Multi-warehouse cooperative state monitoring method and system for intelligent warehouse logistics docking

Abstract

The invention relates to the technical field of radio positioning monitoring, in particular to a multi-warehouse cooperative state monitoring method and system for intelligent warehouse logistics docking. Comprising the following steps: acquiring real-time state data of a plurality of warehouses, including warehouse cargo position data, equipment state data, logistics vehicle position data and personnel distribution data; analyzing the data by adopting a multi-warehouse collaborative state evaluation model, and identifying potential logistics bottleneck points and risk points; and according to an identification result, generating a multi-warehouse collaborative optimization scheme including logistics path planning, a resource allocation strategy and an emergency processing suggestion. Wherein the evaluation model adopts a multi-level architecture, and comprehensive and accurate risk identification is realized through combination of static environment monitoring and dynamic environment monitoring. The system forms a complete optimization-implementation-evaluation closed loop, so that the multi-warehouse cooperative operation efficiency is improved, the logistics cost is reduced, the ability of the system to cope with demand fluctuation and emergencies is enhanced, and a reliable guarantee is provided for upgrading and optimization of the intelligent warehouse logistics system.

Description

Technical Field

[0001] The present invention relates to the field of radio positioning monitoring technology, and in particular to a multi-warehouse collaborative status monitoring method and system for intelligent warehousing and logistics docking. Background Art

[0002] With the rapid development of e-commerce and supply chain globalization, modern warehousing and logistics systems have evolved from the traditional single warehouse model to a complex network of multi-warehouse collaborative operations. The multi-warehouse collaborative operation model can effectively reduce logistics costs, shorten delivery time and improve inventory management efficiency, and thus has become the mainstream trend of industry development. Against this background, warehouse status monitoring systems based on radio positioning technologies (such as RFID, GPS, UWB, etc.) have emerged, providing technical support for multi-warehouse collaborative management. Current warehouse logistics monitoring technology mainly adopts a multi-source data fusion strategy to comprehensively analyze information such as cargo positioning data, equipment operating status, personnel distribution, and logistics vehicle trajectories to build a real-time digital mapping of warehouse logistics. According to research statistics, warehouse logistics systems using advanced monitoring technology can increase order processing efficiency by 25% to 40%, increase inventory accuracy to more than 99.5%, and reduce operating costs by an average of 18%, significantly enhancing the supply chain resilience and market responsiveness of enterprises.

[0003] However, the existing multi-warehouse collaborative status monitoring technology still has many problems that need to be solved. First, traditional monitoring systems mostly adopt a separate architecture, treating cargo positioning, equipment monitoring, vehicle tracking and personnel management as independent functional modules, which makes it difficult to achieve deep data integration and collaborative analysis, resulting in the system being difficult to identify potential bottlenecks across warehouses and links; second, the existing monitoring model is mainly based on static rules, lacks the ability to adapt to dynamic changes in a multi-warehouse environment, and cannot effectively respond to emergencies and temporary demand fluctuations; third, the monitoring results are mostly limited to the problem identification level, lacking a systematic optimization solution generation mechanism and resource allocation strategy, making it difficult to provide direct and effective support for management decisions; finally, the existing system mostly relies on manual experience for status analysis and bottleneck judgment, which is inefficient and the results are highly subjective, making it difficult to meet the monitoring needs of high-frequency and large-scale collaborative operations in modern logistics. These problems have seriously restricted the effectiveness of multi-warehouse collaborative management, and a more systematic, intelligent and standardized multi-warehouse collaborative status monitoring method and system is urgently needed. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problems to be solved by the present invention are as follows: how to solve the following technical problems: first, how to realize the comprehensive collection and fusion analysis of various real-time status data in a multi-warehouse environment, and establish a collaborative status monitoring system covering multiple dimensions such as the location of goods, equipment status, vehicle trajectories, and personnel distribution; second, how to construct an adaptable multi-warehouse collaborative status evaluation model to accurately identify potential bottleneck points and risk points in a complex logistics environment; third, how to automatically generate an operable multi-warehouse collaborative optimization plan based on the monitoring results to provide effective guidance for logistics path planning, resource allocation, and emergency handling; fourth, how to improve the automation level of the monitoring system and the objectivity of the analysis results to meet the needs of the high-frequency iterative development of modern logistics. By solving the above problems, the present invention can significantly improve the efficiency and stability of multi-warehouse collaborative operation and provide strong support for the optimization and upgrading of intelligent warehousing and logistics systems.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking, which includes obtaining real-time status data of multiple warehouses, where the real-time status data at least includes warehouse goods location data, warehouse equipment status data, logistics vehicle location data, and personnel distribution data;

[0008] Analyze the real-time status data using a multi-warehouse collaborative status evaluation model to identify potential logistics bottleneck points and risk points;

[0009] Generate a multi-warehouse collaborative optimization plan according to the identified logistics bottleneck points and risk points, including logistics path planning, resource allocation strategies, and emergency handling suggestions.

[0010] As a preferred solution of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to the present invention, wherein: the real-time status data includes data collected in static and dynamic environments of multiple warehouses.

