Emergency material distribution method, system and device and storage medium

Abstract

The invention provides an emergency material distribution method, system and device and a storage medium, and relates to the technical field of material distribution, and the method comprises the steps: obtaining material information, logistics condition information, user feedback information, logistics network information, material management information and supply chain information; generating material loading information; generating logistics configuration information; generating abnormal recognition result information; generating demand prediction result information; generating parallel processing effect information; generating decision support summary information; acquiring a real-time event data stream; generating real-time processing analysis information; and generating material distribution comprehensive decision support information according to the material loading information, the logistics configuration information, the anomaly identification result information, the demand prediction result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information. And support information for material distribution decision making is provided in multiple aspects, so that quick response and formulation of an emergency material distribution scheme in an emergency are facilitated, and the response speed and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of material distribution, and in particular to an emergency material distribution method, system, equipment and storage medium. Background Art

[0002] In emergencies such as natural disasters, emergency supplies need to be distributed in a timely manner and efficiently transported to where they are needed, such as sending mobile communication vehicles, satellite phones and other emergency supplies to their destinations to quickly restore and ensure communication services, thereby mitigating the impact of emergencies.

[0003] In the prior art, emergency material distribution solutions have problems such as insufficient efficiency and response speed in the face of rapidly changing logistics demands and environmental conditions; inability to identify anomalies and risks in the emergency material distribution process; only passive response to material demands; low efficiency in task scheduling for emergency material distribution; low efficiency in decision-making processing; and slow response to real-time events. Summary of the invention

[0004] The present invention provides an emergency material distribution method, system, device and storage medium, which are used to solve the defects of slow response and low efficiency in the prior art when distributing emergency materials.

[0005] The present invention provides an emergency material distribution method, comprising: Obtain material information, logistics conditions information, user feedback information, logistics network information, material management information and supply chain information; Generate material loading information according to the material information and the logistics condition information, wherein the material loading information represents a loading plan of the material; Generate logistics configuration information according to the material loading information, the logistics condition information and the user feedback information, wherein the logistics configuration information represents a logistics plan for the materials; Generate abnormality identification result information according to the logistics configuration information and the logistics network information, wherein the abnormality identification result information represents an abnormality in the material logistics process; Generate demand forecast result information according to the abnormality identification result information, the material management information and the supply chain information, wherein the demand forecast result information represents the predicted future material demand; Generate parallel processing effect information according to the logistics configuration information and the demand forecast result information, wherein the parallel processing effect information represents the parallel processing effect of material distribution; Generate decision support summary information according to the demand forecast result information and the parallel processing effect information, wherein the decision support summary information represents key information used for decision making; Get real-time event data streams; Generating real-time processing analysis information according to the decision support summary information and the real-time event data stream, wherein the real-time processing analysis information represents analysis information in response to the real-time event; Comprehensive material distribution decision support information is generated based on the material loading information, the logistics configuration information, the abnormality identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information.

[0006] According to an emergency material distribution method provided by the present invention, generating material loading information according to the material information and the logistics condition information includes: According to the material information, a material attribute list is determined based on a decision tree algorithm, wherein the material attribute list includes the type, volume, weight, and fragile attribute of the material; Generate packaging solution information based on the resource attribute list and a mixed integer linear programming algorithm; The material loading information is generated according to the packaging scheme information and the logistics condition information based on a packing algorithm and a simulated annealing algorithm.

[0007] According to an emergency material distribution method provided by the present invention, the generating of logistics configuration information according to the material loading information, the logistics condition information and the user feedback information includes: Calculate the cost-effectiveness ratio of the route and the estimated delivery time based on the material loading information and the logistics condition information, and generate transportation evaluation result information; Performing text analysis on the user feedback information to obtain customer satisfaction information, and generating satisfaction evaluation result information based on the customer satisfaction information and the transportation evaluation result information; The logistics configuration information is generated according to the transportation evaluation result information and the satisfaction evaluation result information.

[0008] According to an emergency material distribution method provided by the present invention, generating abnormality identification result information according to the logistics configuration information and the logistics network information includes: Analyze the logistics speed, logistics node response time and logistics path according to the logistics configuration information and the logistics network information, and generate logistics monitoring result information; Perform stability and abnormal pattern analysis based on the logistics monitoring result information to generate abnormal warning information; According to the abnormal warning information, the abnormal patterns are classified and the risk level is evaluated based on the random forest algorithm to generate abnormal identification result information.

[0009] According to an emergency material distribution method provided by the present invention, generating demand forecast result information according to the abnormality identification result information, the material management information and the supply chain information includes: Perform time series construction and trend analysis processing according to the abnormality identification result information, the material management information and the supply chain information to generate trend analysis result information; Analyzing the information based on the trend results, generating future demand forecast information; According to the future demand forecast information and the supply chain information, the demand forecast result information is generated based on linear programming with the goal of minimizing supply chain costs.

[0010] According to an emergency material distribution method provided by the present invention, generating parallel processing effect information according to the logistics configuration information and the demand forecast result information includes: According to the logistics configuration information and the demand forecast result information, an undirected graph is constructed based on the logistics tasks and dependency relationships, and the undirected graph is sorted based on a topological sorting method to generate task priority sorting result information; Allocate resources based on a greedy algorithm according to the task priority sorting result information to generate resource allocation plan information; According to the resource allocation scheme information, task status information is acquired and parallel processing effect information is generated.

