Enterprise logistics management system based on artificial intelligence

Abstract

The invention relates to the technical field of enterprise management, in particular to an artificial intelligence-based enterprise logistics management system, which comprises the following steps of: acquiring three-dimensional space data of a logistics area, real-time personnel flow track data and material transportation path records, and constructing a comprehensive space model through data fusion; functional partitions are divided through clustering analysis, layout is preliminarily planned, and partition boundaries are corrected by combining equipment maintenance space requirements and flow convenience requirements; multi-dimensional factor weights are optimized through a genetic algorithm, and a candidate layout set is iteratively generated in cooperation with a simulated annealing algorithm; screening schemes according to potential safety hazard threshold values, selecting the scheme with the optimal efficiency, and finally generating visual output and implementing path guidance in combination with a three-dimensional rendering technology, so as to realize intelligent optimization of the logistics space. The system solves the problems that traditional logistics management is single in data dimension, unreasonable in layout planning, unbalanced in multi-objective optimization, difficult in scheme landing and the like, and improves the refinement and intelligence level of logistics management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise management, in particular to an enterprise logistics management system based on artificial intelligence. BACKGROUND

[0002] In the field of enterprise logistics management, space optimization is an important link to improve operational efficiency and reduce management costs. However, traditional methods of logistics space optimization have many shortcomings and cannot meet the needs of modern enterprises for intelligent and refined management.

[0003] Firstly, the data dimension of traditional methods is single and fragmented, relying only on three-dimensional spatial structure data for analysis and planning, without integrating real-time personnel flow trajectories, material transportation paths and other dynamic data, resulting in incomplete data dimensions and independent existence of each data source, which cannot fully reflect the linkage between "people, materials and space", thus making the optimization decision lack comprehensive data support.

[0004] Secondly, the existing division of functional areas is mostly based on space utilization or single function demand, with boundaries determined by experience or simple geometric planning, without fully considering the actual needs of equipment maintenance and operation space, often resulting in insufficient maintenance space. Even if the partition boundaries are adjusted, the convenience of personnel and material flow is easily ignored, leading to a disconnect between layout and actual operational needs.

[0005] In addition, logistics layout optimization needs to consider multiple objectives such as flow efficiency, transportation cost, safety guarantee, etc. However, traditional methods usually use subjective weight allocation and rely on a single algorithm for optimization, which not only makes it difficult to achieve a scientific balance of multiple objectives, but also easily falls into a local optimal solution, resulting in one-sided optimization results that prioritize efficiency over safety or local solutions over global solutions.

[0006] More importantly, the optimization schemes output by existing technologies are mostly abstract data or two-dimensional charts, making it difficult for managers to intuitively perceive the adjustment effects, and lacking specific implementation path guidance, resulting in a poor connection between optimization schemes and actual logistics management operations, and low conversion rate. SUMMARY

[0007] The present application aims to address the above-mentioned shortcomings in the prior art and provides an enterprise logistics management system based on artificial intelligence.

[0008] The purpose of the present application is achieved by the following technical solution: an enterprise logistics management system based on artificial intelligence, comprising the following steps: S1, by collecting three-dimensional spatial data and real-time personnel flow trajectory data of the logistics area, combining with material transportation path records, using data fusion method to integrate into a unified multi-dimensional factor data set, obtaining a comprehensive spatial model containing position coordinates, flow density and transportation distance; S2, according to the personnel flow density distribution extracted in the comprehensive space model and the material transportation distance index, adopting clustering analysis to divide the regional function partition, determining the boundary line and the connecting path between each partition, and obtaining a preliminary space partition scheme; S3, for the preliminary space partition scheme, obtaining equipment maintenance operation space demand data, if the equipment maintenance space is less than a preset threshold, adjusting the partition boundary to expand the operation area, judging whether the adjusted partition meets the flow convenience requirement, and obtaining a corrected partition layout; S4, extracting multi-dimensional factors such as flow path length and transportation efficiency value from the corrected partition layout, iteratively optimizing the weight combination of these factors by genetic algorithm, determining the optimal weight vector, and using it for subsequent layout scheme scoring calculation; S5, applying random disturbance to the optimal weight vector by using simulated annealing algorithm, simulating the layout adjustment process, if the score of the disturbed scheme is higher than the current score, accepting the disturbance and updating the layout scheme, and obtaining an iteratively optimized candidate layout set; S6, obtaining the safety hazard index of each scheme in the candidate layout set, comparing the hazard index with a preset safety threshold, if the hazard index is lower than the threshold, retaining the scheme and calculating its overall efficiency score, judging the one with the highest score in the retained scheme, and obtaining the final preferred layout scheme; S7, generating visual output data according to the final preferred layout scheme, combining three-dimensional rendering technology to present the comparison before and after adjustment, determining the implementation path guidance contained in the output data, and obtaining intelligent space optimization results that can be directly applied to logistics management.

[0009] The application further provides that the comprehensive space model includes a three-dimensional space structure diagram, a personnel flow heat distribution and a material transportation path network, the preliminary space partition scheme includes partition boundary definition, function area classification and connecting path planning, the corrected partition layout includes extended maintenance space area and flow convenience evaluation result, the optimal weight vector includes flow path weight, transportation efficiency weight and safety weight, the candidate layout set includes a plurality of disturbed layout schemes and their scores, and the final preferred layout scheme includes safety hazard evaluation result and overall efficiency score.

