A multi-level cyclic bin allocation scheduling method and system

Abstract

This invention relates to the field of electronic digital data processing technology, and discloses a multi-level cyclic bin allocation and scheduling method and system, comprising: extracting the request value from the resource request instruction and comparing it with a benchmark amount; when the request value exceeds a preset trigger ratio, splitting the resource request instruction into an active probe sub-instruction and a frozen main body sub-instruction; issuing the probe sub-instruction and receiving the processing status update signal fed back by the target node; calculating the state transition rate of the probe sub-instruction; when the state transition rate is higher than a preset transition rate threshold, unfreezing the main body sub-instruction and triggering the issuance operation; otherwise, automatically canceling the main body sub-instruction and reducing the dynamic transition factor of the transition credit account. This invention, through the active interaction mechanism of data instruction sharding and timing logic gating, transforms the prediction of uncertain demand into the deterministic verification of real-time physical feedback, solving the system resource locking and scheduling deadlock problem caused by input data distortion in distributed networks.

Description

Technical Field

[0001] This invention belongs to the field of electronic digital data processing technology, and in particular relates to a multi-level cyclic bin quantity allocation and scheduling method and system. Background Technology

[0002] In current modern large-scale logistics networks and supply chain management systems, the turnover efficiency of reusable containers is related to overall operating costs and response speed. As business scale expands and network layers deepen, data-driven resource allocation mechanisms have become the industry mainstream. Existing technologies generally adopt a declaration-response data interaction model, where lower-level nodes upload demand instructions, and the upper-level system calculates allocation instructions based on the global resource pool and historical quota algorithms. This model relies on the accuracy of input data and linear transmission, and achieves resource supply and demand balance through centralized calculation.

[0003] In the face of discontinuous, sudden high-load or multi-level game scenarios, linear data processing models face the risk of systemic failure. Distributed networks exhibit a tendency for nodes to maximize local interests, and lower-level nodes often incorporate defensive false alarm data into demand commands to avoid stockout risks. To address scheduling challenges, existing technologies attempt to introduce intelligent algorithms to improve decision-making efficiency. For example, Chinese invention patent CN119005572B discloses a packaging reusable box management system and method. This system collects real-time data through Bluetooth and storage units integrated into the box, and uses the DQN reinforcement learning algorithm combined with historical flow and traffic conditions to generate scheduling data. While such strategies address the efficiency issues of path planning and routine scheduling, their core logic remains based on the assumption that requests are the only reality. The system focuses on optimizing received data but lacks the ability to proactively identify the authenticity of data sources. Existing systems lack an endogenous data authenticity verification mechanism, relying solely on mechanical allocation based on reported values. This leads to calculation results deviating from actual needs, and the flawed logic of executing precise calculations based on distorted inputs causes scarce resources to be tilted towards falsely reported nodes, resulting in network-wide resource misallocation. Consequently, the system is unable to identify and remove long-term stagnant static data, leaving a large number of vehicle resources in an ineffective state and creating a backlog of data-level inventory.

[0004] Therefore, the technical problem to be solved by this invention is to construct a method that does not rely on complex external prediction models, automatically filters out false noise through the system's endogenous interactive logic, verifies the authenticity of sudden demands in real time, and cleans up static redundant data for allocation and scheduling. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A multi-level cyclic bin allocation and scheduling method, comprising the following steps:

[0006] Establish a transfer credit account in the database that maps network node attributes, capture resource request instructions initiated by the source node in real time, extract the request value in the resource request instruction and compare it with the benchmark limit execution threshold in the transfer credit account;

[0007] When the requested value exceeds the preset trigger ratio of the baseline amount, the emergency demand active verification process is initiated, splitting the resource request instruction into an active probe sub-instruction and a frozen main sub-instruction. The probe sub-instruction contains a preset small share of the requested value.

[0008] The probe sub-command is issued first through the communication interface, and a lifecycle monitor with a unique identifier for the probe sub-command is instantiated in the system memory to receive the processing status update signal from the target node in response to the probe sub-command in real time.

[0009] Calculate the state transition rate of the probe sub-instruction within a preset probe time window. When the state transition rate is higher than the preset transition rate threshold, generate an unfreezing signal for the main sub-instruction and trigger the distribution of the remaining share. If the transition rate threshold is not reached by the end of the probe time window, automatically cancel the main sub-instruction and reduce the dynamic transition factor of the transition credit account.

[0010] Preferably, before initiating the proactive verification process for sudden demands, the process further includes a data verification step based on a bilateral signaling handshake: in response to the destination node identifier carried in the resource request instruction, a transaction object with a status marked as pending confirmation is generated in the system background, and the splitting operation for the resource request instruction is frozen; an inbound expectation verification signal containing the requested value is constructed based on the destination node identifier, and pushed to the terminal interface associated with the destination node identifier; logic and gating operations are executed, and the pending confirmation status of the transaction object is released and the subsequent process is triggered only when a confirmation feedback signal for the inbound expectation verification signal is received from the terminal interface; if no confirmation feedback signal is received within a preset time window, the resource request instruction is directly discarded and a credit deduction operation is performed on the source node.

