Container visualization big data digital management system

Abstract

The invention relates to the technical field of data management, and discloses a goods substitute visualization big data digital management system, and the system comprises a unified expression conversion module which is used for building a multi-dimensional space-time resonance field unified expression framework, a field state resonance mechanism module, constructing a field state resonance equation, and solving an optimal weight coefficient; the self-adaptive field source control module is used for modeling key nodes of the logistics system into a field source and dynamically adjusting the characteristics of the field source; the multi-target field superposition module is used for converting a plurality of optimization targets into a field superposition problem; the optimal path generation module is used for generating a logistics optimal path based on a field potential energy gradient descent algorithm; the three-layer self-evolution module is used for realizing a three-layer self-evolution structure according to a path optimization result; by fusing various artificial intelligence and deep learning technologies, the technical normal form transformation from discrete point management to continuous field theory management can be realized, so that large-scale logistics management work with high dimension, high complexity and dynamic change can be processed.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and more particularly, to a freight forwarding visualized big data digital management system. Background Art

[0002] The current digital management systems in the freight forwarding and logistics industry mainly use discrete point analysis methods, viewing the logistics system as a network structure composed of independent nodes such as ports, warehouses, logistics centers and transportation routes; most of these systems are based on the linear programming, graph theory algorithms, and heuristic search mathematical models of existing freight forwarding and logistics management technologies for optimization and decision-making, and are unable to handle large-scale logistics management tasks with high dimensions, high complexity and dynamic changes.

[0003] Therefore, how to handle large-scale logistics management with high dimensionality, high complexity and dynamic changes has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention provides a freight forwarding visualized big data digital management system to solve the technical problem of how to handle high-dimensional, high-complexity and dynamically changing large-scale logistics management work in related technologies.

[0005] The present invention provides a freight forwarding visualized big data digital management system, comprising:

[0006] Establish a unified expression framework for multi-dimensional space-time resonance fields, convert the physical, information and value data of the logistics system into a unified field quantity representation, and lay the data foundation for realizing the field state resonance mechanism;

[0007] Based on the unified field quantity representation, the field state resonance mechanism is realized, the field state resonance equation is constructed and the optimal weight coefficient is solved, providing an optimization basis for the construction of the adaptive field source control mechanism;

[0008] Based on the field state resonance results, an adaptive field source control mechanism is constructed, key nodes of the logistics system are modeled as field sources and the field source characteristics are dynamically adjusted, providing a control basis for achieving multi-target field superposition;

[0009] Based on the field source control results, a multi-objective field superposition framework is implemented to transform multiple optimization objectives into field superposition problems, providing an objective function for solving the optimal path of field flow;

[0010] Based on the field superposition results, a field flow optimal path solving system is developed. The optimal logistics path is generated based on the field potential energy gradient descent algorithm, providing an optimized path for the three-layer self-evolution structure.

[0011] According to the results of path optimization, a three-layer self-evolution structure is realized, including field parameter adaptation, field source dynamic adjustment and field topology evolution layer, and feedback is provided to optimize the operating effects of the aforementioned links.

[0012] Furthermore, the field dynamics model in the unified expression framework of the multidimensional space-time resonance field is described by the following equation:

[0013] in, represents the divergence operator, represents the partial derivative with respect to time, F(x,t) represents the field vector function, x is the spatial position vector, t is the time variable, S i (x, t) represents the field source item, i is the number of the i-th field source, which is used to distinguish different field sources.

[0014] Furthermore, the field state resonance mechanism is realized by a multi-field resonance optimization algorithm, and the field state resonance equation is defined as:

[0015] ω1F1(x,t)+ω2F2(x,t)+...+ω n F n (x, t) = R(x, t); where F i is the jth type of field quantity function, representing the mathematical expression of different physical quantities, information flows or value flows in the logistics system, ω i is the weight coefficient, R is the target resonance mode, and n represents the total number of fields participating in the resonance.

[0016] Furthermore, the optimal weight coefficient is solved by the following iterative equation:

[0017] Where D is the distance function, which represents the difference between the current field state and the target resonance mode; η is the learning rate, which controls the step size of each iteration; k is the number of iterations; Represents the parameter ω i The gradient of , which is used to indicate the direction of weight adjustment; Represents the weight coefficient at the kth iteration; Represents the weight coefficient after the k+1th iteration.

[0018] Furthermore, the field source model in the adaptive field source control mechanism is defined as:

[0019] S i (x, t) = A i (t)·φ i ((xx i )); Among them, A i (t) is the field source intensity, which is dynamically adjusted over time; x i is the spatial location of the logistics source; φ i is the source influence function, which describes the law of source intensity attenuation with distance; (xx i ) represents the spatial position x to the source position x iThe Euclidean distance of .

[0020] Furthermore, the field source intensity dynamic adjustment algorithm is defined as: Among them, A i (t+Δt) represents the field source intensity at the next moment t+Δt, A i (t) represents the field source intensity at time t, is the target throughput, is the current throughput, λ is the overall adjustment rate, α is the proportional control parameter, β is the differential control parameter, represents the rate of change of the current throughput, Δt is the time step, and d is the differential.

[0021] Furthermore, the comprehensive optimization field in the multi-objective field superposition framework is defined as:

[0022] Among them, F comp (x, t) is the comprehensive optimization field, which represents the weighted combination of multiple target fields, w i (t) is the dynamic weight coefficient of the i-th objective, which changes over time to adapt to the optimization focus at different stages. is the field function corresponding to the i-th optimization objective, m is the total number of optimization objectives, x is the spatial position vector, and t is the time variable.

[0023] Furthermore, in the field flow optimal path solving system, the flow velocity is defined by using the field potential energy gradient descent method. Among them, v(x, t) is the flow velocity field, which represents the velocity vector of the logistics entity at position x and time t, and k is the proportional coefficient, which controls the sensitivity of the logistics entity to the potential energy gradient of the response field. represents the gradient operator, which is used to calculate the rate of change of the field potential energy function in each direction. Φ(x, t) is the field potential energy function, which describes the potential energy distribution of the logistics system at position x and time t.

[0024] Furthermore, the optimal path is solved by the following path integration method:

[0025] Among them, P(t) is the path position vector at time t, x s are the coordinates of the starting point, v(x(τ), τ) represents the velocity field at position x(τ) at time τ, dτ is the time element, and the integral represents the trajectory of motion along the velocity field from the starting point to the current time t.

[0026] Furthermore, the adaptive parameter update function in the three-layer self-evolution structure is defined as:

[0027] Where Θ represents the parameter vector of the field equation, T is the parameter update period, Θ(t+T) represents the parameter vector of the next update period, Θ(t) represents the current parameter vector, and ΔΘ represents the change in the parameter. represents the gradient operator with respect to the parameter Θ, and J(Θ) is the performance evaluation function used to quantify the overall performance of the system under the current parameter configuration.

