A logistics supply chain management method and system

Abstract

The application discloses a kind of logistics supply chain management method and system, it is related to logistics supply chain management technical field, comprising the following steps: step 1, based on data acquisition unit obtains the real-time data of each link of supply chain, the data of acquisition is standardized and is aggregated processing;Step 2: based on the data of data acquisition and preprocessing module to the demand of market is forecasted, and generates the prediction optimization model of demand;Step 3: based on demand prediction and preliminary optimization module constructs multi-objective optimization model and carries out optimization to preliminary supply chain decision;Step 4: based on the supply chain decision of optimization is applied in logistics supply chain, the state of supply chain is monitored in real time, based on latest data and optimization result, dynamically adjusts strategy;Step 5: based on decision execution and dynamic adjustment module continuously monitors and evaluates the operation effect of supply chain, and based on historical data and real-time monitoring result, optimization multi-objective optimization model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics supply chain management, and particularly relates to a logistics supply chain management method and system. BACKGROUND

[0002] Logistics supply chain management refers to optimizing the operation of a supply chain to achieve the least cost, from procurement to meeting the needs of end customers, and its core lies in logistics activities. It involves coordinating internal and external resources, including production, supply, sales, and logistics, to achieve comprehensive management.

[0003] With the rapid development of the global economy and the diversification of market demand, modern logistics supply chains have become increasingly complex, and traditional supply chain management methods often have the following problems:

[0004] 1. High dependence on technology and strategy: The complex multi-objective optimization algorithm and dynamic adjustment strategy relied on by the present application require high technical support and professional operation knowledge, which not only increases the difficulty of implementation and maintenance, but also may cause the system to be unstable or fail due to technical updates or improper operation.

[0005] 2. Limitations of market demand forecasting: Although the present application can accurately predict market demand and optimize inventory, transportation, and production strategies, market demand itself is uncertain and volatile, and the accuracy of the prediction model may be affected by various external factors such as unexpected events and policy changes, which may cause the prediction results to deviate from reality, thereby affecting the optimization effect of inventory levels and production plans.

[0006] 3. Customer dependence and satisfaction risk: The present application ensures on-time delivery and order fulfillment rate through optimization algorithms to improve customer service experience, however, this method highly depends on the accuracy and real-time performance of the algorithm, if the algorithm fails or data input errors occur, it may cause order processing delays or inaccurate delivery, thereby affecting customer satisfaction and loyalty.

[0007] 4. Supply chain risk and adaptability challenges: Although the present application has the ability to quickly respond to changes in market demand, in actual operation, the complexity and variability of the supply chain may exceed the processing capacity of the system. Although diversified supplier selection and risk management strategies can reduce some risks, they cannot completely eliminate all risks faced by the supply chain, in addition, as the market environment continues to change, the adaptability and effectiveness of the system may also face challenges.

[0008] Therefore, there is an urgent need for an intelligent supply chain management method and system that can dynamically adapt to market changes and optimize resource allocation to achieve efficient operation and sustainable development of the supply chain. SUMMARY

[0009] The purpose of the present invention is to provide a logistics supply chain management method and system to solve the problems raised in the above background technology.

[0010] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0011] A logistics supply chain management method comprises the following steps:

[0012] Step 1, Data Collection and Preprocessing Module: The data collection and preprocessing module includes a data collection unit and a data preprocessing unit. The data collection unit acquires real-time data from all links of the supply chain by collecting multiple layers of data. The data preprocessing unit performs standardization and aggregation based on the data collected by the data collection unit.

[0013] Step 2, Demand Forecasting and Preliminary Optimization Module: Based on the data from the data collection and preprocessing module, the module predicts market demand and generates a demand forecast optimization model. Based on the current supply chain status and predicted market demand, the module performs preliminary optimization calculations and determines preliminary supply chain decisions.