[0011] As a preferred solution of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to the present invention, wherein: the static environment includes fixed radio positioning tag data collection and warehouse infrastructure status monitoring; the dynamic environment includes real-time tracking of mobile devices and monitoring of logistics vehicle navigation trajectories.

[0012] As a preferred solution of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to the present invention, wherein: the multi-warehouse collaborative status evaluation model is constructed based on warehousing and logistics industry standards and historical operation data, and supports automatic identification of logistics bottlenecks and automatic optimization of resource allocation.

[0013] As a preferred solution of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to the present invention, wherein: the logistics path planning in the multi-warehouse collaborative optimization solution is divided into emergency priority, high priority, medium priority and low priority; the resource allocation strategy at least includes personnel scheduling strategy, equipment allocation strategy, storage location optimization strategy, vehicle scheduling strategy and cross-warehouse transfer strategy.

[0014] As a preferred solution of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to the present invention, wherein: it further includes the step of formulating an implementation plan based on the multi-warehouse collaborative optimization solution, and the implementation plan includes system parameter update, resource reallocation and logistics route adjustment.

[0015] As a preferred solution of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to the present invention, wherein: it further includes the step of evaluating the effect of the multi-warehouse system with the implemented optimization solution, verifying the optimization effect and updating the multi-warehouse collaborative status evaluation model.

[0016] In a second aspect, an embodiment of the present invention provides a multi-warehouse collaborative status monitoring system for intelligent warehousing and logistics docking, which includes a status data acquisition module for obtaining real-time status data of multiple warehouses, and the real-time status data at least includes warehouse goods location data, warehouse equipment status data, logistics vehicle location data and personnel distribution data;

[0017] A status analysis module for analyzing the real-time status data by using a multi-warehouse collaborative status evaluation model to identify potential logistics bottleneck points and risk points;

[0018] An optimization solution generation module for generating a multi-warehouse collaborative optimization solution according to the identified logistics bottleneck points and risk points, including logistics path planning, resource allocation strategy and emergency handling suggestions.

[0019] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, and the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking as described in the first aspect of the present invention are implemented.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by the processor, the steps of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking as described in the first aspect of the present invention are implemented.

[0021] The beneficial effects of the present invention are as follows: The multi-warehouse collaborative status monitoring method and system for intelligent warehousing and logistics docking provided by the present invention establish a digital mapping covering the entire multi-warehouse area by comprehensively obtaining multi-dimensional real-time status data including cargo location, equipment status, vehicle location, and personnel distribution, overcoming the problem of information islands caused by traditional separate monitoring. By combining static environment monitoring with dynamic environment monitoring, the system can not only monitor the status of infrastructure but also track dynamic operation processes, significantly improving the comprehensiveness and timeliness of monitoring data.

[0022] By constructing a multi-level collaborative status evaluation model based on industry standards and historical data, the system realizes the accurate identification of potential logistics bottleneck points and risk points. This model covers the facility layer, resource layer, process layer, and system layer, and can not only deeply analyze internal problems in a single warehouse but also discover potential risks in cross-warehouse collaboration, greatly improving the accuracy of risk identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a flowchart of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking;

[0025] Figure 2 It is a computer equipment diagram of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention with reference to the drawings in the specification.

[0027] Many specific details are set forth in the following description in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0028] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate from or selectively exclusive of other embodiments.

[0029] Embodiment 1

[0030] Referring to Figures 1 to 2 , which is the first embodiment of the present invention. This embodiment provides a multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking, including,

[0031] S100: Obtain the real-time status data of multiple warehouses. The real-time status data at least includes warehouse goods location data, warehouse equipment status data, logistics vehicle location data, and personnel distribution data;

[0032] S200: Analyze the real-time status data using a multi-warehouse collaborative status evaluation model to identify potential logistics bottleneck points and risk points;

[0033] S300: Generate a multi-warehouse collaborative optimization plan according to the identified logistics bottleneck points and risk points, including logistics path planning, resource allocation strategies, and emergency handling suggestions.

[0034] It should be noted that the modern enterprise warehousing logistics system is shifting towards a multi-warehouse collaborative operation model, which has brought an exponential increase in management complexity. In a multi-warehouse collaborative environment, goods turnover is frequent, equipment calls are cross-complex, logistics vehicle routes are variable, and personnel scheduling requirements are diverse. These problems make it difficult to achieve the optimal allocation of various logistics resources, there is a lag in information transmission between warehouses, resource scheduling is redundant or insufficient, and logistics path planning lacks overall coordination. Especially in the case of large demand fluctuations, such as e-commerce promotions, seasonal changes, or emergencies, the multi-warehouse system is more likely to have logistics bottlenecks and resource mismatches, resulting in problems such as order delays, inventory backlogs, equipment idleness or overload, and waste of human resources. Traditional single-warehouse monitoring methods are difficult to cope with the complex situations in a multi-warehouse collaborative environment, lack the global monitoring ability for cross-warehouse links, and cannot timely detect potential systematic bottlenecks. At the same time, most existing monitoring systems only focus on data collection and status display, lack in-depth analysis and intelligent optimization capabilities, and are difficult to provide effective optimization plans and warning mechanisms for the decision-making level, and cannot meet the real-time monitoring needs of multi-warehouse collaborative operation in intelligent warehousing logistics docking.