[0028] The beneficial effects of this invention lie in: by establishing a unified expression framework for multidimensional spatiotemporal resonance fields, combined with field-state resonance mechanisms and adaptive field source control, it addresses the high-dimensional, complex, and dynamic nature of logistics management. Through unified data representation, dynamic adaptability, and multi-objective optimization, it improves the system's stability, efficiency, and global optimization capabilities. Furthermore, the system can dynamically adjust strategies based on real-time changes, optimize route planning, and possesses continuous optimization and self-correction capabilities, providing an efficient and adaptable solution for large-scale logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a freight forwarding visual big data digital management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Example 1, as Figure 1 As shown, at least one embodiment of the present invention discloses a freight forwarding visualization big data digital management system that performs the following process:

[0031] Step 100: Establish a unified expression framework for the multi-dimensional space-time resonance field to convert the physical, information, and value data of the logistics system into a unified field quantity representation. The specific implementation is as follows:

[0032] Step 101 collects multi-source heterogeneous data in the logistics management mechanism through the data acquisition unit, including: physical data D phys (spatial location, cargo weight, volume), time data D time (transportation time, loading and unloading time), value data D value (transportation cost, time value), status data D state (cargo status, transportation tool status), generate the original logistics data set D = D phys , D time , D value , D state .

[0033] Step 102 uses the field quantization mapping unit to apply the field quantization mapping function to convert various data into a unified field quantity representation to generate a unified field quantity: Φ phys , Φ info , Φ value ;

[0034] Physical quantity mapping:

[0035] Information mapping:

[0036] Value mapping:

[0037] Among them, x is the multidimensional space coordinate, including physical space and abstract feature space, t is time, f i 、g j and h k are the basic field functions of physical quantity, information quantity and value quantity respectively, α i , β j and γ k is the weight coefficient, which is obtained by machine learning method based on historical data optimization. p 、N i and N v The numbers of basic field functions are physical quantity, information quantity and value quantity respectively.

[0038] Step 103 constructs a unified field expression through the field integration unit, integrates various field quantities into a unified mathematical framework, and generates a comprehensive field intensity vector:

[0039] F(x,t)=w phys ·Φ phys (x,t)+w info ·Φ info (x,t)

[0040] +w value ·Φ value (x, t); where w phys 、w info 、w value is the field type weight, which is used to balance the influence of different types of fields.

[0041] Step 104 establishes the field dynamics equations through the field dynamics modeling unit to describe the evolution of the field over time and generate the field dynamics model F dyn : in, represents the divergence operator, represents the partial derivative with respect to time, F(x, t) represents the integrated field intensity vector function, x is the spatial position vector, t is the time vector, S i (x, t) represents the field source item, which represents the contribution of key nodes (such as warehouses and transfer stations) in the logistics management mechanism to the field. i is the number of the i-th field source, which is used to distinguish different field sources.

[0042] The output of step 100 is a unified expression framework of the multidimensional space-time resonance field, including the unified field quantity representation Φphys , Φ info , Φ value , comprehensive field intensity vector F(x, t) and field dynamics model F dyn , which will be used to implement the field state resonance mechanism in step 200.

[0043] Step 200: Based on the unified field quantity representation, the field state resonance mechanism is realized, the field state resonance equation is constructed and the optimal weight coefficient is solved; based on the multi-field resonance optimization algorithm, the interaction relationship between the comprehensive field intensity vectors F(x, t) generated in step 100 is analyzed to generate the field state resonance condition. The specific implementation is as follows:

[0044] Step 201 uses the resonance state definition unit to define the mathematical expression of field state resonance: when the superposition of multiple fields reaches a certain specific state, the system is in the optimal state, at which time the effects between the fields reach a balance, and the resonance state definition R is generated. def ,

[0045] Step 202 establishes a field state resonance condition equation through a resonance condition construction unit to generate a field state resonance equation:

[0046] R eq =ω1F1(x, t)+ω2F2(x, t)

[0047] +...+ω n F n (x, t);

[0048] Among them, F i are different types of fields (such as cost field, time-effect field, and safety field), and are the components of the comprehensive field strength vector F(x, t) in step 100, ω n is the weight coefficient, which controls the relative importance of each field, R is the target resonance mode, that is, the optimal state of the system, and n is the total number of fields participating in the resonance type.

[0049] Step 203 uses the resonance parameter optimization unit to design a resonance optimization algorithm, solves the optimal weight coefficient through iteration, and generates the optimal weight coefficient set

[0050]

[0051] in, It represents the optimal weight coefficient of the nth field in the field state resonance equation and is the key parameter for making multiple fields superimposed to reach the resonance state. represents the value of the weight coefficient of the i-th field in the k-th iteration, represents the value of the weight coefficient of the i-th field in the k+1-th iteration, Represents the weight coefficient ω iThe gradient operator, D represents the distance function between the current field state and the target resonance state, It represents the comprehensive field after weighted summation of all fields according to the weight coefficient in the kth iteration, and η represents the learning rate, which controls the step size of each iteration.

[0052] Step 204 implements an adaptive weight adjustment mechanism through the adaptive adjustment unit, dynamically adjusts the weight according to the system state and environmental changes, and generates a dynamic weight adjustment function in, is the basic weight, the optimal weight coefficient set Ω, Δω from step 203 i is the weight adjustment calculated based on the system state S(t), where S(t) is the state vector of the system at time t, which contains information about the current environment and task requirements.

[0053] The output of step 200 is the field state resonance mechanism, including the field state resonance equation R eq , optimal weight coefficient set Ω and dynamic weight adjustment function Ω dyn (t), will be used for the adaptive field source control mechanism in step 300 and the multi-target field superposition framework in step 400.

[0054] Step 300: Based on the field source control results, a multi-objective field superposition framework is implemented to transform multiple optimization objectives into field superposition problems; a dynamic field source generation algorithm is used to process the key node data in the logistics management mechanism and the field dynamics model F generated in step 100. dyn , generating a controllable field source distribution. The specific implementation is as follows:

[0055] Step 301 uses the field source modeling unit to identify key nodes in the logistics management mechanism (warehouses, transfer stations, distribution centers), model them as field sources, and generate a field source model set S = S1, S2, ..., S m :S i (x, t) = A i (t)·φ i ((xx i )); Among them, S m Indicates the mth field source, which is the mathematical representation of the key nodes (warehouses, transfer stations, distribution centers) in the logistics system in the field theory model. m is the number of field sources at the key nodes, and A i (t) is the field source intensity of the node at time t, x i is the source position, representing the coordinates of the node in the multidimensional space, φ i is the source influence function, describing how the source influence decays with distance, (xx i ) represents the distance from the spatial point x to the source position x i distance.

