Method and system for global digital collaboration, sharing and processing

Abstract

The application discloses a global digital collaborative sharing and processing method and system. The method performs the following in each sampling period: collecting logistics data of each logistics node; determining the optimal transmission rate of each node and establishing a data transmission channel to share data; determining the synchronization deviation index between each two nodes according to the data update progress difference, the logistics sensitivity coefficient and the nonlinear enhancement coefficient, and triggering synchronization correction when the synchronization deviation index exceeds the maximum tolerance convergence index; determining the elastic load coordination factor based on the optimal transmission rate, the real-time computing power load state and the node synchronization deviation index, distributing the preliminary computing power through the flexible resource redistribution function, and then performing collaborative linkage regulation and control according to the continuous synchronization deviation and the serious weak network state to obtain the final computing power distribution scheme to execute the logistics business. The application realizes the closed-loop collaborative linkage of the transmission rate, data synchronization and computing power scheduling, and improves the operation efficiency of the logistics network and the convergence speed of data consistency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for global digital collaborative sharing and processing. Background Technology

[0002] In collaborative networks comprised of logistics sorting, warehousing and distribution, and vehicle transportation, different nodes need to share data such as orders, inventory, and temperature control in real time. Current technologies typically use fixed transmission rates or simple TCP congestion control for data interaction, failing to dynamically adjust rates based on the size of the data packets to be transmitted, latency constraints, and physical bandwidth. This can easily lead to some nodes having idle bandwidth while others experience queuing and congestion. Furthermore, data synchronization between nodes often relies on periodic full synchronization or simple timestamp-based comparisons, resulting in either excessive synchronization overhead or excessive sensitivity to minor progress differences, leading to frequent synchronization triggers and impacting normal business processing. In addition, computing resources are usually statically allocated per node, unable to be dynamically adjusted based on real-time transmission rates, load intensity, and data synchronization deviations. There is a lack of computing power avoidance mechanisms in weak network conditions and backtracking compensation mechanisms based on historical synchronization deviations. When a single link experiences occasional momentary jitter, it can easily lead to global synchronization misjudgments. Therefore, how to enable multiple logistics nodes to adaptively adjust transmission rates, synchronization strategies, and computing power allocation to achieve coordinated operation is a key technical problem that needs to be solved. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, embodiments of the present invention provide a method and system for global digital collaborative sharing and processing.

[0004] This invention provides a method for global digital collaborative sharing and processing, which performs the following steps in each sampling period:

[0005] Step S1: In the current sampling period The system collects logistics data from each logistics node and obtains the data for each node in the current sampling period. The size of the data packet to be transmitted and the respective sampling period of each pair of cooperating nodes. Data update progress;

[0006] Step S2: Based on each node in the current sampling period The size of the data packet to be transmitted, the preset transmission delay constraint, the node's physical bandwidth, and the preset rate adjustment function based on the hyperbolic tangent function are used to determine the current sampling period for each node. Optimal transmission rate And according to the optimal transmission rate of each node. Establish data transmission channels between nodes to share logistics data through these channels;

[0007] Step S3: Based on the current sampling period between every two collaborative nodes The difference in data update progress and the preset sensitivity function determine the relationship between each pair of cooperating nodes in the current sampling period. Synchronization Deviation Index And when the synchronization deviation index exceeds the maximum tolerance bridging index At that time, a synchronization correction command is triggered to control data synchronization;

[0008] Step S4: Based on the current sampling period, each node is determined in the current sampling period. Optimal transmission rate Real-time computing load status and synchronization deviation index Determine each node in the current sampling period The elastic load coordination factor; based on the elastic load coordination factor and the preset flexible resource reallocation function, each node is allocated in the current sampling period. The computing resources; based on each node in the current sampling period The synchronization deviation index and optimal transmission rate are used to coordinate and regulate computing resources, so as to obtain the current sampling period for each node. The computing power allocation scheme; based on each node in the current sampling period The computing power allocation scheme executes logistics operations on each node; specifically, for each node, the node is linked to all other collaborating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index .

[0009] Preferably, step S2 includes:

[0010] For each node In the current sampling period Obtain the maximum available throughput of the link for this node. Node type damping coefficient Data priority gain factor In the current sampling period Size of the data packet to be transmitted Maximum permissible end-to-end delay and the preset reference rate And determine the node in the current sampling period according to the preset rate adjustment function based on the hyperbolic tangent function. Optimal transmission rate ;

[0011] The rate adjustment function is:

[0012]

[0013] control node According to the description Perform data transmission;

[0014] Among them, if And the current cycle or continuous Demand rate within a period Greater than the optimal transmission rate If this occurs, a degradation process is triggered, which includes temporarily storing the node's low-priority data locally and transmitting only high-priority data; wherein The first preset ratio threshold, , The value range is 0.8 to 1.5. The value range is 0.3 to 0.9; .

[0015] Preferably, step S3 includes:

[0016] For every two collaborative nodes and In the current sampling period Get the current sampling period for each of the two nodes. Data update progress and Logistics sensitivity coefficient Preset zero-adjustment factor Preset nonlinear enhancement coefficient And determine the sensitivity between the two nodes in the current sampling period according to the following preset sensitivity function. Synchronization Deviation Index :

[0017]

[0018] like This triggers a synchronization correction command between the two nodes, controlling the node lagging in data update progress to synchronize with the leading node's data; where This is the preset maximum tolerance bridging index;

[0019] For each node This will establish communication between this node and all other cooperating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index :

[0020]

[0021] in The total number of cooperating nodes, and ; For the two nodes i and j in the current sampling period The synchronization deviation index.

[0022] Preferably, step S4 is performed in each sampling period and includes the following steps:

[0023] Step S41, in the current sampling period For each node Collect data from this node during the current sampling period. Current length of the pending task queue Historical peak queue length Current sampling period CPU utilization The node in the current sampling period is determined according to the following formula. Original load strength :

[0024]

[0025] in, The preset zero-adjustment factor is used for prevention and elimination.

[0026] Step S42: Based on the current sampling period and previous consecutive The synchronization deviation index for each sampling period is used to calculate the node. In the current sampling period Historical Retrospective :

[0027]

[0028] in, For nodes In the Synchronization deviation index for each sampling period; The maximum tolerance bridging index; To determine the window length;

[0029] Step S43: Based on the current sampling period Optimal transmission rate Determine the node In the current sampling period Weak network status indicator :

[0030]

[0031] in, For nodes Maximum available throughput of the link; The damping coefficient is the node type. This is a data priority gain factor; The second preset ratio threshold;

[0032] Step S44, Calculate the node In the current sampling period Elastic load coordination factor :

[0033]

[0034] in The penalty coefficient for weak networks; when When the denominator is greater than 1, then... This reduces the computing power weight of weak network nodes in subsequent computing power allocation;

[0035] Step S45: Obtain the total available computing power Total number of nodes Each node is determined according to the flexible resource reallocation function. In the current sampling period Preliminary computing power :

[0036]

[0037] Step S46: In the current sampling period To implement coordinated control, a first condition and a second condition are defined. The first condition includes the current sampling period. and previous consecutive Synchronization deviation index for each sampling period. All greater than The second condition includes the current sampling period. and previous consecutive The optimal transmission rate for each sampling period within each sampling period. All less than ,in The third preset ratio threshold, and ;

[0038] Determine the node based on the combination of the first and second conditions. In the current sampling period The final computing power as follows:

[0039] If the first condition is not met and the second condition is not met, then determine ;

[0040] If the first condition is true and the second condition is false, then determine. ;

[0041] If the first condition is false and the second condition is true, then determine and will release computing power By index proportion Reassigned to the set of qualified nodes ,in To preset the reduction ratio, the collection By satisfying and nodes composition;

[0042] If both the first and second conditions are met, then first calculate the adjusted median value. Then, the adjusted value is adjusted downwards to obtain the final computing power. and will release computing power Redistribute to the qualified node set according to the stated exponential ratio. Nodes in;

[0043] Step S47: Each node, according to the current sampling period The final computing power is used to execute logistics operations.

