Sudden change early warning model and sudden change control method of e-commerce supply network

Abstract

The invention discloses a sudden change early warning model and sudden change control method of an e-commerce supply network, and belongs to the technical field of e-commerce and supply chain management. Aiming at a cascade failure risk of an e-commerce supply network under an emergent public event, a multi-dimensional network model is constructed through a super network theory, a complex interaction relationship among suppliers, logistics nodes, an e-commerce platform and consumers is represented, and a network structure is dynamically updated based on real-time data. A network critical state is analyzed by adopting a mutation theory, and real-time early warning of mutation risks is realized in combination with an intelligent optimization algorithm and a multi-agent simulation technology. According to the control method, through the process steps of dynamic monitoring, short-term optimization adjustment, intervention opportunity prediction, long-term topology reconstruction and effect evaluation, the sudden change influence is effectively reduced, and the network stability is optimized. Static limitation of a traditional model is broken through, early warning accuracy, response speed and network toughness are remarkably improved, and an innovative solution is provided for an e-commerce platform to maintain efficient operation under emergencies.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce and supply chain management, and in particular to a mutation early warning model and mutation control method for an e-commerce supply network. Background Art

[0002] With the rapid development of e-commerce, e-commerce supply networks have become the core infrastructure for global economic operations and people's livelihoods, encompassing complex interactions between multiple entities such as suppliers, logistics nodes, e-commerce platforms, and consumers. In particular, under the impact of public emergencies such as epidemics and trade frictions, the stability and resilience of e-commerce supply networks face severe challenges. Due to their rapid spread, wide impact, and high uncertainty, emergencies often cause node failures or connection interruptions in the network, leading to cascading failures and, in turn, nonlinear mutations in network efficiency or benefits. For example, during the COVID-19 pandemic in early 2020, some e-commerce platforms experienced a drop in order fulfillment rates of approximately 50% and revenue losses of up to 73.6% due to supply chain disruptions, highlighting the shortcomings of traditional supply network management methods in responding to emergencies.

[0003] Existing research primarily explores the stability of e-commerce supply networks from three perspectives: network optimization, risk assessment, and emergency management. In terms of network optimization, traditional approaches are often based on binary relational network models (such as node-edge graph models) to improve efficiency by optimizing logistics routes or inventory allocation. However, these models struggle to fully characterize the multidimensional characteristics of e-commerce supply networks (such as the interactions among transaction flows, logistics flows, and information flows), resulting in insufficient analysis of the dynamic evolution of complex networks. For example, supply chain optimization models based on bipartite graphs only consider the direct relationship between suppliers and platforms, ignoring the multiple impacts of consumer demand fluctuations and logistics delays. In terms of risk assessment, studies often use static metrics (such as node degree and clustering coefficient) to analyze network vulnerability. However, these studies lack the ability to monitor dynamic mutation mechanisms under emergencies in real time, resulting in low accuracy of early warning signals, with an average false alarm rate of approximately 30%. In terms of emergency management, existing approaches often rely on post-event remedial measures, such as temporarily increasing inventory or switching suppliers. These approaches lack pre-emptive optimization strategies based on network evolutionary laws, resulting in response times often exceeding 24 hours and ineffective containment of cascading failures.

[0004] In recent years, hypernetwork theory has provided a new perspective for modeling multidimensional complex networks. By introducing hyperedges to describe non-binary relationships between multiple agents, it can more accurately characterize the multi-layered structure of e-commerce supply networks. However, existing hypernetwork research has primarily focused on static topological analysis, with limited attention to dynamic update mechanisms (such as the real-time addition and deletion of nodes and hyperedges), making it unable to adapt to the rapid changes in network structure during emergencies. Furthermore, while catastrophe theory has potential for analyzing sudden changes in nonlinear systems, its application to e-commerce supply networks is largely limited to theoretical modeling, lacking dynamic early warning and control methods that combine real-time data with intelligent algorithms. For example, supply chain risk models based on catastrophe theory typically assume static parameters, and their prediction accuracy is limited by data timeliness, with an average error of approximately 25%.