[0056] Step 302 designs a dynamic adjustment algorithm for the field source intensity through the intensity adjustment unit to generate a field source intensity adjustment function:

[0057]

[0058] Among them, A i (t+Δt) represents the field source intensity at the next moment t+Δt, A i (t) represents the field source intensity at time t, is the target throughput, is the current throughput, λ is the overall adjustment rate, α is the proportional control parameter, β is the differential control parameter, represents the rate of change of the current throughput, Δt is the time step, and d is the differential.

[0059] Step 303 uses the location optimization unit to optimize the source location, dynamically adjust the source distribution according to logistics demand, and generate the source location optimization function. Among them, x i (t+Δt) represents the position of the field source at the next moment, x i (t) represents the location of the field source at the current moment, E(x i , t) is the field energy distribution function, which represents the field energy density at the i-th point in space, and is represented by the comprehensive field intensity vector F(x i , t) export: μ is the moving step size, which controls the amplitude of position adjustment. represents the gradient operator with respect to spatial coordinates.

[0060] Step 304 constructs a field source influence function adaptive mechanism through the influence range adjustment unit, dynamically adjusts the influence range and mode according to the field source type and environmental characteristics, and generates an adaptive influence function in, is the basic influence function, which is the basic form of the field source model in step 301, ψ i is the environmental adaptation factor, which dynamically adjusts the influence function shape according to the environmental characteristics. E is the environmental characteristic vector, which includes various characteristics of the logistics environment (traffic conditions, weather conditions), r is the distance variable, and Φ adapt (r, t) represents the influence function at time t, describing how the influence of the field source changes with distance r.

[0061] The output of step 300 is an adaptive field source control mechanism, including a field source model set S, a field source intensity adjustment function A, and a adj (t), field source position optimization function X opt (t) and adaptive influence function Φ adapt (r, t) will be used in the multi-target field superposition framework in step 400 and the field flow optimal path solving system in step 500.

[0062] Step 400: Based on the field source control results, a multi-objective field superposition framework is implemented to transform multiple optimization objectives into field superposition problems; a multi-objective field fusion algorithm is used to process the comprehensive field intensity vector F(x, t) in step 100, the field state resonance mechanism in step 200, and the adaptive field source control mechanism in step 300 to generate a comprehensive optimization field. The specific implementation is as follows:

[0063] Step 401 uses the target field construction unit to construct a corresponding target field for each optimization goal (minimizing cost, maximizing time efficiency, and maximizing safety), and generates a target field set. in, is the target mapping function, which maps the current state of the system to the corresponding target field, D is the current state data of the system, including the current operating state information of the logistics management mechanism, i is the number of optimization targets, The target field corresponding to the i-th optimization target is the mathematical representation of the optimization target at spatial position x and time t.

[0064] Step 402 designs a target weight adaptive algorithm through a weight adaptive unit, dynamically adjusts the weight of each target according to business needs and scenario characteristics, and generates a target weight vector W(t)=w1(t),w2(t),...,w i (t): w i (t) = g i (P(t), H(t), E(t)); where, g i is the weight generation function, which calculates the weight based on multiple factors. P(t) is the business priority vector, which indicates the current priority of each business goal. H(t) is the historical performance data, which contains the system's past operation effect information. E(t) is the environment state vector, which describes the current state of the logistics environment. w i (t) represents the dynamic change weight coefficient of the i-th optimization objective at time t.

[0065] Step 403 uses the field superposition unit to realize multi-target field superposition and generate a comprehensive optimization field Among them, w i (t) is the component of the target weight vector W(t) generated in step 402, is the target field set F generated in step 401 obj Step 404 builds a conflict detection and coordination mechanism through the conflict detection and coordination unit to identify and resolve conflicts between different target fields and generate an optimized field after conflict adjustment: Among them, C i，j(x, t) is the conflict measurement function, which obtains the degree of conflict between target i and target j at the spatial vector x and time t. h is the conflict evaluation function, which determines the degree of conflict based on the relationship between the field vectors. δ i，j is the conflict indication function, which is activated when the conflict degree exceeds the threshold: i，j (x, t)>τ, R i，j is the conflict resolution item, which is used to adjust the conflict field vector. τ is the conflict threshold, which indicates the maximum conflict level acceptable to the system. When this value is exceeded, conflict coordination is required.

[0066] The output of step 400 is a multi-target field superposition framework, including the target field set F obj , target weight vector W(t), comprehensive optimization field F comp (x, t) and the conflict-adjusted optimized field F adj (x, t) will be used in the field flow optimal path solving system in step 500. This framework transforms the complex multi-objective optimization problem into a field superposition problem, which not only preserves the independent characteristics of each objective but also naturally handles the interaction between objectives through field superposition. At the same time, it introduces conflict detection and coordination mechanisms to ensure the rationality of the final solution.

[0067] Step 500: Based on the field superposition results, develop a field flow optimal path solving system, generate the optimal logistics path based on the field potential energy gradient descent algorithm; process the conflict-adjusted optimized field F generated in step 400 based on the field potential energy gradient descent algorithm. adj (x, t), generates the optimal path planning of the logistics management mechanism. The specific implementation is as follows:

[0068] Step 501 uses the field potential energy calculation unit to calculate the optimized field F after conflict adjustment. adj (x, t) calculates the field potential energy function and generates the field potential energy function: Where Φ(x, t) represents the value of the field potential energy function at position x and time t, x0 is the reference point, which is usually a fixed node in the logistics network, x′ is the integration variable, which represents the point on the path from the reference point x0 to the target point x, and · represents the vector dot product operation.

[0069] Step 502 uses the velocity field generation unit to calculate the fastest descent direction as the flow velocity field based on the field potential energy function Φ(x,t) to generate the flow velocity field Among them, k is the proportional coefficient, which controls the flow velocity. Represents the gradient of the field potential energy function φ, pointing to the direction in which the field potential energy increases fastest, and its negative value Then it points to the direction where the field potential energy decreases fastest, that is, the direction in which the logistics should flow. v(x, t) represents the flow velocity vector at position x and time t, x represents the spatial position vector, and t represents the time vector. Step 503 applies the path integral through the path integral unit to solve the problem of the velocity vector from the starting point x. s To the end point x e The optimal path of In actual calculations, the numerical integration method is used to gradually construct the path: Among them, x s is the starting point coordinate, x(τ) represents the location of the logistics entity at time τ, represents the integral from time 0 to time t, x i and x i+1 Respectively represent the i-th point and the i+1-th point on the path, t i represents the i-th time step, Δt is the time step, P(t) represents the location of the logistics entity at time t, which constitutes the initial optimal path, τ represents the integral variable, representing the time point from 0 to t, x e Indicates the end point coordinates.