[0035] Therefore, the present invention constructs a complete multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking through steps S100 - S300. In step S100, the system comprehensively obtains multi-dimensional real-time status data including cargo location, equipment status, vehicle location, and personnel distribution, establishes a digital mapping covering the entire multi-warehouse area, realizes real-time and comprehensive monitoring of the operation status of the logistics system, and provides a complete data basis for subsequent analysis. In step S200, the system uses a specially constructed multi-warehouse collaborative status evaluation model to deeply analyze the real-time status data, which can not only identify bottlenecks within a single warehouse but also discover potential risk points in cross-warehouse operations, realizing global evaluation and early warning of the logistics system. In step S300, based on the identified bottleneck points and risk points, the system automatically generates a multi-warehouse collaborative optimization plan including logistics path planning, resource allocation strategies, and emergency handling suggestions, providing a directly operable solution for management and realizing closed-loop management from problem discovery to solution generation. Through this series of steps, the present invention effectively solves the problems of incomplete status monitoring, insufficient analysis, and unsystematic optimization in the multi-warehouse collaborative environment, significantly improves the efficiency and stability of multi-warehouse collaborative operations, and provides strong support for the optimization and upgrading of intelligent warehousing logistics systems.

[0036] Embodiment 2

[0037] Referring to Figures 1 - 2 , this is the second embodiment of the present invention.

[0038] In the embodiment of the present application, in step S100, real-time status data of multiple warehouses is obtained. The real-time status data at least includes warehouse cargo location data, warehouse equipment status data, logistics vehicle location data, and personnel distribution data, and includes the following steps A1 - A2:

[0039] A1: The real-time status data includes data collected in static and dynamic environments of multiple warehouses.

[0040] A2: The static environment includes fixed radio positioning tag data collection and warehouse infrastructure status monitoring; the dynamic environment includes mobile device real-time tracking and logistics vehicle navigation trajectory monitoring.

[0041] Specifically, in A1, the static environment refers to the relatively fixed facilities, equipment, and item status in the warehouse, usually with a low change frequency, and data collection can be carried out according to a predetermined cycle. The data of this type of environment mainly reflects the basic operation status and resource configuration of the warehouse, which is of great significance for global resource planning and infrastructure optimization. The dynamic environment refers to the continuously changing operation process and mobile resource status in the warehouse, usually with a high change frequency, and data needs to be collected in real-time or near real-time. The data of this type of environment mainly reflects the real-time operation situation and resource utilization efficiency of the logistics system, which is crucial for immediate decision-making and dynamic scheduling.

[0042] In an alternative embodiment, the collection of real-time status data can also be achieved through an Internet of Things sensor network. That is, various types of sensors such as temperature and humidity sensors, light sensors, vibration sensors, and gas sensors are deployed at key nodes in the warehouse to monitor the environmental parameters of the warehouse and the storage conditions of specific goods in real time. This method can not only obtain data related to logistics operations, but also monitor environmental factors that affect the quality of goods, and is particularly suitable for goods with special requirements for storage conditions, such as medicines, foods, precision electronic equipment, etc. Through the real-time monitoring of environmental parameters, the system can give early warnings of abnormal conditions that may affect the quality of goods, providing more comprehensive safety guarantees for multi-warehouse collaborative operations.

[0043] In another alternative embodiment, the collection of real-time status data can also be achieved through a computer vision system. That is, a high-definition camera network is deployed inside the warehouse and in the loading and unloading areas, and image recognition technology is used to monitor the stacking situation of goods, the congestion status of channels, the operation standardization of operators, etc. in real time. This method can overcome the limitations of traditional RFID or barcode recognition, without attaching tags to each piece of goods, and at the same time can obtain more intuitive scene information and discover abnormal situations that may be overlooked by other monitoring methods, such as irregular placement of goods, channel blockage, safety hazards, etc.

[0044] In the embodiment of the present application, the specific implementation method of collecting the fixed radio positioning tag data in A2 is to deploy an RFID tag and reader system inside the warehouse and on the goods. Each piece of goods or shelf position is equipped with a uniquely identified RFID tag, and the accurate position and inventory information of the goods are obtained through RFID readers distributed throughout the warehouse. The system uses UHF RFID technology, and the reading distance can reach more than 10 meters, realizing the coverage of large warehouses. At the same time, the system supports batch reading, and a single reader can process hundreds of tag data per second, meeting the data collection requirements of high-density storage areas. The read data is transmitted to the central server in real time through a wired network or a wireless network to ensure the timeliness and accuracy of the data.

[0045] The monitoring of the warehouse infrastructure status is mainly used to evaluate the operating status of the equipment and infrastructure in the warehouse, including but not limited to:

[0046] Monitoring the operating status of automated storage equipment, such as the operating parameters, fault status, and usage efficiency of automated stereoscopic warehouses, roadway stackers, automatic sorting lines, etc.;

[0047] Power system monitoring, such as the operating status and energy consumption data of distribution equipment, lighting systems, and emergency power supplies; safety system monitoring, such as the operating status and abnormal event records of fire protection equipment, access control systems, and video surveillance systems; temperature and humidity control system monitoring, such as the operating status and environmental parameter data of air conditioning equipment, dehumidification equipment, and cold chain facilities; loading and unloading equipment monitoring, such as the usage conditions and maintenance requirements of forklifts, lifting platforms, and loading bridges.

