layer, including the following steps:
[0016] Positive and negative sample construction: Positive sample pairs: matching order-resource pairs; Negative sample pairs: random unmatched pairs;
[0017] Contrast loss calculation: The loss function increases the feature distance between positive and negative samples. The calculation formula is expressed as in, is the cosine similarity, τ = 0.1 is the temperature coefficient, and N is the set of negative samples;
[0018] Feature output: Output standardized order and resource feature vectors to the dynamic demand forecasting layer.
[0019] Furthermore, the dynamic demand forecasting layer uses short-term memory units to model the specific steps of short-term Markov state transition:
[0020] Input feature standardization and state initialization: Order features are standardized to eliminate dimensional differences;
[0021] Update gate calculation: Use update gate to calculate weight matrix and bias term through Sigmoid function to determine the proportion of historical state retention, which is expressed as Among them, it is expressed as is the weight matrix, b z is the bias term, σ(·) is the Sigmoid function;
[0022] Reset gate calculation: Reset gate is based on the Sigmoid function to filter irrelevant historical information to control the information flow, which is expressed as r t Control the fusion ratio of historical state and current input;
[0023] Candidate hidden state generation: Combine the reset history state with the current input to generate candidate hidden states, and compress the value range through the tanh activation function;
[0024] Hidden state update: Generate the current hidden state by updating the gated fusion of the new and old states.
[0025] Furthermore, the dynamic demand forecasting layer captures cross-cycle dependencies and external event impacts by fusing long-term historical memory with external event embedding through temporal self-attention, quantifies event impacts with SHAP values to achieve gated fusion, and dynamically generates a demand state transition probability matrix and order priority scores, including the following steps:
[0026] Event Enhanced Coding: Through event enhanced coding, external events are mapped into embeddable vectors, concatenated with the short-term order status output by the short-term memory unit, and then linearly transformed into key-value pairs;
[0027] Temporal self-attention calculation: Utilize the temporal self-attention mechanism to calculate the attention weights of historical moments to focus on cross-period dependency patterns;
[0028] Quantification of external event impact and embedding adjustment: Quantify the marginal contribution of external events to demand forecast based on SHAP value and dynamically adjust the event embedding weight. The SHAP value calculation formula is: Where D is the event set, f(·) is the demand forecast function;
[0029] Fusion of long-term context and short-term state: The gated fusion module balances the contribution of long-term and short-term memory, achieving a deep fusion of historical long-term patterns and real-time external disturbances, providing contextual support for the dynamic demand forecasting layer with both a global perspective and event response capabilities.
[0030] Furthermore, the specific steps of the intelligent resource matching layer to capture the fine-grained correlation features between orders and resources through multi-head spatiotemporal attention feature interaction are as follows:
[0031] Feature mapping and linear projection: Linear projection technology is used to map order features and resource features into query, key, and value vectors, respectively, to construct a high-dimensional semantic representation space to capture deep feature associations.
[0032] Scaled dot product attention calculation: The similarity matrix between the query and the key is calculated through the scaled dot product attention mechanism. The Softmax function is used to generate probabilistic attention weights, and the value vector is weighted and aggregated to quantify the matching strength between order demand and resource supply and suppress irrelevant feature interference.
[0033] Multi-head parallelism and feature fusion: Introducing a multi-head parallel computing architecture, using multiple sets of independent weight matrices to extract multi-dimensional feature relationships in parallel, and generating a comprehensive representation with both global correlation and local sensitivity through feature splicing and linear fusion.
[0034] Furthermore, the intelligent resource matching layer combines time-sensitive gating to suppress timeout resource matching and strengthen time constraints. The specific steps are:
[0035] Parameterization of time constraint: Order commitment time t order Converted into time penalty coefficient w time =1 / t order And calculate the resource estimated delivery time t resource , through the timeout judgment function IsOvertime, the business timeliness requirement is converted into a computable binary constraint;
[0036] Sigmoid gate function design: using formula g timer =σ(w timer ·(t order -t resource ))=1 / (1+exp(-w timer ·(t order -t resource ))Map the time margin into a continuous value in the interval [0,1], and adjust w timer Control the gating sensitivity, thereby completing the gradient control of fully opening non-timed resources, linearly suppressing slightly timed resources, and strongly suppressing severely timed resources;
[0037] Gating and attention score fusion: through MatchScore final =g timer AttentionScore integrates time constraints into the matching scores generated by multi-head attention and combines it with dynamic threshold adjustment for business scenario adaptation.