[0070] Step 504 uses the path optimization unit to build a path optimizer to further optimize the initial path and generate the final optimized path. Among them, L is the Lagrangian function, which comprehensively considers multiple optimization objectives such as path length, time, and cost:

[0071]

[0072] in, represents the time derivative of the path, i.e., speed, C(P(t)) is the path cost function, R(P(t), t) is the path risk function, α, β, and γ are weight coefficients, T is the total transportation time, argmin P Represents finding the path P that minimizes the objective function, P opt represents the final optimal path after optimization, It represents the square of the speed and is related to the path energy consumption.

[0073] The output of step 500 is the field flow optimal path solution system, including the field potential energy function Φ(x, t), the flow velocity field v(x, t), the initial optimal path P(t) and the final optimized path P opt , through deep learning technology, field parameter optimization, weight coefficient calculation and multi-objective fusion are used to generate and optimize the optimal path of field flow, which will be used for the performance evaluation of the three-layer self-evolution structure and the field visualization and interaction mechanism in step 600.

[0074] Step 600: Based on the path optimization results, a three-layer self-evolution structure is implemented, including field parameter adaptation, field source dynamic adjustment, and field topology evolution layers. The system's self-evolution mechanism is generated by combining the multi-scale adaptive evolutionary algorithm to process system operation history data, the field state resonance mechanism in step 200, the adaptive field source control mechanism in step 300, and the optimal path in step 500. The specific implementation is as follows:

[0075] Step 601: Continuously optimize the field equation parameters based on historical operation data through the field parameter adaptive layer to generate an adaptive parameter update function Θ adapt (t) = Θ(t+T d )=Θ(t)+ΔΘ;where Θ represents the parameter vector of the field equation, T d is the parameter update cycle, Θ(t+T) represents the parameter vector of the next update cycle, Θ(t) represents the current parameter vector, ΔΘ represents the change in the parameter, represents the gradient operator with respect to the parameter Θ, and J(Θ) is the performance evaluation function, which measures the operating effect of the system under the parameter Θ and controls the amplitude of parameter update. It is obtained by gradient descent or evolutionary algorithm optimization: Among them, J(Θ) is the performance evaluation function, which measures the operating effect of the system under parameter Θ and is defined as Controls the magnitude of parameter updates, represents the gradient with respect to the parameter vector Θ, M i is the i-th performance indicator (such as path length, transportation time, energy consumption), w i is the corresponding weight, n is the number of performance indicators, Θ adapt (t) represents the adaptive parameter update function at time t, It represents the sum from i=1 to i=n.

[0076] Step 602 uses the field source dynamic adjustment layer to automatically increase, decrease and adjust the field source according to real-time environmental changes to generate a field source dynamic adjustment strategy S dyn (t): Field source intensity adjustment: According to the field source intensity adjustment function A in step 300 adj (t) Dynamically adjust the field source intensity and the field source position: According to the field source position optimization function X in step 300 opt (t) Automatically adjust the source position and source type: Automatically select the appropriate source type based on task requirements and environmental characteristics i (t) = f type (Task(t), Env(t)); among them, Type i (t) represents the type of the i-th source at time t, f type is the type selection function, Task(t) is the current task feature vector, Env(t) is the current environment feature vector, Sdyn (t) represents the field source dynamic adjustment strategy function at time t, A adj (t) represents the field source intensity adjustment function at time t, X opt (t) represents the optimization function of the field source position at time t. 6.3 The field topology evolution layer automatically adjusts the field space topology structure as the logistics network changes, generating a topology structure update function G evolve (t): G t+1 =Evolve(G t , Δ t ).

[0077] Among them, G t Represents the field topology at time t, represented by graph G t =(V t , E t ), V t is the node set, E t is the edge set, G t represents the field space topology at time t (which can be represented by graph theory), G t+1 represents the field space topology at time t+1, Evolve is the topology evolution function, which defines how the topology changes, Δ t Represents the environment and demand changes at time t. 6.4 Design an inter-layer coordination mechanism using inter-layer coordination units, build a closed-loop system of perception-adjustment-optimization, and generate an inter-layer coordination control function C coord (t): Bottom-up information flow: low-level changes trigger high-level adjustments, represented by Info up (t)=I1(t),I2(t),...,I k (t); Top-down control flow: high-level decisions guide low-level execution, represented by Ctrl down (t) = C1(t), C2(t), ..., C m (t); Horizontal collaboration: coordination and cooperation between components at the same level, represented by Coord horizontal (t)=H1(t),H2(t),...,H p (t); where Info up (t) contains the information set passed from the lower layer to the higher layer, Ctrl down (t) contains the set of control instructions passed from the high level to the low level, Coord horizontal (t) contains the coordination information set shared between components at the same layer, I k (t), C m (t) and H p (t) represents the specific information, control and collaboration content respectively, and k, m and p represent the size of the corresponding set respectively.

[0078] The output of step 600 is a three-layer self-evolution structure, including the adaptive parameter update function Θ adapt (t), field source dynamic adjustment strategy S dyn (t), topology update function G evolve (t) and inter-layer collaborative control function C coord (t), which will be used in subsequent visualization and interaction mechanisms and emergent characteristic analysis and application systems. This structure builds a multi-level, adaptive system evolution framework, enabling the freight forwarding visualization big data digital management system to autonomously learn and adapt to environmental changes, continuously optimize its own performance, and break through the limitations of traditional static optimization systems. At the technical implementation level, through the deep integration of the field visualization and interaction module and the emergent characteristic analysis module, a complete freight forwarding visualization big data digital management system ecosystem is built. The two modules are seamlessly integrated through a unified field data interface: the field expression V field directly as the input of the emergent property analysis, and the self-organizing channel set C generated by the emergent property analysis self , collaborative behavior pattern set B collab and key influence point set P key The data is fed back to the visualization system in real time for highlighting and interactive control. This two-way data flow enables the system to form a self-reinforcing intelligent loop: users can interact Adjust the field parameters, the system will immediately analyze the characteristics of the adjusted changes and provide decision support reports R decision and Logistics Situational Awareness Report R aware Provide optimization suggestions. This enables the logistics management mechanism to continuously self-optimize and adapt to the complex and ever-changing logistics environment, ultimately achieving the technical effects of global coordination, efficient resource utilization, and intelligent self-adaptation. This implementation method utilizes a multi-dimensional spatiotemporal resonance freight forwarding visualization big data digital management system mechanism, achieving a technological shift from discrete point management to continuous field theory management. This significantly improves the planning efficiency, resource utilization, and transportation and warehousing efficiency of the logistics network, while enhancing adaptability, robustness, and decision-making accuracy, providing an innovative technical path for freight forwarding visualization big data digital management.