[0048] Real-time tracking of mobile devices is mainly used to monitor the location and status of mobile operation equipment inside the warehouse, including but not limited to:

[0049] AGV (Automated Guided Vehicle) position tracking and task status monitoring; real-time position and operation status monitoring of intelligent forklifts; position and operation efficiency monitoring of robotic picking equipment; position and operation status monitoring of handheld terminal devices; position and working status monitoring of wearable devices.

[0050] Logistics vehicle navigation trajectory monitoring is mainly used to track the operation of logistics vehicles connecting multiple warehouses, including but not limited to:

[0051] Real-time position and driving speed monitoring of logistics vehicles; monitoring of vehicle loading status and capacity utilization rate; deviation analysis of driving routes from planned routes; early warning of abnormal vehicle operation status, such as speeding, long-term stagnation, deviation from routes, etc.; monitoring of cross-warehouse transportation time and efficiency.

[0052] It should be noted that the above four types of data collection complement and cooperate with each other, constituting a comprehensive and systematic state monitoring data collection system. Among them, fixed radio positioning tag data collection and warehouse infrastructure status monitoring mainly focus on relatively stable elements in the static environment, providing data support for the basic resource planning and facility optimization of the logistics system; real-time tracking of mobile devices and logistics vehicle navigation trajectory monitoring mainly focus on continuously changing elements in the dynamic environment, providing a basis for real-time scheduling decisions and handling of abnormal situations. The combination of the four types of data can comprehensively reflect the system operation status in a multi-warehouse collaborative environment, providing a complete data basis for bottleneck identification and optimization plan generation.

[0053] In the embodiment of the present application, in step S200, a multi-warehouse collaborative state evaluation model is used to analyze the real-time state data to identify potential logistics bottleneck points and risk points, including the following steps B1 - B2:

[0054] B1: The multi-warehouse collaborative state evaluation model is constructed based on warehouse logistics industry standards and historical operation data, supporting automatic identification of logistics bottlenecks and automatic optimization of resource allocation.

[0055] In B2, the multi-warehouse collaboration status evaluation model adopts a multi-level evaluation architecture, including facility layer evaluation, resource layer evaluation, process layer evaluation, and system layer evaluation. By comprehensively analyzing the status of each layer, a comprehensive evaluation of the overall operation efficiency of the multi-warehouse system is achieved.

[0056] Specifically, in B1, the multi-warehouse collaboration status evaluation model is mainly constructed based on the following warehousing and logistics industry standards:

[0057] ISO 28001:2007 "Supply Chain Security Management System" standard, which stipulates the requirements and evaluation methods for supply chain security risk management;

[0058] GBT 30334-2013 "Warehousing Operation Specification for Logistics Enterprises", which puts forward specific requirements for warehousing operation processes and efficiency indicators;

[0059] GBT 31867-2015 "Classification and Evaluation Indicators for Logistics Enterprises", which stipulates the evaluation index system for logistics enterprises;

[0060] VDI 3601 "Key Performance Indicators for Warehousing and Logistics", which provides key indicators and methods for evaluating the performance of warehousing and logistics systems.

[0061] At the same time, the model also integrates a large amount of historical operation data, including:

[0062] Historical order processing records and time data in the multi-warehouse collaboration environment; equipment usage records and maintenance data; personnel operation efficiency and distribution data; inventory turnover rate and accuracy data; logistics vehicle operation records and route data; historical bottleneck and risk event records and solution data.

[0063] These industry standards and historical data provide a rich knowledge base for the model, enabling it to accurately evaluate the operation status of the multi-warehouse system, identify potential bottleneck points and risk points.

[0064] In B2, the multi-level evaluation architecture specifically includes:

[0065] Facility layer evaluation: mainly evaluate the status and effectiveness of warehouse infrastructure and equipment, including storage facility utilization rate, equipment operation status, maintenance demand prediction, etc. Evaluation indicators include equipment utilization rate, equipment failure rate, equipment energy consumption efficiency, equipment maintenance cycle, etc.

[0066] Resource layer evaluation: mainly evaluate the allocation and use efficiency of mobile resources such as human resources and logistics vehicles, including personnel work load, vehicle scheduling rationality, resource matching degree, etc. Evaluation indicators include per capita operation volume, vehicle full load rate, resource idle time ratio, resource usage peak-valley difference, etc.

[0067] Process layer evaluation: mainly evaluate the efficiency and stability of the internal operation process and cross-warehouse logistics process in the warehouse, including order processing time, picking efficiency, loading and unloading efficiency, cross-warehouse transportation time, etc. The evaluation indicators include order turnover time, picking accuracy rate, loading and unloading time, cross-warehouse logistics delay rate, etc.