[0038] Furthermore, the intelligent resource matching layer optimizes operating costs and delivery timeliness through dynamic cost verification and resource screening.
[0039] Design of nonlinear cost adjustment function: Design of cost correction formula C based on exponential decay function adjusted =C base (1+λ·exp(-MatchScore))C, using the matching score MatchScore to calculate the base cost C base Perform nonlinear adjustments and control the adjustment range through the cost sensitivity coefficient λ, so that high-matching resources are fine-tuned due to the attenuation of the exponential term, while low-matching resources are significantly increased due to the amplification of the exponential term, thus building an incentive mechanism of "high quality matching and low cost";
[0040] Multi-resource sorting and optimal selection: A multi-resource sorting algorithm sorts the adjusted costs in ascending order, and threshold filtering is used to eliminate over-budget resources. Ultimately, the resource at the head of the queue is selected as the optimal solution.
[0041] The present invention has the following beneficial effects:
[0042] 1. In this paper, the hybrid memory state transfer network uses short-term memory units to capture short-term Markov transitions, while simultaneously integrating long-term historical memory and external event embedding using temporal self-attention. The gated fusion coefficient dynamically balances the contributions of both. This increases the weight of long-term memory to pre-allocate capacity, breaking through the limitations of traditional hidden Markov models that rely solely on the current state.
[0043] 2. In the present invention, the data governance layer adopts MLP+Transformer dual encoders to process static features such as order amount / address and dynamic features such as timeliness requirements / user behavior respectively, and realizes nonlinear fusion through feature splicing, breaking through the linear limitation of cosine similarity. The feature distance between positive and negative samples is widened through Contrastive Loss, so that the model automatically learns the weights associated with key features, which is equivalent to introducing an implicit nonlinear kernel function, significantly improving the recognition accuracy of the contribution of key features.
[0044] 3. In the present invention, the intelligent resource matching layer globally ranks candidate resources through a nonlinear cost calibration formula, rather than adjusting the cost of a single resource in isolation. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] Figure 1 This is a system block diagram of the intelligent logistics distribution data management platform proposed by the present invention. DETAILED DESCRIPTION
[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0047] See also Figure 1 As shown, the present invention is a smart logistics distribution data management platform, including:
[0048] Data Governance Layer: Responsible for multimodal data fusion, feature decoupling and enhancement. It uses a dual-channel feature encoding network combined with contrastive reinforcement learning (using contrastive loss to increase the feature distance between positive and negative samples) to construct feature vectors. The dual-channel feature encoding network includes a static feature encoder (MLP) and a dynamic feature encoder (Transformer).
[0049] Dynamic demand forecasting layer: Based on a hybrid memory network, it models demand evolution patterns. Based on a hybrid memory state transition network (HM-TransNet), it uses GRUs (Grounded Reinforcement Units) to model short-term Markov state transitions. It fuses long-term historical memory with external event embeddings (such as promotions and weather codes) through temporal self-attention. It also uses SHAP values to quantify event impacts for gated fusion, dynamically generating a demand state transition probability matrix and order priority scores.
[0050] Intelligent resource matching layer: This layer achieves joint optimization of cost and timeliness through the spatiotemporal attention mechanism. Through the multi-head spatiotemporal attention mechanism (using order features as queries and resource features as key-value pairs to calculate matching weights), combined with timeliness-sensitive gating (the sigmoid function suppresses timed resources) and a dynamic cost correction formula (adjusting operating costs based on matching scores), it ultimately outputs a distribution resource decision that has both timeliness and cost advantages.