[0079] Company A has 37 key port nodes worldwide, 128 regional logistics centers, a fleet of over 5,000 transport vehicles, and processes approximately 82,000 freight orders daily. The company faces the following business challenges: Using traditional planning systems, Company A faces challenges with complex global networks, highly conflicting multi-objective optimization, poorly adaptable static planning, uneven resource utilization, and high decision complexity. This results in low optimization efficiency, a ship loading rate of only 73.2%, and an on-time delivery rate of 81.7%. As shown in Tables 1 and 2, a unified expression framework for multidimensional spatiotemporal resonance fields is established to transform heterogeneous data into field quantity representations. Table 1: Mapping of multi-source heterogeneous data into unified field quantities.

[0080]

[0081] Table 2: Calculation results of comprehensive field strength vector (Asia-Pacific region sample);

[0082]

[0083] As shown in Table 3, through the field resonance mechanism, the system optimizes the balance between cost control and delivery time for Company A on the Asia-Europe route. Table 3: Multi-field resonance parameter optimization process (iterative sample);

[0084]

[0085] As shown in Tables 4 and 5, through the adaptive field source control mechanism, when Company A encountered congestion in the Suez Canal, the system automatically adjusted the flow of cargo within 245 milliseconds, successfully avoiding delay losses of approximately US$183 million.

[0086] Table 4: Key node source parameter configuration;

[0087]

[0088] Table 5: Field source dynamic adjustment response (a sample of an emergency);

[0089]

[0090] As shown in Table 6, through the multi-target field overlay framework, Company A integrated and optimized multiple business objectives, significantly shortening the calculation time for complex decision-making scenarios from several hours to 3.7 minutes, achieving real-time optimized decision-making. Table 6: Multi-target field configuration and overlay effect;

[0091]

[0092] As shown in Table 7, using the field flow optimal path solving system, Company A optimized its global route planning, shortening routes by 2.6%, saving time by 8.4%, reducing transportation costs by 5.7%, and effectively reducing delays by 28.3 hours under complex weather conditions. Table 7: Field Potential Energy Gradient Path Planning vs. Traditional Path Planning (Asia-Europe Route Sample);

[0093]

[0094] As shown in Table 8, the system shortened the optimization calculation time by 99.04%, achieved real-time decision-making, increased the ship loading rate to 91.5%, improved the on-time delivery rate by 15.3%, saved US$158 million in shipping costs, increased business growth by 8.7%, and reduced costs by 21.6% and carbon emissions by 21.4%, successfully resolving the contradiction between local and global optimality and achieving global optimization.

[0095] Table 8: Comparison of global optimization performance before and after system application;

[0096]

[0097] As shown in Table 9, the system improves emergency response time by 99.98%, achieving millisecond-level response, quickly restoring normal operations and automatically reconstructing the logistics network, demonstrating excellent self-learning and adaptive capabilities, and effectively responding to various complex scenarios.

[0098] Table 9: Verification of the system’s adaptive capabilities to different types of events;

[0099]

[0100] Implementation 2 makes a key improvement to the source control mechanism of Implementation 1 by constructing an asynchronous adaptive source control mechanism. Step 1 introduces time-sensitive autonomous decision-making capabilities and a distributed coordination mechanism to achieve adaptive, asynchronous, and coordinated source control. The input of this step is the source model set generated in Implementation 1, and the output is an asynchronous adaptive source control mechanism with time-sensitive characteristics. The specific implementation is as follows:

[0101] Step 1.1 Establish a time-varying field source model: Use the time-varying field source model to characterize the key nodes of the logistics management mechanism and generate an asynchronous field source model set: S async =S1(t1), S2(t2), ..., S m (t m ); where S m (t m ) represents the mth source at its local time t m Each source has independent time parameters to achieve autonomous update: S i (x, t i )=A i (t i )·φ i ((xx i (t i )), t i ); where S i (x, t i ) represents the source i at position x and local time t i The field strength A i (t i ) represents the field source i at time t i Strength coefficient x i (t i ) represents the field source i at time t i The position coordinates φ iRepresents the influence function of the field source i, describing the attenuation law of the field source intensity with distance t i The output represents the asynchronous source model set S async , as the basic model for subsequent field source autonomous decision-making.

[0102] Step 1.2: Implement the asynchronous autonomous decision-making mechanism: Implement the independent update of the field source attributes through the asynchronous autonomous decision-making mechanism and generate the field source autonomous decision function D auto =D i (t i , Δt i )=f D (S i (t i ), L i (t i ), G(t ref ), E i (t i )); where the decision function f D This is achieved through the following formula:

[0103] f D =α·f local +(1-α)·f global +β·f adapt ; The autonomous decision weight α and the adaptive adjustment weight β are dynamically adjusted by the following formula: α(t i )=min(0.8,max(0.2,1exp(-λ·(t i t ref )))); Among them, D i represents the decision function of source i, Δt i represents the decision update cycle, which is dynamically adjusted according to the importance of the source and the complexity of the environment. fD represents the comprehensive decision function, which integrates local and global information. S i (t i ) represents the field source i at time t i The current state of L i (t i ) represents the field source i at time t i Local perception information, G(t ref ) represents the timestamp t ref The most recent global reference information, E i (t i ) represents the field source i at time t i The environmental state, f local represents the decision component based on local information, f global represents the decision component based on global information, f adaptrepresents the adaptive adjustment component, α(t i ) represents the autonomous decision weight, which controls the balance between local and global information, β(t i ) represents the adaptive adjustment weight, which controls the strength of environmental adaptability, λ represents the time attenuation coefficient, which controls the rate of aging of global information over time, and γ represents the state change sensitivity coefficient, which controls the degree of response to changes in the field source state. It represents the gradient of the source state with respect to time, that is, the rate of change of the state, ∈ represents a small constant to prevent the denominator from being zero, t i represents the local time of source i, t ref The output represents the field source autonomous decision function D auto , enabling the field source to autonomously adjust its decision-making strategy based on the timeliness of local and global information.

[0104] Step 1.3: Construct a multi-time-scale field source timing coordination algorithm: Use the multi-time-scale field source timing coordination algorithm to coordinate field sources with different update frequencies and generate an asynchronous coordination control strategy C. async First, classify the field sources according to the update frequency: S fast Represents a fast field source set with an update period of T fast S mid Represents a medium-speed source set with an update period of T mid S slow Represents the slow field source set, with an update period of T slow ; Among them, ensure T fast < <T mid < <T slow ;Then, design the timing coordination function;

[0105] Build asynchronous collaboration strategies based on time-series collaboration functions; Among them, H represents the temporal synergy strength between field sources, ω ij represents the spatial correlation weight between sources i and j, τ ij represents the time-correlated attenuation coefficient between sources i and j, K(S i , S j ) represents the characteristic similarity function between source i and j, C async represents the asynchronous cooperative control strategy, which defines how the source coordinates according to the state of the adjacent source. i Indicates the current local time of source i, t j represents the current local time of source j, (t i -t j ) represents the time difference between source i and j, that is, the degree of asynchrony in decision time, τ ijRepresents the time correlation attenuation coefficient between field sources i and j, and controls the tolerance of time difference. Represents the time decay function, which decreases as the time difference increases, S i (t i ) represents the source i at its local time t i Status, S j (t j ) represents the source j at its local time t j Status, S j (t j )-S i (t i ) represents the difference in the state of the field source, that is, the direction and amplitude that need to be coordinated. The output result represents the asynchronous cooperative control strategy C async , to achieve smooth coordination between field sources with different update frequencies.