[0010] The application further provides that the three-dimensional space data and real-time personnel flow trajectory data of the logistics area are collected, combined with the material transportation path record, integrated into a unified multi-dimensional factor data set by using data fusion method, and the steps of obtaining the comprehensive space model including position coordinates, flow density and transportation distance are as follows: S101, based on the three-dimensional space data of the logistics area, using multi-source data synchronization technology, time stamp calibration is carried out on the data collected by different sensors, and the data is mapped to a unified spatial coordinate system through a geographic information system, and a standardized spatial data set is generated; S102, based on the standardized spatial data set, combining real-time personnel flow trajectory data, using dynamic trajectory tracking technology, analyzing the directionality and aggregation characteristics of personnel flow, generating a personnel flow density distribution map; S103, based on the personnel flow density distribution map, combining the material transportation path record, using path optimization technology, analyzing the distance and frequency characteristics of material transportation, generating a material transportation path network map; S104, based on the material transportation path network map and the personnel flow density distribution map, using data fusion method, integrating multi-dimensional factors into a unified comprehensive spatial model, generating a comprehensive spatial model containing position coordinates, flow density and transportation distance.

[0011] The application further provides that, according to the personnel flow density distribution and material transportation distance index extracted from the comprehensive spatial model, the step of using clustering analysis to divide the region functional partition, determining the boundary line and connection path between each partition, and obtaining the preliminary spatial partition scheme is specifically: S201, based on the comprehensive spatial model, using density clustering algorithm, according to the personnel flow density distribution, identifying high-density area and low-density area, generating a preliminary functional partition division; S202, based on the preliminary functional partition division, combining the material transportation distance index, using hierarchical clustering algorithm, analyzing the connectivity and coverage range of material transportation path, generating connection path planning between partitions; S203, based on the connection path planning, using boundary optimization technology, adjusting the partition boundary to ensure the continuity and integrity of the functional partition, generating a preliminary spatial partition scheme; S204, based on the preliminary spatial partition scheme, using spatial topology analysis technology, verifying the logical relationship and functional matching between partitions, generating the final definition of partition boundary line and connection path.

[0012] The application further provides that, for the preliminary spatial partition scheme, obtaining equipment maintenance operation space demand data, if the equipment maintenance space is less than the preset threshold, adjusting the partition boundary to expand the operation area, judging whether the adjusted partition meets the flow convenience requirement, and obtaining the corrected partition layout, the step is specifically: S301, based on the preliminary spatial partition scheme, obtaining equipment maintenance operation space demand data, using spatial demand evaluation technology, analyzing the minimum operation space required for equipment maintenance, generating an equipment maintenance space demand report; S302. Based on the equipment maintenance space requirement report, a boundary expansion technique is used. If the equipment maintenance space is less than a preset threshold, the partition boundary is adjusted to expand the operation area and generate an expanded partition layout. S303. Based on the expanded partition layout, the flow convenience assessment technology is used to analyze the flow efficiency of personnel and materials within the partition, determine whether the adjusted partition meets the flow convenience requirements, and generate the flow convenience assessment result. S304. Based on the aforementioned mobility convenience assessment results, a revised partition layout is generated by adopting partition optimization technology, taking into account both equipment maintenance space and mobility convenience requirements.

[0013] The present invention is further configured such that the step of extracting multi-dimensional factors, such as flow path length and transportation efficiency value, from the modified partition layout, and iteratively optimizing the weight combination of these factors through a genetic algorithm to determine the optimal weight vector for subsequent layout scheme scoring calculations is as follows: S401. Based on the revised partition layout, extract multi-dimensional factors such as flow path length, transportation efficiency value and safety index, and use factor analysis technology to quantify the influence of each factor on layout optimization and generate an initial weight vector. S402. Based on the initial weight vector, a genetic algorithm is used to iteratively optimize the weight combination through selection, crossover and mutation operations to generate multiple candidate weight vectors; S403. Based on the multiple candidate weight vectors, a scoring calculation technique is used to evaluate the overall adaptability of each weight vector to the layout scheme and generate the optimal weight vector. S404. Based on the optimal weight vector, a weight allocation technique is used to apply the weights to the scoring calculation of subsequent layout schemes, thereby generating scoring calculation rules.

[0014] The present invention is further configured such that the step of applying a random perturbation to the optimal weight vector using a simulated annealing algorithm to simulate the layout adjustment process, and accepting the perturbation and updating the layout scheme to obtain an iteratively optimized candidate layout set, specifically includes: S501. Based on the optimal weight vector, the simulated annealing algorithm is used to apply random perturbation to the weight vector to generate multiple perturbated weight vectors. S502. Based on the multiple perturbated weight vectors, the layout scheme score corresponding to each perturbated weight vector is evaluated using the scoring calculation rules, and the perturbated scheme score result is generated. S503. Based on the perturbation-reset scheme score, a perturbation acceptance technique is used. If the perturbation-reset scheme score is higher than the current score, the perturbation is accepted and the layout scheme is updated to generate an updated layout scheme. S504. Based on the updated layout scheme, an iterative optimization technique is used to repeatedly perturb and score the layout, generating a set of candidate layouts for iterative optimization.

[0015] The present invention is further configured such that the step of obtaining the safety hazard index of each scheme in the candidate layout set, comparing the hazard index with a preset safety threshold, retaining the scheme if the hazard index is lower than the threshold, calculating its overall efficiency score, and determining the scheme with the highest score among the retained schemes to obtain the final preferred layout scheme is specifically as follows: S601. Based on the candidate layout set, obtain the safety hazard index of each scheme, use safety hazard assessment technology to analyze the potential risk points in the layout scheme, and generate safety hazard assessment results; S602. Based on the safety hazard assessment results, a threshold comparison technique is used. If the hazard index is lower than the preset safety threshold, the scheme is retained and its overall efficiency score is calculated to generate a list of retained schemes. S603. Based on the list of retained solutions, use efficiency scoring technology to calculate the overall efficiency score of each retained solution and generate an efficiency scoring result. S604. Based on the efficiency score results, an optimization technique is used to determine the highest-scoring scheme among the retained schemes, and a final optimal layout scheme is generated.