[0005] In summary, existing technologies have the following limitations when dealing with sudden changes in e-commerce supply networks:

[0006] Model limitations: Traditional binary network models cannot effectively represent multidimensional and multi-layer structures, resulting in an incomplete analysis of mutation mechanisms and ignoring the coupling effects of transactions, logistics, and information flows.

[0007] Insufficient dynamic monitoring: The lack of real-time monitoring and early warning mechanisms makes it difficult to accurately identify the critical state of mutations. The early warning response time is long, with an average delay of about 12 hours.

[0008] Control strategy lags: Existing mutation control methods are mostly passive responses and lack active optimization strategies based on dynamic evolution laws, making it difficult to effectively reduce the risk of cascading failures.

[0009] Therefore, in view of the shortcomings of the existing technology, the present invention proposes a mutation warning model and mutation control method for e-commerce supply networks to solve the above problems. Summary of the Invention

[0010] The purpose of the present invention is to solve the existing technical problems raised in the above background technology, and to provide a mutation warning model and mutation control method for e-commerce supply networks.

[0011] The present invention achieves the above-mentioned object through the following scheme:

[0012] The sudden change warning model for e-commerce supply networks includes:

[0013] Hypernetwork construction module: used to collect multi-subject data in the e-commerce supply network. Multi-subject data includes supplier data, logistics node data, e-commerce platform data, and consumer data. Based on multidimensional relationships, a hypernetwork model is constructed. The graph theory method is used to define it as G = (N, E, W), where N represents the node set, which contains the identification information of all subjects, E represents the hyperedge set, which represents the complex interaction between multiple subjects, W is the weight matrix, and the weight w ij Through the node load Li and connection strength S ij Determine the node load L i Based on the calculation of transaction volume and inventory, the connection strength S ij Based on the weighted average of data transmission frequency and logistics efficiency, the calculation formula is w ij =k1·L i +k2·S ij , where L i =0.6·T i +0.4·S i , T i is the node transaction volume, S i is the inventory occupancy rate, S ij =0.5·(1 / T ij )+0.5·P ij , T ij is the logistics time, P ij is the information transmission success rate, k1 and k2 are empirical weight coefficients, satisfying k1+k2=1;

[0014] Hypernetwork dynamic update module: used to dynamically adjust N and E based on real-time collected time series data T(t). The update formulas are N(t+1)=N(t)+ΔN(t) and E(t+1)=E(t)+ΔE(t), where ΔE(t) is the number of newly added nodes and ΔE(t) is the number of newly added hyperedges. New hyperedges are automatically generated based on node relevance. Time series data T(t) includes transaction volume, logistics delay, and node status.

[0015] State set analysis module based on mutation theory: uses state set theory to analyze the critical state of network mutation;

[0016] Laser intelligent algorithm real-time monitoring module: Dynamically monitor network parameters through iterative optimization technology;

[0017] Multi-agent simulation module: Based on agent technology, it simulates the impact of emergencies on the network and generates risk assessment reports.

[0018] As a preferred technical solution of the present invention, the super network dynamic update module adjusts the topology structure based on the constraint condition ΔE(t)≤α·ΔN(t), where α is the network connection density parameter, which is determined by Monte Carlo simulation optimization, ΔN(t) is determined by the number of backup suppliers or logistics nodes detected by the system, and ΔE(t) is generated by the correlation analysis between the newly added nodes and the existing nodes.

[0019] As a preferred technical solution of the present invention, the state set analysis module uses the mutation critical state formula Identify the mutation region, where is the load ratio of node i, θ iis the load critical threshold, determined based on historical failure data statistics, n is the total number of nodes, when C exceeds the preset critical value C crit When the warning signal is triggered, the preset critical value C crit Derived through regression analysis of historical data.