[0014] Step 3, multi-objective optimization calculation module: Based on the demand forecast and preliminary optimization modules, a multi-objective optimization model is constructed to optimize the preliminary supply chain decision;

[0015] Step 4, decision execution and dynamic adjustment: Apply the optimized supply chain decision to the logistics supply chain, monitor the status of the supply chain in real time, based on the latest data and optimization results;

[0016] Step 5, the feedback and continuous optimization: based on the decision execution and dynamic adjustment module, continuously monitor and evaluate the operational effect of the supply chain, and optimize the multi-objective optimization model based on historical data and real-time monitoring results.

[0017] A further improvement of the technical solution of the present invention is that: the data acquisition unit acquires real-time data of each link of the supply chain by collecting multi-layer data, where x is a supply chain decision variable vector, including inventory level I, transportation status T, transportation speed V and purchase quantity Q;

[0018] The data preprocessing unit performs standardization processing and aggregation processing based on the data collected by the data collection unit as follows:

[0019] Normalization formula:

[0020]

[0021] Among them D 标准 The original data set, even if it is inventory level I, transportation status T, transportation speed V and purchase quantity Q, D 原始For the standardized data set, μ is the mean of D 标准 , σ is the standard deviation of D 标准 .

[0022] The data set formed by the multi-level data aggregation of the data preprocessing unit is specifically as follows:

[0023]

[0024] Where ω i is the data weight of different levels, is the standardized data of the i-th level, D agg is the multi-level data set formed, wherein n represents the total number of levels of data.

[0025] The further improvement of the technical scheme of the application is that the multi-objective optimization model is specifically as follows:

[0026]

[0027] Where C(x), E(x), S(x), C(x), P(x), R(x), and A(x) represent the total cost function of the supply chain, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain, and the adaptability function of the supply chain under given production parameters, respectively, α, β, γ, δ, ∈ are weight coefficients, F(x) is the objective function, and x is the supply chain decision variable vector.

[0028] The further improvement of the technical scheme of the application is that the objective function includes the total cost function of the supply chain, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain, and the adaptability function of the supply chain, which are represented as C(x), E(x), S(x), C(x), P(x), R(x), and A(x), respectively, and the specific analysis is as follows:

[0029] The total cost function of the supply chain is C(x) = C p (Q) + C s (I) + C t (T) + C h (V)

[0030] Where C p (Q), C s (I), C t (T), and C h (V) represent the procurement cost, the storage cost, the transportation cost, and the labor cost, respectively.

[0031] The further improvement of the technical scheme of the application is that the overall efficiency function formula of the supply chain is further represented as:

[0032]

[0033] Among them E P 、E t are production efficiency and transportation efficiency respectively, λ is the penalty coefficient of transportation delay, and d(T) is the transportation delay time.

[0034] A further improvement of the technical solution of the present invention is that the customer service level function formula is further expressed as:

[0035] S(x)=ω d ·S d (T)+ω O ·S O (Q)

[0036] where ω d 、ω O are the weights of on-time delivery rate and order fulfillment rate, S d 、S O They are on-time delivery rate and order fill rate respectively.

[0037] A further improvement of the technical solution of the present invention is that the environmental impact function formula is further expressed as:

[0038] P(x)=θ c ·P c (T)+θ e ·P e (V)

[0039] Among them, P c 、P e Expressed as carbon emissions and energy consumption respectively;

[0040] The risk function formula of the supply chain is further expressed as:

[0041] R(x)=κ i ·R i (Q)+κ m ·R m (D agg )

[0042] where κ i , κ m Expressed as the weight of each risk type, R i 、R m They are respectively expressed as interruption risk and market volatility risk.

[0043] A further improvement of the technical solution of the present invention is that the adaptability function of the supply chain is further expressed as:

[0044]

[0045] Wherein η is the adjustment coefficient of the response speed, M(t) is the market demand prediction function, dM(t), dQ(x) respectively represent the change rate of market demand and the change rate of production or supply capacity of the supply chain with time t.