[0016] The present invention is further configured such that the step of generating visual output data based on the final preferred layout scheme, integrating 3D rendering technology to present a comparison before and after adjustment, determining the implementation path guidance contained in the output data, and obtaining intelligent space optimization results that can be directly applied to logistics management specifically includes: S701. Based on the final preferred layout scheme, use three-dimensional modeling technology to construct spatial layout models before and after adjustment, and generate a three-dimensional spatial comparison diagram. S702. Based on the three-dimensional spatial comparison map, using rendering technology, lighting, material and texture effects are integrated to generate a high-quality three-dimensional rendering effect map; S703. Based on the aforementioned 3D rendering effect diagram, path planning technology is used to determine the implementation path guidance and generate an implementation path document containing adjustment steps and operation instructions; S704. Based on the implementation path document, data integration technology is used to merge the 3D rendering effect map and the implementation path guidance into unified visual output data, generating intelligent spatial optimization results that can be directly applied to logistics management.

[0017] The present invention is further configured such that the data fusion method includes multi-source data synchronization technology and spatial mapping processing of geographic information systems; the clustering analysis includes density clustering algorithm and hierarchical clustering algorithm; the genetic algorithm includes selection, crossover and mutation operations; the simulated annealing algorithm includes random perturbation and scoring calculation rules; the safety hazard assessment technology includes fuzzy comprehensive evaluation method; the efficiency scoring technology includes analytic hierarchy process (AHP) and TOPSIS method; and the 3D rendering technology includes B-rep representation method and Phong lighting model.

[0018] The beneficial effects of this invention are: 1. By integrating multi-source dynamic data such as three-dimensional space, personnel flow, and material transportation, a comprehensive spatial model is constructed through data fusion methods. This breaks through the limitations of traditional data fragmentation and fully reflects the linkage relationship between "people-objects-space", providing comprehensive and reliable data support for layout optimization.

[0019] 2. Functional zones are divided based on cluster analysis, and the boundaries are adjusted in combination with equipment maintenance space thresholds. At the same time, the convenience of personnel and material flow is taken into account, avoiding problems such as insufficient maintenance space and inefficient flow in traditional layouts, making the zoning layout more in line with the actual logistics operation scenario.

[0020] 3. By optimizing the weights of multi-dimensional factors through genetic algorithms and combining them with simulated annealing algorithm for random perturbation iteration, a dynamic balance of multiple objectives such as flow efficiency, transportation cost, and safety assurance is achieved. This effectively avoids the local optima problem caused by traditional subjective weight allocation and single algorithms, and improves the global optimality of the layout scheme.

[0021] 4. Integrating 3D rendering technology to present before-and-after comparisons of layout adjustments, along with specific implementation path guidance, transforms abstract solutions into intuitive and actionable visual results, solving the problem of low implementation conversion rates of traditional solutions, helping managers to quickly implement and optimize solutions, and reducing logistics management implementation costs and operational difficulties. Attached Figure Description

[0022] The invention will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the invention. For those skilled in the art, other drawings can be obtained based on the following drawings without any creative effort.

[0023] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0024] The present invention will be further described in conjunction with the following embodiments.

[0025] Depend on Figure 1 As can be seen, the enterprise logistics management system based on artificial intelligence described in this embodiment includes the following steps: S1. By collecting three-dimensional spatial data and real-time personnel flow trajectory data of the logistics area, and combining them with material transportation route records, a unified multi-dimensional factor dataset is integrated using data fusion methods to obtain a comprehensive spatial model that includes location coordinates, flow density, and transportation distance. Three-dimensional spatial coordinate data of logistics areas (such as warehouses, office building logistics areas, canteen kitchens, etc.) are collected by a 3D laser scanner. Real-time personnel flow trajectory data are obtained through UWB positioning and video surveillance trajectory analysis. The material transportation route records of transport vehicles / robots (including transportation start point, destination, transit nodes, and transportation time) are exported through the material management system, achieving full-dimensional coverage of the three core data categories of "space-personnel-materials". In addition, a two-dimensional fusion method of timestamp calibration and spatial coordinate system unification is adopted. First, the time dimension of data collected by different devices is aligned through multi-source data synchronization technology (such as controlling the time error of sensor data and positioning data within 100ms). Then, all data are mapped to the same Mercator coordinate system through Geographic Information System (GIS) to eliminate spatial dimension deviation. Finally, through data fusion methods (such as association rules based on location overlap, multi-source data synchronization technology, and GIS spatial mapping processing), the three-dimensional spatial data, personnel flow density (number of people per unit area), and material transportation distance (straight line / actual path distance between two points) are integrated into a structured dataset to form a comprehensive spatial model containing the three elements of "location coordinates-flow density-transportation distance".

[0026] S2. Based on the personnel flow density distribution and material transportation distance indicators extracted from the comprehensive spatial model, cluster analysis is used to divide the area into functional zones, determine the boundary lines and connection paths between each zone, and obtain a preliminary spatial zoning scheme. Functional zoning is achieved using a combination of density clustering and hierarchical clustering algorithms. The first step uses the density clustering algorithm (DBSCAN) with personnel flow density as the core indicator, setting density thresholds (e.g., 1 person / m² for low density, 5 people / m² for high density) to automatically identify high-density activity areas (e.g., logistics personnel operation areas) and low-density storage areas (e.g., material temporary storage areas), generating preliminary functional zoning. The second step uses the hierarchical clustering algorithm, with material transportation distance as a constraint, to analyze the connectivity of transportation paths between different preliminary zoning areas (e.g., transportation distance < 50m for strong connectivity), planning the shortest connection paths between zoning areas (e.g., direct access from the warehouse to the canteen kitchen). Spatial topology analysis technology is used to verify the zoning logic, constructing a zoning adjacency matrix (e.g., the adjacency relationship between storage area, operation area, and passageway area), determining whether there are overlapping / omissions at zoning boundaries (e.g., avoiding intersections between storage area and passageway boundaries), and verifying whether connection paths cover all zoning areas (e.g., ensuring each functional area has at least one direct access path). Finally, the zoning boundary lines and connection paths are determined, forming a preliminary spatial zoning scheme.