[0020] As the preferred technical solution of the present invention, the laser intelligent algorithm real-time monitoring module adopts the least squares optimization objective function Monitoring node load deviation, where L opt is the optimal load value, calculated based on the percentage of the node's maximum capacity, and the update formula is Where η is the learning rate, and the dynamic adjustment range of η is 0.001-0.1. The algorithm is based on TensorFlow's optimizer and iterates every predetermined time interval.

[0021] As a preferred technical solution of the present invention, the multi-agent simulation module predicts the mutation risk through the simulation equation R(t)=β·F(t)+(1-β)·H(t), where β is a weight factor (range 0 to 1), F(t)=1-e -λ·C , where λ is the failure rate parameter, based on the failure event frequency fitting, N r is the number of repair personnel, T r is the mean time to repair, and the simulation step size is a configurable time interval.

[0022] The present invention also provides a method for controlling mutations in an e-commerce supply network, comprising the following steps:

[0023] Step a: Dynamically monitor and evaluate network status and calculate mutation status in real time When C exceeds C crit When the mutation control process is triggered, the network efficiency is evaluated. T i is the node throughput, T max To maximize throughput, generate the initial optimization objective function U = max(E-γ·C), where γ is the penalty coefficient;

[0024] Step b: Short-term optimization adjustment, according to the C value of step a, the adaptive mutation suppression algorithm is used to adjust the hyperedge weight w ij , the update formula is w ij (t+1)=w ij (t)·(1-δ·ΔC), where ΔC=C(t+1)-C(t) and δ is the adjustment coefficient. Optimization is performed on a distributed computing cluster to reduce C to a safe range.

[0025] Step c: Predicting the timing of intervention, based on the network status adjusted in step b, using big data to drive the optimization model and train the linear regression model T opt =a·C+b·F(t)+c (a, b, c are fitted by the least square method, F(t) comes from claim 5), when T opt <T curr (current time), trigger intervention instructions to perform load redistribution or logistics path optimization;

[0026] Step d: Long-term topology reconstruction, based on the intervention results of step c, is performed by formula Adding a spare node, ΔN k Filter based on response time, meet (μ is the upper limit of growth), and after reconstruction, the network connectivity and efficiency E are verified;

[0027] Step e: Optimization effect evaluation. Based on the execution results of steps a to d, calculate the comprehensive index Q = ω1·E + ω2·(1-F) + ω3·R, where ω1, ω2, and ω3 are weight factors, satisfying ω1+ω2+ω3=1, and R comes from claim 5). When ΔQ exceeds the preset threshold, the optimization is confirmed to be successful.

[0028] As a preferred technical solution of the present invention, the adaptive mutation suppression algorithm in step b triggers weight adjustment when ΔC exceeds a preset threshold, and the dynamic adjustment range of δ is 0.1 to 0.5. After adjustment, the decrease in C is verified.

[0029] As a preferred technical solution of the present invention, the big data driven optimization model in step c is trained based on historical emergency records. opt The unit is hour. The intervention instructions are distributed to the load balancer through the message queue to optimize the logistics path or node allocation.

[0030] As a preferred technical solution of the present invention, in the topology reconstruction in step d, m is the number of spare nodes, the screening condition is that the supplier response time is less than a preset value, and the efficiency verification includes minimum path length and throughput tests.

[0031] As a preferred technical solution of the present invention, the comprehensive indicator Q in step e is optimized successfully when ΔQ exceeds Q base The evaluation period is a configurable time interval.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention greatly improves the early warning capability of the e-commerce supply network in emergencies (such as epidemics) through an innovative super-network modeling method. Compared with traditional methods that can only handle simple two-point relationships, the present invention can comprehensively capture the multiple complex interactions between suppliers, logistics nodes, e-commerce platforms and consumers, and monitor changes in network status in real time. The system can quickly issue early warning signals, and the response speed is significantly faster than traditional methods. In addition, by predicting possible chain failures through advanced simulation technology, the accuracy of early warnings is greatly improved, and false alarms are significantly reduced. This enables e-commerce platforms to identify potential risks more promptly and reliably, laying a solid foundation for subsequent responses.