[0068] System layer evaluation: mainly evaluate the overall collaborative efficiency and response ability of the multi-warehouse system, including system throughput, response time, resource collaboration degree, system stability, etc. The evaluation indicators include system order processing capacity, peak response ability, cross-warehouse collaboration efficiency, system fluctuation range, etc.

[0069] The automatic identification function of logistics bottlenecks is mainly implemented based on the following technologies:

[0070] Threshold-based anomaly detection: Set the threshold range of each indicator according to industry standards and historical data, and trigger a bottleneck alarm when the real-time data exceeds the threshold;

[0071] Trend-based early warning mechanism: Through time series analysis, identify the development trends of each indicator and predict possible bottleneck problems;

[0072] Bottleneck identification based on association rules: Analyze the association relationships between different indicators to discover hidden systematic bottlenecks;

[0073] Bottleneck verification based on simulation: Verify the impact degree of potential bottlenecks on the overall system performance through system simulation;

[0074] Pattern matching based on historical cases: Match the current system state with historical bottleneck cases to identify possible bottlenecks in similar situations.

[0075] The automatic optimization function of resource allocation is mainly comprehensively evaluated based on the following factors:

[0076] Resource utilization rate: Evaluate the current usage efficiency of various resources and identify resource overload or idle situations; Demand forecasting: Based on historical data and current trends, predict the short-term changes in resource requirements; Job priority: Determine the priority ranking of jobs according to factors such as order urgency and customer importance; Resource availability: Consider factors such as resource maintenance plans and usage restrictions to evaluate the actual available status of resources; Cross-warehouse collaboration benefit: Evaluate the overall benefit of resources in a cross-warehouse collaboration environment rather than single-warehouse optimization.

[0077] In an alternative embodiment, the multi-warehouse collaboration status evaluation model can also adopt a simulation evaluation method based on digital twin, that is, constructing a virtual digital model corresponding to the actual warehousing logistics system, real-time simulating and predicting the operation performance of the system under different states, and identifying non-optimal links and potential bottleneck points in the system through comparative analysis with the ideal state. This method can conduct virtual tests and effect evaluations on various optimization schemes without interfering with the operation of the actual system, providing a more reliable basis for decision-making.

[0078] In another alternative embodiment, the multi-warehouse collaboration status evaluation model can also adopt an adaptive evaluation method based on reinforcement learning, that is, by establishing a learning framework of environment-state-action-reward, enabling the evaluation model to learn and optimize the evaluation strategy from continuous system interactions, continuously adjusting the weights and thresholds of evaluation indicators, and adapting to the demand characteristics of different business scenarios and seasonal changes. This method has strong adaptability and can automatically adjust the evaluation criteria as the business environment changes, maintaining the accuracy and practicality of the evaluation results.

[0079] It should be noted that the multi-warehouse collaboration status evaluation model adopts a hierarchical modular design. Each layer of evaluation module can operate independently or work collaboratively, and can be flexibly configured according to specific requirements. The model supports incremental learning and can be continuously optimized and improved through continuous data collection and feedback, improving the accuracy and adaptability of the evaluation. At the same time, the model has a rich built-in industry knowledge base and best practice cases, and can provide targeted evaluation results and optimization suggestions according to the characteristics of different types of warehouses and different business scenarios.

[0080] In the embodiment of the present application, in step S300, according to the identified logistics bottleneck points and risk points, a multi-warehouse collaboration optimization plan is generated, including logistics path planning, resource allocation strategies, and emergency handling suggestions, including the following steps C1 - C3:

[0081] C1: The logistics path planning in the multi-warehouse collaboration optimization plan is divided into emergency priority, high priority, medium priority, and low priority; the resource allocation strategies include at least personnel scheduling strategies, equipment allocation strategies, storage location optimization strategies, vehicle scheduling strategies, and cross-warehouse transfer strategies.

[0082] C2: It also includes the step of formulating an implementation plan based on the multi-warehouse collaboration optimization plan. The implementation plan includes system parameter updates, resource reallocation, and logistics route adjustments.

[0083] C3: It also includes the step of evaluating the effect of the multi-warehouse system with the implemented optimization plan, verifying the optimization effect, and updating the multi-warehouse collaboration status evaluation model.

[0084] Specifically, in C1, the priority division criteria for the logistics path planning are as follows:

[0085] Emergency Priority: Refers to logistics tasks that have a significant impact on business continuity and customer satisfaction and require immediate handling. Such tasks usually involve urgent orders from important customers, orders at high risk of serious delays, emergency allocation of critical supplies, etc. The system will assign the optimal path and resources to tasks with emergency priority and can adjust or suspend other tasks with lower priority when necessary.

[0086] High Priority: Refers to logistics tasks that have a relatively large impact on business efficiency and need to be processed preferentially but do not affect business continuity. Such tasks usually involve regular orders from important customers, material flow with high time sensitivity, planned important inventory adjustments, etc. The system will assign better paths and sufficient resources to high-priority tasks to ensure that the tasks can be completed as planned.

[0087] Medium Priority: Refers to logistics tasks that have a certain impact on daily business operations and need to be processed according to the plan. Such tasks usually involve standard orders from regular customers, periodic inventory replenishment, regular material transfer between warehouses, etc. The system will assign reasonable paths and appropriate resources to medium-priority tasks to ensure that the tasks can be completed within the normal time range.