[0051] In one embodiment, the data governance layer uses a dual-channel feature encoding network to map the original order and resource data into structured feature vectors, decoupling static / dynamic attributes and enhancing temporal correlation, including the following steps:
[0052] Static feature encoding: Use the fully connected layer (MLP) to perform full connection mapping and nonlinear activation on structured static data such as order amount and address code to generate a low-dimensional static feature vector to decouple the core attributes, expressed as f static =ReLU(W1x static +b1),W1∈R 128×1024 ,b1∈R 128 ;
[0053] Dynamic feature encoding: Transformer is used to capture temporal dynamic data such as timeliness requirements and user historical behavior. The multi-head self-attention mechanism is used to model temporal dependencies and generate dynamic feature vectors that include contextual awareness.
[0054] Resource feature encoding: CNN is used to extract the spatial grid features of the delivery resource route data, and combined with the resource type embedding vector to generate a resource feature vector. Subsequently, static and dynamic features are fused through layer normalization to form a unified order representation.
[0055] In one embodiment, the data governance layer constructs positive and negative sample pairs on this dual-channel feature encoding network, applies a contrastive loss function to increase the feature distance between matching order-resource pairs and random mismatched pairs, forcing the model to learn more discriminative feature representations, and ultimately outputs standardized order and resource feature vectors to the dynamic demand forecasting layer, including the following steps:
[0056] Positive and negative sample construction: Positive sample pairs: matched order-resource pairs (e.g., fresh food orders → refrigerated trucks); negative sample pairs: random unmatched pairs (e.g., fresh food orders → ordinary trucks);
[0057] Contrast loss calculation: The loss function increases the feature distance between positive and negative samples. The calculation formula is expressed as in, is the cosine similarity, τ = 0.1 is the temperature coefficient, and N is the set of negative samples (e.g., containing 5 negative samples);
[0058] Feature output: Output standardized order and resource feature vectors to the dynamic demand forecasting layer.
[0059] In one embodiment, the dynamic demand forecasting layer uses the following specific steps to model short-term Markov state transition using a GRU:
[0060] Input feature standardization and state initialization: Order features are standardized to eliminate dimensional differences;
[0061] Update Gate calculation: Update gate is used to calculate the weight matrix and bias term through the Sigmoid function to determine the proportion of historical state retention, which is expressed as Among them, it is expressed as is the weight matrix, b z is the bias term, σ(·) is the Sigmoid function (output range [0,1]);
[0062] Reset Gate calculation: Reset gate is based on the Sigmoid function to filter irrelevant historical information to control the information flow, which is expressed as rt Control the fusion ratio of historical state and current input;
[0063] Candidate hidden state generation: Combine the reset history state with the current input to generate a candidate hidden state, and compress the value range through the tanh activation function;
[0064] Hidden state update: Generate the current hidden state by updating the gated fusion of the new and old states.
[0065] The entire process strictly adheres to the Markov assumption (depending only on the state at the previous moment). For example, when processing continuous fresh food orders, the GRU triggers resource warnings by gradually strengthening the cold chain resource occupancy status, or uses high update gate weights to capture state mutations for time-sensitive orders, ultimately providing a lightweight and efficient short-term context representation for the hybrid memory network.
[0066] In one embodiment, the dynamic demand forecasting layer captures cross-period dependencies (such as holiday recurrence patterns) and external event impacts through temporal self-attention fusion of long-term historical memory and external event embedding, and quantifies event impacts with SHAP values to achieve gated fusion. It dynamically generates a demand state transition probability matrix and order priority scores, including the following steps:
[0067] Event Enhancement Coding: Through event enhancement coding, external events are mapped into embeddable vectors, concatenated with the short-term order status output by the GRU (Growth Reinforcement Unit), and then linearly transformed into key-value pairs.
[0068] Temporal self-attention calculation: Utilizes the temporal self-attention mechanism to calculate the attention weights of historical moments to focus on cross-period dependency patterns (such as the Double 11 order peak).