[0034] The present invention has designed a set of efficient control processes, which effectively respond to the challenges brought by network mutations through five steps: real-time monitoring, rapid adjustment, intervention timing prediction, long-term optimization and effect evaluation. Once a risk is discovered, the system can quickly adjust the logistics route or redistribute the workload to quickly alleviate network pressure, and the intervention speed far exceeds the traditional passive response method. Data analysis technology helps the system accurately determine the best time to intervene and automatically trigger optimization measures, such as selecting more efficient logistics routes. In the long run, the system optimizes the network structure by adding backup suppliers and other measures to further improve operational efficiency. This process-based control method achieves a smooth transition from emergency response to long-term stability, significantly improving management efficiency.

[0035] By closely integrating early warning and control, the present invention significantly enhances the resilience and economic benefits of e-commerce supply networks in the face of emergencies. The system can effectively reduce the risk of cascading failures, accelerate network recovery, and ensure service continuity. Thanks to timely intervention and optimization of the network structure, the platform's operational stability has been significantly improved, and it can maintain a higher order fulfillment capacity in the face of emergencies. From an economic perspective, rapid response and long-term optimization measures have effectively reduced losses caused by network interruptions, saving operating costs for the platform. Through a comprehensive solution of real-time monitoring and proactive optimization, the present invention provides a sustainable solution for e-commerce platforms to maintain stable operations during crises. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to 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 any creative work.

[0037] Figure 1 This is a model diagram of the sudden change warning model of the e-commerce supply network of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] The present invention provides a mutation warning model and mutation control method for e-commerce supply networks, which aims to cope with the dynamic monitoring and optimization needs of e-commerce supply networks under public emergencies through super network theory, mutation theory and intelligent optimization algorithm. The following specific implementation methods describe in detail the technical implementation steps, parameter calculation methods and implementation processes to ensure that technical personnel can understand and reproduce. The implementation of this method is based on a distributed computing architecture, and the core development environment includes Python's NetworkX library (for super network modeling), TensorFlow (for laser intelligent algorithm optimization), AnyLogic (for multi-agent simulation) and Kafka message queues (for real-time data distribution). The hardware environment is a server cluster with 8-core CPUs and 32GB of memory. Data collection is completed through the ERP system API of the e-commerce platform, and the collection frequency is once per hour.

[0040] Supernetwork construction and dynamic update: Based on the supernetwork construction module, multi-subject data in the e-commerce supply network is collected, including 50 suppliers (providing goods), 30 logistics nodes (responsible for transportation), 10 e-commerce platforms (processing orders) and 10 consumer areas (representing demand). The initial total number of nodes is n = 100.

[0041] Based on multidimensional relationships (transaction flow, logistics flow, information flow), a hypernetwork model G = (N, E, W) is constructed, where N is a node set containing the identity, location, and capacity information of the subject; E is a hyperedge set with an initial number of 300 hyperedges, representing the transaction, logistics, and information interaction between subjects; W is a weight matrix with weight w ij Through the node load L i and connection strength S ij Calculation, the formula is w ij =k1·L i +k2·S ij .

[0042] Node load L i =0.6·T i +0.4·S i , where T i is the transaction volume (average 5000 pieces / hour), S i is the inventory occupancy rate (average 60%); connection strength S ij =0.5·(1 / T ij)+0.5·P ij , where T ij is the logistics time (average 8 hours), P ij The success rate of information transmission (95% on average).

[0043] The weight coefficients k1=0.6 and k2=0.4 are fitted by analyzing the transaction data of the past 6 months, satisfying k1+k2=1.