[0027] S3. For the preliminary spatial partitioning scheme, obtain the equipment maintenance operation space requirement data. If the equipment maintenance space is less than the preset threshold, adjust the partition boundary to expand the operation area, determine whether the adjusted partition meets the flow convenience requirements, and obtain the corrected partition layout. Based on equipment technical parameters (such as air conditioner outdoor units, generators, material transport robots, etc.), the minimum maintenance operation space threshold is determined (e.g., generator maintenance requires a reserved 2m×3m operation space). Buffer analysis technology is used to delineate a "maintenance space buffer" for each piece of equipment in the initial zoning scheme. The buffer area is compared with the preset threshold to identify zones with insufficient maintenance space (e.g., the generator zone with a buffer area < 6㎡). The maintenance space is expanded through a boundary offset algorithm, offsetting outward along the non-channel side boundary of the insufficient zone (the offset is calculated based on the gap area, e.g., a 0.5m offset for a 2㎡ gap). At the same time, accessibility analysis technology is used to evaluate the flow convenience of the adjusted zones, calculating the average walking time for personnel from the entrance to the operation point (e.g., ≤ 30s is considered satisfactory) and the average passage efficiency of material transport (e.g., the throughput of transport robots ≥ 95%). If the convenience requirements are met, a corrected zoning layout is formed, avoiding the traditional problem of prioritizing maintenance space over flow efficiency.

[0028] S4. Extract multi-dimensional factors such as flow path length and transportation efficiency value from the corrected partition layout, and iteratively optimize the weight combination of these factors through a genetic algorithm to determine the optimal weight vector for subsequent layout scheme scoring calculation. Three core optimization factors are extracted from the revised zoning layout: flow path length (the total actual path length of personnel / materials from the starting point to the destination), transportation efficiency value (the amount of materials transported per unit time, such as 10 tons / hour), and basic safety indicators (such as aisle width and emergency exit distance). Factor analysis is used to quantify the impact of each factor on the layout (e.g., the initial weight of transportation efficiency on the warehouse logistics area is set to 0.4), generating an initial weight vector. With the goal of maximizing the overall layout fit, a genetic algorithm is used to iteratively optimize the weight combination. The first step uses a roulette wheel selection to retain weight vectors with high fit (e.g., the top 30% of vectors). The second step uses single-point crossover to recombine different weight vectors (e.g., swapping flow path weights and transportation efficiency weights). The third step uses random mutation (mutation probability set to 0.05) to avoid local optima. After 50-100 iterations, the weight vectors that contribute best to the layout score are selected (e.g., "flow path weight 0.3, transportation efficiency weight 0.4, safety weight 0.3"), providing an objective basis for subsequent scheme evaluation.

[0029] S5. Apply random perturbation to the optimal weight vector using simulated annealing algorithm to simulate the layout adjustment process. If the score of the scheme after perturbation is higher than the current score, accept the perturbation and update the layout scheme to obtain a set of candidate layouts for iterative optimization. Drawing inspiration from the physical process of metal annealing and cooling, and using the optimal weight vector as the initial solution, random perturbations are applied under set temperature parameters (initial temperature T0=100, cooling coefficient α=0.95). For example, for a 1500㎡ canteen kitchen and logistics area, the simulated annealing initial temperature T0=100, and the cooling coefficient α=0.95 for the first 100 iterations to ensure scheme diversity; from the 101st to the 200th iteration, α decreases to 0.9, prompting the schemes to converge quickly to the optimal solution, ultimately increasing the proportion of high-quality schemes in the candidate layout set to 85%; then, the scoring rules (such as "weight") are determined. The layout score corresponding to the new vector is calculated by multiplying the actual values ​​of the factors by (the sum of the weights and factors). The Metropolis criterion is used to determine whether to accept the perturbation. If the score of the scheme after the perturbation is higher than the current score (e.g., from 80 to 85), it is directly accepted and the layout is updated. If the score decreases (e.g., from 80 to 78), it is accepted according to the probability formula P=exp(-(score difference) / T) (the higher the temperature, the higher the probability of acceptance, to avoid falling into local optima too early). After 200-300 iterations, a candidate layout set containing 10-20 high-quality schemes is generated.

[0030] S6. Obtain the safety hazard index of each scheme in the candidate layout set. By comparing the hazard index with the preset safety threshold, if the hazard index is lower than the threshold, retain the scheme and calculate its overall efficiency score. Determine the scheme with the highest score among the retained schemes to obtain the final preferred layout scheme. The fuzzy comprehensive evaluation method is used to quantify safety hazard indicators. Four secondary indicators are constructed: passage width (weight 0.3), number of emergency exits (weight 0.2), equipment spacing (weight 0.3), and fire protection facility coverage (weight 0.2). The total safety hazard score of each candidate scheme is calculated by expert scoring (1-5 points, 5 points for no hazard). If the total score is <3 points, it is considered low hazard. Schemes with hazard indicators below a preset threshold (e.g., threshold 3 points) are selected as qualified schemes. The combined method of Analytic Hierarchy Process (AHP) and TOPSIS is used to calculate the overall efficiency score of qualified schemes. First, the weights of flow efficiency, transportation cost, and space utilization rate are determined by AHP (e.g., flow efficiency 0.4, transportation cost 0.3, space utilization rate 0.3). Then, the index values ​​of each scheme are standardized by TOPSIS, and the relative closeness to the ideal optimal scheme is calculated (e.g., closeness of 0.92 is the highest). Finally, the scheme with the highest closeness is selected as the final preferred layout scheme.