[0011] Preferably, the step of calculating the state transition rate of the probe sub-instruction within a preset probe time window specifically includes: recording the first moment when the probe sub-instruction is sent to the target node, and the second moment when the final completion status signal for the probe sub-instruction is received from the target node; calculating the time difference between the second moment and the first moment, dividing a preset small fraction by the time difference to obtain the state transition rate; comparing the state transition rate with the benchmark transition rate stored in the system configuration table, and if the state transition rate is greater than a preset multiple of the benchmark transition rate, then determining that the transition rate threshold condition is met.

[0012] Preferably, the multi-level cyclic container allocation and scheduling method further includes an inventory data cleaning process based on time-dimensional entropy. This process is configured as follows: associating the last state change timestamp with the in-stock resource data packets belonging to each network node, and periodically obtaining the current system time; based on the time difference between the current system time and the timestamp, calculating the occupancy cost entropy value of the static inventory data held by each network node using a non-linear time decay function. Occupation cost entropy value The calculation formula is as follows: ,in, This represents the total number of batches of resource data packets in the database under this network node. For the first The number of resource data packets in the batch. The current system time. For the first The timestamp of the last status change of the batch resource data packet. A preset time decay coefficient greater than 1; real-time monitoring of occupancy cost entropy value. When the entropy exceeds the preset upper limit of the entire network, a reverse allocation instruction is automatically generated to transfer the ownership of the resource data packets under the network node name to other nodes.

[0013] Preferably, the step of splitting the resource request instruction into an active probe sub-instruction and a frozen main body sub-instruction specifically includes: invoking a preset fragmentation algorithm to calculate the subordinate share of the probe sub-instruction based on the dynamic ratio of the request value to the current total network load; constructing a probe data packet containing a first unique identifier as the probe sub-instruction and writing the first unique identifier into a high-priority sending queue; constructing a main body data packet containing a second unique identifier as the main body sub-instruction, writing the second unique identifier into a pending buffer, and establishing a parent-child logical mapping relationship between the first and second unique identifiers; and configuring the unfreezing signal to retrieve and activate the second unique identifier in the pending buffer according to the parent-child logical mapping relationship.

[0014] Preferably, the process of executing the multi-level cyclic bin allocation scheduling method also includes a dynamic flow factor update step based on historical flow data: within a preset historical sliding time window, the total amount of resource data packets actually completed by the network node is collected as the effective throughput value; the total amount of resource allocation instructions issued by the system to the network node within the same time window is obtained as the allocation benchmark value; the ratio of the effective throughput value to the allocation benchmark value is calculated, and the ratio is mapped to a normalized value. The dynamic flow factor in the flow credit account is updated with weights using this value. The dynamic flow factor is used to adjust the effective judgment range of the benchmark amount in the next threshold comparison. The step of the lifecycle monitor receiving the processing status update signal fed back by the target node for the probe sub-instruction in real time specifically includes: while issuing the probe sub-instruction, subscribing to the topic channel that matches the unique identifier of the probe sub-instruction in the message middleware; listening to the status change events in the topic channel, filtering and extracting specific status codes containing preset final state markers; when a specific status code is captured, triggering an interrupt signal to stop the timing of the probe time window, and passing the current time-consuming data to the subsequent calculation steps.

[0015] Preferably, the step of constructing an inbound expectation verification signaling containing the requested value based on the destination node identifier further includes: retrieving historical reception capability data corresponding to the destination node identifier, determining whether the requested value exceeds the physical reception threshold of the destination node; if it exceeds, adding a warning mark to the inbound expectation verification signaling and requiring confirmation feedback signal to contain a digital signature with secondary authorization; if it does not exceed, generating a standard format verification data packet, the data packet containing the source node identifier, the requested value, and the expected arrival time window.

[0016] Preferably, the step of automatically generating reverse allocation instructions further includes: traversing other candidate nodes in the network besides the network node, calculating the current resource holding and occupancy cost entropy value of each candidate node; calculating the receiving priority of each candidate node based on the distance parameter and entropy value parameter, selecting the node with the highest priority as the allocation target; generating a scheduling instruction data packet containing the source address, target address and allocation quantity, and locking the network node's permission to request new resources until the occupancy cost entropy value falls below the upper limit of the entropy value.

[0017] Preferably, the steps for lowering the dynamic transfer factor of the transfer credit account specifically include: determining the cumulative value of the number of times the main sub-instruction has been cancelled within a preset statistical period; retrieving the corresponding penalty ladder table based on the cumulative value to determine the current credit deduction step size; subtracting the credit deduction step size from the current dynamic transfer factor, and synchronizing the updated dynamic transfer factor to the shared credit database of all network nodes to restrict the resource acquisition permissions of the source node in other related businesses.

[0018] A multi-level cyclic bin allocation and scheduling system, the system comprising:

[0019] The circulation credit account management module is used to establish circulation credit accounts that map network node attributes in the database, capture resource request instructions initiated by the source node in real time, extract the request value in the resource request instruction and compare it with the benchmark limit execution threshold in the circulation credit account;

[0020] The sudden demand verification module is used to initiate the sudden demand active verification process when the requested value exceeds the preset trigger ratio of the baseline amount. It splits the resource request instruction into an active probe sub-instruction and a frozen main sub-instruction. The probe sub-instruction contains a preset small share of the requested value.