[0044] The hypernetwork dynamic update module adjusts N and E based on time series data T(t), including hourly transaction volume, logistics delays, and node status. The update formulas are N(t+1)=N(t)+ΔN(t) and E(t+1)=E(t)+ΔE(t), where ΔN(t) is the number of newly added nodes (determined by screening alternative suppliers with response time <24 hours), and ΔE(t) is the number of newly added hyperedges (generated through correlation analysis, such as the connection between new suppliers and existing logistics nodes).

[0045] The update must satisfy the constraint ΔE(t) ≤ α·ΔN(t), where α is the network connection density parameter, optimized through Monte Carlo simulation (1000 simulations to select the optimal value for connectivity). In the implementation, α is initially set to 0.5 and dynamically adjusted from 0.3 to 0.7 to ensure that the minimum path length is less than 5 (verified by the Dijkstra algorithm).

[0046] Mutation warning model: The state set analysis module calculates the mutation state in is the node load ratio, θ i is the load critical threshold, based on 5000 historical failure records (node ​​downtime rate>50%), the typical value is 0.8, ranging from 0.7 to 0.9. Initially n = 100, when C exceeds the preset critical value C crit (Through regression analysis of three years of e-commerce failure data, the typical value is 0.7, ranging from 0.6 to 0.8), an early warning signal is triggered and the warning is sent to the control system through the Kafka message queue, with a response time of <1 second.

[0047] Laser intelligent algorithm real-time monitoring module optimizes the objective function Among them L opt is the optimal load value, set as the maximum capacity of the node L max 60% (typical value 3000 pieces / hour). Update formula is Where η is the learning rate (initial 0.01, dynamic adjustment range 0.001 to 0.1), The algorithm is based on TensorFlow's Adam optimizer, iterates every 10 seconds, the training data is the load log of the past 6 months, and the number of iterations is set to 100 or convergence (J change < 0.01).

[0048] According to claim 5, the multi-agent simulation module uses AnyLogic software to define 100 agents (50 suppliers, 30 logistics, 10 platforms, and 10 consumers) to simulate the network response to an emergency (such as an epidemic blockade). The simulation equation is R(t) = β·F(t) + (1-β)·H(t), where β is a weight factor (initial 0.6, ranging from 0 to 1, adjusted based on risk priority), and F(t) = 1-e -λ·C is the probability of cascading failure, λ is the failure rate parameter (fitted by the failure event frequency, typical value 0.5, range 0.3 to 0.7), is the node recovery rate (N r is the number of repair personnel, with a typical value of 5; T r is the average repair time, with a typical value of 4 hours), the simulation step size is 5 minutes, and it is run 100 times to generate a risk assessment report.

[0049] Mutation control method: The mutation control method includes the following ordered steps to form a complete process, and the steps are connected by data and state transfer:

[0050] Step a: Dynamically monitor and evaluate network status

[0051] Based on the early warning model, C is calculated in real time. When C>C crit , triggering the control process and evaluating the network efficiency Where T i is the node throughput (typical value 40,000 items / hour), T max For the maximum throughput (typical value 6000 pieces / hour), the optimization objective function U = max(E-γ·C) is generated, where γ is the penalty coefficient (typical value 0.3, range 0.2 to 0.4, based on the mutation impact weight). This step collects real-time data through Kafka, the calculation period is once per minute, and the results are stored in the Redis cache.

[0052] Step b: Short-term optimization adjustment: According to the C value of step a, the hyperedge weight w is adjusted using the adaptive mutation suppression algorithm ij , the update formula is w ij (t+1)=w ij(t)·(1-δ·ΔC), where ΔC = C(t+1)-C(t), and δ is the adjustment coefficient (initial 0.2, dynamic range 0.1 to 0.5). When ΔC > 0.1 (a preset threshold based on historical data fluctuation analysis), an adjustment is triggered. This is executed in parallel on a distributed computing cluster (using the Spark framework). The adjustment verifies the decrease in C (target 10%) and ensures network stability (connectivity test passes). This step follows the C in step a and provides an optimized network state for subsequent interventions.