[0011] According to an emergency material distribution method provided by the present invention, generating decision support summary information according to the demand forecast result information and the parallel processing effect information includes: According to the demand forecast result information and the parallel processing effect information, the data is processed in layers to generate hierarchical data summary information; According to the hierarchical data summary information, grouping and aggregation processing are performed to generate processing flow summary information; According to the processing flow summary information, the decision support summary information is generated based on keyword extraction and analysis.

[0012] According to an emergency material distribution method provided by the present invention, generating real-time processing analysis information according to the decision support summary information and the real-time event data stream includes: According to the real-time event data stream, grouping and integrating are performed to generate real-time data integration result information; Performing identification processing according to the data integration result information to generate key event identification information; The real-time processing analysis information is generated according to the key event identification information and the decision support summary information.

[0013] According to an emergency material distribution method provided by the present invention, the material distribution comprehensive decision support information is generated according to the material loading information, the logistics configuration information, the abnormal identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information, including: Obtaining strategy comprehensive analysis result information by analyzing the decision model according to the material loading information, the logistics configuration information, the abnormality identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information; According to the comprehensive analysis result information, obtaining response plan design information based on scenario simulation; According to the response plan design information, a plan evaluation is performed to generate the material distribution comprehensive decision support information.

[0014] The present invention also provides an emergency material distribution system, comprising: A material optimization packaging module, used to generate material loading information according to the material information and the logistics condition information; A logistics network optimization module, used to generate logistics configuration information according to the material loading information, the logistics condition information and the user feedback information; An abnormal pattern recognition module, used to generate abnormal recognition result information according to the logistics configuration information and the logistics network information; A demand forecasting module, used to generate demand forecasting result information according to the abnormality identification result information, the material management information and the supply chain information; A task parallel processing module, used to generate parallel processing effect information according to the logistics configuration information and the demand forecast result information; A data hierarchical processing module, used to generate decision support summary information according to the demand forecast result information and the parallel processing effect information; A stream data processing module, used for generating real-time processing analysis information according to the decision support summary information and the real-time event data stream; A comprehensive decision support module, used to generate comprehensive decision support information for material distribution based on the material loading information, the logistics configuration information, the abnormal identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information; The material optimization packaging module, the logistics network optimization module, the abnormal pattern recognition module, the demand prediction module, the task parallel processing module, the data hierarchical processing module, the stream data processing module and the comprehensive decision support module cooperate with each other to execute the above-mentioned emergency material distribution method.

[0015] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an emergency material distribution method as described above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the emergency material distribution method as described in any one of the above is implemented.

[0017] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned emergency material distribution methods.

[0018] The present invention provides an emergency material distribution method, system, device and storage medium, which have at least the following beneficial effects: according to material information and logistics condition information, based on the spatial conditions of logistics, a material packaging and loading plan is obtained, and material loading information is generated, which is conducive to improving the loading efficiency of materials and the space utilization rate of material loading. According to material loading information, logistics condition information and user feedback information, based on the weight of material loading and logistics route, combined with user feedback on logistics, the logistics route and resource allocation are determined, and logistics configuration information is generated, which is conducive to improving the logistics speed and efficiency of materials. According to logistics configuration information and logistics network information, anomalies in the logistics process are identified, and anomaly identification result information is generated, which is conducive to timely discovering anomalies such as logistics delays and material damage to take corresponding measures. According to anomaly identification result information, material management information and supply chain information, based on the impact of logistics anomalies on material replenishment, combined with material management information and supply chain information, the change trend of materials is known, and then demand forecast result information is generated, which is conducive to actively responding to material demand based on future forecasts of material demand, and timely adjusting material distribution strategies to improve the efficiency of material distribution. According to the logistics configuration information and demand forecast result information, based on the supply and demand of materials, the execution order of the material allocation task is adjusted, and the completion effect of the material allocation task is obtained, and the parallel processing effect information is generated, so as to optimize the adjustment strategy of the material allocation task, which is conducive to efficient parallel material allocation tasks and improve the execution efficiency of material allocation. According to the renewal forecast result information and parallel processing effect information, based on the material demand situation and the completion effect of the material allocation task, the decision effect of material allocation can be known, and decision support summary information is generated, which is convenient for providing support information for subsequent decisions, which is conducive to improving the efficiency and accuracy of decision-making. According to the implementation event data flow and decision support summary information in the material allocation process, the recommended decision for real-time events is analyzed, and real-time processing analysis information is generated, which is conducive to improving the response efficiency to real-time events. According to the material loading information, logistics configuration information, abnormal identification result information, demand forecast result information, parallel processing effect information, decision support summary information and real-time processing analysis information, comprehensive material allocation decision support information is generated, so as to provide support information for material allocation decisions in many aspects, which is conducive to quickly responding to the formulation of efficient and feasible emergency material allocation plans in emergencies, and improving the response speed and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of an emergency material distribution method provided by the present invention.

[0021] Figure 2 This is one of the structural schematic diagrams of an emergency material distribution system provided by the present invention.