[0046] Further improvement of the technical scheme of the present application is that in the supply chain optimization process, the system optimization cycle step comprises:

[0047] S1: demand prediction and preprocessing: based on the multi-objective optimization model, the real-time data collected from each link of the supply chain is detected and processed to form a market demand prediction function M(t), and the prediction result is preprocessed, Wherein M(t) is the prediction function, g(t-τ) is the response function or weight function, indicating the influence decay of past data, X(τ) is the observation value at time τ, and M(t) mainly predicts any one value of inventory level I, transportation state T, transportation speed V and purchase quantity Q;

[0048] S2: based on the current supply chain state and demand prediction result, preliminary optimization calculation is performed to obtain preliminary supply chain decision;

[0049] S3: based on the preliminary optimization result, a complex optimization engine is applied for multi-objective optimization calculation to solve the optimal decision variable x, so that the objective function F(X) reaches the minimum value;

[0050] S4: based on the real-time monitoring data, the intelligent decision support module dynamically adjusts the multi-objective optimization model, and implements rapid re-optimization according to the changes of market and supply chain state.

[0051] Further improvement of the technical scheme of the present application is that a logistics supply chain management system is used to realize the logistics supply chain management method, characterized by comprising a data acquisition and preprocessing module, a demand prediction and preliminary optimization module, a multi-objective optimization calculation module, a decision execution and dynamic adjustment module, a feedback and continuous optimization module and an electrical connection.

[0052] Due to the adoption of the above technical scheme, the present application has the following technical progress compared with the prior art:

[0053] 1. The present application provides an innovative logistics supply chain management method and system, which applies complex multi-objective optimization algorithm and dynamic adjustment strategy. This method not only considers multiple key factors such as cost, time, resource utilization rate, but also can intelligently adjust in the real-time changing market environment, so as to ensure that the overall efficiency of the supply chain is significantly improved.

[0054] 2、The logistics supply chain management method and system provided by the application can significantly reduce the total operation cost of the supply chain by optimizing inventory, transportation and production strategies in detail, can accurately predict market demand, reasonably arrange production plan and inventory level, avoid cost waste caused by excessive inventory or shortage, further reduces logistics cost and transportation time by optimizing transportation route and distribution mode, so that the overall operation cost of the supply chain is effectively controlled, and the profitability and market competitiveness of the enterprise are significantly improved.

[0055] 3、The logistics supply chain management method and system provided by the application can significantly improve customer service experience by ensuring on-time delivery and order satisfaction rate through advanced optimization algorithm, can track order status in real time, accurately predict delivery time, and make rapid adjustment when necessary to ensure that orders can be delivered on time, at the same time, reduces order delay and shortage by optimizing production plan and inventory management, improves order satisfaction rate, under the joint action of these measures, customers can enjoy more reliable and efficient service, and customer satisfaction and loyalty are enhanced.

[0056] 4、The logistics supply chain management method and system provided by the application can maintain high adaptability and low risk level of the supply chain through the ability to quickly respond to market demand changes, can timely adjust the multi-objective optimization model by monitoring market dynamics and customer demand in real time to ensure that the changing market demand can be met, at the same time, reduces the risk faced by the supply chain through diversified supplier selection and risk management strategy, improves the stability and reliability of the supply chain. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0058] Figure 1 The flowchart of the logistics supply chain management method and system of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0060] Embodiment 1, as shown, the present application provides a logistics supply chain management method, step 1, data acquisition and pretreatment module: data acquisition and pretreatment module includes data acquisition unit and data pretreatment unit, data acquisition unit through the multi-layer data acquisition, obtain the real-time data of each link of supply chain, data pretreatment unit based on the data collected by data acquisition unit standardization processing and aggregation processing; Figure 1