[0044] On the other hand, embodiments of the present invention also provide a global digital collaborative sharing and processing system, including:

[0045] The data acquisition module is used for the current sampling period. The system collects logistics data from each logistics node and obtains the data for each node in the current sampling period. The size of the data packet to be transmitted and the respective sampling period of each pair of cooperating nodes. Data update progress;

[0046] The channel establishment module is used to determine the current sampling period for each node. The size of the data packet to be transmitted, the preset transmission delay constraint, the node's physical bandwidth, and the preset rate adjustment function based on the hyperbolic tangent function are used to determine the current sampling period for each node. Optimal transmission rate And according to the optimal transmission rate of each node. Establish data transmission channels between nodes to share logistics data through these channels;

[0047] The synchronization processing module is used to determine the time between each pair of cooperating nodes in the current sampling period. The difference in data update progress and the preset sensitivity function determine the relationship between each pair of cooperating nodes in the current sampling period. Synchronization Deviation Index And when the synchronization deviation index exceeds the maximum tolerance bridging index At that time, a synchronization correction command is triggered to control data synchronization;

[0048] The control and processing module is used to control each node in the current sampling period based on the current sampling period. Optimal transmission rate Real-time computing load status and synchronization deviation index Determine each node in the current sampling period The elastic load coordination factor; based on the elastic load coordination factor and the preset flexible resource reallocation function, each node is allocated in the current sampling period. The computing resources; based on each node in the current sampling period The synchronization deviation index and optimal transmission rate are used to coordinate and regulate computing resources, so as to obtain the current sampling period for each node. The computing power allocation scheme; based on each node in the current sampling period The computing power allocation scheme executes logistics operations on each node; specifically, for each node, the node is linked to all other collaborating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index .

[0049] Preferably, the channel establishment module is further configured to:

[0050] For each node In the current sampling period Obtain the maximum available throughput of the link for this node. Node type damping coefficient Data priority gain factor In the current sampling period Size of the data packet to be transmitted Maximum permissible end-to-end delay and the preset reference rate And determine the node in the current sampling period according to the preset rate adjustment function based on the hyperbolic tangent function. Optimal transmission rate ;

[0051] The rate adjustment function is:

[0052]

[0053] control node According to the description Perform data transmission;

[0054] Among them, if And the current cycle or continuous Demand rate within a period Greater than the optimal transmission rate If this occurs, a degradation process is triggered, which includes temporarily storing the node's low-priority data locally and transmitting only high-priority data; wherein The first preset ratio threshold, , The value range is 0.8 to 1.5. The value range is 0.3 to 0.9; .

[0055] Preferably, the synchronization processing module is further configured to:

[0056] For every two collaborative nodes and In the current sampling period Get the current sampling period for each of the two nodes. Data update progress and Logistics sensitivity coefficient Preset zero-adjustment factor Preset nonlinear enhancement coefficient And determine the sensitivity between the two nodes in the current sampling period according to the following preset sensitivity function. Synchronization Deviation Index :

[0057]

[0058] like This triggers a synchronization correction command between the two nodes, controlling the node lagging in data update progress to synchronize with the leading node's data; where This is the preset maximum tolerance bridging index;

[0059] For each node This will establish communication between this node and all other cooperating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index :

[0060]

[0061] in The total number of cooperating nodes, and ; For the two nodes i and j in the current sampling period The synchronization deviation index.

[0062] Preferably, the control processing module is further configured to perform the following steps in each sampling period:

[0063] Step S41, in the current sampling period For each node Collect data from this node during the current sampling period. Current length of the pending task queue Historical peak queue length Current sampling period CPU utilization The node in the current sampling period is determined according to the following formula. Original load strength :

[0064]

[0065] in, The preset zero-adjustment factor is used for prevention and elimination.

[0066] Step S42: Based on the current sampling period and previous consecutive The synchronization deviation index for each sampling period is used to calculate the node. In the current sampling period Historical Retrospective :

[0067]

[0068] in, For nodes In the Synchronization deviation index for each sampling period; The maximum tolerance bridging index; To determine the window length;

[0069] Step S43: Based on the current sampling period Optimal transmission rate Determine the node In the current sampling period Weak network status indicator :

[0070]

[0071] in, For nodes Maximum available throughput of the link; The damping coefficient is the node type. This is a data priority gain factor; The second preset ratio threshold;

[0072] Step S44, Calculate the node In the current sampling period Elastic load coordination factor :

[0073]

[0074] in The penalty coefficient for weak networks; when When the denominator is greater than 1, then... This reduces the computing power weight of weak network nodes in subsequent computing power allocation;

[0075] Step S45: Obtain the total available computing power Total number of nodes Each node is determined according to the flexible resource reallocation function. In the current sampling period Preliminary computing power :

[0076]

[0077] Step S46: In the current sampling period To implement coordinated control, a first condition and a second condition are defined. The first condition includes the current sampling period. and previous consecutive Synchronization deviation index for each sampling period. All greater than The second condition includes the current sampling period. and previous consecutive The optimal transmission rate for each sampling period within each sampling period. All less than ,in The third preset ratio threshold, and ;

[0078] Determine the node based on the combination of the first and second conditions. In the current sampling period The final computing power as follows:

[0079] If the first condition is not met and the second condition is not met, then determine ;

[0080] If the first condition is true and the second condition is false, then determine. ;

[0081] If the first condition is false and the second condition is true, then determine and will release computing power By index proportion Reassigned to the set of qualified nodes ,in To preset the reduction ratio, the collection By satisfying and nodes composition;

[0082] If both the first and second conditions are met, then first calculate the adjusted median value. Then, the adjusted value is adjusted downwards to obtain the final computing power. and will release computing power Redistribute to the qualified node set according to the stated exponential ratio. Nodes in;

[0083] Step S47: Each node, according to the current sampling period The final computing power is used to execute logistics operations.