[0053] Step c: Prediction of intervention timing: Based on the adjusted C and F(t) in step b, use the big data driven optimization model to predict the intervention timing. Train the linear regression model T opt =a·C+b·F(t)+c, where a, b, and c are fitted to 5000 historical emergency records using the least squares method (typical values ​​are a=0.4, b=0.3, and c=-0.1, with the range adjusted based on data distribution). opt The unit is hours. When T opt <T curr At (current time), an intervention command is triggered and distributed to the load balancer via Kafka, performing load redistribution (reducing reliance on high-load nodes) or logistics route optimization (preferring routes with a duration of less than 6 hours). This step continues the optimization results of step b and prepares data for long-term reconstruction.

[0054] Step d: Long-term topology reconstruction: According to the intervention results of step c, the formula Adding a spare node, ΔN k Based on the supplier screening with response time < 24 hours (querying the supplier database through API), m is the number of spare nodes (initial 5, range 3 to 10), satisfying the constraints μ is the upper limit of growth (typical value 0.2, range 0.1 to 0.3, based on cost constraints).

[0055] The reconstruction steps include:

[0056] (1) Node evaluation (screening supplier credit > 90%);

[0057] (2) Connectivity test (verify that the path length is < 5 after adding a hyperedge);

[0058] (3) Efficiency verification (E improvement > 5%).

[0059] This step follows the intervention in step c and enhances network resilience.

[0060] Step e: Optimization effect evaluation: Based on the execution results of steps a to d, calculate the comprehensive index Q = ω1·E + ω2·(1-F) + ω3·R, where ω1, ω2, and ω3 are weight factors (typical values ​​are 0.4, 0.3, and 0.3, satisfying ω1+ω2+ω3=1, and the range is adjusted based on priority), and F and R are from claim 5. The evaluation cycle is once a week, and when ΔQ>0.1·Q base (Q base The optimization was successful when ∑ is the baseline value before optimization (typical value is 0.5). The results were stored in a MySQL database for subsequent analysis.

[0061] During the implementation process, it is important to note that:

[0062] Data processing: Real-time data is cleaned using the Flink stream processing framework, outliers (such as transaction volume > 10,000 items / hour) are filtered, and stored in HDFS.

[0063] Algorithm deployment: The laser intelligent algorithm and regression model are deployed in Kubernetes containers, and load balancing is achieved through Nginx.

[0064] Simulation Environment: AnyLogic simulations were run on a standalone server, with event triggering based on Poisson distribution (on average, 1 event per hour).

[0065] Network verification: Connectivity testing uses NetworkX's Dijkstra algorithm, and efficiency testing is performed by comparing throughput (difference before and after optimization).

[0066] Example 1: In the data of a certain e-commerce platform in March 2025, the initial N=100 (50 suppliers, 30 logistics, 10 platforms, 10 consumers), E=300. Calculate L i (Node 1 transaction volume 5000 pieces / hour, inventory occupancy rate 60%), S ij (Logistics time 8 hours, information success rate 95%), w ij =0.6·L i +0.4·S ij Due to foreign tariff policies, five new backup suppliers were added, with ΔN(t) = 5. The association analysis generated two hyperedges, with ΔE(t) = 2 and α = 0.5, satisfying the constraints. After the update, N(t+1) = 105 and E(t+1) = 302. A connectivity test confirmed that the minimum path length, 4.2, is less than 5.

[0067] Example 2: Using the above hypernetwork, the load ratio of node 1 is P1 = 0.2, θ1 = 0.8, C = 0.65 < 0.7, and no warning is triggered. The epidemic situation intensifies, P1 = 0.3, C = 0.75 > 0.7, and a warning is triggered. Laser algorithm optimizes J, L opt=3000 pieces / hour, η=0.01, after 10 iterations L1=2800 pieces. Simulation β=0.6, λ=0.5, F(t)=1-e -0.5·0.75 =0.68, H(t)=0.5, R(t)=0.61, generating a high risk report.