[0061] Step 2, demand forecasting and preliminary optimization module: based on the data of data acquisition and pretreatment module, the demand of market is predicted, and the prediction optimization model of demand is generated, based on the current supply chain state and the predicted market demand, the preliminary optimization calculation is executed, and the preliminary supply chain decision is determined;

[0062] Step 3, multi-objective optimization calculation module: based on the demand forecasting and preliminary optimization module, a multi-objective optimization model is constructed to optimize the preliminary supply chain decision;

[0063] Step 4, decision execution and dynamic adjustment: based on the optimized supply chain decision, the application is carried out in the logistics supply chain, the state of supply chain is monitored in real time, based on the latest data and optimization result;

[0064] Step 5, feedback and continuous optimization: based on the decision execution and dynamic adjustment module, the operation effect of supply chain is monitored and evaluated continuously, and based on the historical data and real-time monitoring result, the multi-objective optimization model is optimized.

[0065] Demand forecasting and supply chain simulation, dynamic management of inventory level, avoid the cost waste caused by too much or insufficient inventory, in the aspect of transportation strategy, the system can optimize the transportation route and transportation mode based on real-time traffic data and historical transportation record, reduce the transportation time and transportation cost, at the same time, in the optimization of production strategy, the system can flexibly adjust the production plan according to the real-time change of market demand and production capacity, maximize the utilization efficiency of equipment and human resources, avoid overproduction or shortage, through the above optimization measures, the system not only significantly reduces the operation cost of supply chain, but also improves the resource utilization rate and production efficiency, thereby bringing greater economic benefits to enterprises.

[0066] The data acquisition unit acquires real-time data of each link of supply chain through multi-layer data acquisition, wherein x is the supply chain decision variable vector, including inventory level I, transportation state T, transportation speed V and purchase quantity Q;

[0067] The data pretreatment unit carries out standardization processing and aggregation processing based on the data collected by the data acquisition unit, which is as follows:

[0068] Standardization formula:

[0069] ​

[0070] where D 标准 is the original data set, even for inventory level I, transportation status T, transportation speed V and purchase quantity Q, D 原始 is the standardized data set, μ is the mean of D 标准 , and σ is the standard deviation of D 标准 .

[0071] The data set formed by the multi-level data aggregation of the data preprocessing unit is as follows:

[0072]

[0073] where ω i is the data weight of different levels, is the standardized data of the i-th level, D agg is the multi-level data set formed, where n represents the total number of levels of data.

[0074] The multi-objective optimization model is as follows:

[0075]

[0076] where C(x), E(x), S(x), C(x), P(x), R(x), A(x) respectively represent the total cost function of the supply chain, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain and the adaptability function of the supply chain under given production parameters, α, β, γ, δ, ∈ are weight coefficients, F(x) is the objective function, and x is the supply chain decision variable vector.

[0077] The objective function includes the total cost function of the supply chain, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain and the adaptability function of the supply chain, which are represented as C(x), E(x), S(x), C(x), P(x), R(x), A(x) respectively, and the specific analysis is as follows:

[0078] The total cost function of the supply chain is: C(x) = C p (Q) + C s (I) + C t (T) + C h (V)

[0079] where C p (Q), C s (I), C t (T), C h (V) represent the purchase cost, storage cost, transportation cost and labor cost respectively.

[0080] The overall efficiency function of the supply chain is further represented as:

[0081]

[0082] where E P , E t are production efficiency and transportation efficiency, respectively, λ is the penalty coefficient of transportation delay, and d(T) is the transportation delay time.

[0083] The customer service level function is further represented as:

[0084] S(x) = ω d · S d (T) + ω O · S O (Q)

[0085] where ω d , ω O are the weights of on-time delivery rate and order fulfillment rate, respectively, S d , S O are on-time delivery rate and order fulfillment rate, respectively.

[0086] The environmental impact function is further represented as:

[0087] P(x) = θ c · P c (T) + θ e · P e (V)

[0088] where P c , P e are carbon emission and energy consumption, respectively.