[0031] S7. Generate visual output data based on the final preferred layout scheme, integrate 3D rendering technology to present a comparison before and after the adjustment, determine the implementation path guidance contained in the output data, and obtain intelligent space optimization results that can be directly applied to logistics management. B-rep (boundary representation) was used to construct 3D models of the spatial layout before and after adjustments (e.g., a warehouse with insufficient maintenance space before adjustment and a warehouse with expanded maintenance area after adjustment). Phong lighting models were used to simulate realistic lighting effects (e.g., changes in light and shadow under natural and artificial light), generating 3D rendered images that can be rotated 360° to visually present the layout differences. Based on the final optimized layout scheme, [the following was adopted]. The path planning algorithm breaks down the implementation steps, such as: Step 1: Remove the 1m wide partition wall on the west side of the warehouse (to expand the maintenance space); Step 2: Redefine the passageway marking lines (to ensure the passageway width is 2m); Step 3: Adjust the position of the material storage racks (to match the new transportation route), and mark the construction period (e.g., 3 days) and required materials (e.g., partition wall removal tools, marking paint) for each step, forming an implementation path guidance document that can be directly implemented, achieving seamless connection between the plan and execution.

[0032] This embodiment integrates multi-source dynamic data such as three-dimensional space, personnel flow, and material transportation, and constructs a comprehensive spatial model through data fusion methods. This breaks through the limitations of traditional methods that rely on fragmented data, and fully reflects the linkage relationship between "people-objects-space", providing comprehensive and reliable data support for layout optimization.

[0033] This embodiment divides functional zones based on cluster analysis, adjusts the boundaries by combining equipment maintenance space thresholds, and takes into account the convenience of personnel and material flow. It avoids problems such as insufficient maintenance space and inefficient flow in traditional layouts, making the zoning layout more in line with the actual logistics operation scenario.

[0034] This embodiment optimizes the weights of multi-dimensional factors through a genetic algorithm, and combines it with simulated annealing algorithm for random perturbation iteration to achieve a dynamic balance of multiple objectives such as flow efficiency, transportation cost, and safety assurance. This effectively avoids the local optima problem caused by traditional subjective weight allocation and single algorithms, and improves the global optimality of the layout scheme.

[0035] This embodiment integrates 3D rendering technology to present a before-and-after comparison of layout adjustments, along with specific implementation path guidance. It transforms abstract solutions into intuitive and actionable visual results, solving the problem of low implementation conversion rates of traditional solutions. This helps managers quickly implement and optimize solutions, reducing logistics management implementation costs and operational difficulties.

[0036] In this embodiment, an AI-based enterprise logistics management system, in step S101, multi-source data synchronization technology ensures the consistency of data collected by different sensors in the time dimension. The geographic information system then maps these data to a unified spatial coordinate system, generating a standardized spatial dataset. Subsequently, in step S102, dynamic trajectory tracking technology is used to analyze the directional and clustering characteristics of personnel flow, generating a personnel flow density distribution map. This process quantifies the flow density by calculating the dwell time and movement direction of personnel within each grid cell, and presents it in the form of a heat map. The formula is expressed as D(x, y) = Σ(Ti × Vi) / A, where D(x, y) represents the flow density of the grid cell (x, y), Ti is the dwell time of personnel in that cell, Vi is the movement speed, and A is the cell area. In step S103, the system combines material transportation path records and uses path optimization technology to analyze material transportation... Based on the distance and frequency characteristics of the transport, a material transport route network map is generated. The core algorithm for route optimization adopts an improved Dijkstra algorithm, with the formula f(n) = g(n) + h(n), where g(n) represents the actual transport cost from the starting point to node n, and h(n) is a heuristic estimate used to predict the minimum cost from node n to the destination. Through this algorithm, the system can identify the optimal transport route and calculate its transport efficiency value. Finally, in step S104, the data fusion method integrates the above multi-dimensional factors into a unified comprehensive spatial model, generating a comprehensive spatial model that includes location coordinates, flow density, and transport distance.

[0037] A corporate logistics management system based on artificial intelligence described in this embodiment. In step S201, a density clustering algorithm is used to identify high-density areas and low-density areas, thereby generating a preliminary functional partition division. The core formula of the density clustering algorithm is ρ(i)=|{j:d(i,j)<dc}|, where ρ(i) represents the local density of point i, d(i,j) is the distance between point i and point j, and dc is the cut-off distance. By setting different density thresholds, the system can automatically divide different functional areas; in step S202, a hierarchical clustering algorithm is used to analyze the connectivity and coverage of the material transportation path, generating an inter-zone connection path plan. The hierarchical clustering algorithm uses the Ward method, and its objective function is E=ΣΣd²(xi,xj), where d(xi,xj) represents the distance between two clusters, and xi and xj are sample points in the clusters respectively; in step S203, a boundary optimization technique is used to adjust the partition boundary to ensure the continuity and integrity of the functional partition. The boundary optimization uses an elastic boundary model, and the formula is Eb=α×Lb+β×Cb, where Eb represents the boundary energy, Lb is the boundary length, Cb is the boundary curvature, and α and β are weight coefficients. By minimizing the boundary energy, the system can obtain a more reasonable partition boundary; in step S204, a spatial topology analysis technique is used to verify the logical relationship and functional matching between zones, generating the final definition of the partition boundary line and the connection path. The topology analysis uses an adjacency matrix A, where the element aij represents the connectivity between area i and area j. If the two areas are adjacent, then aij=1, otherwise it is 0.