[0069] Quantification of external event impact and embedding adjustment: Based on the SHAP value, the marginal contribution of external events to demand forecast is quantified and the event embedding weight is dynamically adjusted (for example, when a rainstorm affects delivery time, its embedding strength is amplified). The SHAP value calculation formula is: Where D is the event set, f(·) is the demand prediction function (such as state transition probability);
[0070] Fusion of long-term context and short-term state: The gated fusion module balances the contribution of long-term and short-term memory, achieving a deep fusion of historical long-term patterns and real-time external disturbances. This provides the dynamic demand forecasting layer with contextual support that combines a global perspective with event response capabilities. For example, in the Double 11 (Singles' Day) scenario, high attention weight can be used to replicate the order surge patterns of previous years and trigger resource pre-allocation.
[0071] In one embodiment, the specific steps of the intelligent resource matching layer to capture the fine-grained correlation features between orders and resources through multi-head spatiotemporal attention feature interaction are as follows:
[0072] Feature mapping and linear projection: Linear projection technology is used to map order features (such as amount, address, and timeliness) and resource features (such as vehicle type, location, and capacity) into query, key, and value vectors, respectively, to construct a high-dimensional semantic representation space to capture deep feature associations.
[0073] Scaled dot product attention calculation: The similarity matrix between the query and the key is calculated through the scaled dot product attention mechanism. The Softmax function is used to generate probabilistic attention weights, and the value vector is weighted and aggregated to quantify the matching strength between order demand and resource supply and suppress irrelevant feature interference.
[0074] Multi-head parallelism and feature fusion: Introducing a multi-head parallel computing architecture, using multiple sets of independent weight matrices to parallelly extract multi-dimensional feature relationships such as "amount-capacity matching", "address-location proximity", and "timeliness-transportation capacity", and through feature splicing and linear fusion, generate a comprehensive representation that has both global correlation and local sensitivity.
[0075] In one embodiment, the intelligent resource matching layer combines time-sensitive gating to suppress timed resource matching and strengthen time constraints. The specific steps are:
[0076] Parameterization of time constraint: Order commitment time t order Converted into time penalty coefficient w time =1 / t order And calculate the resource estimated delivery time t resource , through the timeout judgment function IsOvertime, the business timeliness requirement is converted into a computable binary constraint;
[0077] Sigmoid gate function design: using formula g timer =σ(w timer ·(t order -t resource ))=1 / (1+exp(-w timer ·(t order -t resource ))Map the time margin into a continuous value in the interval [0,1], and adjust w timer Control the gating sensitivity to complete the full opening of resources without timeout (g timer ≈1), linear suppression of slight timeout resources (such as t resource =1.1t order Time timer =0.48) and severe timeout resource suppression (such as t resource =2torder Time timer =0.38) gradient control;
[0078] Gating and attention score fusion: through MatchScore final =g timer AttentionScore integrates time constraints into the matching scores generated by multi-head attention and combines it with dynamic threshold adjustment for business scenario adaptation.
[0079] In one embodiment, the intelligent resource matching layer optimizes operating costs and delivery timeliness through dynamic cost verification and resource screening.
[0080] Design of nonlinear cost adjustment function: Design of cost correction formula C based on exponential decay function adjusted =C base (1+λ·exp(-MatchScore))C, using the matching score MatchScore to calculate the base cost C base Perform nonlinear adjustments and control the adjustment range through the cost sensitivity coefficient λ. This causes high-matching resources (such as refrigerated trucks with a matching score of 0.85) to undergo fine-tuning due to exponential attenuation (e.g., a base cost of 50 yuan is adjusted to 50.7 yuan), while low-matching resources (such as small trucks with a matching score of 0.65) to undergo significant upward adjustments due to exponential amplification (e.g., a base cost of 40 yuan is adjusted to 40.72 yuan), thereby establishing an incentive mechanism of "high quality matching and low cost";
[0081] Multi-resource sorting and optimal selection: A multi-resource sorting algorithm sorts the adjusted costs in ascending order. Threshold filtering is used to eliminate over-budget resources (for example, eliminating options with costs exceeding 45 yuan). Ultimately, the resource at the head of the queue is selected as the optimal solution (for example, a medium-sized truck with an adjusted cost of 45.72 yuan is selected instead of an overdue resource with a lower base cost). This ensures that both time constraints and cost control objectives are met in complex scenarios such as e-commerce promotions.