[0089] The risk function of the supply chain is further represented as:

[0090] R(x) = κ i · R i (Q) + κ m · R m (D agg )

[0091] where κ i , κ m are the weights of each risk type, R i , R m are disruption risk and market fluctuation risk, respectively.

[0092] The adaptability function of the supply chain is further represented as:

[0093]

[0094] Wherein η is the adjustment coefficient of the response speed, M(t) is the market demand prediction function, dM(t), dQ(x) respectively represent the change rate of market demand and the change rate of production or supply capacity of the supply chain with time t.

[0095] Based on the optimization step of the cycle, the collected real-time data of each link of the supply chain is detected and processed based on a multi-objective optimization model to form a market demand prediction function M(t), and the prediction result is preprocessed and dynamically adjusted strategy, which further optimizes the logistics supply chain management system.

[0096] The multi-objective optimization algorithm combines multiple key factors in the supply chain, including the total cost function, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain, and the adaptability function of the supply chain. By optimizing these factors, the system can make optimal decisions under different operating conditions. The algorithm can balance cost, efficiency, and service quality in different logistics scenarios to achieve overall optimization of the supply chain. Compared with traditional static planning methods, the dynamic adjustment strategy of the present application can significantly reduce supply chain disruptions or delays caused by unexpected events and improve overall operational efficiency.

[0097] In the supply chain optimization process, the system optimization cycle step is:

[0098] S1: Demand prediction and preprocessing: based on a multi-objective optimization model, the collected real-time data of each link of the supply chain is detected and processed to form a market demand prediction function M(t), and the prediction result is preprocessed.

[0099]

[0100] Wherein M(t) is the prediction function, g(t-τ) is the response function or weight function, indicating the influence decay of past data, X(τ) is the observation value at time τ, M(t) mainly predicts any one of the values of inventory level I, transportation status T, transportation speed V and purchase quantity Q, so as to detect whether the multi-objective optimization model

[0101]

[0102] Meets the standards of logistics supply chain management.

[0103] S2: Based on the current supply chain state and demand prediction result, preliminary optimization calculation is carried out.

[0104] Based on The current supply chain state and demand prediction result are judged, and the model is further optimized, i.e. the collected data including inventory level I, transportation status T, transportation speed V and purchase quantity Q are updated.

[0105] S3: On the basis of the preliminary optimization results, a complex optimization engine is applied for multi-objective optimization calculation to solve the optimal decision variable x, so that the objective function F(X) reaches a minimum value;

[0106] The decision variable x, even for the inventory level I, the transportation state T, the transportation speed V and the purchase quantity Q, the objective function F(X) is Minimize

[0107] Wherein C(x), E(x), S(x), C(x), P(x), R(x), A(x) respectively represent the total cost function of the supply chain under the given production parameters, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain and the adaptability function of the supply chain, and α, β, γ, δ, ∈ are weight coefficients, and F(x) is the objective function.

[0108] S4: Based on the intelligent decision support module, the multi-objective optimization model X0 is dynamically adjusted according to real-time monitoring data, and rapid re-optimization is carried out according to the changes of market and supply chain state.

[0109] The system of the application can track the order state, the transportation progress and the production process in real time through an advanced optimization algorithm, ensure that each link is within the plan, intelligently allocate resources according to the priority and urgency of customer orders, adjust the transportation and production plan to ensure that the orders are delivered on time, and at the same time, the system has an automatic exception handling mechanism, which can immediately start an emergency plan such as reallocating transportation resources or adjusting production progress as soon as potential delay risks are detected, thereby avoiding order delays to the greatest extent. In addition, by maintaining real-time information communication with customers, the system can inform customers in advance about possible delays or changes, thereby enhancing customer trust and satisfaction.

[0110] A logistics supply chain management system for realizing a logistics supply chain management method, characterized by comprising a data acquisition and preprocessing module, a demand prediction and preliminary optimization module, a multi-objective optimization calculation module, a decision execution and dynamic adjustment module, a feedback and continuous optimization module and an electrical connection.