[0084] The beneficial effects of the above-mentioned technical solutions provided in the embodiments of the present invention include at least the following:

[0085] The global digital collaborative sharing and processing method and system provided in this invention achieves closed-loop collaborative control of transmission, synchronization, and computing power by dynamically adapting the transmission rate, detecting and correcting nonlinear data synchronization deviations, and flexibly reallocating computing power based on an elastic load coordination factor within each sampling period. Specifically, this method automatically adjusts the optimal transmission rate based on the real-time data packet size, latency constraints, and physical bandwidth of the nodes, avoiding network congestion and resource idleness. It accurately identifies data synchronization deviations using a nonlinear enhancement coefficient and a logistics sensitivity coefficient, triggering synchronization correction only when the deviation continuously exceeds the limit, reducing ineffective synchronization overhead. By fusing the optimal transmission rate, synchronization deviation index, and real-time load status to construct an elastic load coordination factor, it allocates the total available computing power using an exponential normalization mechanism and performs hierarchical linkage control based on continuous synchronization deviations and severely weak network conditions (temporarily increasing computing power when synchronization deviation is large, and actively relinquishing computing power and reallocating it to healthy nodes when the network is severely weak), forming an adaptive feedback loop. This scheme effectively improves the operating efficiency, resource utilization, and data consistency convergence speed of the logistics collaborative network, and is particularly suitable for heterogeneous logistics scenarios including sorting centers, fixed storage points, and vehicle-mounted mobile terminals.

[0086] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0087] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0088] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0089] Figure 1 This is a flowchart of the global digital collaborative sharing and processing method provided in the embodiments of the present invention. Detailed Implementation

[0090] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0091] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," "far," "near," "front," and "rear," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0092] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0093] This invention provides a method for global digital collaborative sharing and processing, such as... Figure 1 As shown, it includes:

[0094] Step S1: In the current sampling period The system collects logistics data from each logistics node and obtains the data for each node in the current sampling period. The size of the data packet to be transmitted and the respective sampling period of each pair of cooperating nodes. Data update progress;

[0095] Step S2: Based on each node in the current sampling period The size of the data packet to be transmitted, the preset transmission delay constraint, the node's physical bandwidth, and the preset rate adjustment function based on the hyperbolic tangent function are used to determine the current sampling period for each node. Optimal transmission rate And according to the optimal transmission rate of each node. Establish data transmission channels between nodes to share logistics data through these channels;

[0096] Step S3: Based on the current sampling period between every two collaborative nodes The difference in data update progress and the preset sensitivity function determine the relationship between each pair of cooperating nodes in the current sampling period. Synchronization Deviation Index And when the synchronization deviation index exceeds the maximum tolerance bridging index At that time, a synchronization correction command is triggered to control data synchronization;

[0097] Step S4: Based on the current sampling period, each node has been determined in the current sampling period. Optimal transmission rate Real-time computing load status and synchronization deviation index Determine each node in the current sampling period The elastic load coordination factor; based on the elastic load coordination factor and the preset flexible resource reallocation function, each node is allocated in the current sampling period. The computing resources; based on each node in the current sampling period The synchronization deviation index and optimal transmission rate are used to coordinate and regulate computing resources, so as to obtain the current sampling period for each node. The computing power allocation scheme; based on each node in the current sampling period The computing power allocation scheme executes logistics operations on each node; specifically, for each node, the node is linked to all other collaborating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index .

[0098] In this embodiment, steps S1 to S4 are executed once in each sampling period (the sampling period length can be preset according to the system load, such as 5 seconds, 10 seconds, or 30 seconds). In step S1, real-time logistics data is collected from each logistics node (sorting center, fixed storage point, vehicle mobile terminal, etc.), including order status, inventory, temperature and humidity records, vehicle location, etc., and the size of the data packet to be transmitted at each node and the data update progress between each two nodes are parsed from it (for example, expressed as the ratio of the latest transaction timestamp to the full data timestamp).

[0099] Step S2 uses a rate adjustment function based on the hyperbolic tangent function, combined with the size of the data packet to be transmitted, the maximum allowable end-to-end delay, and the physical bandwidth of the nodes, to calculate the optimal transmission rate for each node in the current period. This rate increases with the increase of the data packet size and decreases with the tightening of the delay constraint, and is further adjusted accordingly. The function naturally saturates near the physical bandwidth. The system then establishes a data transmission channel and shares logistics data according to the calculated rate.

[0100] Step S3 uses a preset sensitivity function to convert the data update progress differences between nodes into a dimensionless synchronization deviation index. This index reflects the severity of data inconsistency. When the index exceeds the preset maximum tolerance level, the index is closed. When the system triggers a synchronization correction command, it forces the lagging node to pull the missing data from the leading node until the two are consistent.

[0101] In step S4, an elastic load coordination factor is determined based on the optimal transmission rate calculated for the current period, the real-time collected computing power load status (task queue length and CPU utilization), and the synchronization deviation index obtained in step S3. This factor comprehensively reflects the node's network communication capability, data synchronization status, and computing pressure. Next, the system uses a flexible resource reallocation function (e.g., a Softmax mechanism based on exponential normalization) to proportionally allocate the total available computing power of the system to each node. Furthermore, the system also performs coordinated control based on the synchronization deviation index and the optimal transmission rate: for example, temporarily increasing the computing power of nodes with persistently large synchronization deviations, temporarily reducing the computing power of severely weak network nodes, and transferring the released computing power to nodes with smooth network connections. Finally, each node executes logistics operations, such as order scheduling, cargo tracking, or inventory management, according to its allocated computing power.

[0102] It should be noted that the synchronization deviation index of each node in step S4 The arithmetic mean of the synchronization deviation index between the node and all other cooperating nodes is taken. This design can avoid global misjudgment caused by occasional instantaneous jitter of a single link, and is especially suitable for multi-node mesh cooperative networks.

[0103] The beneficial effects of the above technical solution are as follows: Through the collaborative design of steps S1 to S4, the linkage between transmission rate, data synchronization, and computing power scheduling is realized. Step S2 dynamically determines the optimal transmission rate based on the size of the data packets to be transmitted, latency constraints, and physical bandwidth of each node, avoiding bandwidth idleness or congestion caused by a fixed rate. Step S3 uses a nonlinear sensitivity function to calculate the synchronization deviation index between nodes, triggering synchronization correction only when the deviation exceeds the tolerance threshold, reducing invalid synchronization operations. Step S4 uses the optimal transmission rate obtained in step S2, the synchronization deviation index obtained in step S3, and the real-time computing power load status as inputs to determine the elastic load coordination factor, and allocates computing power resources through a flexible resource reallocation function. Simultaneously, it performs coordinated linkage control based on the synchronization deviation index and the optimal transmission rate, enabling computing power to flow from weak network nodes or low-load nodes to synchronously lagging nodes, accelerating the overall data consistency recovery of the system. Furthermore, the synchronization deviation index of each node is taken as the arithmetic mean of the deviations between it and all other cooperating nodes, avoiding excessive interference from occasional jitter of a single link on global decision-making. The above technical solution realizes closed-loop adaptive adjustment of transmission, synchronization and computing power in logistics collaborative networks, improving operational efficiency and data consistency convergence speed.

[0104] In one embodiment, step S2 may include:

[0105] For each node In the current sampling period Obtain the maximum available throughput of the link for this node. (This represents the maximum data transmission rate that the node can currently achieve, which can be obtained through link adaptive algorithms or actual measurements), Node type damping coefficient. Data priority gain factor In the current sampling period Size of the data packet to be transmitted Maximum permissible end-to-end delay and the preset reference rate And determine the node in the current sampling period according to the preset rate adjustment function based on the hyperbolic tangent function. Optimal transmission rate ;

[0106] The rate adjustment function is:

[0107]

[0108] control node According to the description Perform data transmission;

[0109] Among them, if And the current cycle or continuous Demand rate within a cycle Greater than the optimal transmission rate If this occurs, a degradation process is triggered, which includes temporarily storing the node's low-priority data locally and transmitting only high-priority data; wherein The first preset ratio threshold is defined. High-priority data includes cold chain temperature control data and urgent order information, while low-priority data includes regular express delivery status updates and non-real-time statistical information.