[0106] Step 1.4 Implement the distributed source conflict detection and reconciliation algorithm: Use the distributed source conflict detection and reconciliation algorithm to handle conflicts in asynchronous decision-making and generate a conflict reconciliation source set S harm First, define the source decision conflict measurement function;

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[0108] When the conflict metric exceeds the threshold, the conflict reconciliation mechanism is triggered: S harm，i (x, t) = S i (x, t) + ΔS i (x, t); where the adjustment amount is calculated as: Among them, F i and F j represents the field intensity vector generated by field sources i and j, θ ij represents the angle between two field vectors, C conflict represents the source decision conflict measurement function μ represents the reconciliation coefficient, which controls the intensity of the adjustment, S harm，i represents the state of source i after conflict reconciliation, ΔS i Indicates the adjustment amount of source i, C conflict (S i , S j , x, t) represents the decision conflict metric function of source i and j at position x and time t, (F i (x, t)·F j (x, t)) represents the absolute value of the dot product of the field intensity vectors generated by field sources i and j, (F i (x,t))·(F j (x, t)) represents the product of the vector norms of the field strengths generated by the field sources i and j, cos(θ ij) represents the cosine value of the angle between the field vectors generated by the field sources i and j, represents the directional similarity, μ represents the harmonic coefficient, and controls the intensity of the conflict adjustment, ΔS i (x, t) represents the state adjustment of source i at position x and time t, S represents the projection ratio of source i in the direction of source j, harm The output result represents the conflict reconciliation source set S. harm , ensuring that the conflicts of asynchronous source decisions are effectively reconciled. The final output of step 1 is the asynchronous adaptive source control mechanism, which includes the asynchronous source model set S async , Field source autonomous decision function D auto , asynchronous cooperative control strategy C async and conflict mediation source set S harm This mechanism enables each source to make quick decisions based on local information while maintaining coordination and consistency with other sources. It effectively solves the communication bottleneck and delay problems of the centralized source control mechanism in an asynchronous environment, and provides basic support for the hierarchical coordination framework in step 2.

[0109] Step 2: Implementing a multi-level asynchronous decision-making hierarchical collaborative framework: This step is the core of implementation method 2. Based on the asynchronous adaptive field source control mechanism of step 1, a complete asynchronous decision-making hierarchical collaborative framework is constructed. The input includes the asynchronous field source model set S in step 1. async , Field source autonomous decision function D auto , asynchronous cooperative control strategy C async and conflict mediation source set S harm The output is a multi-level asynchronous decision-making framework that can operate collaboratively at different time scales and spatial levels. The specific implementation is as follows:

[0110] Step 2.1 Construct a multi-scale asynchronous decision-making hierarchical framework: Divide logistics management mechanism decisions into three mutually nested time scale levels to form an asynchronous decision-making hierarchical network Strategic Layer strat Represents a long time scale (day / week / month), responsible for network structure optimization and overall resource allocation, with an update cycle of T strat ; Tactical layer L tact Represents the medium time scale (hours / days), responsible for regional scheduling and task allocation, with an update period of T tact ; Operation layer L oper Represents a short time scale (minutes / hours), responsible for real-time execution and local optimization, with an update cycle of T oper ; Ensure that the time scale meets the separation condition T oper < <T tact < <T strat ; The decision state update equation of each layer is: Li (t+Δt i )=f i (L i (t)·L i+1 (t i+1 )·L i-1 (t i-1 )·E i (t)); where L i (t+Δt i ) represents level i at time point t+Δt i The decision state, that is, the new state after a decision cycle, L i (t) represents the decision state of level i at the current time t, f i represents the state update function of level i, mapping the current state to the new state, L i+1 (t i+1 ) indicates that the previous level is at its most recent decision time point t i+1 The state of L is used as the constraint condition of the current level. i-1 (t i-1 ) represents the next level at its most recent feedback time point t i-1 Status, providing execution feedback E i (t) represents the environmental state perceived by level i at time t, Δt i It represents the decision cycle of level i, and different levels have different cycle lengths.

[0111] Step 2.2 Design an asynchronous information transmission mechanism between layers: Build a multi-layer information flow framework To achieve effective information exchange between different layers. Define two basic inter-layer information transmission: Downstream decision flow D down Represents the transfer of decision constraints and goal setting from high-level to low-level upload feedback flow F up Represents the transfer of execution status and environmental awareness from low-level to high-level layers.

[0112] The asynchronous transmission of information is achieved through two mechanisms: time triggering and event triggering:

[0113] Design inter-layer information compression and abstraction functions to reduce communication burden: down Represents the downward decision flow, including the constraints and guidance from the upper layer to the lower layer, F up Represents the upload feedback flow, which contains the execution status feedback from the lower layer to the upper layer, T trigger (i, j) represents the time to trigger communication from level i to level j, δT i，j represents the fixed communication period from level i to level j, f E represents the event evaluation function, which evaluates whether the environmental change needs to trigger communication, ΔEi Indicates the environmental change at level i, I i→j A represents the information transmitted from level i to level j. i，j represents the information abstraction function from level i to level j, τ i，j Represents the abstract granularity parameter, which is dynamically adjusted by the level difference. The output represents the multi-layer information flow framework It realizes efficient asynchronous information exchange between different layers.

[0114] Step 2.3 Implement a multi-level decision consistency maintenance mechanism: Build an inter-layer consistency assurance system C hier , to ensure the coordination and consistency of decisions at different levels. Design conflict detection function: C detect (D i , D j )=||P i (D i )P j (D j )||; Conflict reconciliation is achieved through soft constraints:

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[0116] Among them, C detect represents the conflict detection function, which measures the inconsistency between decisions at different levels, P i and P j Denote the projection function that maps the decisions of level i and j to the common influence space, D harmony represents the consensus decision after reconciliation, w i represents the decision weight of level i, w ij represents the consistency weight between layers i and j, λ represents the consistency adjustment parameter, which controls the degree of enforcement of consistency between layers, and D i represents the decision set of level i, D j represents the decision set of level j, ||ID-D i || represents decision D and level i decision D i The deviation measure between them, ||P i (D)-P j (D)|| represents the inconsistency measure of decision D after projection at different levels, C consist represents the overall decision consistency evaluation index, N represents the total number of decision levels, C max Represents the maximum possible value of the conflict detection function, argmin D It represents the decision D that minimizes the objective function. The output represents the inter-layer consistency assurance system C. hier , maintaining coordination and consistency of decision-making at different levels in an asynchronous environment.