[0038] This embodiment describes an AI-based enterprise logistics management system. For the initial spatial zoning scheme, the system needs to consider the equipment maintenance operation space requirements. In step S301, space requirement assessment technology is used to analyze the minimum operation space required for equipment maintenance and generate an equipment maintenance space requirement report. The space requirement assessment uses a buffer analysis method, with the formula B(P,r)={Q:d(P,Q)≤r}, where B(P,r) represents a buffer zone centered at point P with radius r, and d(P,Q) is the distance between point P and point Q. In step S302, if the equipment maintenance space is less than a preset threshold, boundary expansion technology is used to adjust the zoning boundary to expand the operation area. Boundary expansion uses an offset algorithm, with the formula O(S,d)={P:d(P,S)=d}, where O(S, d) represents the result after the geometric object S is offset by a distance d along the normal direction; In step S303, the mobility assessment technology is used to analyze the mobility efficiency of personnel and materials within the partition and to determine whether the adjusted partition meets the mobility requirements. The mobility assessment adopts the accessibility analysis method, and the formula is A(i)=Σexp(-dij / λ), where A(i) represents the accessibility of region i, dij is the distance from region i to region j, and λ is the attenuation coefficient; In step S304, the partition optimization technology comprehensively considers the equipment maintenance space and mobility requirements to generate the corrected partition layout. The partition optimization adopts the multi-objective optimization method, and the objective function is F=w1×Em+w2×Ea, where Em represents the maintenance space adaptability, Ea represents the mobility adaptability, and w1 and w2 are weight coefficients.

[0039] This embodiment describes an AI-based enterprise logistics management system. From the corrected zoning layout, the system extracts multi-dimensional factors such as flow path length, transportation efficiency value, and safety indicators. It then uses a genetic algorithm to iteratively optimize the weight combination of these factors to determine the optimal weight vector. In step S401, factor analysis is used to quantify the influence of each factor on layout optimization and generate an initial weight vector. Principal component analysis is employed for factor analysis, with the formula Z = X × W, where Z is the factor score matrix, X is the original variable matrix, and W is the factor loading matrix. In step S402, the genetic algorithm iteratively optimizes the weight combination through selection, crossover, and mutation operations, generating multiple candidate weight vectors. The selection probability formula of the genetic algorithm is Pi = fi / Σfi, where Pi represents the selection probability of individual i and fi is its fitness value. In step S403, the scoring calculation technique is used to evaluate the overall adaptability of each weight vector to the layout scheme and generate the optimal weight vector. The scoring calculation adopts the weighted summation method, with the formula S = Σwi × fi, where S is the total score, wi is the weight of the i-th factor, and fi is its evaluation value. In step S404, the weight allocation technique applies the optimal weight vector to the scoring calculation of subsequent layout schemes to generate scoring calculation rules. The weight allocation adopts normalization processing, with the formula wi' = wi / Σwi, where wi' is the normalized weight value.

[0040] This embodiment describes an AI-based enterprise logistics management system that uses a simulated annealing algorithm to apply random perturbations to the optimal weight vector, simulating a layout adjustment process. In step S501, the simulated annealing algorithm applies random perturbations to the weight vector, generating multiple perturbed weight vectors. The perturbation formula is X' = X + ΔX, where X' is the perturbed solution, X is the current solution, and ΔX is the random perturbation amount. In step S502, a scoring calculation rule is used to evaluate the layout scheme score corresponding to each perturbed weight vector, generating a perturbed scheme score result. The scoring calculation employs an exponential cooling strategy. The formula is T(k+1)=α×T(k), where T is the temperature, α is the cooling coefficient, and k is the number of iterations. In step S503, the perturbation acceptance technique is used to determine whether to accept the perturbed layout scheme. The acceptance probability formula is P=exp(-(S'-S) / T), where S' is the score of the new solution, S is the score of the current solution, and T is the current temperature. In step S504, the iterative optimization technique repeats the perturbation and scoring process to generate a set of candidate layouts for iterative optimization. The iteration termination condition is |S'-S|<ε or the maximum number of iterations is reached, where ε is the convergence threshold.

[0041] This embodiment describes an AI-based enterprise logistics management system that acquires safety hazard indicators for each scheme in a candidate layout set. By comparing these hazard indicators with preset safety thresholds, the final preferred layout scheme is determined. In step S601, safety hazard assessment technology is used to analyze potential risk points in the layout schemes and generate safety hazard assessment results. The safety hazard assessment adopts a fuzzy comprehensive evaluation method, with the formula R=W×F, where R is the risk value, W is the weight vector, and F is the risk factor matrix. In step S602, threshold comparison technology is used to filter layout schemes that meet safety requirements and generate a list of retained schemes. Threshold comparison employs an interval judgment method, with the formula R∈[Rmin,Rmax], where Rmin and Rmax are the lower and upper limits of the safety threshold, respectively. In step S603, efficiency scoring technology is used to calculate the overall efficiency score of each retention scheme, generating efficiency scoring results. Efficiency scoring uses the analytic hierarchy process (AHP), with the formula V=Σwi×vi, where V is the overall efficiency value, wi is the weight of the i-th indicator, and vi is its evaluation value. In step S604, optimization technology is used to determine the retention scheme with the highest score, generating the final optimal layout scheme. Optimization uses the TOPSIS method, with the formula... Where Ci represents the relative proximity. For the positive ideal solution distance, The distance is the negative ideal solution distance.