[0110] in, Represents a node The maximum available throughput of the link can be obtained directly from the network card specifications or link speed test, and the unit is, for example, Mbps. This represents the node type damping coefficient, with a value ranging from 0.3 to 0.9. It can be preset according to the node's role: 0.7 to 0.9 for sorting centers, 0.5 to 0.7 for fixed warehouse outlets, and 0.3 to 0.5 for vehicle-mounted mobile terminals. This coefficient reflects the reduction in bandwidth utilization due to differences in link stability at different nodes; a smaller value indicates a more unstable link. It is a dimensionless value. This is a data priority gain factor, ranging from 0.8 to 1.5. It is a dimensionless value used to distinguish the urgency of transmission for different data types, with higher values ​​assigned to urgent data. It can be preset according to the data type. For example, 1.2 to 1.5 is used for urgent orders or cold chain temperature-controlled data, while 0.8 to 1.0 is used for ordinary express orders or inventory data. Indicates the current sampling period internal nodes The size of the data packets to be transmitted is obtained through statistics from the node data cache queue. This indicates the maximum permissible end-to-end latency, which is preset by the service SLA (Service Level Agreement). This represents the reference rate, which can be set to a constant of 100 Mbps for dimensionless processing. The first preset proportional threshold value ranges from 0.2 to 0.4 and is dimensionless. This is to ensure... In this embodiment, the maximum available throughput of the link is not exceeded. .

[0111] In this embodiment, for any node The system first obtains the above parameters, and then substitutes them into the rate adjustment function to calculate the optimal transmission rate. The independent variable in the formula It is a dimensionless ratio that represents the urgency of the required rate relative to the baseline rate for the current data packet size. The larger the ratio, the more demanding the data packet size. The closer the value is to 1, Approaching The smaller the ratio, Approximately linear The transmission rate decreases linearly with demand. The introduction of the hyperbolic tangent function allows the transmission rate to naturally saturate near the physical bandwidth, avoiding over-transmission.

[0112] In the downgrade judgment sub-step, downgrade is triggered when two conditions are met simultaneously: one is... That is, the current optimal transmission rate is lower than the reference rate by a certain percentage; secondly, This means that the theoretically required data rate exceeds the actual available data rate. In this case, the system temporarily stores low-priority data in a local queue and only transmits high-priority data to ensure that critical business operations are not interrupted.

[0113] In the above embodiments, by using node type damping coefficients and data priority gain factors, rate adjustment can differentiate the transmission needs of different nodes (e.g., fixed facilities and mobile terminals) and different services (e.g., cold chain and regular express delivery). The hyperbolic tangent function allows the rate to saturate naturally, avoiding over-bandwidth transmission. The degradation mechanism automatically protects high-priority data when resources are insufficient, improving the system's robustness.

[0114] In one embodiment, step S3 may include:

[0115] For every two collaborative nodes and In the current sampling period Get the current sampling period for each of the two nodes. Data update progress and Logistics sensitivity coefficient Preset zero-adjustment factor Preset nonlinear enhancement coefficient And determine the sensitivity between the two nodes in the current sampling period according to the following preset sensitivity function. Synchronization Deviation Index :

[0116]

[0117] like This triggers a synchronization correction command between the two nodes, controlling the node lagging in data update progress to synchronize with the leading node's data; where This is the preset maximum tolerance bridging index;

[0118] For each node This will establish communication between this node and all other cooperating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index :

[0119]

[0120] in The total number of cooperating nodes, and .

[0121] in, All values ​​are dimensionless and range from 0 to 1, representing the node in the current sampling period. The data update progress is obtained by comparing the latest transaction timestamp within a node with the timestamp of all data.

[0122] Dimensionless, representing the logistics sensitivity coefficient; used to quantify the sensitivity of different logistics scenarios to data synchronization errors between nodes. The value is determined by pre-setting the value based on the type of goods transmitted between the two collaborative nodes; for example, 1.5–2.0 for cold chain or valuable goods scenarios, and 0.8–1.2 for ordinary express delivery scenarios.

[0123] To prevent the zero adjustment factor from being removed, take the smallest positive number (e.g., This ensures that the denominator is not zero. It is a nonlinear enhancement coefficient, dimensionless, with a value greater than 1, and preferably adjustable within the range of 1.5 to 2.0.

[0124] This is the maximum tolerance bridging index, a preset value. This index is the synchronization deviation index. The trigger threshold is set as follows: the value is preset according to business sensitivity, which can be set to 0.3 for cold chain scenarios and 0.6 for ordinary scenarios; this value can be determined through historical data statistics or on-site debugging, and represents the maximum allowable degree of data inconsistency.

[0125] Preferably, the logistics sensitivity coefficient With the maximum tolerance bridging index With proper settings, this allows for adjustments within the normal business fluctuation range. It will not accidentally trigger synchronization correction, for example, it is advisable. .

[0126] This is used to non-linearly amplify moderate to severe data progress differences while suppressing minor differences (such as sampling jitter), thereby reducing invalid synchronization triggers. The preferred value range is 1.5 to 2.0.

[0127] This embodiment describes the calculation of the synchronization deviation index and the synchronization triggering rules. For every two cooperating nodes... and The system obtains the data update progress of their current sampling period. , and logistics sensitivity coefficient Then substitute it into the sensitivity function to calculate. The formula has the following property: when the schedule difference is zero... When the schedule difference is much greater than The hour fraction term is close to 1. near .because The function exhibits a non-linear amplification effect on medium to large progress differences, while suppressing extremely small progress differences (such as those caused by sampling jitter), thus avoiding frequent false synchronization triggers. In highly sensitive scenarios such as cold chain logistics... A higher value means that even a moderate deviation can be amplified and quickly trigger synchronization.

[0128] if The system triggers a synchronization correction command between the two nodes, and the lagging node actively pulls the missing data from the leading node.

[0129] For each node Its node-level synchronization deviation index Defined as the pairwise deviation index between it and all other nodes. The arithmetic mean of the nodes is used. Using the average value instead of the maximum value can avoid nodes being misjudged as having high deviation due to brief jitter in individual links, which is especially suitable for multi-node mesh cooperative topologies.

[0130] The beneficial effects of the above technical solution are: nonlinear enhancement coefficient This makes the synchronization deviation index sensitive to moderate and greater deviations but tolerant of minor deviations, reducing the number of times ineffective synchronization is triggered; logistics sensitivity coefficient It enables different synchronization strengths for different business scenarios; the arithmetic mean improves the stability of synchronization decisions and adapts to multi-node mesh collaborative structures.