[0022] Figure 3 This is the second structural schematic diagram of an emergency material distribution system provided by the present invention.

[0023] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0025] Combine the following Figure 1 A method for distributing emergency supplies of the present invention is described, comprising: S100: Acquire material information, logistics condition information, user feedback information, logistics network information, material management information and supply chain information; S200: Generate material loading information according to the material information and the logistics condition information, wherein the material loading information represents a loading plan of the material; S300: Generate logistics configuration information according to the material loading information, the logistics condition information and the user feedback information, wherein the logistics configuration information represents a logistics plan of the material; S400: generating abnormality identification result information according to the logistics configuration information and the logistics network information, wherein the abnormality identification result information represents an abnormality in the material logistics process; S500: generating demand forecast result information according to the abnormality identification result information, the material management information and the supply chain information, wherein the demand forecast result information represents the predicted future material demand; S600: Generate parallel processing effect information according to the logistics configuration information and the demand forecast result information, wherein the parallel processing effect information represents the parallel processing effect of material distribution; S700: Generate decision support summary information according to the demand forecast result information and the parallel processing effect information, wherein the decision support summary information represents key information used for decision making; S110: Acquire real-time event data stream; S800: Generate real-time processing analysis information according to the decision support summary information and the real-time event data stream, wherein the real-time processing analysis information represents analysis information in response to the real-time event; S900: Generate comprehensive decision support information for material distribution based on the material loading information, the logistics configuration information, the abnormality identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information.

[0026] According to the material information and logistics condition information, based on the spatial conditions of logistics, the material packaging and loading plan is obtained, and the material loading information is generated, which is conducive to improving the loading efficiency of materials and the space utilization rate of material loading. According to the material loading information, logistics condition information and user feedback information, based on the weight of the material loading and the logistics route, combined with the user's feedback on logistics, the logistics route and resource allocation are determined, and the logistics configuration information is generated, which is conducive to improving the logistics speed and efficiency of materials. According to the logistics configuration information and logistics network information, the anomalies in the logistics process are identified, and the anomaly identification result information is generated, which is conducive to timely discovering the anomalies such as logistics delays and material damage to take corresponding measures. According to the anomaly identification result information, material management information and supply chain information, based on the impact of logistics anomalies on material replenishment, combined with the material management information and supply chain information, the change trend of materials is known, and then the demand forecast result information is generated, which is conducive to actively responding to material demand based on the future forecast of material demand, and timely adjusting the material allocation strategy to improve the efficiency of material allocation. According to the logistics configuration information and demand forecast result information, based on the supply and demand of materials, the execution order of the material allocation task is adjusted, and the completion effect of the material allocation task is obtained, and the parallel processing effect information is generated, so as to optimize the adjustment strategy of the material allocation task, which is conducive to efficient parallel material allocation tasks and improve the execution efficiency of material allocation. According to the renewal forecast result information and parallel processing effect information, based on the material demand situation and the completion effect of the material allocation task, the decision effect of material allocation can be known, and decision support summary information is generated, which is convenient for providing support information for subsequent decisions, which is conducive to improving the efficiency and accuracy of decision-making. According to the implementation event data flow and decision support summary information in the material allocation process, the recommended decision for real-time events is analyzed, and real-time processing analysis information is generated, which is conducive to improving the response efficiency to real-time events. According to the material loading information, logistics configuration information, abnormal identification result information, demand forecast result information, parallel processing effect information, decision support summary information and real-time processing analysis information, comprehensive material allocation decision support information is generated, so as to provide support information for material allocation decisions in many aspects, which is conducive to quickly responding to the formulation of efficient and feasible emergency material allocation plans in emergencies, and improving the response speed and efficiency.

[0027] In some embodiments of the present invention, material loading information may include information such as material space utilization, weight distribution balance, and protection level of fragile items; logistics configuration information may include information such as logistics routes, cost-effectiveness, and estimated arrival time; abnormal identification result information may include information such as potential logistics delays, material damage risks, and supply and demand imbalance warnings; demand forecasting result information may include information such as peak demand period forecasts, material gap warnings, and excess inventory risks; parallel processing effect information may include optimized task completion time, resource utilization, task conflicts, and other information; decision support summary information may include information such as key demand change points, emergency response priorities, and resource allocation optimization plans; real-time processing and analysis information may include information such as instant logistics adjustment instructions, real-time inventory status updates, and emergency event response triggers; comprehensive material distribution decision support information may include comprehensive risk management plans, emergency material distribution optimization strategies, and resource allocation adjustment plans.

[0028] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S200 includes: S210: Determine a material attribute list based on the material information and a decision tree algorithm, wherein the material attribute list includes the type, volume, weight, and fragile attribute of the material; S220: Generate packaging solution information according to the resource attribute list based on a mixed integer linear programming algorithm; S230: Generate the material loading information according to the packaging scheme information and the logistics condition information based on a packing algorithm and a simulated annealing algorithm.

[0029] According to the material information, the type and characteristics of the materials can be determined. Based on the decision tree algorithm, for example, the decision tree algorithm can be called in the Python language using the DecisionTreeClassifier command to classify the data. The parameter max_depth is set to 5 to control the depth of the decision tree. The material attributes are analyzed, including the basic attributes of the material's volume, weight, and whether it is fragile, and a material attribute list is generated.