[0082] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. A smart logistics distribution data management platform, characterized by: include: Data governance layer: Responsible for multimodal data fusion, feature decoupling and enhancement, and constructing feature vectors using a dual-channel feature encoding network combined with contrast enhancement learning. The dual-channel feature encoding network includes static feature encoding and dynamic feature encoding. Dynamic demand forecasting layer: Based on a hybrid memory network, it models demand evolution patterns. Based on a hybrid memory state transition network, it uses short-term memory units to model short-term Markov state transitions. It fuses long-term historical memory with external event embedding through temporal self-attention, and uses SHAP values to quantify event impacts for gated fusion. It then dynamically generates a demand state transition probability matrix and order priority scores. Intelligent resource matching layer: This layer achieves joint cost-timeliness optimization through the spatiotemporal attention mechanism. By combining the multi-head spatiotemporal attention mechanism with time-sensitive gating and a dynamic cost correction formula, it ultimately outputs a distribution resource decision that has both timeliness and cost advantages.
2. A smart logistics distribution data management platform according to claim 1, characterized in that: The data governance layer uses a dual-channel feature encoding network to map the original order and resource data into structured feature vectors, decoupling static / dynamic attributes and enhancing temporal correlation. The following steps are involved: Static feature encoding: Use the fully connected layer to perform full connection mapping and nonlinear activation on the structured static data to generate a low-dimensional static feature vector to decouple the core attributes, expressed as f static =ReLU(W1x static +b1),W1∈R 128×1024 ,b1∈R 128 ; Dynamic feature encoding: Transformer is used to capture temporal dynamic data, and a multi-head self-attention mechanism is used to model temporal dependencies to generate dynamic feature vectors that include context awareness. Resource feature encoding: CNN is used to extract the spatial grid features of the delivery resource route data, and combined with the resource type embedding vector to generate a resource feature vector. Subsequently, static and dynamic features are fused through layer normalization to form a unified order representation.
3. The intelligent logistics distribution data management platform according to claim 2 is characterized in that: The data governance layer constructs positive and negative sample pairs on this dual-channel feature encoding network and uses a contrastive loss function to increase the feature distance between matched order-resource pairs and randomly mismatched pairs, forcing the model to learn more discriminative feature representations. The model ultimately outputs standardized order and resource feature vectors to the dynamic demand forecasting layer, including the following steps: Positive and negative sample construction: Positive sample pairs: matching order-resource pairs; Negative sample pairs: random unmatched pairs; Contrast loss calculation: The loss function increases the feature distance between positive and negative samples. The calculation formula is expressed as in, is the cosine similarity, τ = 0.1 is the temperature coefficient, and N is the set of negative samples; Feature output: Output standardized order and resource feature vectors to the dynamic demand forecasting layer.
4. The intelligent logistics distribution data management platform according to claim 1 is characterized in that: The specific steps of the dynamic demand forecasting layer using short-term memory units to model short-term Markov state transitions are as follows: Input feature standardization and state initialization: Order features are standardized to eliminate dimensional differences; Update gate calculation: Use update gate to calculate weight matrix and bias term through Sigmoid function to determine the proportion of historical state retention, which is expressed as Among them, it is expressed as is the weight matrix, b z is the bias term, σ(·) is the Sigmoid function; Reset gate calculation: Reset gate is based on the Sigmoid function to filter irrelevant historical information to control the information flow, which is expressed as r t Control the fusion ratio of historical state and current input; Candidate hidden state generation: Combine the reset history state with the current input to generate candidate hidden states, and compress the value range through the tanh activation function; Hidden state update: Generate the current hidden state by updating the gated fusion of the new and old states.