[0021] The signaling interaction and monitoring module is used to prioritize sending probe sub-commands through the communication interface, and instantiate a lifecycle monitor with a unique identifier for the probe sub-command in the system memory, and receive the processing status update signal from the target node in response to the probe sub-command in real time.

[0022] The logic gating and execution module is used to calculate the state transition rate of the probe sub-instruction within a preset probe time window. When the state transition rate is higher than the preset transition rate threshold, an unfreezing signal is generated for the main sub-instruction and the remaining share is issued. If the transition rate threshold is not reached by the end of the probe time window, the main sub-instruction is automatically cancelled and the dynamic transition factor of the transition credit account is reduced.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] 1. In multi-level cyclic bin allocation, a logic for active verification of instruction sharding and real-time status feedback is constructed. The system does not rely on external correlation data for probabilistic prediction. Excess request instructions are decomposed into high-priority probe sub-instructions and frozen status main instructions. The system monitors the status update frequency of the data objects corresponding to the probe sub-instructions in the target database and establishes an instant logic gating channel. When the status flow speed of the probe sub-instruction meets the preset timing requirements, the main instruction is unfrozen and issued. Real-time interactive feedback using small sample data is used as a verification tool. Without introducing third-party verification sources or complex prediction models, the system solves the technical challenge of distinguishing between real business needs and abnormal data noise in non-continuous, sudden high-load scenarios. This ensures the system's agility in responding to emergencies and improves the confidence and security of resource allocation instruction generation.

[0025] 2. By introducing data timeliness decay logic, the problem of static data occupying system resources for a long time in distributed storage networks and being difficult to quantify and identify by flow indicators is solved. The timestamp is associated with the inventory data objects residing in each node. Based on the time difference between the current time and the timestamp, a non-linear weighted calculation is performed to generate an inventory entropy value that represents the cost of data retention. When the entropy value exceeds the upper limit set by the system, a reverse transfer instruction is automatically triggered, which transforms the time dimension into a negative feedback variable for system control. The system automatically identifies and removes dead data nodes that have not been updated for a long time, so that the data resources of the entire network are kept in a high-frequency flow state. The self-cleaning logic based on time calculation eliminates data accumulation points in the system, and maintains the high availability and dynamic balance of the overall system resources without relying on manual inspection.

[0026] 3. Integrating a historical throughput data flow factor update mechanism, a bilateral handshake signal transaction confirmation mechanism, and a detection verification and entropy cleanup mechanism, a closed-loop control system covering the past, present, and future of data processing is constructed. The flow factor uses historical interaction performance to smooth out input deviations under normal conditions. The bilateral handshake logic uses the target node to receive confirmation signals to block single-point false requests. The detection verification mechanism handles sudden data anomalies, and the entropy cleanup mechanism solves historical static backlogs. The various logic modules are nested and complementary to each other, forming an endogenous defense system that does not rely on physical sensor hardware upgrades and is purely based on data interaction protocols and calculation rules. In the face of input data distortion, network fluctuations, or abnormal behavior of local nodes, robust and reasonable scheduling instructions are output based on interactive feedback and calculation results, ensuring the accuracy and anti-interference capability of data processing and resource scheduling in a large-scale distributed network environment. Attached Figure Description

[0027] Figure 1 This is a flowchart of the active verification scheduling process for instruction fragmentation and timing gating in this invention.

[0028] Figure 2 This is a comparison chart of the evolution trends of dynamic transfer factors for nodes with different credit attributes in this invention.

[0029] Figure 3 This is a system logic architecture diagram for integrating the core scheduling cluster and distributed interaction in this invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0031] This invention provides a multi-level cyclic bin allocation and scheduling method and system, which establishes a circulation credit account mapping network node attributes in a database, and uses this as the core data structure to store the static baseline quota of each node. and dynamic flow factor In the resource request processing flow, the system captures the resource request commands initiated by the source node in real time and extracts the request values ​​from the resource request commands through a parser. The processor will request the value A logical comparison is performed with the current valid credit limit in the circulating credit account, which is determined by the base credit limit. With dynamic flow factor The product is determined when the requested value is... When the preset trigger ratio does not exceed the effective limit, the system determines that the instruction conforms to normal logic and generates a regular allocation instruction; when the requested value... Exceeding the benchmark amount When the preset trigger ratio is reached, the system determines that it is currently in a state of sudden demand and initiates a sudden demand active verification process. The system calls the instruction fragmentation algorithm to split the resource request instruction into two independent data entities: an active probe sub-instruction and a frozen main sub-instruction. The probe sub-instruction contains the requested value. The system assigns a pre-defined small share to the probe sub-instruction and sets it as a specific percentage or fixed value of the total amount; the main sub-instruction carries the remaining request share. The system assigns a first unique identifier to the probe sub-instruction and a second unique identifier to the main sub-instruction, and establishes a parent-child logical mapping relationship between the first unique identifier and the second unique identifier in memory.