[0068] Example 3: Step a: C = 0.75, E = 0.8, γ = 0.3, U = 0.575. Step b: ΔC = 0.1, δ = 0.2, adjust w ij =0.5·(1-0.2·0.1)=0.49, C drops to 0.68. Step c: T opt =0.4·0.68+0.3·0.68-0.1=0.378 hours, T curr = 1 hour, triggering load redistribution, E increases to 0.85. Step d: Add 3 new suppliers, N new =108, μ = 0.2 satisfies the constraint, E = 0.87. Step e: Q = 0.4 0.87 + 0.3 0.32 + 0.3 0.5 = 0.594, Q base =0.5, ΔQ=0.094<0.1·0.5, the optimization failed to meet the criteria, so we adjusted δ=0.3 and tried again.

[0069] Example 4: Repeat steps a to d, after adjustment, C = 0.65, E = 0.9, F = 0.3, R = 0.6, Q = 0.4 0.9 + 0.3 0.7 + 0.3 0.6 = 0.75, Q base =0.5, ΔQ = 0.25 > 0.05, the optimization is successful. After reconstruction, N new =112, path length 4.5, throughput increased by 10%.

[0070] 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 various embodiments of the present invention.

Claims

1. The sudden change warning model of e-commerce supply network is characterized by: include: Hypernetwork construction module: used to collect multi-subject data in the e-commerce supply network. Multi-subject data includes supplier data, logistics node data, e-commerce platform data, and consumer data. Based on multidimensional relationships, a hypernetwork model is constructed. The graph theory method is used to define it as G = (N, E, W), where N represents the node set, which contains the identification information of all subjects, E represents the hyperedge set, which represents the complex interaction between multiple subjects, W is the weight matrix, and the weight w ij Through the node load L i and connection strength S ij Determine the node load L i Based on the calculation of transaction volume and inventory, the connection strength S ij Based on the weighted average of data transmission frequency and logistics efficiency, the calculation formula is w ij =k1·L i +k2·S ij , where L i =0.6·T i +0.4·S i , T i is the node transaction volume, S i is the inventory occupancy rate, S ij =0.5·(1 / T ij )+0.5·P ij , T ij is the logistics time, P ij is the information transmission success rate, k1 and k2 are empirical weight coefficients, satisfying k1+k2=1; Hypernetwork dynamic update module: used to dynamically adjust N and E based on real-time collected time series data T(t). The update formulas are N(t+1)=N(t)+ΔN(t) and ε(t+1)=E(t)+ΔE(t), where ΔN(t) is the number of newly added nodes and ΔE(t) is the number of newly added hyperedges. New hyperedges are automatically generated based on node relevance. Time series data T(t) includes transaction volume, logistics delay, and node status. State set analysis module based on mutation theory: uses state set theory to analyze the critical state of network mutation; Laser intelligent algorithm real-time monitoring module: Dynamically monitor network parameters through iterative optimization technology; Multi-agent simulation module: Based on agent technology, it simulates the impact of emergencies on the network and generates risk assessment reports.

2. The sudden change warning model for e-commerce supply network according to claim 1 is characterized in that: The super network dynamic update module adjusts the topology structure based on the constraint condition ΔE(t)≤α·ΔN(t), where α is the network connection density parameter determined by Monte Carlo simulation optimization, ΔN(t) is determined by the number of backup suppliers or logistics nodes detected by the system, and ΔE(t) is generated by analyzing the correlation between new nodes and existing nodes.