[0111] Working principle: The staff obtains real-time data of each link of the supply chain by using the data acquisition unit in the data acquisition and preprocessing module, that is, x is a supply chain decision variable vector, including inventory level I, transportation state T, transportation speed V and purchase quantity Q, and the data preprocessing unit is used to standardize and aggregate the data collected by the data acquisition unit, and the standardization and aggregation are as follows:

[0112] Standardization formula:

[0113]

[0114] where D 标准 The original data set, even for inventory level I, transportation status T, transportation speed V and purchase quantity Q, D 原始 is the standardized data set, μ is the mean of D 标准 , and σ is the standard deviation of D 标准 ;

[0115] The data set formed by the multi-level data aggregation of the data preprocessing unit is specifically as follows:

[0116]

[0117] where ω i is the data weight of different levels, is the standardized data of the i-th level, D agg is formed into a multi-level data set, where n represents the total number of levels of data,

[0118] After that, the demand of the market is predicted by using the data based on the data acquisition and preprocessing module, and a prediction optimization model of demand is generated, based on the current supply chain state and the predicted market demand, a preliminary optimization calculation is performed to determine the preliminary supply chain decision, and then a multi-objective optimization model is constructed based on the demand prediction and preliminary optimization module to optimize the preliminary supply chain decision, and the multi-objective optimization model is specifically as follows:

[0119]

[0120] Wherein C (x), E (x), S (x), C (x), P (x), R (x), A (x) respectively represent the total cost function of the supply chain, the overall efficiency function of the supply chain, the customer service level function, the environmental impact function, the risk function of the supply chain and the adaptability function of the supply chain under given production parameters, alpha, beta, gamma, delta, epsilon are weight coefficients, F (x) is the objective function, x is the supply chain decision variable vector, then based on the optimized supply chain decision, it is applied to the logistics supply chain, the state of the supply chain is monitored in real time, based on the latest data and the optimization result, then based on the decision execution and dynamic adjustment module, the operation effect of the supply chain is continuously monitored and evaluated, and based on the historical data and real-time monitoring result, the multi-objective optimization model is optimized, the system of the application can quickly analyze market trends and demand changes and make corresponding adjustments by integrating advanced prediction models and flexible scheduling strategies, the system can not only adjust inventory levels and production plans in real time, but also optimize supplier selection and transportation arrangements to respond to market changes, in addition, the risk management module of the system can monitor potential risks in the supply chain in real time, such as supplier supply capacity, natural disasters during transportation, and develop response strategies in advance to reduce the risk of supply chain interruption.Through these functions, the system ensures the high adaptability of the supply chain, so that enterprises can maintain an advantage in the competitive market while minimizing operational risks.