[0131] In one embodiment, step S4 is performed in each sampling period and includes the following steps:

[0132] Step S41, in the current sampling period ( The sampling period number is the number of the sampling period, with adjacent numbers separated by one sampling period (the sampling period length is a preset constant). For each node... Collect data from this node during the current sampling period. Current length of the pending task queue (Unit: shards, read in real-time via the node task scheduler) Historical peak queue length (Unit: 100 samples, maximum value within the past 24 hours; if the system has been running for less than 24 hours, maximum value during the current running period will be used.) Current sampling period. CPU utilization (Dimensionless, obtained through the operating system monitoring interface), and the node in the current sampling period is determined according to the following formula. Original load strength :

[0133]

[0134] in, The preset zero-adjustment factor (take a positive number, for example) is used to prevent zeroing. , used to avoid (when the denominator is zero)

[0135] Step S42: Based on the current sampling period and previous consecutive Number of sampling periods (total) The synchronization deviation index (in each sampling period) is used to calculate the node. In the current sampling period Historical Retrospective This is used to reflect the persistence of recent synchronization deviations of nodes:

[0136]

[0137] in, For nodes In the Synchronization deviation index for each sampling period (dimensionless, ranging from 0 to 1, calculated by step S3); The maximum tolerance bridging index; To determine the window length (positive integer, (This is used to determine how many consecutive sampling periods are needed to trigger regulation).

[0138] Wherein, if the current sampling period number If the data is from an existing sampling period, then only the data from that period will be used for calculation, and the missing periods will not be included in the calculation.

[0139] Step S43: Based on the current sampling period Optimal transmission rate Determine the node In the current sampling period Weak network status indicator :

[0140]

[0141] in, For nodes Maximum available throughput of the link; This is the node type damping coefficient (dimensionless, ranging from 0.3 to 0.9, preset according to the node role); this coefficient reflects the reduction in bandwidth utilization due to differences in link stability among different nodes. The value ranges from 0.3 to 0.9. Specific values ​​can be assigned based on the node role; for example, 0.7 to 0.9 for sorting centers, 0.5 to 0.7 for fixed warehouse outlets, and 0.3 to 0.5 for vehicle-mounted mobile terminals. A smaller value indicates a more unstable link and lower bandwidth utilization.

[0142] This is a data priority gain factor used to differentiate the transmission priority of different data types, with a value ranging from 0.8 to 1.5. It is preset according to the data type; for example, 1.2 to 1.5 is used for urgent orders or cold chain temperature-controlled data, while 0.8 to 1.0 is used for ordinary express orders or inventory data. A higher value indicates that the data requires a higher transmission rate.

[0143] The second preset proportional threshold is dimensionless; this threshold is used to determine whether a node is in a weak network state. Its value ranges from 0.3 to 0.5. (When the optimal transmission rate...) At this time, the node is marked as a weak network. This value can be calibrated through network simulation or actual testing.

[0144] Here, we still need to ensure To ensure that the threshold does not exceed ;

[0145] Step S44, Calculate the node In the current sampling period Elastic load coordination factor :

[0146]

[0147] in, This is a dimensionless, preset constant representing a weak network penalty coefficient. This coefficient amplifies the negative impact of weak network nodes on computing resource allocation. Its value ranges from 0.5 to 1.0. It is also used as an elastic load coordination factor when a node is marked as a weak network node. Divide by This reduces its computing power allocation weight. It can be adjusted according to the network environment; the initial default value can be set to 0.75.

[0148] This formula is essentially a product-gated model. Reflecting the current computing pressure, Reflecting the degree of historical synchronization lag, the weak network penalty item implements conditional weight reduction in the form of division;

[0149] when When the denominator is greater than 1, it makes This reduces the computing power weight of weak network nodes in subsequent computing power allocation;

[0150] Step S45: Obtain the total available computing power Total number of nodes Each node is determined according to the flexible resource reallocation function. In the current sampling period Preliminary computing power

[0151] Total available computing power The sum of CPU core counts for all collaborating nodes is calculated to eliminate performance differences between heterogeneous hardware architectures. The method is as follows: with a baseline CPU single-core performance of 1, a relative conversion factor is obtained for other architecture cores through standard performance tests (such as SPECint). The physical core count of each node is then multiplied by the corresponding factor and summed. This calculation is performed during initialization and dynamically updated when a node joins or leaves.

[0152] Specifically, the flexible resource reallocation function is expressed as the following formula:

[0153]

[0154] This function is in the form of Softmax and utilizes an exponential function. The elastic load coordination factor is mapped to a positive weight to ensure smooth allocation of computing power and that the sum equals the total available computing power.

[0155] Step S46: In the current sampling period Execute coordinated control: Define the first condition and the second condition, wherein the first condition includes the current sampling period. and previous consecutive Number of sampling periods (total) Synchronization deviation index for each sampling period (in each sampling period). All greater than The second condition includes the current sampling period. and previous consecutive Number of sampling periods (total) The optimal transmission rate for each sampling period (in 1 sampling period). All less than ,in The third preset ratio threshold (dimensionless, ranging from 0.1 to 0.2) is used, and ; Used to determine whether a node is in a severely weak network state, with a value ranging from 0.1 to 0.2, and satisfying the following conditions: When continuous In each sampling period When this occurs, the computing power is directly reduced. This value is set to be higher than... Smaller size to enable hierarchical weak network processing.

[0156] Determine the node based on the combination of the first and second conditions. In the current sampling period The final computing power as follows:

[0157] If the first condition is not met and the second condition is not met, then determine ;

[0158] If the first condition is true and the second condition is false, then determine. ;

[0159] If the first condition is false and the second condition is true, then determine and will release computing power By index proportion Reassigned to the set of qualified nodes ,in For the preset reduction ratio (dimensionless, with a value range of, for example, 0.2~0.4), the set By satisfying and nodes Composition; where, if set If empty, the released computing power is temporarily stored in the system resource pool and not redistributed; the original node retains its reduced computing power. This exponential allocation is based on the elastic load coordination factor of each qualified node, ensuring that the released computing power flows to nodes with higher overall load pressure and better network conditions, avoiding excessive concentration of computing power on already heavily loaded nodes, because... This comprehensively reflects the actual needs of the nodes and the network conditions;

[0160] If both the first and second conditions are met, then first calculate the adjusted median value. Then, the adjusted value is adjusted downwards to obtain the final computing power. and will release computing power Redistribute to the qualified node set according to the stated exponential ratio. The nodes in. Similarly, if If the value is empty, the released computing power is temporarily stored.

[0161] The rationale behind the "increase first, then decrease" strategy is that nodes with persistent synchronization deviations need to temporarily increase computing power to catch up with data progress. However, if the node is also in a severely weak network state, the network has become a bottleneck, and excessively increasing computing power is not beneficial. Therefore, a certain increase incentive is given first, and then some computing power is recovered according to the degree of network weakness to prevent resource waste and maintain a balance between incentives and constraints.

[0162] Step S47: Each node, according to the current sampling period The final computing power is used to execute logistics operations.

[0163] in, and The range of values ​​for are respectively and And satisfy ;in Used to determine the weak network status (when...) Time-marked weak network, using weak network penalty coefficient (Reduce computing power weight) Used to determine a severely weak network condition (when continuous...) In each cycle (This triggers a direct reduction in computing power and releases it to healthy nodes), thus creating a tiered processing mechanism: minor rate drops only reduce the weight, while severe rate drops force the nodes to relinquish computing power. The reason is that the network conditions of logistics nodes vary. A slight decrease in speed (e.g., brief signal fluctuations) should not immediately strip nodes of their computing power, otherwise it will lead to frequent migration of computing power and system instability. Conversely, if severely weak networks (e.g., entering signal blind spots) continue to occupy computing power, it will result in resource waste. The benefit is: it establishes a tiered processing mechanism. As an early warning threshold, the weak network penalty coefficient is used. Reduce node competitiveness at the load weight level, but do not directly reduce allocated computing power, thus preserving the possibility of processing local tasks. As a threshold for severely weak networks, computing power is forcibly relinquished and redistributed only when a lower rate threshold is consistently met, thus avoiding idle computing power and reducing false triggers. This design enables the system to respond sensitively to minor fluctuations and decisively reclaim resources from severely weak network nodes, improving computing power utilization efficiency and collaborative stability.