[0030] According to the material attribute list, a mixed integer linear programming algorithm is used. For example, it can be calculated through the scipy.optimize.linprog function in Python language. The function parameter bounds sets the limit of the variable. The objective function is to minimize the sum of free space and unbalanced weight. The optimal material packaging and weight distribution plan is obtained, and the packaging plan information is generated.

[0031] According to the packing plan information, the logistics situation and loading conditions are determined based on the logistics condition information, and the packing algorithm for solving the three-dimensional packing problem is combined with the simulated annealing algorithm to iteratively optimize the plan. For example, the BinPacking function of the py3dbp library can be used as the packing algorithm in the Python language, and the Annealer class of the simanneal library can be used to process the simulated annealing algorithm process. The parameter coolingRate is set to 0.99 to control the annealing rate, so as to obtain the configuration with the highest material loading efficiency and generate material loading information. Based on the material loading information, the loading efficiency and the protection level of the materials can be improved.

[0032] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S300 includes: S310: Calculate the route cost-effectiveness ratio and estimated delivery time according to the material loading information and the logistics condition information, and generate transportation evaluation result information; S320: Performing text analysis on the user feedback information to obtain customer satisfaction information, and generating satisfaction evaluation result information according to the customer satisfaction information and the transportation evaluation result information; S330: Generate the logistics configuration information according to the transportation evaluation result information and the satisfaction evaluation result information.

[0033] According to the material loading information, the logistics situation is determined based on the logistics condition information, such as the transportation time, cost and route efficiency of each logistics route, and the cost-effectiveness and estimated arrival time of the logistics route are calculated. For example, the NumPy library of the Python language can be used for numerical analysis to calculate the cost-effectiveness ratio and estimated arrival time of each route, generate transportation efficiency evaluation results, and facilitate the identification of the most efficient logistics route.

[0034] Based on user feedback information, customer satisfaction information of each logistics route is determined through text analysis and processing. For example, user feedback information can be processed through the NLTK library of the Python language, including using the tokenize library to segment the text of the feedback information, performing sentiment analysis on the segmented text, obtaining customer satisfaction scores, and generating satisfaction evaluation result information.

[0035] According to the transport evaluation result information and satisfaction evaluation result information, the optimal logistics route and configuration are determined based on the cost-effectiveness ratio of the logistics route, the estimated arrival time and customer satisfaction, and the logistics configuration information is generated. For example, based on the transport evaluation result information and the satisfaction evaluation result information, a deep deterministic policy gradient algorithm can be used in the Python language to adjust the logistics configuration, build a neural network model through the TensorFlow library, and perform model training. During the model training process, the Adam optimizer is used, and the learning rate is set to 0.001. The effect of the logistics configuration plan is evaluated through the reward function, and then the optimal logistics configuration is determined, and the logistics configuration information is generated. Based on the logistics configuration information, it is beneficial to improve the response speed and overall efficiency of the logistics network for material distribution.

[0036] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S400 includes: S410: Analyze the logistics speed, logistics node response time and logistics path according to the logistics configuration information and the logistics network information, and generate logistics monitoring result information; S420: Perform stability and abnormal pattern analysis based on the logistics monitoring result information to generate abnormal warning information; S430: According to the abnormal warning information, the abnormal pattern is classified and the risk level is evaluated based on the random forest algorithm to generate abnormal identification result information.

[0037] According to the logistics configuration information combined with the logistics network information, real-time data monitoring technology is adopted. For example, the data information flow of the logistics network can be processed and analyzed through the Apache Kafka platform, and the threshold parameters can be set to use the Kafka Streams API provided by the platform to analyze the logistics speed of materials, logistics node response time and logistics path changes, and generate logistics monitoring result information.

[0038] Based on the logistics monitoring results, possible anomalies in the logistics network can be identified. For example, the stability and abnormal pattern analysis can be performed by using the Lyapunov index calculation and phase space reconstruction. The SciPy library in Python can be used to calculate the Lyapunov index to evaluate stability. The Lyapunov index threshold is set to 0.1. The phase space reconstruction is set through the embedding dimension and delay time of the time series data to reflect the changes in the data flow, thereby identifying anomalies and generating abnormal warning information; According to the abnormal warning information, the abnormalities are classified and evaluated. For example, the random forest algorithm is used to classify the abnormal patterns and evaluate the risk level. The random forest model can be constructed using the sklearn library of the Python language. The model parameters include the number of trees set to 100 and the maximum depth set to 10. The abnormal patterns are classified into risk levels through model training and prediction, and the abnormal identification result information is generated. Based on the abnormal identification result information, it is helpful to timely discover abnormal warnings of transportation delays, material damage and imbalance between supply and demand, and provide a basis for taking preventive or response measures in a timely manner.

[0039] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S500 includes: S510: Perform time series construction and trend analysis processing according to the abnormality identification result information, the material management information and the supply chain information to generate trend analysis result information; S520: Generate future demand forecast information based on the trend result analysis information; S530: Generate the demand forecast result information according to the future demand forecast information and the supply chain information based on linear programming with the goal of minimizing supply chain costs.