[0117] Step 2.4: Build a decision constraint propagation network: Implement a hierarchical decision constraint system D constraint , to ensure that decision constraints are effectively transmitted between different levels. Establish a decision constraint propagation network:

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[0119] Among them, the decision constraint mapping is defined as:

[0120] C i→j (D j )={(d j , s j )|d j ∈D j , s j ∈{valid, invalid, warning}};

[0121] The constraint evaluation function is implemented as: j =f eval (d j , P i→j (D i )); Constraint propagation follows rules that reflect the two-way information transmission and feedback between the strategic layer, tactical layer, and operational layer, involving constraints such as resource boundaries, network structure, scheduling plan, task allocation, execution feasibility, resource requirements, and bottlenecks. Among them, D constraint represents the decision constraint propagation network, C i→j represents the decision constraint mapping from level i to level j, d j represents the decision of level j, s j Indicates the decision status, which can be valid, invalid or warning. eval It represents the constraint evaluation function, which is the projection function that maps the decision of level i to the decision space of level j. strat represents the strategic level, tact represents the tactical level, and oper represents the operational level. The output represents the hierarchical decision constraint system D constraint , ensuring the consistency and feasibility of decisions at different levels through constraint propagation.

[0122] Step 2.5 Develop a multi-scale time window collaborative decision-making mechanism: Construct a time window collaborative framework W collab , to solve the coordination problem of decision-making at different time scales. Design multi-scale time window: W i (t)=(tα i ·T i , t+β i ·T i ); define the collaborative decision-making area as the overlapping area of ​​time windows at different levels: W i，j (t) = Wi (t)∩W j (t); Achieve time-aligned decision collaboration within the collaborative decision-making area: Among them, W i (t) represents the decision window of level i at time t, T i represents the standard decision cycle of level i, α i and β i Represent the backtracking and forward-looking coefficients, respectively, to control the window size, W i，j (t) represents the collaborative decision-making area of ​​levels i and j at time t, and They represent the collaborative decision-making of optimized levels i and j, L i and L j Represent the optimization objective functions of level i and j respectively, C align represents the decision alignment metric function within the time window, λ represents the collaborative weight parameter, which controls the collaborative strength, and D collab represents the collaborative decision-making result, f sync represents the decision synchronization function, P i (t) represents the decision priority of level i at time t, H i (t) represents the historical decision data of level i, F i (t) represents the future forecast data of level i, f α represents the retrospective coefficient adjustment function, f β Represents the forward coefficient adjustment function. The output represents the time window coordination framework W collab , to achieve effective coordination of decisions at different time scales.

[0123] The final output of step 2 is a multi-level asynchronous decision-making hierarchical collaborative framework, which includes an asynchronous decision-making hierarchical network , multi-layer information flow framework Inter-layer consistency assurance system C hier , inter-level decision-making constraint system D constraint and time window collaborative framework W collab . This framework enables each node and level in the logistics network to make autonomous decisions at an appropriate time scale based on its own characteristics and environmental status, while ensuring overall coordination through the designed information transmission and consistency maintenance mechanism, providing hierarchical decision-making architecture support for step 3. Step 3 represents the construction of a distributed resource scheduling and adaptive learning mechanism: This step is based on the asynchronous adaptive field source control mechanism of step 1 and the multi-level asynchronous decision-making hierarchical coordination framework of step 2, realizing distributed resource scheduling and adaptive learning mechanism in an asynchronous decision-making environment, and enhancing the system's adaptability and learning efficiency in a dynamic environment. The input includes the asynchronous field source model set S async , multi-layer decision-making framework The output of the system is a complete system including a resource adaptive allocation framework, a multi-level adaptive learning system, and a multi-layer consensus framework. The specific implementation is as follows:

[0124] Step 3.1 Build a dynamic scheduling mechanism for distributed computing resources: Form a resource adaptive allocation framework through the dynamic scheduling mechanism for distributed computing resources Achieve efficient utilization of computing resources. Design a dynamic resource allocation strategy to allocate computing resources based on decision importance and urgency: Among them, the urgency formula is: Among them, R i (t) represents the computing resources allocated to level i at time t, R total represents the total available computing resources of the system, w i (t) represents the importance weight of level i at time t, u i (t) represents the urgency of level i at time t, t last，i represents the time of the last decision update at level i, T i represents the standard decision cycle of level i, γ represents the sensitivity coefficient of environmental change, which controls the impact of environmental changes on the degree of urgency, and E i (t) represents the environmental state of level i at time t, E i (t last，i ) represents the state of the environment when level i made the last decision, ||E i (t)-E i (t last，i )|| represents the norm of the change of environmental state, that is, the degree of environmental change, ||E i (t last，i )|| represents the norm of the last environment state, which is used to normalize the environment change. The output represents the resource adaptive allocation framework Realize dynamic and optimized allocation of computing resources among different decision-making levels.

[0125] Step 3.2 Develop a distributed learning mechanism in an asynchronous decision-making environment: Build a multi-level adaptive learning system , so that the system can continuously optimize the decision-making strategy in an asynchronous decision-making environment. Design learning objectives and strategies for each decision-making level:

[0126] Strategic learning updates:

[0127] (θ,D strat , F up (t-τ:t));

[0128] Tactical learning updates: (θ,D tact , F up (t-τ:t), Ddown (t));

[0129] Operational layer learning update: (θ,D oper , E(t), D down (t));

[0130] Among them, θ i represents the decision model parameter of level i, η i represents the learning rate of layer i, L i represents the loss function of level i, D i represents the decision set of level i, F up Indicates the upload feedback information flow, D down represents the downlink decision information flow, E(t) represents the environmental state at time t, Δt i represents the learning update cycle of level i, t last，i Indicates the time of the last learning update of level i, T i represents the standard learning cycle of level i, τ represents the length of the historical time window, represents the parameter gradient operator, C sync represents the inter-level learning coordination function, φ represents the coordination evaluation function, ||D j (t)-D j (t last，j )|| represents the norm of decision change, ||D j (t last，j )|| represents the norm of the last decision. The output represents a multi-level adaptive learning system Achieve continuous optimization of decision-making models at different levels. Step 3.3 Build a distributed asynchronous consensus formation algorithm: Implement a multi-layer consensus framework A consensus , solve the consensus formation problem in asynchronous decision-making environment. Design basic distributed consensus algorithm: To adapt to the asynchronous environment, a delay-tolerant consensus algorithm is designed: In order to solve the potential decision oscillation problem, a dynamic damping factor is introduced: The final asynchronous consensus update formula is:

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[0132] in, represents the decision value of node i in the kth iteration, N i represents the neighbor set of node i, w ij (t) represents the influence weight of node j on node i at time t, satisfying τ ij (t) represents the communication delay from node j to node i at time t, αi Represents the local information retention coefficient, which controls the degree to which the node maintains its own decision, β i (t) represents the dynamic damping factor to prevent decision oscillation, γ represents the base value of the damping coefficient, and λ represents the sensitivity coefficient of decision change. The output result represents the multi-layer consensus framework A consensus , achieving effective consensus among distributed nodes in an asynchronous environment. The final output of step 3 is a distributed resource scheduling and adaptive learning mechanism, including a resource adaptive allocation framework Multi-level adaptive learning system and multi-layer consensus framework A consensus .

[0133] This implementation significantly improves the response speed, computing resource utilization, and decision consistency of the operation layer by introducing an asynchronous decision-making hierarchical collaboration mechanism. It also enhances robustness, fault tolerance, and cross-regional collaborative decision-making efficiency, and has the ability to continuously learn, thereby optimizing the efficiency of decision-making model updates and knowledge transfer.

Claims

1. A freight forwarding visual big data digital management system, characterized by: include: Unified expression conversion module: Establish a unified expression framework for multi-dimensional space-time resonance fields to convert the physical, information and value data of the logistics system into a unified field quantity representation; Field state resonance mechanism module: Based on the unified field quantity representation, it realizes the field state resonance mechanism, constructs the field state resonance equation and solves the optimal weight coefficient; Adaptive field source control module: Based on the field state resonance results, an adaptive field source control mechanism is constructed to model key nodes of the logistics system as field sources and dynamically adjust the field source characteristics; Multi-objective field superposition module: Based on the field source control results, a multi-objective field superposition framework is implemented to transform multiple optimization objectives into field superposition problems; Optimal path generation module: Based on the field superposition results, develop a field flow optimal path solution system, and generate the optimal logistics path based on the field potential energy gradient descent algorithm; Three-layer self-evolution module: Based on the path optimization results, a three-layer self-evolution structure is implemented, including field parameter adaptation, field source dynamic adjustment and field topology evolution layer.

2. The freight forwarding visualization big data digital management system according to claim 1 is characterized in that: The field dynamics model in the unified expression framework of the multidimensional space-time resonance field is described by the following equations: in, represents the divergence operator, represents the partial derivative with respect to time, F(x, t) represents the field vector function, X is the spatial position vector, t is the time variable, S i (x, t) represents the field source item, i is the number of the i-th field source, which is used to distinguish different field sources.

3. The freight forwarding visualization big data digital management system according to claim 1 is characterized in that: The field state resonance mechanism is realized by a multi-field resonance optimization algorithm, and the field state resonance equation is defined as: ω1F1(x,t)+ω2F2(x,t)+...+ω n F n (x,t)=R(x,t); Among them, F i is the jth type of field quantity function, representing the mathematical expression of different physical quantities, information flows or value flows in the logistics system, ω i is the weight coefficient, R is the target resonance mode, and n represents the total number of fields participating in the resonance.

4. The freight forwarding visualization big data digital management system according to claim 3 is characterized in that: The optimal weight coefficient is solved by the following iterative equation: Where D is the distance function, which represents the difference between the current field state and the target resonance mode; η is the learning rate, which controls the step size of each iteration; k is the number of iterations; Represents the parameter ω i The gradient of , which is used to indicate the direction of weight adjustment; Represents the weight coefficient at the kth iteration; Represents the weight coefficient after the k+1th iteration.

5. The freight forwarding visualization big data digital management system according to claim 1 is characterized in that: The field source model in the adaptive field source control mechanism is defined as: S i (x,t)=A i (t)·φ i ((x-x i )); Among them, A i (t) is the field source intensity, which is dynamically adjusted over time; x i is the spatial location of the logistics source; φ i is the field source influence function, which describes the law of field source intensity attenuation with distance; (xx i ) represents the spatial position x to the source position x i The Euclidean distance of .

6. The freight forwarding visualized big data digital management system according to claim 5 is characterized in that: The field source intensity dynamic adjustment algorithm is defined as: A i (t+Δt)=A i (t)+λ. Among them, A i (t+Δt) represents the field source intensity at the next moment t+Δt, A i (t) represents the field source intensity at time t, is the target throughput, is the current throughput, λ is the overall adjustment rate, α is the proportional control parameter, β is the differential control parameter, represents the rate of change of the current throughput, Δt is the time step, and d is the differential.

7. The freight forwarding visualization big data digital management system according to claim 1 is characterized in that: The comprehensive optimization field in the multi-objective field superposition framework is defined as: Among them, F comp (x, t) is the comprehensive optimization field, which represents the weighted combination of multiple target fields, w i (t) is the dynamic weight coefficient of the i-th objective, which changes over time to adapt to the optimization focus at different stages. is the field function corresponding to the i-th optimization objective, m is the total number of optimization objectives, x is the spatial position vector, and t is the time variable.

8. The freight forwarding visualized big data digital management system according to claim 1 is characterized in that: In the field flow optimal path solving system, the flow velocity field is defined using the field potential energy gradient descent method: Among them, v(x, t) is the flow velocity field, which represents the velocity vector of the logistics entity at position x and time t, and k is the proportional coefficient, which controls the sensitivity of the logistics entity to the potential energy gradient of the response field. represents the gradient operator, which is used to calculate the rate of change of the field potential energy function in each direction. Φ(x, t) is the field potential energy function, which describes the potential energy distribution of the logistics system at position X and time t.

9. The freight forwarding visualized big data digital management system according to claim 8 is characterized in that: The optimal path is solved by the following path integral method: Among them, P(t) is the path position vector at time t, x s is the starting point coordinate, v(x(τ), τ) represents the velocity field at position X(τ) at time τ, and dτ is the time element, which represents the trajectory of motion along the velocity field from the starting point to the current time t.

10. The freight forwarding visualized big data digital management system according to claim 1 is characterized in that: The adaptive parameter update function in the three-layer self-evolution structure is defined as: Where Θ represents the parameter vector of the field equation, T is the parameter update period, Θ(t+T) represents the parameter vector of the next update period, Θ(t) represents the current parameter vector, and ΔΘ represents the change in the parameter. represents the gradient operator with respect to the parameter Θ, and J(Θ) is the performance evaluation function used to quantify the overall performance of the system under the current parameter configuration.

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