[0164] This embodiment fully describes the specific execution process of step S4, including load intensity calculation, historical backtracking, weak network detection, construction of elastic coordination factor, preliminary computing power allocation, and coordinated linkage control.

[0165] In step S41, the original load strength It combines task queue length and CPU utilization. (Logarithmic function) The effect of increasing queue length on load is gradually saturated, preventing individual nodes from excessively preempting computing power due to instantaneous backlog; fractional terms The sharp increase in CPU utilization when it approaches 1 reflects the nonlinear stress under high load. This ensures that the denominator is not zero.

[0166] In step S42, the historical backtracking item Based on the current and previous continuum The average synchronization deviation index of each sampling period and The ratio is set with a lower limit of 0.1, ensuring that the backtracking term will not return to zero even when there is no deviation. This value reflects the persistence of recent synchronization deviations of nodes, thus ensuring the temporal continuity of computing power allocation.

[0167] In step S43, the weak network status indicator factor By comparing the current optimal transmission rate with To determine. If If the value is below this threshold, it is marked as a weak network. ), otherwise it is 0.

[0168] In step S44, the elastic load coordination factor Will , Multiply by the weak network penalty factor, where the weak network penalty is expressed as a division: .when hour, Divide by ,make The size of the node is reduced, thereby decreasing the node's computing power weight in subsequent Softmax allocations. This correction ensures that weak network nodes do not receive excessive computing power due to high load.

[0169] In step S45, the initial computing power The total available computing power is calculated using the Softmax function. By index weight Allocation ensures that resources are always fully utilized and that there are no negative values.

[0170] In step S46, the coordinated control further adjusts the computing power based on the continuous synchronization deviation (first condition) and the continuous severe network weakness (second condition). Both conditions require continuous... Each sampling period is satisfied to avoid instantaneous fluctuations. Four scenarios are handled separately: initial computing power is maintained when there is no trigger; computing power is increased only when the synchronization deviation is large (multiplied by...). Only reduce computing power (multiplied by) during severely weak network conditions. The released computing power will be redistributed exponentially to healthy nodes. When both conditions are met, the value is first increased and then decreased, with the released computing power calculated based on the increased value. This composite strategy of incentivizing first and then recovering retains the catching-up intention of lagging nodes while ensuring that resources are prioritized for healthy nodes in severely weak networks.

[0171] In step S47, each node calculates its final computing power. Perform specific logistics tasks (order scheduling, cargo tracking, inventory management, etc.).

[0172] In the above technical solution, the original load intensity, combined with queue length and CPU utilization, accurately reflects the node's computational pressure. Historical backtracking incorporates the long-term trend of synchronization deviation into power allocation, giving the system time memory. Weak network penalty uses division, correctly reducing the computing power weight of nodes in weak networks. Softmax allocation ensures full utilization of total computing power and smooth allocation. The upward adjustment compensation and weak network degradation mechanisms in the coordinated control enhance the system's self-healing ability under continuous deviation or extremely weak network environments. The hierarchical design of the system means that it only adjusts the weights for mildly weak networks, and only forcibly reclaims computing power for persistently severe weak networks, thus reducing unnecessary computing power fluctuations.

[0173] Corresponding to the methods provided in any of the foregoing embodiments, this embodiment of the invention provides a global digital collaborative sharing and processing system, including:

[0174] The data acquisition module is used for the current sampling period. The system collects logistics data from each logistics node and obtains the data for each node in the current sampling period. The size of the data packet to be transmitted and the respective sampling period of each pair of cooperating nodes. Data update progress;

[0175] The channel establishment module is used to determine the current sampling period for each node. The size of the data packet to be transmitted, the preset transmission delay constraint, the node's physical bandwidth, and the preset rate adjustment function based on the hyperbolic tangent function are used to determine the current sampling period for each node. Optimal transmission rate And according to the optimal transmission rate of each node. Establish data transmission channels between nodes to share logistics data through these channels;

[0176] The synchronization processing module is used to determine the time between each pair of cooperating nodes in the current sampling period. The difference in data update progress and the preset sensitivity function determine the relationship between each pair of cooperating nodes in the current sampling period. Synchronization Deviation Index And when the synchronization deviation index exceeds the maximum tolerance bridging index At that time, a synchronization correction command is triggered to control data synchronization;

[0177] The control and processing module is used to control each node in the current sampling period based on the current sampling period. Optimal transmission rate Real-time computing load status and synchronization deviation index Determine each node in the current sampling period The elastic load coordination factor; based on the elastic load coordination factor and the preset flexible resource reallocation function, each node is allocated in the current sampling period. The computing resources; based on each node in the current sampling period The synchronization deviation index and optimal transmission rate are used to coordinate and regulate computing resources, so as to obtain the current sampling period for each node. The computing power allocation scheme; based on each node in the current sampling period The computing power allocation scheme executes logistics operations on each node; specifically, for each node, the node is linked to all other collaborating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index .

[0178] Preferably, the channel establishment module is further configured to:

[0179] For each node In the current sampling period Obtain the maximum available throughput of the link for this node. Node type damping coefficient Data priority gain factor In the current sampling period Size of the data packet to be transmitted Maximum permissible end-to-end delay and the preset reference rate And determine the node in the current sampling period according to the preset rate adjustment function based on the hyperbolic tangent function. Optimal transmission rate ;

[0180] The rate adjustment function is:

[0181]

[0182] control node According to the description Perform data transmission;

[0183] Among them, if And the current cycle or continuous Demand rate within a cycle Greater than the optimal transmission rate If this occurs, a degradation process is triggered, which includes temporarily storing the node's low-priority data locally and transmitting only high-priority data; wherein The first preset ratio threshold, , The value range is 0.8 to 1.5. The value range is 0.3 to 0.9; .

[0184] Preferably, the synchronization processing module is further configured to:

[0185] For every two collaborative nodes and In the current sampling period Get the current sampling period for each of the two nodes. Data update progress and Logistics sensitivity coefficient Preset zero-adjustment factor Preset nonlinear enhancement coefficient And determine the sensitivity between the two nodes in the current sampling period according to the following preset sensitivity function. Synchronization Deviation Index :

[0186]

[0187] like This triggers a synchronization correction command between the two nodes, controlling the node lagging in data update progress to synchronize with the leading node's data; where This is the preset maximum tolerance bridging index;

[0188] For each node This will establish communication between this node and all other cooperating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index :

[0189]

[0190] in The total number of cooperating nodes, and ; For the two nodes i and j in the current sampling period The synchronization deviation index.

[0191] Preferably, the control processing module is further configured to perform the following steps in each sampling period:

[0192] Step S41, in the current sampling period For each node Collect data from this node during the current sampling period. Current length of the pending task queue Historical peak queue length Current sampling period CPU utilization The node in the current sampling period is determined according to the following formula. Original load strength :

[0193]

[0194] in, The preset zero-adjustment factor is used for prevention and elimination.