[0032] The system prioritizes sending probe sub-commands to the target node via the communication interface and instantiates a lifecycle monitor for the first unique identifier in the system memory. This lifecycle monitor subscribes to state change events related to the first unique identifier in the message middleware, listens for and receives processing status update signals from the target node in response to the probe sub-commands in real time, and records the first moment the probe sub-command is sent. And the second moment after receiving the final completion status signal Finally, the status signal characterizes that the physical resource corresponding to the probe sub-instruction has been substantially consumed or transferred by the target node; the logic gating module calculates the status transition rate of the probe sub-instruction based on the recorded time data. Calculation logic execution formula ,in, To determine the number of shares contained in the probe subinstruction, the logic gating module calculates the state transition rate of the probe subinstruction. At that time, the network transmission delay elimination procedure is executed to obtain the node's net processing capacity, and the system reads the signaling round-trip time recorded during the bilateral signaling handshake phase. Set the one-way network transmission time to The processor will detect the total lifecycle time of the subinstruction. Subtracting the one-way network transmission time, we obtain the corrected node physical processing time. The processor uses the formula Calculate the net turnover rate; if the calculated value is... If the value is lower than the system's minimum physical response threshold (e.g., 5ms), the logic gating module will mark the validity of this probe data as 0, discard the instruction, and not trigger subsequent flow rate threshold comparison operations. The system will then calculate the value in real time. The value is compared with the preset flow rate threshold stored in the system configuration table. When the state flow rate... When the flow rate exceeds the preset threshold, the logic gating module generates a unfreezing signal for the main sub-instruction. The system retrieves and activates the main sub-instruction corresponding to the second unique identifier in the pending buffer based on the parent-child logical mapping relationship, triggering the distribution of the remaining share. If the monitor does not receive a completion signal or the calculated value is not obtained by the deadline of the preset detection time window... If the threshold is not reached, the system automatically cancels the main sub-instruction and calls the credit update algorithm to lower the dynamic transfer factor in the source node's credit account. .

[0033] Before initiating the proactive verification process for sudden demands, the system performs a data verification step based on a two-way signaling handshake to verify business duality. In response to the destination node identifier carried in the resource request command, the system generates a transaction object with a status marked as pending confirmation in the background and suspends the splitting operation for the resource request command. The system constructs a data structure containing the requested values ​​based on the destination node identifier. The system receives the expected verification signal for the inbound resource request and pushes it to the terminal interface associated with the destination node identifier. The system executes logic and gating operations, releasing the pending confirmation state of the transaction object and triggering subsequent processes only when a confirmation feedback signal for the inbound expected verification signal is received from the terminal interface within a preset time window. If no confirmation feedback signal is received within the preset time window, the system directly discards the resource request instruction and executes a credit deduction operation for the source node. The system also performs an inventory data cleaning process based on time-dimensional entropy values ​​to handle static backlog data. The system associates the timestamp of the last state change with the in-stock resource data packets belonging to each network node. And periodically obtain the current system time. The processor is based on the current system time. With timestamp The time difference is used to calculate the occupancy cost entropy value of the static inventory data held by each network node using a nonlinear time decay function. The cost of entropy The calculation formula is as follows: ,in, This represents the total number of batches of resource data packets in the database under this network node. For the first The number of resource data packets in the batch. The current system time. For the first The timestamp of the last status change of the batch resource data packet. The system monitors the occupancy cost entropy value in real time, using a preset time decay coefficient greater than 1. When the entropy exceeds the preset upper limit of the entire network, a reverse allocation instruction is automatically generated to transfer the ownership of the resource data packets under the name of the network node to other nodes and lock the network node's permission to make new resource requests.

[0034] Regarding dynamic flow factors For maintenance, the system performs dynamic update steps based on historical data. Within a preset historical sliding time window, the system collects the total amount of resource data packets actually transferred by the network node as the effective throughput value, and obtains the total amount of resource allocation instructions issued by the system to the network node within the same time window as the allocation benchmark value. The system calculates the ratio of the effective throughput value to the allocation benchmark value, maps this ratio to a normalized value, and uses this value to adjust the dynamic transfer factor in the transfer credit account. Perform weighted updates for dynamic flow factors. Periodic numerical updates are performed, the system executes the exponentially weighted moving average (EWMA) calculation procedure, and the processor reads the actual throughput within the current sliding time window from the historical database. Call the formula Calculate the current period factor value; among which variables The smoothing coefficient is fixed at [value]. ,variable The turnover factor of the previous cycle, To update the flow factor, for credit deductions, the system calls the discrete mapping table stored in the configuration area, defining the cumulative cancellation count. With deduction step size The linear functional relationship is The processor, based on the retrieved... value pairs The subtraction is performed, and the final result is written into the shared credit database as the input parameter for the next stage of threshold comparison, updating the dynamic turnover factor. Used to adjust the base amount in the next threshold comparison. The effective range of judgment.