[0040] According to the abnormal identification result information, combined with the material management information and supply chain information, based on the historical data of the three, through time series analysis, for example, the time series of historical data can be constructed through the Pandas library of the Python language, and the ARIMA model can be applied to perform historical data trend analysis. The ARIMA function in the statsmodels library is used to configure the model parameters to 2 autoregressive terms, 1 difference, and 2 moving average terms to generate trend analysis result information.

[0041] Based on the trend analysis results, future demand forecast information is generated. For example, the Transformer architecture can be used to process time series data, and the model can be built using the TensorFlow library in the Python language. The model configuration includes setting the number of layers to 6, the number of heads to 8, and the feed-forward network dimension to 2048. The Adam optimizer is used for model training, and the learning rate is adjusted to 0.001. The model analyzes the long-term dependencies of historical changes and combines them with trend changes to generate future demand forecast information.

[0042] According to the future demand forecast information, the supply chain configuration is adjusted through linear programming. For example, the linprog function of the SciPy library can be used for calculation in Python. The objective function is set to minimize the total cost of the supply chain. The constraints are the availability of resources and the satisfaction of the forecast for demand. The supply chain configuration is adjusted to match the predicted future demand to generate demand forecast result information. The demand forecast result information is helpful to guide the optimization of inventory management strategies and formulate more efficient material allocation strategies.

[0043] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S600 includes: S610: According to the logistics configuration information and the demand forecast result information, an undirected graph is constructed based on the logistics tasks and dependency relationships, and the undirected graph is sorted based on a topological sorting method to generate task priority sorting result information; S620: Allocate resources based on a greedy algorithm according to the task priority sorting result information to generate resource allocation solution information; S630: Acquire task status information according to the resource allocation scheme information and generate parallel processing effect information.

[0044] According to the logistics configuration information and demand forecast result information, multiple tasks are prioritized through graph theory task scheduling, and the dependencies between material distribution tasks are represented by directed acyclic graphs. For example, the NetworkX library of Python language can be used to create and operate directed acyclic graphs, and the topological sorting algorithm is used to select the execution order of material distribution tasks. The urgency, importance and resource requirements of material distribution tasks are used as the basis for sorting, and task priority sorting result information is generated.

[0045] According to the task priority sorting result information, a dynamic resource allocation strategy is adopted to allocate resources according to the material allocation task requirements and the current material allocation task load. By analyzing the available resources in the resource pool and the resource requirements of the material allocation task, a greedy algorithm is used to allocate resources to each material allocation task to generate resource allocation plan information.

[0046] According to the resource allocation plan information, the task status information is obtained. For example, the Prometheus tool can be used to collect and analyze the execution status, resource usage, and completion time indicators of the material allocation task. The collection configuration includes setting the data information collection frequency and threshold alarm. The obtained task status information is visualized, for example, the Grafana tool can be used to visualize the data information, and the parallel processing effect information is generated by analyzing various indicators. Based on the parallel processing effect information, it is easy to understand the resource allocation problem and the material allocation task problem, which is conducive to adjusting and optimizing the resource allocation strategy or task scheduling strategy, and improving the resource allocation utilization and the efficiency of the material allocation task execution.

[0047] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S700 includes: S710: hierarchically process the data according to the demand forecast result information and the parallel processing effect information to generate hierarchical data summary information; S720: performing grouping and aggregation processing according to the hierarchical data summary information to generate processing flow summary information; S730: Generate the decision support summary information based on keyword extraction and analysis according to the processing flow summary information.

[0048] According to the prediction result information and the parallel processing effect information, the data information is stratified. For example, the decision tree algorithm can be used. The model can be built in Python using the DecisionTreeClassifier command in the sklearn library. The parameter max_depth is 5, and the parameter criterion is set to gini to evaluate the quality of data stratification. The data information is trained using the fit method, the data set is stratified, and hierarchical data summary information is generated.

[0049] According to the hierarchical data summary information, grouping and aggregation processing are performed. For example, the Apache Flink platform can be used to process the data stream through the DataStream API provided by the platform, and the timeWindow can be set to 5 seconds to divide the data into windows. The flatMap function and keyBy function can be used to pre-process and group the data. The apply method can be used to perform customized aggregation logic to generate processing flow summary information.

[0050] According to the summary information of the processing flow, key information is extracted. For example, natural language processing technology can be used to segment the text using the tokenize.word_tokenize function provided by the Natural Language Toolkit library in Python, use pos_tag for part-of-speech tagging, and use the nltk.FreqDist command to count the key words in the word frequency analysis data to extract key information from the data information and generate decision support summary information. Based on the decision support summary information, it can effectively support decision making and provide a basis for decision making, which is conducive to improving the speed and accuracy of decision making.

[0051] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S800 includes: S810: performing grouping integration processing according to the real-time event data stream to generate real-time data integration result information; S820: performing identification processing according to the data integration result information to generate key event identification information; S830: Generate the real-time processing analysis information according to the key event identification information and the decision support summary information.

[0052] According to the real-time event data stream, data integration and analysis are performed. For example, the Apache Flink platform can be used for data processing. The DataStream API provided by the platform is used to receive and integrate the real-time event data stream. The time window is set to 5 seconds. The flatMap function and keyBy function are used to pre-process and group the data. The data is integrated according to the preset logic to generate real-time data integration result information.