[0195] Step S42: Based on the current sampling period and previous consecutive The synchronization deviation index for each sampling period is used to calculate the node. In the current sampling period Historical Retrospective :

[0196]

[0197] in, For nodes In the Synchronization deviation index for each sampling period; The maximum tolerance bridging index; To determine the window length;

[0198] Step S43: Based on the current sampling period Optimal transmission rate Determine the node In the current sampling period Weak network status indicator :

[0199]

[0200] in, For nodes Maximum available throughput of the link; The damping coefficient is the node type. This is a data priority gain factor; The second preset ratio threshold;

[0201] Step S44, Calculate the node In the current sampling period Elastic load coordination factor :

[0202]

[0203] in The penalty coefficient for weak networks; when When the denominator is greater than 1, it makes This reduces the computing power weight of weak network nodes in subsequent computing power allocation;

[0204] Step S45: Obtain the total available computing power Total number of nodes Each node is determined according to the flexible resource reallocation function. In the current sampling period Preliminary computing power :

[0205]

[0206] Step S46: In the current sampling period To implement coordinated control, a first condition and a second condition are defined. The first condition includes the current sampling period. and previous consecutive Synchronization deviation index for each sampling period. All greater than The second condition includes the current sampling period. and previous consecutive The optimal transmission rate for each sampling period within each sampling period. All less than ,in The third preset ratio threshold, and ;

[0207] Determine the node based on the combination of the first and second conditions. In the current sampling period The final computing power as follows:

[0208] If the first condition is not met and the second condition is not met, then determine ;

[0209] If the first condition is true and the second condition is false, then determine. ;

[0210] If the first condition is false and the second condition is true, then determine and will release computing power By index proportion Reassigned to the set of qualified nodes ,in To preset the reduction ratio, the collection By satisfying and nodes composition;

[0211] If both the first and second conditions are met, then first calculate the adjusted median value. Then, the adjusted value is adjusted downwards to obtain the final computing power. and will release computing power Redistribute to the qualified node set according to the stated exponential ratio. Nodes in;

[0212] Step S47: Each node, according to the current sampling period The final computing power is used to execute logistics operations.

[0213] The global digital collaborative sharing and processing method and system provided in this invention achieves closed-loop collaborative control of transmission, synchronization, and computing power by dynamically adapting the transmission rate, detecting and correcting nonlinear data synchronization deviations, and flexibly reallocating computing power based on an elastic load coordination factor within each sampling period. Specifically, this method automatically adjusts the optimal transmission rate based on the real-time data packet size, latency constraints, and physical bandwidth of the nodes, avoiding network congestion and resource idleness. It accurately identifies data synchronization deviations using a nonlinear enhancement coefficient and a logistics sensitivity coefficient, triggering synchronization correction only when the deviation continuously exceeds the limit, reducing ineffective synchronization overhead. By fusing the optimal transmission rate, synchronization deviation index, and real-time load status to construct an elastic load coordination factor, it allocates the total available computing power using an exponential normalization mechanism and performs hierarchical linkage control based on continuous synchronization deviations and severely weak network conditions (temporarily increasing computing power when synchronization deviation is large, and actively relinquishing computing power and reallocating it to healthy nodes when the network is severely weak), forming an adaptive feedback loop. This scheme effectively improves the operating efficiency, resource utilization, and data consistency convergence speed of the logistics collaborative network, and is particularly suitable for heterogeneous logistics scenarios including sorting centers, fixed storage points, and vehicle-mounted mobile terminals.

[0214] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. This disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims. Thus, if these modifications and variations of the invention fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for global digital collaborative sharing and processing, characterized in that, Perform the following steps in each sampling period: Step S1: In the current sampling period The system collects logistics data from each logistics node and obtains the data for each node in the current sampling period. The size of the data packet to be transmitted and the respective sampling period of each pair of cooperating nodes. Data update progress; Step S2: Based on each node in the current sampling period The size of the data packet to be transmitted, the preset transmission delay constraint, the node's physical bandwidth, and the preset rate adjustment function based on the hyperbolic tangent function are used to determine the current sampling period for each node. Optimal transmission rate And according to the optimal transmission rate of each node. Establish data transmission channels between nodes to share logistics data through these channels; Step S3: Based on the current sampling period between every two collaborative nodes The difference in data update progress and the preset sensitivity function determine the relationship between each pair of cooperating nodes in the current sampling period. Synchronization Deviation Index And when the synchronization deviation index exceeds the maximum tolerance bridging index At that time, a synchronization correction command is triggered to control data synchronization; Step S4: Based on the current sampling period, each node is determined in the current sampling period. Optimal transmission rate Real-time computing load status and synchronization deviation index Determine each node in the current sampling period The elastic load coordination factor; based on the elastic load coordination factor and the preset flexible resource reallocation function, each node is allocated in the current sampling period. The computing resources; based on each node in the current sampling period The synchronization deviation index and optimal transmission rate are used to coordinate and regulate computing resources, so as to obtain the current sampling period for each node. The computing power allocation scheme; Based on each node in the current sampling period The computing power allocation scheme executes logistics operations on each node; specifically, for each node, the node is linked to all other collaborating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index .

2. The method according to claim 1, characterized in that, Step S2 includes: For each node In the current sampling period Obtain the maximum available throughput of the link for this node. Node type damping coefficient Data priority gain factor In the current sampling period Size of the data packet to be transmitted Maximum permissible end-to-end delay and the preset reference rate And determine the node in the current sampling period according to the preset rate adjustment function based on the hyperbolic tangent function. Optimal transmission rate ; The rate adjustment function is: control node According to the description Perform data transmission; Among them, if And the current cycle or continuous Demand rate within a period Greater than the optimal transmission rate If this occurs, a degradation process is triggered, which includes temporarily storing the node's low-priority data locally and transmitting only high-priority data; wherein The first preset ratio threshold, , The value range is 0.8 to 1.

5. The value range is 0.3 to 0.9; .

3. The method according to claim 1, characterized in that, Step S3 includes: For every two collaborative nodes and In the current sampling period Get the current sampling period for each of the two nodes. Data update progress and Logistics sensitivity coefficient Preset zero-adjustment factor Preset nonlinear enhancement coefficient And determine the sensitivity between the two nodes in the current sampling period according to the following preset sensitivity function. Synchronization Deviation Index : like This triggers a synchronization correction command between the two nodes, controlling the node with lagging data update progress to synchronize with the leading node's data; where This is the preset maximum tolerance bridging index; For each node This will establish communication between this node and all other cooperating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index : in The total number of cooperating nodes, and ; For the two nodes i and j in the current sampling period The synchronization deviation index.