[0035] Example 1: In the actual operation of a large regional logistics distribution center facing a sudden surge in orders during the 618 e-commerce promotion season, lower-level outlets, in order to avoid the risk of supply disruptions due to the central warehouse's resources running out, generally artificially inflate the values ​​when submitting resource requests. This causes the total demand received by the system to far exceed the physical inventory limit. The system captures the resource request instruction initiated by a source node in real time, such as the regional warehouse numbered Node_A, and extracts the request value. and the benchmark amount in the circulation credit account of that node. Perform the comparison, when detected achieve When the resource usage reaches 150%, the system determines that the instruction has triggered a proactive verification process for sudden demand. Instead of immediately releasing all resources, it will... It splits into a probe sub-instruction containing only 5% of the share and a main sub-instruction containing 95% of the share.

[0036] The probe sub-instruction sets a first unique identifier and issues it first. The system then subscribes to and monitors the processing status of the probe sub-instruction within Node_A in real time using the lifecycle monitor for the first unique identifier in memory. When the Node_A operating system performs an inventory upload operation on this batch of probe resources, a status update signal is generated and sent back to the system. The logic gating module calculates the status transition rate of the probe sub-instruction. ,like If the throughput rate exceeds twice the baseline rate within the preset detection time window, it indicates that Node_A currently possesses extremely high resource throughput and processing capabilities, verifying the rigidity and authenticity of the sudden demand. The system then generates a unfreezing signal, activates the main sub-instruction corresponding to the second unique identifier, and distributes the remaining 95% share to Node_A. This mechanism, without relying on external prediction models, utilizes real-time feedback from small sample detection data to effectively filter false alarms and ensure that scarce resources are prioritized for nodes with genuine high turnover capabilities. For nodes that fail the detection verification, the system automatically cancels the main sub-instruction and lowers their dynamic turnover factor. .

[0037] Example 2: This example constructs a simulation test platform comprising one central control node (simulating a regional distribution center) and 50 distributed leaf nodes (simulating lower-level outlets). Developed based on an event-driven discrete event simulation engine, it is configured to simulate concurrent requests, network latency, and node behavior patterns in a real logistics network. All nodes have independent circulation credit accounts, with a base credit limit... The initial dynamic turnover factor is uniformly set to 1000 units. Set to version 1.0; the experimental design includes a set of control experiments to compare the system performance of the direct response mode (control group) in the prior art with the fragmented detection verification mode (experimental group) of this invention when facing mixed traffic surges. The mixed traffic consists of two parts: one part is normal business requests that conform to historical patterns (accounting for 80% of the total requests), and the other part is abnormally high requests that simulate malicious false reporting or panic hoarding (accounting for 20%, with the request value...). Set as the base amount (200% to 500%), and in addition to the uncertainties in the simulated environment, the experiment introduced random delays (0.1s to 0.5s) and packet loss rate (1%) into the node feedback signal.

[0038] In the control group, the system performs a linear allocation based solely on the requested value and the remaining total inventory. In the experimental group, the system executes the verification logic of this invention: when... At that time, a probe sub-command containing 5% of the resources was split off. The experiment ran for 24 hours of simulation time, and abnormal traffic was injected intensively during the high-load period (hours 10 to 14). The system recorded and analyzed two key indicators: effective resource allocation rate (i.e., the proportion of resources allocated to nodes with real demand) and system deadlock count (i.e., the number of times normal requests could not be responded to due to resource exhaustion). The experimental data showed that in the early stage of traffic injection, the performance of the two systems was similar. However, as abnormally high-value requests appeared intensively, the available inventory of the control group was quickly locked, causing subsequent normal requests to be forced to queue or be rejected. In contrast, the experimental group quickly identified and froze most of the main body of false requests through the real-time feedback of the probe sub-command. Specifically, the data showed that when faced with a false request impact of 300% of the baseline amount, the probe sub-command of the experimental group triggered the timeout mechanism within an average of 0.2 seconds after being issued because no actual flow feedback was received, thus avoiding 95% of the main resources being ineffectively locked. See Table 1, which clearly shows the performance difference between the two modes under different load conditions.

[0039] Table 1: System Performance Comparison Data Table

[0040]

[0041] The above data reveals the advantages of the mechanism of this invention: by introducing probe sub-instructions as a touchstone, the system transforms the prediction of an uncertain future into the verification of the current physical state. In mixed high-load scenarios, the effective resource allocation rate is significantly increased from 42.3% in the control group to 94.1% in the experimental group, eliminating the system deadlock phenomenon.

[0042] Example 3: This example combines Figures 1 to 3 This document describes a multi-level cyclic bin allocation and scheduling method and system, such as... Figure 1As shown, the credit account management module stores the baseline credit limit and dynamic transfer factor and provides data support. After capturing the resource request instruction, the system extracts the request value in real time and performs a threshold comparison step. If the request value does not exceed the baseline credit limit trigger ratio, it enters the regular allocation process to generate a standard allocation instruction. If it is determined to be a sudden demand, the resource request instruction is split and the sharding algorithm is called. On the one hand, a probe sub-instruction containing a preset small share and in an active state is generated and sent with high priority. On the other hand, a main sub-instruction containing the remaining share and in a frozen state is generated and temporarily stored in the pending cache area and an ID mapping relationship is established. The lifecycle monitor subscribes to the probe ID and receives the processing status update signal in real time to calculate the status transfer rate. It enters the logic gating stage to determine whether the transfer rate is higher than the threshold. If the verification is successful, the main sub-instruction is activated and unfrozen to trigger the issuance of the remaining share. If it fails or times out, the instruction is canceled and a penalty is implemented. The dynamic transfer factor is lowered and fed back to the account management module.