[0053] According to the real-time data integration result information, key event identification and processing are performed. For example, complex event processing logic can be used to identify key events. In the Apache Flink platform, the data information is pattern matched through the CEP library, and matching parameters are set, including event sequence and time interval, to identify key events in material distribution in real time and generate key event identification information.

[0054] According to the key event identification information, combined with the decision support summary information, the event processing strategy is automatically analyzed. For example, the stream data processing logic in the Apache Flink platform is used, combined with the decision support summary information, to automatically trigger the corresponding response strategy, and define the response strategy of multiple event types by setting the platform's processing logic to generate the real-time processing analysis information. Based on the real-time processing analysis information, it is possible to respond to real-time events in a timely manner, which is conducive to improving the response speed of real-time events and optimizing material distribution.

[0055] refer to Figure 1 In some embodiments of the emergency material distribution method of the present invention, the S900 includes: S910: Obtaining strategy comprehensive analysis result information by analyzing a decision model according to the material loading information, the logistics configuration information, the abnormality identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information, and the real-time processing analysis information; S920: Acquire response solution design information based on scenario simulation according to the comprehensive analysis result information; S930: Performing scheme evaluation according to the response scheme design information, and generating the comprehensive decision support information for material allocation.

[0056] According to the material loading information, flow configuration information, abnormal identification result information, demand forecast result information, parallel processing effect information, decision support summary information and real-time processing analysis information, through the analysis decision model processing, for example, the multi-level decision analysis method is used to conduct comprehensive evaluation and analysis of the strategy, which can be the DecisionTreeClassifier function in the Scikit-learn library in the Python language, to build a decision tree with the parameter max_depth set to 10. Through model training, an analysis decision model is formed to reflect the interaction and priority between factors and generate comprehensive strategy analysis result information.

[0057] According to the comprehensive analysis results of the strategy, scenario simulation technology is used. For example, a simulation model can be built through the SimPy library of the Python language to simulate various resource allocations and emergency responses, obtain the resource requirements and response time under each emergency scenario, and generate corresponding response plan design information.

[0058] According to the response plan design information, each response plan is evaluated. For example, a real-time data feedback mechanism can be used to monitor and process data streams in real time through the Apache Kafka platform, set the time window for stream processing to 5 seconds, and use the platform's DataStream API for event-driven analysis to analyze the key performance indicators of the corresponding plan to evaluate the effect of the response plan in the emergency scenario and generate comprehensive decision support information for material allocation. Based on the comprehensive decision support information for material allocation, it can provide a basis for the response plan for material allocation in emergency scenarios, which is conducive to improving the response speed and execution efficiency of material allocation.

[0059] An emergency material distribution system provided by the present invention is described below. The emergency material distribution system described below and the emergency material distribution method described above can correspond to each other.

[0060] refer to Figures 1 to 3 The present invention also provides an emergency material distribution system, comprising: A material optimization packaging module, used to generate material loading information according to the material information and the logistics condition information; A logistics network optimization module, used to generate logistics configuration information according to the material loading information, the logistics condition information and the user feedback information; An abnormal pattern recognition module, used to generate abnormal recognition result information according to the logistics configuration information and the logistics network information; A demand forecasting module, used to generate demand forecasting result information according to the abnormality identification result information, the material management information and the supply chain information; A task parallel processing module, used to generate parallel processing effect information according to the logistics configuration information and the demand forecast result information; A data hierarchical processing module, used to generate decision support summary information according to the demand forecast result information and the parallel processing effect information; A stream data processing module, used for generating real-time processing analysis information according to the decision support summary information and the real-time event data stream; A comprehensive decision support module, used to generate comprehensive decision support information for material distribution based on the material loading information, the logistics configuration information, the abnormal identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information; The material optimization packaging module, the logistics network optimization module, the abnormal pattern recognition module, the demand prediction module, the task parallel processing module, the data hierarchical processing module, the stream data processing module and the comprehensive decision support module cooperate with each other to execute the above-mentioned emergency material distribution method.

[0061] refer to Figure 3 In some embodiments of an emergency material distribution system of the present invention: The material optimization packaging module includes a material classification and attribute analysis submodule executing S210, a space and weight optimization strategy submodule executing S220, and a dynamic loading plan update submodule executing S230; The logistics network optimization module includes a transportation efficiency evaluation submodule executing S310, a satisfaction feedback integration submodule executing S320, and a configuration dynamic adjustment submodule executing S330; The abnormal pattern recognition module includes a data flow dynamic monitoring submodule executing S410, an abnormal early warning mechanism submodule executing S420, and a risk level assessment submodule executing S430; The demand forecasting module includes a historical trend analysis submodule for executing S510, a future demand forecasting submodule for executing S520, and a supply chain adjustment guidance submodule for executing S530; The task parallel processing module includes a multi-task priority sorting submodule for executing S610, a resource dynamic allocation submodule for executing S620, and an execution efficiency monitoring submodule for executing S630; The data hierarchical processing module includes a data refinement and grading submodule for executing S710, a processing efficiency optimization submodule for executing S720, and a key information extraction submodule for executing S730; The stream data processing module includes a real-time data access and integration submodule executing S810, a key event identification submodule executing S820, and a response strategy automatic triggering submodule executing S830; The comprehensive decision support module includes a strategy comprehensive analysis submodule for executing S910, an emergency response plan design submodule for executing S920, and a decision execution effect feedback submodule for executing S930.