3. The sudden change warning model for e-commerce supply network according to claim 1 is characterized in that: The state set analysis module uses the mutation critical state formula Identify the mutation region, where is the load ratio of node i, θ i is the load critical threshold, determined based on historical failure data statistics, n is the total number of nodes, when C exceeds the preset critical value C crit When the warning signal is triggered, the preset critical value C crit Derived through regression analysis of historical data.

4. The sudden change warning model for e-commerce supply network according to claim 1 is characterized in that: The laser intelligent algorithm real-time monitoring module adopts the least squares optimization objective function Monitoring node load deviation, where L opt is the optimal load value, calculated based on the percentage of the node's maximum capacity, and the update formula is Where η is the learning rate, and the dynamic adjustment range of η is 0.001-0.

1. The algorithm is based on TensorFlow's optimizer and iterates every predetermined time interval.

5. The sudden change warning model for e-commerce supply network according to claim 1 is characterized in that: The multi-agent simulation module predicts the mutation risk through the simulation equation R(t)=β·F(t)+(1-β)·H(t), where β is a weight factor (range 0 to 1), F(t)=1-e -λ·C , where λ is the failure rate parameter, based on the failure event frequency fitting, R i =N r / T r , N r is the number of repair personnel, T r is the mean time to repair, and the simulation step size is a configurable time interval.

6. A method for controlling sudden changes in an e-commerce supply network based on the sudden change warning model of the e-commerce supply network according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step a: Dynamically monitor and evaluate network status and calculate mutation status in real time When C exceeds C crit When the mutation control process is triggered, the network efficiency is evaluated. T i is the node throughput, T max To maximize throughput, generate the initial optimization objective function U = max(E-γ·C), where γ is the penalty coefficient; Step b: Short-term optimization adjustment, according to the C value of step a, the adaptive mutation suppression algorithm is used to adjust the hyperedge weight w ij , the update formula is w ij (t+1)=w ij (t)·(1-δ·ΔC), where ΔC=C(t+1)-C(t) and δ is the adjustment coefficient. Optimization is performed on a distributed computing cluster to reduce C to a safe range. Step c: Predicting the timing of intervention, based on the network status adjusted in step b, using big data to drive the optimization model and train the linear regression model T opt =a·C+b·F(t)+c (a, b, c are fitted by the least square method, and F(t) comes from claim 5), when T opt <T curr (current time), trigger intervention instructions to perform load redistribution or logistics path optimization; Step d: Long-term topology reconstruction, based on the intervention results of step c, is performed by formula Adding a spare node, ΔN k Filter based on response time, meet (μ is the upper limit of growth), and after reconstruction, the network connectivity and efficiency E are verified; Step e: Optimization effect evaluation. Based on the execution results of steps a to d, calculate the comprehensive index Q = ω1·E + ω2·(1-F) + ω3·R, where ω1, ω2, and ω3 are weight factors, satisfying ω1+ω2+ω3=1, and R comes from claim 5). When ΔQ exceeds the preset threshold, the optimization is confirmed to be successful.

7. The method for controlling sudden changes in an e-commerce supply network according to claim 6, characterized in that: The adaptive mutation suppression algorithm in step b triggers weight adjustment when ΔC exceeds a preset threshold, with a dynamic adjustment range of δ from 0.1 to 0.5, and the decrease in C is verified after adjustment.

8. The method for controlling sudden changes in an e-commerce supply network according to claim 6, characterized in that: The big data driven optimization model in step c is trained based on historical emergency records. opt The unit is hour. The intervention instructions are distributed to the load balancer through the message queue to optimize the logistics path or node allocation.

9. The method for controlling sudden changes in an e-commerce supply network according to claim 6, wherein: In the topology reconstruction in step d, m is the number of spare nodes, the screening condition is that the supplier response time is less than a preset value, and the efficiency verification includes minimum path length and throughput tests.

10. The method for controlling sudden changes in an e-commerce supply network according to claim 6, characterized in that: The comprehensive indicator Q in step e is optimized successfully when ΔQ exceeds Q base The evaluation period is a configurable time interval.

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