[0043] like Figure 2 As shown, the horizontal axis represents the statistical period, and the vertical axis represents the dynamic turnover factor. The graph illustrates the factor change trajectory of three types of nodes with different credit attributes. The turnover factor of high-credit nodes shows a steady upward trend over the statistical period, gradually increasing from an initial value of 1.0 to above 1.2. The turnover factor of ordinary nodes remains stable around 1.0 with only slight increases, while the turnover factor of low-credit nodes shows a downward trend, continuously declining from an initial value of 1.0 to below 0.6. Figure 3 As shown, the core scheduling server cluster serves as the computing and control center. Internally, it integrates a burst demand verification module for instruction sharding algorithms and probe sub-instruction generation, a lifecycle monitor for real-time status monitoring and flow rate calculation, a logic gating and execution module for threshold comparison and judgment and main instruction unfreezing or cancellation, and an inventory cleaning service for inventory entropy calculation and reverse allocation. This cluster connects to a shared credit database via JDBC or a data access interface to read the flow credit account table and the benchmark quota configuration table for persistent storage. At the same time, it connects to a message middleware server through a message subscription mechanism to listen for status change events in topic channels to achieve asynchronous communication bus functionality. It also interacts with source network nodes and target network nodes through a distributed communication network. The former is responsible for reporting resource requests and running the resource request generation component, while the latter is responsible for receiving probe sub-instructions, pushing status updates, and running status feedback and handshake components.

[0044] Example 4: In the specific diagnosis of the dual risks of occasional fraud by high-credit nodes and the botnetization of inventory data in this invention, two potential logical problems were identified: first, relying solely on the historical credit of the source node may lead to a vulnerability of large-scale fraud in a single instance; second, the lack of a time-dimensional penalty mechanism for static inventory data leads to long-term ineffective resource occupation. To solve the above problems, this example constructs and implements a two-sided handshake verification based on business duality and a time decay and clearing mechanism based on entropy value; in the scenario where a regional distribution center initiates a bulk transfer request to a lower-level branch, the system captures the source node. resource request command The system does not directly rely on the source node. Instead of releasing credit limits based on high credit scores, the system generates a transaction object with a pending confirmation status, freezing the processing flow of the request. The system parses the destination node declared in the instruction. And send to the destination node through message middleware The terminal pushes an input verification signal containing the requested value and requests the destination node to perform an input verification. Explicit confirmation is required within a preset short time window, such as 10 minutes, only if the destination node... The system only performs a logical AND operation upon receiving an acknowledgment (ACK) signal, unfreezing the transaction object and triggering subsequent allocation calculations. If no acknowledgment is received within a timeout period, the system directly determines that the request lacks business duality, automatically discards the instruction, and deducts the amount from the source node. Credit score.

[0045] In addition to managing the timeliness of inventory data, the system introduces inventory entropy as a dynamic indicator to measure the cost of resource occupancy. The system associates the timestamp of the last state change with each batch of in-stock resource data packets belonging to each node. The background cleaning process is periodically activated, such as every hour, to obtain the current system time. The system iterates through all static data packets, and for each data packet, it calculates its dwell time. The entropy value of the inventory occupancy cost at this node is calculated using a nonlinear time decay function. The calculation logic is configured to exponentially weight the dwell time, i.e. ,in A time sensitivity coefficient greater than 1, when a certain node's The accumulated value exceeds the preset entropy limit for the entire network. When the system determines that the node has a serious backlog of zombie inventory, the system will automatically trigger the circuit breaker mechanism, generate a mandatory reverse transfer instruction, transfer the ownership of the resources under the node to the nearest node in the network with the lowest entropy value that is out of stock, and simultaneously freeze the node's new request permissions until its entropy value falls back to a safe level.

[0046] Example 5: To ensure the static benchmark credit limit in the circulating credit account To accurately reflect the true physical carrying capacity of each network node, this invention introduces a standardized offline calibration and data filling procedure. The system extracts the average daily throughput data of all nodes over the past 12 months from the historical database and combines it with the physical storage area, maximum number of operating stations, and geographical location coefficient of each node to construct a multi-dimensional feature vector. A clustering algorithm is used to divide all network nodes into several typical categories, and a two-week stress test is conducted on representative nodes of each category. During the test, the resource allocation is gradually increased until an inflection point is observed where node congestion or increased processing latency is observed. The resource allocation at this point is recorded as the physical limit threshold for that category of node, and a baseline quota is set. To reserve a safety buffer, the value is set at 85% of this limit threshold. Finally, the calibrated value is... The values ​​are written to the system configuration table in batches to complete the account initialization.