[0062] The material optimization packaging module optimizes the material packaging and loading strategy according to the type, volume, weight and other characteristics of the materials through a mixed integer linear programming algorithm. It can be a material characteristic parameter model that quantitatively describes the size, weight and fragility of each material, and uses the solution method of the three-dimensional packing problem to simulate the best arrangement of materials in the container, achieve the highest space utilization, generate material loading information, and effectively improve loading efficiency and material protection level.

[0063] The logistics network optimization module can dynamically optimize the logistics network based on the material loading information and logistics condition information, combined with user feedback information, using a deep deterministic policy gradient algorithm. By analyzing the current operating status of the logistics network and user satisfaction, the logistics configuration strategy is adjusted to adjust the logistics route, allocate resources, and arrange logistics plans. The deep deterministic policy gradient algorithm optimizes the decision-making process by continuously learning the operating status of the logistics network to adapt to the ever-changing logistics needs and environmental conditions. The generated logistics configuration information includes the optimized logistics route and resource allocation strategy, which is conducive to improving the response speed and overall efficiency of the logistics network.

[0064] The abnormal pattern recognition module obtains the corresponding data stream based on the logistics configuration information, and uses Lyapunov index calculation and phase space reconstruction to analyze the stability of the logistics network and identify possible anomalies. Lyapunov index calculation is used to evaluate stability, while phase space reconstruction technology reveals the inherent change pattern in the data stream. The combination of the two identifies anomalies and generates abnormal identification result information, including potential logistics transportation delays, material damage, and supply and demand imbalance warnings, providing a scientific basis for timely prevention or response measures.

[0065] The demand forecasting module uses the Transformer architecture to predict future material demand based on the anomaly identification result information, material management information, and historical data of supply chain information. The Transformer architecture processes time series data through the self-attention mechanism to capture long-term dependencies, thereby accurately predicting the changing trend of material demand. The Transformer model can predict key indicators such as material peak demand period, material gap warning, and excess inventory by combining historical data analysis with abnormal pattern recognition information, and generate demand forecast result information, which is conducive to guiding inventory management optimization and formulating more efficient material allocation strategies.

[0066] The task parallel processing module uses graph-theoretic task scheduling methods to divide and allocate resources for the parallel execution of multiple material distribution subtasks based on the logistics configuration information and demand forecasting results. By constructing a material distribution task dependency graph, the topological sorting algorithm is used to determine the execution order of material distribution tasks. At the same time, the material distribution tasks are prioritized according to their urgency and importance. The resource allocation strategy calculates the allocated logistics resources based on the needs of the material distribution tasks and the current material distribution load. The completion status information of the material distribution tasks is collected, and the parallel processing effect information is generated by analyzing the completion indicators. In this way, efficient utilization of resource allocation is achieved, which is conducive to improving resource utilization, processing efficiency and responsiveness.

[0067] The data hierarchical processing module uses adaptive data processing adjustment strategies to adjust the hierarchy and accuracy of the data processing process based on demand forecasting results and information on parallel processing effects. By analyzing the importance and urgency of the data, the priority and depth of data processing are automatically adjusted to ensure that key information can be extracted and processed quickly and accurately. Information summary technology is used to extract key information from the data and generate decision support summary information, including key demand change points, emergency response priorities, etc., to effectively support decision making and improve the speed and accuracy of decision making.

[0068] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the above-mentioned emergency material distribution method.

[0069] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0070] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute an emergency material distribution method provided by the above methods.

[0071] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute an emergency material distribution method provided by the above methods.

[0072] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0073] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0074] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0075] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0076] All actions to obtain signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for distributing emergency supplies, characterized in that: include: Obtain material information, logistics conditions information, user feedback information, logistics network information, material management information and supply chain information; Generate material loading information according to the material information and the logistics condition information, wherein the material loading information represents a loading plan of the material; Generate logistics configuration information according to the material loading information, the logistics condition information and the user feedback information, wherein the logistics configuration information represents a logistics plan for the materials; Generate abnormality identification result information according to the logistics configuration information and the logistics network information, wherein the abnormality identification result information represents an abnormality in the material logistics process; Generate demand forecast result information according to the abnormality identification result information, the material management information and the supply chain information, wherein the demand forecast result information represents the predicted future material demand; Generate parallel processing effect information according to the logistics configuration information and the demand forecast result information, wherein the parallel processing effect information represents the parallel processing effect of material distribution; Generate decision support summary information according to the demand forecast result information and the parallel processing effect information, wherein the decision support summary information represents key information used for decision making; Get real-time event data streams; Generating real-time processing analysis information according to the decision support summary information and the real-time event data stream, wherein the real-time processing analysis information represents analysis information in response to the real-time event; Comprehensive material distribution decision support information is generated based on the material loading information, the logistics configuration information, the abnormality identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information.