[0088] Low Priority: Refers to logistics tasks that have a relatively small impact on short-term business and can be flexibly scheduled for processing. Such tasks usually involve inventory adjustments with non-urgent needs, preventive material replenishment, internal auxiliary material flow, etc. The system will assign paths and resources to low-priority tasks when resources are sufficient to ensure the efficient use of overall system resources.

[0089] The specific content of the resource allocation strategy is as follows:

[0090] Personnel Scheduling Strategy: Optimize the allocation of personnel in different warehouses and different operation areas according to factors such as workload distribution, skill requirements, and work efficiency. Specific measures include:

[0091] Pre-scheduling of personnel based on peak workload prediction; Dynamically adjusting personnel configuration according to real-time workload; Allocation of professional personnel based on skill matching degree; Personnel rotation mechanism considering work intensity and continuous working hours; Flexible cross-warehouse personnel scheduling mechanism to cope with sudden demands.

[0092] Equipment Allocation Strategy: Optimize the allocation and scheduling of equipment resources according to factors such as equipment performance characteristics, operation requirements, and usage efficiency. Specific measures include:

[0093] Allocation strategy based on equipment characteristics and task matching degree; Usage plan considering equipment maintenance cycle; Strategy for equalizing equipment usage frequency; Cross-warehouse sharing mechanism for high-value equipment; Real-time monitoring and dynamic adjustment mechanism for equipment load.

[0094] Warehouse location optimization strategy: Optimize the storage location allocation of goods within the warehouse and across warehouses according to factors such as the turnover frequency, relevance, and storage requirements of goods. Specific measures include:

[0095] Warehouse location zoning strategy based on goods turnover rate; Proximity storage strategy considering the relevance of goods; Dynamic warehouse location adjustment strategy for seasonal goods; Cross-warehouse inventory balance strategy; Optimization and dynamic adjustment mechanism for warehouse location utilization.

[0096] Vehicle scheduling strategy: Optimize the scheduling and route arrangement of logistics vehicles according to factors such as transportation demand, route efficiency, and vehicle characteristics. Specific measures include:

[0097] Matching allocation based on transportation demand and vehicle load; Route optimization strategy considering multi-point distribution; Vehicle return load optimization strategy; Consolidation and optimization of cross-warehouse transportation routes; Dynamic route adjustment mechanism under real-time traffic conditions.

[0098] Cross-warehouse transfer strategy: Optimize the goods transfer plan between multiple warehouses according to factors such as the inventory level of each warehouse, demand forecast, and transportation cost. Specific measures include:

[0099] Transfer trigger mechanism based on inventory level and safety stock; Transfer source selection strategy considering transfer cost and time; Balance strategy between batch transfer and immediate transfer; Advance transfer plan under seasonal demand forecast; Quick response transfer mechanism in case of emergency.

[0100] In an alternative implementation, the multi-warehouse collaborative optimization solution may further include inventory strategy optimization, that is, optimize the inventory structure and level of each warehouse based on factors such as sales forecast, supply chain risk, and warehousing cost, including adjustment of safety stock level, optimization of inventory distribution, and optimization of replenishment strategy, etc. This optimization can reduce the overall inventory cost and improve the inventory turnover rate on the premise of ensuring the service level, which is particularly important for businesses with strong seasonality and diverse categories.

[0101] In another alternative implementation, the multi-warehouse collaborative optimization solution may further include energy consumption optimization, that is, reduce the overall energy consumption of the multi-warehouse system, reduce operating costs and environmental impacts by reasonably arranging operation time, optimizing equipment usage plan, and adjusting temperature control system, etc. This optimization is particularly applicable to warehousing facilities with high energy consumption such as cold chain warehousing and large-scale automated warehouses, and can achieve the goal of sustainable development without affecting the normal operation of the business.

[0102] In C2, the implementation plan formulated based on the multi-warehouse collaborative optimization solution specifically includes:

[0103] System parameter update: Update various parameters and rules in the system according to the optimization solution, including but not limited to:

[0104] Update of goods classification and priority parameters; adjustment of storage location allocation rules; optimization of path planning algorithm parameters; adjustment of resource allocation weights; update of warning thresholds and triggering conditions.

[0105] Resource reallocation: Reallocate resources such as personnel, equipment, and vehicles according to the optimization plan, including but not limited to:

[0106] Personnel work arrangement and skills training plan; equipment maintenance and deployment plan; adjustment of vehicle routes and scheduling plan; re-planning of warehouse space and storage locations; implementation of cross-warehouse resource sharing mechanism.

[0107] Logistics route adjustment: Adjust internal logistics and cross-warehouse logistics routes according to the optimization plan, including but not limited to:

[0108] Optimization of in-warehouse operation routes; re-planning of cross-warehouse transportation routes; adjustment of delivery routes and time windows; setting of emergency backup routes; optimization of multimodal transportation plans.