5. The intelligent logistics distribution data management platform according to claim 4 is characterized in that: The dynamic demand forecasting layer captures cross-cycle dependencies and external event impacts through temporal self-attention fusion of long-term historical memory and external event embedding, and uses SHAP values to quantify event impacts to achieve gated fusion. It dynamically generates a demand state transition probability matrix and order priority scores, including the following steps: Event Enhanced Coding: Through event enhanced coding, external events are mapped into embeddable vectors, concatenated with the short-term order status output by the short-term memory unit, and then linearly transformed into key-value pairs; Temporal self-attention calculation: Utilize the temporal self-attention mechanism to calculate the attention weights of historical moments to focus on cross-period dependency patterns; Quantification of external event impact and embedding adjustment: Quantify the marginal contribution of external events to demand forecast based on SHAP value and dynamically adjust the event embedding weight. The SHAP value calculation formula is: Where D is the event set, f(·) is the demand forecast function; Fusion of long-term context and short-term state: The gated fusion module balances the contribution of long-term and short-term memory, achieving a deep fusion of historical long-term patterns and real-time external disturbances, providing contextual support for the dynamic demand forecasting layer with both a global perspective and event response capabilities.
6. The intelligent logistics distribution data management platform according to claim 1 is characterized in that: The specific steps of the intelligent resource matching layer to capture the fine-grained correlation features between orders and resources through the interaction of multi-head spatiotemporal attention features are as follows: Feature mapping and linear projection: Linear projection technology is used to map order features and resource features into query, key, and value vectors, respectively, to construct a high-dimensional semantic representation space to capture deep feature associations. Scaled dot product attention calculation: The similarity matrix between the query and the key is calculated through the scaled dot product attention mechanism. The Softmax function is used to generate probabilistic attention weights, and the value vector is weighted and aggregated to quantify the matching strength between order demand and resource supply and suppress irrelevant feature interference. Multi-head parallelism and feature fusion: Introducing a multi-head parallel computing architecture, using multiple sets of independent weight matrices to extract multi-dimensional feature relationships in parallel, and generating a comprehensive representation with both global correlation and local sensitivity through feature splicing and linear fusion.
7. The intelligent logistics distribution data management platform according to claim 6, characterized in that: The intelligent resource matching layer combines time-sensitive gating to suppress timed resource matching and strengthen time constraints. The specific steps are: Parameterization of time constraint: Order commitment time t order Converted into time penalty coefficient w time =1 / t order And calculate the resource estimated delivery time t resource , through the timeout judgment function IsOvertime, the business timeliness requirement is converted into a computable binary constraint; Sigmoid gate function design: using formula g timer =σ(w timer ·(t order -t resource ))=1 / (1+exp(-w timer ·(t order -t resource ))Map the time margin into a continuous value in the interval [0,1], and adjust w timer Control the gating sensitivity, thereby completing the gradient control of fully opening non-timed resources, linearly suppressing slightly timed resources, and strongly suppressing severely timed resources; Gating and attention score fusion: through MatchScore final =g timer AttentionScore integrates time constraints into the matching scores generated by multi-head attention and combines it with dynamic threshold adjustment for business scenario adaptation.
8. The intelligent logistics distribution data management platform according to claim 7 is characterized in that: The specific steps of the intelligent resource matching layer to optimize operating costs and delivery time through dynamic cost verification and resource screening are as follows: Design of nonlinear cost adjustment function: Design of cost correction formula C based on exponential decay function adjusted =C base (1+λ·exp(-MatchScore))C, using the matching score MatchScore to calculate the base cost C base Perform nonlinear adjustments and control the adjustment range through the cost sensitivity coefficient λ, so that high-matching resources are fine-tuned due to the exponential attenuation, while low-matching resources are significantly increased due to the exponential amplification, thus establishing an incentive mechanism of "high quality matching and low cost"; Multi-resource sorting and optimal selection: A multi-resource sorting algorithm sorts the adjusted costs in ascending order, and threshold filtering is used to eliminate over-budget resources. Ultimately, the resource at the head of the queue is selected as the optimal solution.