[0047] To address the system's adaptability in different deployment environments, this invention also includes a pre-deployment calibration procedure. Before the system goes live or connects to a new regional network, a 72-hour silent run and parameter fine-tuning process is performed. During this period, the system operates in shadow mode, receiving real-time business data and calculating various indicators, but without issuing any control commands. The system monitors the proportion of sudden demand triggers, detects the flow rate distribution of sub-commands, and assesses the growth trend of inventory entropy. By comparing simulation expectations with actual observation data, the gradient descent method is used to adjust the time decay coefficient. and smoothing coefficient Fine-tuning is performed to bring the false alarm rate and false negative rate of the system to a preset minimum range under specific network conditions. Only after all key parameters have passed this calibration process and been verified can the system be officially switched to active control mode to ensure its stability and accuracy in actual operation.

[0048] Example 6: This example uses an adaptive parameter calibration procedure based on network load characteristics. By combining offline analysis and online learning, it determines the optimal share ratio of probe sub-instructions and the flow rate threshold for judging the authenticity of sudden demand, thus balancing probe cost and verification accuracy. In the parameter calibration procedure, the system extracts resource consumption rate samples of each node under normal and sudden loads from historical operating data. The processor performs statistical analysis on the sample data to construct a probability density function of node consumption rate. Based on this probability density function, the system uses Monte Carlo simulation to evaluate the distribution of the time required for probe sub-instructions to complete flow under different probe share ratios. By setting an expected verification confidence level, such as 95%, the system reverse-engineers the minimum probe share ratio that can distinguish between normal and sudden demand. This ratio must satisfy the condition that within a preset probe time window, the probability of probe sub-instructions completing flow under real sudden demand is greater than the confidence level, while the probability of completion under false demand is less than the false alarm tolerance.

[0049] The minimum detection share ratio is determined and written into the system configuration table as a benchmark parameter. Based on this, the system further performs the calibration of the flow rate threshold. A group of representative nodes is selected, and sudden demands of different intensities are simulated in a controlled experimental environment. The actual flow rate of the probe sub-instruction is recorded. By analyzing the correlation between the flow rate and the demand intensity, a functional relationship between the flow rate threshold and the benchmark amount is fitted and configured as a dynamic threshold calculation model for the system during online operation. In actual operation, the system uses this model to calculate the corresponding flow rate threshold in real time based on the benchmark amount of the current node, which serves as a quantitative basis for judging whether the probe sub-instruction has passed verification. In addition, the system is also configured with an adaptive calibration mechanism, which can periodically fine-tune the parameters in the above functional model based on the feedback of false alarms and missed alarms during online operation.

[0050] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-level cyclic bin allocation and scheduling method, characterized in that, Includes the following steps: Establish a transfer credit account that maps network node attributes, capture resource request instructions initiated by the source node in real time, extract the request value and compare it with the benchmark amount in the transfer credit account; When the requested value exceeds the preset trigger ratio of the baseline amount, the emergency demand active verification process is initiated, splitting the resource request instruction into an active probe sub-instruction and a frozen main sub-instruction. The probe sub-instruction contains a preset small share of the requested value. Prioritize issuing probe sub-commands and instantiate a lifecycle monitor with a unique identifier for the probe sub-command, and receive real-time processing status update signals from the target node in response to the probe sub-commands; Calculate the state transition rate of the probe sub-instruction within the preset probe time window. When the state transition rate is higher than the preset transition rate threshold, generate an unfreezing signal and trigger the distribution of the remaining share. If the transition rate threshold is not reached by the end of the probe time window, automatically cancel the main sub-instruction and reduce the dynamic transition factor of the credit account. The specific steps for calculating the state transition rate of the probe sub-instruction within a preset probe time window include: recording the first moment when the probe sub-instruction is sent to the target node, and the second moment when the final completion status signal for the probe sub-instruction is received from the target node; calculating the time difference between the second moment and the first moment, dividing the preset small fraction by the time difference to obtain the state transition rate; comparing the state transition rate with the benchmark transition rate stored in the system configuration table, and if the state transition rate is greater than a preset multiple of the benchmark transition rate, then the transition rate threshold condition is determined to be met. The process of executing the multi-level cyclic bin allocation scheduling method also includes a dynamic circulation factor update step based on circulation history data: within a preset historical sliding time window, the total amount of resource data packets actually completed by the network node is collected as the effective throughput value; the total amount of resource allocation instructions issued by the system to the network node within the same time window is obtained as the allocation benchmark value; the ratio of the effective throughput value to the allocation benchmark value is calculated, and the ratio is mapped to a normalized value. This value is used to weight and update the dynamic circulation factor in the circulation credit account. The dynamic circulation factor is used to adjust the effective judgment range of the benchmark amount in the next threshold comparison. The step of the lifecycle monitor receiving the processing status update signal fed back by the target node for the probe sub-instruction in real time includes: while issuing the probe sub-instruction, subscribing to the topic channel that matches the unique identifier of the probe sub-instruction in the message middleware; listening to the status change events in the topic channel, filtering and extracting specific status codes containing preset final state markers; when a specific status code is captured, triggering an interrupt signal to stop the timing of the probe time window, and passing the current time-consuming data to the subsequent calculation steps.