[0042] This embodiment describes an AI-based enterprise logistics management system. Based on the final optimized layout scheme, the system generates visual output data and integrates 3D rendering technology to present a comparison before and after adjustments. In step S701, 3D modeling technology is used to construct spatial layout models before and after adjustments, generating a 3D spatial comparison image. 3D modeling uses the B-rep representation method, with the formula M={V,E,F}, where V is the vertex set, E is the edge set, and F is the face set. In step S702, rendering technology is used to integrate lighting, material, and texture effects to generate a high-quality 3D rendering image. Lighting calculation uses the Phong model, with the formula I=Ia+Id+Is, where Ia is ambient light, Id is diffuse light, and Is is specular light. In step S703, path planning technology is used to determine the implementation path guidance, generating an implementation path document containing adjustment steps and operation instructions. Path planning uses... The algorithm, with the formula f(n)=g(n)+h(n), where g(n) is the actual cost and h(n) is the heuristic estimate; in step S704, data integration technology is used to merge the 3D rendering effect map and the implementation path guidance into unified visual output data, generating intelligent spatial optimization results that can be directly applied to logistics management. The data integration adopts XML format, with the formula D={G,M,P}, where G is geometric data, M is material data, and P is path data.

[0043] Through the specific implementation methods described above, this embodiment achieves intelligent optimization of enterprise logistics management, not only improving space utilization efficiency but also significantly enhancing the security and convenience of logistics management. By applying multi-dimensional data analysis and intelligent algorithms, the system can automatically generate optimal layout schemes and provide detailed implementation guidance, offering strong technical support for enterprise logistics management.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An enterprise logistics management system based on artificial intelligence, characterized in that: Includes the following steps: S1. By collecting three-dimensional spatial data and real-time personnel flow trajectory data of the logistics area, and combining them with material transportation route records, a unified multi-dimensional factor dataset is integrated using data fusion methods to obtain a comprehensive spatial model that includes location coordinates, flow density, and transportation distance. S2. Based on the personnel flow density distribution and material transportation distance indicators extracted from the comprehensive spatial model, cluster analysis is used to divide the area into functional zones, determine the boundary lines and connection paths between each zone, and obtain a preliminary spatial zoning scheme. S3. For the preliminary spatial partitioning scheme, obtain the equipment maintenance operation space requirement data. If the equipment maintenance space is less than the preset threshold, adjust the partition boundary to expand the operation area, determine whether the adjusted partition meets the flow convenience requirements, and obtain the corrected partition layout. S4. Extract multi-dimensional factors from the corrected partition layout, iteratively optimize the weight combination of these factors through a genetic algorithm, determine the optimal weight vector, and use it for the scoring calculation of subsequent layout schemes. S5. Apply random perturbation to the optimal weight vector using simulated annealing algorithm to simulate the layout adjustment process. If the score of the scheme after perturbation is higher than the current score, accept the perturbation and update the layout scheme to obtain a set of candidate layouts for iterative optimization. S6. Obtain the safety hazard index of each scheme in the candidate layout set. By comparing the hazard index with the preset safety threshold, if the hazard index is lower than the threshold, retain the scheme and calculate its overall efficiency score. Determine the scheme with the highest score among the retained schemes to obtain the final preferred layout scheme. S7. Generate visual output data based on the final preferred layout scheme, integrate 3D rendering technology to present a comparison before and after the adjustment, determine the implementation path guidance contained in the output data, and obtain intelligent space optimization results that can be directly applied to logistics management.

2. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The comprehensive spatial model includes a three-dimensional spatial structure diagram, a thermal distribution of personnel flow, and a network of material transportation routes. The preliminary spatial zoning scheme includes the definition of zoning boundaries, the classification of functional areas, and the planning of connection routes. The revised zoning layout includes the expansion and maintenance of spatial areas and the assessment results of mobility convenience. The optimal weight vector includes the weight of the flow path, the weight of transportation efficiency, and the weight of safety. The candidate layout set includes multiple perturbation-based layout schemes and their scores. The final preferred layout scheme includes the assessment results of safety hazards and the overall efficiency score. The visualization output data includes a three-dimensional rendering and an implementation path guidance document.

3. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The specific steps involved in collecting three-dimensional spatial data and real-time personnel flow trajectory data from the logistics area, combining them with material transportation route records, and integrating them into a unified multi-dimensional factor dataset using a data fusion method to obtain a comprehensive spatial model containing location coordinates, flow density, and transportation distance are as follows: S101. Based on the three-dimensional spatial data of the logistics area, multi-source data synchronization technology is used to calibrate the timestamps of data collected by different sensors, and the data is mapped to a unified spatial coordinate system through a geographic information system to generate a standardized spatial dataset. S102. Based on the standardized spatial dataset and combined with real-time personnel flow trajectory data, dynamic trajectory tracking technology is used to analyze the directional and clustering characteristics of personnel flow and generate a personnel flow density distribution map. S103. Based on the personnel flow density distribution map and combined with the material transportation route records, the route optimization technology is used to analyze the distance and frequency characteristics of material transportation and generate a material transportation route network map. S104. Based on the material transportation route network map and the personnel flow density distribution map, a data fusion method is used to integrate multi-dimensional factors into a unified comprehensive spatial model, generating a comprehensive spatial model that includes location coordinates, flow density, and transportation distance.

4. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The steps for obtaining a preliminary spatial zoning scheme by using cluster analysis to divide the region into functional zones based on the personnel flow density distribution and material transportation distance indicators extracted from the comprehensive spatial model, determining the boundary lines and connection paths between each zone, are as follows: S201. Based on the comprehensive spatial model, a density clustering algorithm is used to identify high-density and low-density areas according to the distribution of personnel flow density, and a preliminary functional zoning is generated. S202. Based on the preliminary functional zoning, and combined with the material transportation distance index, a hierarchical clustering algorithm is used to analyze the connectivity and coverage of the material transportation path and generate a connection path plan between the zoning areas. S203. Based on the connection path planning, boundary optimization technology is used to adjust the partition boundaries to ensure the continuity and integrity of functional partitions, and a preliminary spatial partitioning scheme is generated. S204. Based on the preliminary spatial partitioning scheme, spatial topology analysis technology is used to verify the logical relationships and functional matching between partitions, and to generate the final definition of partition boundary lines and connection paths.

5. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The steps for obtaining equipment maintenance and operation space requirement data for the preliminary spatial zoning scheme, adjusting the zoning boundaries to expand the operation area if the equipment maintenance space is less than a preset threshold, and determining whether the adjusted zoning meets the requirements for convenient mobility to obtain the corrected zoning layout are as follows: S301. Based on the preliminary spatial partitioning scheme, obtain equipment maintenance operation space requirement data, use space requirement assessment technology to analyze the minimum operating space required for equipment maintenance, and generate an equipment maintenance space requirement report. S302. Based on the equipment maintenance space requirement report, a boundary expansion technique is used. If the equipment maintenance space is less than a preset threshold, the partition boundary is adjusted to expand the operation area and generate an expanded partition layout. S303. Based on the expanded partition layout, the flow convenience assessment technology is used to analyze the flow efficiency of personnel and materials within the partition, determine whether the adjusted partition meets the flow convenience requirements, and generate the flow convenience assessment result. S304. Based on the aforementioned mobility convenience assessment results, a revised partition layout is generated by adopting partition optimization technology, taking into account both equipment maintenance space and mobility convenience requirements.

6. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The specific steps for extracting multi-dimensional factors from the corrected partition layout, iteratively optimizing the weight combination of these factors using a genetic algorithm, and determining the optimal weight vector for subsequent layout scheme scoring calculations are as follows: S401. Based on the revised partition layout, extract multi-dimensional factors such as flow path length, transportation efficiency value and safety index, and use factor analysis technology to quantify the influence of each factor on layout optimization and generate an initial weight vector. S402. Based on the initial weight vector, a genetic algorithm is used to iteratively optimize the weight combination through selection, crossover and mutation operations to generate multiple candidate weight vectors; S403. Based on the multiple candidate weight vectors, a scoring calculation technique is used to evaluate the overall adaptability of each weight vector to the layout scheme and generate the optimal weight vector. S404. Based on the optimal weight vector, a weight allocation technique is used to apply the weights to the scoring calculation of subsequent layout schemes, thereby generating scoring calculation rules.

7. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The specific steps of applying a random perturbation to the optimal weight vector using the simulated annealing algorithm to simulate the layout adjustment process, and accepting the perturbation and updating the layout scheme if the score of the perturbed scheme is higher than the current score, to obtain the candidate layout set for iterative optimization, are as follows: S501. Based on the optimal weight vector, the simulated annealing algorithm is used to apply random perturbation to the weight vector to generate multiple perturbated weight vectors. S502. Based on the multiple perturbated weight vectors, the layout scheme score corresponding to each perturbated weight vector is evaluated using the scoring calculation rules, and the perturbated scheme score result is generated. S503. Based on the perturbation-reset scheme score, a perturbation acceptance technique is used. If the perturbation-reset scheme score is higher than the current score, the perturbation is accepted and the layout scheme is updated to generate an updated layout scheme. S504. Based on the updated layout scheme, an iterative optimization technique is used to repeatedly perturb and score the layout, generating a set of candidate layouts for iterative optimization.

8. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The steps of obtaining the safety hazard index for each scheme in the candidate layout set, comparing the hazard index with a preset safety threshold, retaining the scheme if the hazard index is lower than the threshold, calculating its overall efficiency score, and determining the scheme with the highest score among the retained schemes to obtain the final preferred layout scheme are as follows: S601. Based on the candidate layout set, obtain the safety hazard index of each scheme, use safety hazard assessment technology to analyze the potential risk points in the layout scheme, and generate safety hazard assessment results; S602. Based on the safety hazard assessment results, a threshold comparison technique is used. If the hazard index is lower than the preset safety threshold, the scheme is retained and its overall efficiency score is calculated to generate a list of retained schemes. S603. Based on the list of retained solutions, use efficiency scoring technology to calculate the overall efficiency score of each retained solution and generate an efficiency scoring result. S604. Based on the efficiency score results, an optimization technique is used to determine the highest-scoring scheme among the retained schemes, and a final optimal layout scheme is generated.

9. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The steps of generating visual output data based on the final preferred layout scheme, integrating 3D rendering technology to present a comparison before and after the adjustment, determining the implementation path guidance contained in the output data, and obtaining intelligent space optimization results that can be directly applied to logistics management are as follows: S701. Based on the final preferred layout scheme, use three-dimensional modeling technology to construct spatial layout models before and after adjustment, and generate a three-dimensional spatial comparison diagram. S702. Based on the three-dimensional spatial comparison map, using rendering technology, lighting, material and texture effects are integrated to generate a high-quality three-dimensional rendering effect map; S703. Based on the aforementioned 3D rendering effect diagram, path planning technology is used to determine the implementation path guidance and generate an implementation path document containing adjustment steps and operation instructions; S704. Based on the implementation path document, data integration technology is used to merge the 3D rendering effect map and the implementation path guidance into unified visual output data, generating intelligent spatial optimization results that can be directly applied to logistics management.

10. The enterprise logistics management system based on artificial intelligence according to claim 1, characterized in that: The data fusion method includes multi-source data synchronization technology and spatial mapping processing of geographic information systems; the clustering analysis includes density clustering algorithm and hierarchical clustering algorithm; the genetic algorithm includes selection, crossover and mutation operations; the simulated annealing algorithm includes random perturbation and scoring calculation rules; the safety hazard assessment technology includes fuzzy comprehensive evaluation method; the efficiency scoring technology includes analytic hierarchy process (AHP) and TOPSIS method; and the 3D rendering technology includes B-rep representation method and Phong lighting model.

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