[0109] The formulation of the implementation plan follows the principles of step-by-step implementation and continuous optimization, usually divided into three levels: short-term plan (1 - 7 days), medium-term plan (1 - 4 weeks), and long-term plan (1 - 6 months), to ensure that the optimization plan can be implemented smoothly and orderly while maintaining the stable operation of the system.

[0110] In C3, the effect evaluation of the multi-warehouse system with the implemented optimization plan mainly includes the following steps:

[0111] Effect verification test: For the implemented optimization measures, collect and analyze relevant performance indicators to verify the optimization effect. The test methods include:

[0112] Comparative analysis of key performance indicators (KPIs) to compare the system performance changes before and after optimization; A / B test to verify the effect of specific optimization measures through controlled experiments; simulation test to verify the overall effect of complex optimization plans in a virtual environment; real-time monitoring and analysis to continuously monitor the system operation status and discover potential problems.

[0113] Optimization effect evaluation: Conduct a comprehensive evaluation of the effects of optimization measures, including:

[0114] Evaluation of system efficiency improvement, such as reduction of order processing time and increase of resource utilization rate; evaluation of cost impact, such as reduction of operation cost and energy consumption; evaluation of user experience impact, such as improvement of customer satisfaction and reduction of complaints; evaluation of system stability, such as reduction of abnormal events and system fluctuations.

[0115] Update of the evaluation model: Based on the effect evaluation results, update the multi-warehouse collaboration status evaluation model, including:

[0116] Model parameter adjustment to optimize the weights and thresholds of evaluation metrics; rule base update to add newly discovered bottleneck patterns and solutions; prediction algorithm optimization to improve the accuracy and timeliness of early warnings; knowledge base expansion to record successful cases and lessons learned to enrich the knowledge base of the model.

[0117] In an alternative implementation, the effect evaluation can also adopt the balanced scorecard method, that is, comprehensively evaluate the optimization effect from four dimensions: finance, customers, internal processes, and learning and growth, to ensure a balance between short-term benefits and long-term development of the optimization measures. This evaluation method can measure the comprehensive value of the optimization plan more comprehensively and avoid the problem of overemphasizing short-term indicators while ignoring long-term development.

[0118] In another alternative implementation, the effect evaluation can also adopt a scenario-based stress testing method, that is, test the adaptability and stability of the optimized system by simulating various extreme scenarios (such as sudden increase in order volume, equipment failure, route interruption, etc.). This method is particularly suitable for evaluating the risk resistance ability and emergency response ability of the system and is of great significance for enhancing the overall resilience of the system.

[0119] It should be noted that the effect evaluation of the multi-warehouse system with the implemented optimization plan is a continuous process, including not only the immediate evaluation after the implementation of the optimization but also the follow-up evaluation of the medium- and long-term effects. Through regular effect evaluation and model update, the system can continuously learn and improve, adapt to changes in the business environment and new challenges, and achieve the goal of continuous optimization. At the same time, the evaluation results also provide valuable reference and basis for future optimization decisions, forming a virtuous cycle of optimization-evaluation-improvement.

[0120] In the implementation mode of this application, the specific execution of the implementation plan follows the principle of batch implementation and gradual progress to avoid systemic risks and business interruptions. System parameter updates usually adopt the method of gray release, first verify the effect of parameter adjustment in a small-scale test environment, and then gradually promote it to the production environment after confirmation. Resource reallocation combines the characteristics of the business cycle and is carried out during the relatively off-peak season or operation gaps of the business to reduce the impact on normal operations. The adjustment of logistics routes also adopts a progressive implementation strategy, with the old and new routes running in parallel for a period of time to ensure a smooth transition.

[0121] For larger-scale optimization plans, the implementation plan usually includes a detailed risk assessment and emergency plan to ensure rapid response and handling in case of problems during the implementation process. At the same time, the implementation plan also includes a comprehensive communication and training plan to ensure that all relevant personnel fully understand the content and implementation key points of the optimization plan, and improve the collaborative efficiency and accuracy of the implementation.

[0122] During the process of verifying the optimization effect, the system adopts a variety of data collection and analysis methods, including automated monitoring, manual spot checks, and user feedback, etc., to comprehensively evaluate the optimization effect from different perspectives. For the improvement effect of key indicators, the system will generate detailed data reports and visualization charts to intuitively display the comparison results before and after optimization. At the same time, the system will also collect various feedbacks and problems during the implementation process as the basis for continuous improvement.

[0123] The update of the evaluation model adopts the incremental learning method, retaining the effective model structure and parameters, and at the same time, pertinently adjusting and improving the parts that need to be improved. During the update process, new collected data and feedback will be fully utilized, especially paying attention to those bottleneck points with inaccurate predictions or being ignored, to improve the comprehensiveness and accuracy of the model. At the same time, the model update will also consider the changes in the business environment and new industry standards to ensure that the model always meets the latest business requirements and standard requirements.