2. The method for distributing emergency supplies according to claim 1, characterized in that: Generating material loading information according to the material information and the logistics condition information includes: According to the material information, a material attribute list is determined based on a decision tree algorithm, wherein the material attribute list includes the type, volume, weight, and fragile attribute of the material; Generate packaging solution information based on the resource attribute list and a mixed integer linear programming algorithm; The material loading information is generated according to the packaging scheme information and the logistics condition information based on a packing algorithm and a simulated annealing algorithm.

3. The method for distributing emergency supplies according to claim 1, characterized in that: Generating logistics configuration information according to the material loading information, the logistics condition information and the user feedback information includes: Calculate the cost-effectiveness ratio of the route and the estimated delivery time based on the material loading information and the logistics condition information, and generate transportation evaluation result information; Performing text analysis on the user feedback information to obtain customer satisfaction information, and generating satisfaction evaluation result information based on the customer satisfaction information and the transportation evaluation result information; The logistics configuration information is generated according to the transportation evaluation result information and the satisfaction evaluation result information.

4. The method for distributing emergency supplies according to claim 1, characterized in that: The generating of abnormality identification result information according to the logistics configuration information and the logistics network information includes: Analyze the logistics speed, logistics node response time and logistics path according to the logistics configuration information and the logistics network information, and generate logistics monitoring result information; Perform stability and abnormal pattern analysis based on the logistics monitoring result information to generate abnormal warning information; According to the abnormal warning information, the abnormal patterns are classified and the risk level is evaluated based on the random forest algorithm to generate abnormal identification result information.

5. The method for distributing emergency supplies according to claim 1, characterized in that: The generating demand forecast result information according to the abnormality identification result information, the material management information and the supply chain information includes: Perform time series construction and trend analysis processing according to the abnormality identification result information, the material management information and the supply chain information to generate trend analysis result information; Analyzing the information based on the trend results, generating future demand forecast information; According to the future demand forecast information and the supply chain information, the demand forecast result information is generated based on linear programming with the goal of minimizing supply chain costs.

6. The method for distributing emergency supplies according to claim 1, characterized in that: The generating of parallel processing effect information according to the logistics configuration information and the demand forecast result information includes: According to the logistics configuration information and the demand forecast result information, an undirected graph is constructed based on the logistics tasks and dependency relationships, and the undirected graph is sorted based on a topological sorting method to generate task priority sorting result information; Allocate resources based on a greedy algorithm according to the task priority sorting result information to generate resource allocation plan information; According to the resource allocation scheme information, task status information is acquired and parallel processing effect information is generated.

7. The method for distributing emergency supplies according to claim 1, characterized in that: Generating decision support summary information according to the demand forecast result information and the parallel processing effect information includes: According to the demand forecast result information and the parallel processing effect information, the data is processed in layers to generate hierarchical data summary information; According to the hierarchical data summary information, grouping and aggregation processing are performed to generate processing flow summary information; According to the processing flow summary information, the decision support summary information is generated based on keyword extraction and analysis.

8. The method for distributing emergency supplies according to claim 1, characterized in that: Generating real-time processing analysis information according to the decision support summary information and the real-time event data stream includes: According to the real-time event data stream, grouping and integrating are performed to generate real-time data integration result information; Performing identification processing according to the data integration result information to generate key event identification information; The real-time processing analysis information is generated according to the key event identification information and the decision support summary information.

9. The method for distributing emergency supplies according to claim 1, characterized in that: The generating of material allocation comprehensive decision support information according to the material loading information, the logistics configuration information, the abnormal identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information includes: Obtaining strategy comprehensive analysis result information by analyzing the decision model according to the material loading information, the logistics configuration information, the abnormality identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information; According to the comprehensive analysis result information, obtaining response plan design information based on scenario simulation; According to the response plan design information, a plan evaluation is performed to generate the material distribution comprehensive decision support information.

10. An emergency material distribution system, characterized in that: include: A material optimization packaging module, used to generate material loading information according to the material information and the logistics condition information; A logistics network optimization module, used to generate logistics configuration information according to the material loading information, the logistics condition information and the user feedback information; An abnormal pattern recognition module, used to generate abnormal recognition result information according to the logistics configuration information and the logistics network information; A demand forecasting module, used to generate demand forecasting result information according to the abnormality identification result information, the material management information and the supply chain information; A task parallel processing module, used to generate parallel processing effect information according to the logistics configuration information and the demand forecast result information; A data hierarchical processing module, used to generate decision support summary information according to the demand forecast result information and the parallel processing effect information; A stream data processing module, used for generating real-time processing analysis information according to the decision support summary information and the real-time event data stream; A comprehensive decision support module, used to generate comprehensive decision support information for material distribution based on the material loading information, the logistics configuration information, the abnormal identification result information, the demand forecast result information, the parallel processing effect information, the decision support summary information and the real-time processing analysis information; The material optimization packaging module, the logistics network optimization module, the abnormal pattern recognition module, the demand forecasting module, the task parallel processing module, the data hierarchical processing module, the stream data processing module and the comprehensive decision support module cooperate with each other to execute an emergency material distribution method as described in any one of claims 1 to 9.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, an emergency material distribution method as claimed in any one of claims 1 to 9 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, an emergency material distribution method as claimed in any one of claims 1 to 9 is implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, an emergency material distribution method as claimed in any one of claims 1 to 9 is implemented.

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