[0121] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of managing a supply chain of goods, characterized by: Comprising the following steps: Step 1, data acquisition and preprocessing module: the data acquisition and preprocessing module comprises a data acquisition unit and a data preprocessing unit, the data acquisition unit acquires real-time data of each link of the supply chain by collecting multi-layer data, and the data preprocessing unit performs standardization processing and aggregation processing based on the data collected by the data acquisition unit; Step 2, demand prediction and preliminary optimization module: based on the data of the data acquisition and preprocessing module, the demand of the market is predicted, and a demand prediction optimization model is generated, based on the current supply chain state and the predicted market demand, a preliminary optimization calculation is performed to determine the preliminary supply chain decision; Step 3, multi-objective optimization calculation module: based on the demand prediction and preliminary optimization module, a multi-objective optimization model is constructed to optimize the preliminary supply chain decision, the multi-objective optimization model is as follows: ； wherein respectively represent a total cost function of the supply chain, an overall efficiency function of the supply chain, a customer service level function, an environmental impact function, a risk function of the supply chain and an adaptability function of the supply chain under given production parameters, is a weight coefficient, is an objective function, is a supply chain decision variable vector; Step 4, decision execution and dynamic adjustment: based on the optimized supply chain decision, it is applied in the logistics supply chain, the state of the supply chain is monitored in real time, and the latest data and optimization results are based on; Step 5, feedback and continuous optimization: based on the decision execution and dynamic adjustment module, the operation effect of the supply chain is continuously monitored and evaluated, and based on the historical data and real-time monitoring results, the multi-objective optimization model is optimized, and in the supply chain optimization process, the system optimization cycle step: S1: demand prediction and preprocessing: based on the multi-objective optimization model, the real-time data collected from each link of the supply chain is detected and processed to form a market demand prediction function M(t), and the prediction results are preprocessed: wherein is a prediction function, is a response function or a weight function, indicating the decay of the influence of past data, is time of the observation value, any one of the inventory level I, the transport status T, the transport speed V and the purchase quantity Q is mainly predicted; S2: based on the current supply chain state and the demand prediction result, a preliminary optimization calculation is performed; S3: On the basis of the preliminary optimization result, a complex optimization engine is applied to carry out multi-objective optimization calculation to solve the optimal decision variable x so that the objective function reaches a minimum value. S4: based on the real-time monitoring data, the intelligent decision support module dynamically adjusts the multi-objective optimization model, and quickly re-optimizes according to the changes of the market and the supply chain state.

2. The method of claim 1, wherein: The data acquisition unit acquires real-time data of each link of the supply chain by collecting multi-layer data, wherein x is a supply chain decision variable vector, including inventory level I, transportation state T, transportation speed V and purchase quantity Q; The data preprocessing unit performs standardization processing and aggregation processing based on the data collected by the data acquisition unit, which is as follows: The standardization formula is as follows: ； wherein denotes the original data set comprising the inventory level I, the transport status T, the transport speed V and the purchase quantity Q, is the standardized data set, is the mean value of is the standard deviation of is the mean value of is the standard deviation of The data set formed by the multi-level data aggregation of the data preprocessing unit is as follows: ； wherein is the data weight for different levels, is the standardized data for the i-th level, is the formation of a multi-level data set, wherein n levels of data in total are represented.

3. The method of claim 1, wherein: The objective function includes a total cost function of the supply chain, an overall efficiency function of the supply chain, a customer service level function, an environmental impact function, a risk function of the supply chain, and an adaptability function of the supply chain, respectively represented as The specific analysis is as follows: The total cost function of the supply chain is: ​ wherein are respectively represented as procurement cost, storage cost, transportation cost and labor cost.

4. The method of claim 1, wherein: The overall efficiency function formula of the supply chain is further represented as: ； wherein respectively production efficiency and transport efficiency, is a penalty coefficient for transport delay, is a transport delay time.

5. The method of claim 1, wherein: The customer service level function formula is further represented as: ； wherein respectively the weight of on-time delivery rate and order fulfillment rate, respectively the on-time delivery rate and order fulfillment rate.

6. The method of claim 1, wherein: The environmental impact function formula is further represented as: ； wherein respectively represent carbon emissions and energy consumption, respectively represent weights of carbon emissions and energy consumption; The risk function formula of the supply chain is further represented as: ； wherein are represented as weights for each risk type, are represented as interruption risk and market fluctuation risk, respectively.

7. The method of claim 1, wherein: The adaptability function of the supply chain is further represented as: ； wherein is the adjustment coefficient for the speed, is the market demand prediction function, respectively represent the rate of change of the market demand and the rate of change of the production or supply capacity of the supply chain over time t.

8. A logistics supply chain management system for implementing the logistics supply chain management method according to any one of claims 1 to 7, characterized in that: The data acquisition and preprocessing module, the demand prediction and preliminary optimization module, the multi-objective optimization calculation module, the decision execution and dynamic adjustment module, the feedback and continuous optimization module are electrically connected.

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