4. The method according to claim 3, characterized in that, Step S4 is performed in each sampling period and includes the following steps: Step S41, in the current sampling period For each node Collect data from this node during the current sampling period. Current length of the pending task queue Historical peak queue length Current sampling period CPU utilization The node in the current sampling period is determined according to the following formula. Original load strength : in, The preset zero-adjustment factor is used for prevention and elimination. Step S42: Based on the current sampling period and previous consecutive The synchronization deviation index for each sampling period is used to calculate the node. In the current sampling period Historical Retrospective : in, For nodes In the Synchronization deviation index for each sampling period; The maximum tolerance bridging index; To determine the window length; Step S43: Based on the current sampling period Optimal transmission rate Determine the node In the current sampling period Weak network status indicator : in, For nodes Maximum available throughput of the link; The damping coefficient is the node type. This is a data priority gain factor; The second preset ratio threshold; Step S44, Calculate the node In the current sampling period Elastic load coordination factor : in The penalty coefficient for weak networks; when When the denominator is greater than 1, it makes This reduces the computing power weight of weak network nodes in subsequent computing power allocation; Step S45: Obtain the total available computing power Total number of nodes Each node is determined according to the flexible resource reallocation function. In the current sampling period Preliminary computing power : Step S46: In the current sampling period To implement coordinated control, a first condition and a second condition are defined. The first condition includes the current sampling period. and previous consecutive Synchronization deviation index for each sampling period. All greater than The second condition includes the current sampling period. and previous consecutive The optimal transmission rate for each sampling period within each sampling period. All less than ,in The third preset ratio threshold, and ; Determine the node based on the combination of the first and second conditions. In the current sampling period The final computing power as follows: If the first condition is not met and the second condition is not met, then determine ; If the first condition is true and the second condition is false, then determine. ; If the first condition is false and the second condition is true, then determine and will release computing power By index proportion Reassigned to the set of qualified nodes ,in To preset the reduction ratio, the collection By satisfying and nodes composition; If both the first and second conditions are met, then first calculate the adjusted median value. Then, the adjusted value is adjusted downwards to obtain the final computing power. and will release computing power Redistribute to the qualified node set according to the stated exponential ratio. Nodes in; Step S47: Each node, according to the current sampling period The final computing power is used to execute logistics operations.

5. A global digital collaborative sharing and processing system, characterized in that, include: The data acquisition module is used in the current sampling period. The system collects logistics data from each logistics node and obtains the data for each node in the current sampling period. The size of the data packet to be transmitted and the respective sampling period of each pair of cooperating nodes. Data update progress; The channel establishment module is used to determine the current sampling period for each node. The size of the data packet to be transmitted, the preset transmission delay constraint, the node's physical bandwidth, and the preset rate adjustment function based on the hyperbolic tangent function are used to determine the current sampling period for each node. Optimal transmission rate And according to the optimal transmission rate of each node. Establish data transmission channels between nodes to share logistics data through these channels; The synchronization processing module is used to determine the time between each pair of cooperating nodes in the current sampling period. The difference in data update progress and the preset sensitivity function determine the relationship between each pair of cooperating nodes in the current sampling period. Synchronization Deviation Index And when the synchronization deviation index exceeds the maximum tolerance bridging index At that time, a synchronization correction command is triggered to control data synchronization; The control and processing module is used to control each node in the current sampling period based on the current sampling period. Optimal transmission rate Real-time computing load status and synchronization deviation index Determine each node in the current sampling period The elastic load coordination factor; based on the elastic load coordination factor and the preset flexible resource reallocation function, each node is allocated in the current sampling period. The computing resources; based on each node in the current sampling period The synchronization deviation index and optimal transmission rate are used to coordinate and regulate computing resources, so as to obtain the current sampling period for each node. The computing power allocation scheme; Based on each node in the current sampling period The computing power allocation scheme executes logistics operations on each node; specifically, for each node, the node is linked to all other collaborating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index .

6. The system according to claim 5, characterized in that, The channel establishment module is also used for: For each node In the current sampling period Obtain the maximum available throughput of the link for this node. Node type damping coefficient Data priority gain factor In the current sampling period Size of the data packet to be transmitted Maximum permissible end-to-end delay and the preset reference rate And determine the node in the current sampling period according to the preset rate adjustment function based on the hyperbolic tangent function. Optimal transmission rate ; The rate adjustment function is: control node According to the description Perform data transmission; Among them, if And the current cycle or continuous Demand rate within a period Greater than the optimal transmission rate If this occurs, a degradation process is triggered, which includes temporarily storing the node's low-priority data locally and transmitting only high-priority data; wherein The first preset ratio threshold, , The value range is 0.8 to 1.

5. The value range is 0.3 to 0.9; .

7. The system according to claim 5, characterized in that, The synchronization processing module is also used for: For every two collaborative nodes and In the current sampling period Get the current sampling period for each of the two nodes. Data update progress and Logistics sensitivity coefficient Preset zero-adjustment factor Preset nonlinear enhancement coefficient And determine the sensitivity between the two nodes in the current sampling period according to the following preset sensitivity function. Synchronization Deviation Index : like This triggers a synchronization correction command between the two nodes, controlling the node with lagging data update progress to synchronize with the leading node's data; where This is the preset maximum tolerance bridging index; For each node This will establish communication between this node and all other cooperating nodes during the current sampling period. The arithmetic mean of the synchronization deviation index is used as the node's value in the current sampling period. Synchronization Deviation Index : in The total number of cooperating nodes, and ; For the two nodes i and j in the current sampling period The synchronization deviation index.

8. The system according to claim 7, characterized in that, The control processing module is also used to perform the following steps in each sampling period: Step S41, in the current sampling period For each node Collect data from this node during the current sampling period. Current length of the pending task queue Historical peak queue length Current sampling period CPU utilization The node in the current sampling period is determined according to the following formula. Original load strength : in, The preset zero-adjustment factor is used for prevention and elimination. Step S42: Based on the current sampling period and previous consecutive The synchronization deviation index for each sampling period is used to calculate the node. In the current sampling period Historical Retrospective : in, For nodes In the Synchronization deviation index for each sampling period; The maximum tolerance bridging index; To determine the window length; Step S43: Based on the current sampling period Optimal transmission rate Determine the node In the current sampling period Weak network status indicator : in, For nodes Maximum available throughput of the link; The damping coefficient is the node type. This is a data priority gain factor; The second preset ratio threshold; Step S44, Calculate the node In the current sampling period Elastic load coordination factor : in The penalty coefficient for weak networks; when When the denominator is greater than 1, it makes This reduces the computing power weight of weak network nodes in subsequent computing power allocation; Step S45: Obtain the total available computing power Total number of nodes Each node is determined according to the flexible resource reallocation function. In the current sampling period Preliminary computing power : Step S46: In the current sampling period To implement coordinated control, a first condition and a second condition are defined. The first condition includes the current sampling period. and previous consecutive Synchronization deviation index for each sampling period. All greater than The second condition includes the current sampling period. and previous consecutive The optimal transmission rate for each sampling period within each sampling period. All less than ,in The third preset ratio threshold, and ; Determine the node based on the combination of the first and second conditions. In the current sampling period The final computing power as follows: If the first condition is not met and the second condition is not met, then determine ; If the first condition is true and the second condition is false, then determine. ; If the first condition is false and the second condition is true, then determine and will release computing power By index proportion Reassigned to the set of qualified nodes ,in To preset the reduction ratio, the collection By satisfying and nodes composition; If both the first and second conditions are met, then first calculate the adjusted median value. Then, the adjusted value is adjusted downwards to obtain the final computing power. and will release computing power Redistribute to the qualified node set according to the stated exponential ratio. Nodes in; Step S47: Each node, according to the current sampling period The final computing power is used to execute logistics operations.

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