2. The multi-level cyclic bin allocation and scheduling method according to claim 1, characterized in that, Before initiating the proactive verification process for sudden demands, a data verification step based on bilateral signaling handshake is also included: in response to the destination node identifier carried in the resource request instruction, a transaction object with a status marked as pending confirmation is generated in the system background, and the splitting operation for the resource request instruction is frozen; Based on the destination node identifier, construct an inbound expectation verification signal containing the requested value and push it to the terminal interface associated with the destination node identifier; execute logic and gating operations, and only when a confirmation feedback signal for the inbound expectation verification signal is received from the terminal interface, release the pending confirmation state of the transaction object and trigger the subsequent process; if no confirmation feedback signal is received within the preset time window, directly discard the resource request instruction and execute the credit deduction operation for the source node.

3. The multi-level cyclic bin allocation and scheduling method according to claim 1, characterized in that, The process of executing the multi-level cyclic box quantity allocation and scheduling method also includes executing an inventory data cleaning process based on time dimension entropy value. The process is configured as follows: associate the timestamp of the last state change with the in-stock resource data packets belonging to each network node, and periodically obtain the current system time. Based on the time difference between the current system time and the timestamp, the occupancy cost entropy value of the static inventory data held by each network node is calculated using a nonlinear time decay function. Occupation cost entropy value The calculation formula is as follows: ,in, This represents the total number of batches of resource data packets in the database under this network node. For the first The number of resource data packets in the batch. The current system time. For the first The timestamp of the last status change of the batch resource data packet. The preset time decay coefficient is greater than 1; Real-time monitoring of occupancy cost entropy value When the entropy exceeds the preset upper limit of the entire network, a reverse allocation instruction is automatically generated to transfer the ownership of the resource data packets under the network node name to other nodes.

4. The multi-level cyclic bin allocation and scheduling method according to claim 1, characterized in that, The steps of splitting a resource request instruction into an active probe sub-instruction and a frozen main body sub-instruction specifically include: invoking a preset fragmentation algorithm to calculate the subordinate share of the probe sub-instruction based on the dynamic ratio of the request value to the current total network load; constructing a probe data packet containing a first unique identifier as the probe sub-instruction and writing the first unique identifier into a high-priority sending queue; constructing a main body data packet containing a second unique identifier as the main body sub-instruction, writing the second unique identifier into a pending buffer, and establishing a parent-child logical mapping relationship between the first and second unique identifiers; and configuring the unfreezing signal to retrieve and activate the second unique identifier in the pending buffer based on the parent-child logical mapping relationship.

5. The multi-level cyclic bin allocation and scheduling method according to claim 2, characterized in that, The step of constructing an inbound expectation verification signaling containing the requested value based on the destination node identifier further includes: retrieving historical reception capability data corresponding to the destination node identifier, determining whether the requested value exceeds the physical reception threshold of the destination node; if it exceeds, adding a warning mark to the inbound expectation verification signaling and requiring confirmation feedback signal to contain a digital signature with secondary authorization; if it does not exceed, generating a standard format verification data packet, the data packet containing the source node identifier, the requested value, and the expected arrival time window.

6. The multi-level cyclic bin allocation and scheduling method according to claim 3, characterized in that, The steps for automatically generating reverse allocation instructions also include: traversing other candidate nodes in the network besides the network node, calculating the current resource holdings and occupancy cost entropy value of each candidate node; calculating the receiving priority of each candidate node based on the distance parameter and entropy value parameter, selecting the node with the highest priority as the allocation target; generating a scheduling instruction data packet containing the source address, target address and allocation quantity, and locking the network node's permission to request new resources until the occupancy cost entropy value falls below the upper limit of the entropy value.

7. The multi-level cyclic bin allocation and scheduling method according to claim 1, characterized in that, The steps to lower the dynamic transfer factor of a credit account include: determining the cumulative number of times a main sub-instruction has been cancelled within a preset statistical period; retrieving the corresponding penalty ladder table based on the cumulative value to determine the current credit deduction step size; subtracting the credit deduction step size from the current dynamic transfer factor and synchronizing the updated dynamic transfer factor to the shared credit database of all network nodes.

8. A multi-level cyclic bin allocation and scheduling system, used to implement the method according to any one of claims 1-7, characterized in that the system include: The circulation credit account management module is used to establish circulation credit accounts that map network node attributes in the database, capture resource request instructions initiated by the source node in real time, extract the request value in the resource request instruction and compare it with the benchmark limit execution threshold in the circulation credit account; The sudden demand verification module is used to initiate the sudden demand active verification process when the requested value exceeds the preset trigger ratio of the baseline amount. It splits the resource request instruction into an active probe sub-instruction and a frozen main sub-instruction. The probe sub-instruction contains a preset small share of the requested value. The signaling interaction and monitoring module is used to prioritize the issuance of probe sub-commands through the communication interface, and to instantiate a lifecycle monitor with a unique identifier for the probe sub-command in the system memory, and to receive the processing status update signals fed back by the target node for the probe sub-command in real time. The logic gating and execution module is used to calculate the state transition rate of the probe sub-instruction within a preset probe time window. When the state transition rate is higher than the preset transition rate threshold, an unfreezing signal is generated for the main sub-instruction and the remaining share is issued. If the transition rate threshold is not reached by the end of the probe time window, the main sub-instruction is automatically cancelled and the dynamic transition factor of the transition credit account is reduced.

Images