[0124] In summary, the present invention realizes the comprehensive real-time monitoring of the operation status of the multi-warehouse collaboration system by constructing a comprehensive real-time status data collection system covering static and dynamic environments; realizes the accurate identification of potential logistics bottleneck points and risk points by constructing a multi-level evaluation model based on industry standards and historical data; generates a multi-warehouse collaboration optimization plan including logistics path planning, resource allocation strategies, and emergency handling suggestions, and formulates an implementation plan and an effect evaluation mechanism to form a complete optimization-implementation-evaluation closed loop, realizing the continuous optimization of the logistics system. This series of technical solutions together constitute a systematic, intelligent, and standardized multi-warehouse collaboration status monitoring method for intelligent warehousing and logistics docking, effectively solving problems such as incomplete monitoring, in-depth analysis, unsystematic optimization, and non-closed implementation in the multi-warehouse collaboration environment, and providing strong support for the optimization and upgrading of the intelligent warehousing and logistics system.

[0125] Embodiment 3

[0126] The above is a schematic solution of a multi-warehouse collaboration status monitoring method for intelligent warehousing and logistics docking. It should be noted that the technical solution of the multi-warehouse collaboration status monitoring system for intelligent warehousing and logistics docking belongs to the same concept as the technical solution of the above multi-warehouse collaboration status monitoring method for intelligent warehousing and logistics docking. For the details not described in detail in the technical solution of the multi-warehouse collaboration status monitoring system for intelligent warehousing and logistics docking in this embodiment, reference can be made to the description of the technical solution of the above multi-warehouse collaboration status monitoring method for intelligent warehousing and logistics docking.

[0127] This embodiment also provides a multi-warehouse collaboration status monitoring system for intelligent warehousing and logistics docking, including:

[0128] A status data acquisition module, which is used to obtain real-time status data of multiple warehouses. The real-time status data at least includes warehouse goods location data, warehouse equipment status data, logistics vehicle location data, and personnel distribution data;

[0129] A status analysis module, which is used to analyze the real-time status data by using a multi-warehouse collaborative status evaluation model to identify potential logistics bottleneck points and risk points;

[0130] An optimization plan generation module, which is used to generate a multi-warehouse collaborative optimization plan according to the identified logistics bottleneck points and risk points, including logistics path planning, resource allocation strategies, and emergency handling suggestions.

[0131] This embodiment also provides an electronic device, which is applicable to the situation of multi-warehouse collaborative status monitoring for intelligent warehousing and logistics docking, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking as proposed in the above embodiment.

[0132] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking as proposed in the above embodiment.

[0133] The storage medium proposed in this embodiment and the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0134] Through the above description of the implementation manners, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk, or an optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking, characterized by: Including, obtaining real-time status data of multiple warehouses, wherein the real-time status data at least includes warehouse cargo location data, warehouse equipment status data, logistics vehicle location data and personnel distribution data; A multi-warehouse collaborative status evaluation model is used to analyze the real-time status data to identify potential logistics bottlenecks and risk points; Based on the identified logistics bottlenecks and risk points, a multi-warehouse collaborative optimization plan is generated, including logistics route planning, resource allocation strategy and emergency response suggestions.

2. The multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking according to claim 1 is characterized by: The real-time status data includes data collected by multiple warehouses in a static environment and a dynamic environment.

3. The multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking according to claim 2 is characterized by: The static environment includes fixed radio positioning tag data collection and warehouse infrastructure status monitoring; the dynamic environment includes real-time tracking of mobile devices and navigation trajectory monitoring of logistics vehicles.

4. The multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking according to claim 3 is characterized by: The multi-warehouse collaborative status evaluation model is constructed based on warehousing and logistics industry standards and historical operation data, and supports automatic identification of logistics bottlenecks and automatic optimization of resource allocation.

5. The multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking according to claim 4 is characterized by: The logistics path planning in the multi-warehouse collaborative optimization scheme is divided into emergency priority, high priority, medium priority and low priority; the resource allocation strategy includes at least personnel scheduling strategy, equipment allocation strategy, warehouse location optimization strategy, vehicle scheduling strategy and cross-warehouse allocation strategy.

6. The multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking according to claim 5 is characterized by: It also includes the step of formulating an implementation plan based on the multi-warehouse collaborative optimization plan, and the implementation plan includes system parameter updating, resource reallocation and logistics route adjustment.

7. The multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking according to claim 6 is characterized by: It also includes the step of evaluating the effectiveness of the multi-warehouse system that has implemented the optimization plan, verifying the optimization effect and updating the multi-warehouse collaborative status evaluation model.

8. A multi-warehouse collaborative status monitoring system for intelligent warehousing and logistics docking, based on the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking according to any one of claims 1 to 7, characterized in that: It also includes a status data acquisition module for acquiring real-time status data of multiple warehouses, wherein the real-time status data includes at least warehouse cargo location data, warehouse equipment status data, logistics vehicle location data, and personnel distribution data; A status analysis module is used to analyze the real-time status data using a multi-warehouse collaborative status evaluation model to identify potential logistics bottlenecks and risk points; The optimization plan generation module is used to generate multi-warehouse collaborative optimization plans based on the identified logistics bottlenecks and risk points, including logistics route planning, resource allocation strategies and emergency response suggestions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-warehouse collaborative status monitoring method for intelligent warehousing and logistics docking described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the multi-warehouse collaborative status monitoring method for intelligent warehousing logistics docking described in any one of claims 1 to 7 are implemented.

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