A logistics and warehousing management system based on intelligent algorithm

Abstract

The present application relates to the technical field of data processing system, especially to a logistics warehouse management system based on intelligent algorithm, which comprises a collection module, a preliminary screening module, a first screening module, a second screening module, a final screening module and an adjustment module. Through multi-layer screening and dynamic adjustment, the present application realizes efficient selection and optimization of warehouse transportation path, can comprehensively consider multiple key parameters such as loading and unloading complexity, order urgency, channel congestion rate, obstacle avoidance frequency and deceleration frequency, and carry out joint analysis based on their time and space variation characteristics. Congestion rate and deceleration frequency often present synchronous fluctuation, and there is a synergistic effect between loading and unloading complexity and obstacle avoidance frequency, and order urgency preferentially drives path screening sequence, effectively solving the problem of high route congestion rate and low response speed when facing complex and variable scenes due to excessive reliance on static threshold judgment and artificial decision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing systems, and in particular to a logistics warehouse management system based on intelligent algorithms. BACKGROUND

[0002] With the rapid development of e-commerce and instant delivery, the dependence of the logistics warehouse system on handling efficiency and path optimization is increasing, and the traditional method of relying on manual experience or fixed path scheduling has been difficult to cope with the problems of rapid increase in order scale, complex distribution of goods, and frequent congestion of transportation channels. How to consider the order urgency, the complexity of goods loading and unloading, and the dynamic state of the transportation channel in a multi-dimensional parameter fluctuation environment, and then quickly filter out the optimal handling path, has become an important challenge in the current intelligent warehouse field.

[0003] Chinese patent application publication No. CN119168535A discloses a logistics warehouse management system, which includes a goods distribution module, a warehouse configuration module, a warehouse operation module, a material distribution module, and a query statistics module. The goods distribution module is used to optimize warehouse space utilization, improve goods access speed, reduce manual errors and losses, and accurately manage and predict inventory. The factory can improve overall warehouse efficiency and operational level. The warehouse configuration module is used to identify and manage warehouse resources by configuring warehouse entity parameters. Through warehouse configuration management, optimized warehouse operation plans can be developed according to actual operation needs, and efficient use of warehouse environment can be achieved. That is, more goods can be stored in limited warehouse volume, and greater warehouse throughput can be obtained with limited resources, and higher operation efficiency and speed can be obtained with limited manpower and resources. The warehouse operation module is used for warehouse management, mainly including receipt management, shipment management, goods inventory, and goods scrap. The material distribution module is used for transportation management department to develop transportation plans according to customer package quantity, customer address, transportation mode, and transportation capacity. The query statistics module is used to provide comprehensive query of business information related to warehouse business and output reports.

[0004] It can be seen that the logistics warehouse management system has the following problems: the system often relies on manual experience or a single parameter when handling urgent orders, resulting in some uncertainty in goods location selection results; the system lacks a dynamic decision-making mechanism in the matching process of order urgency and goods location selection, which may cause some urgent orders to be processed first; the screening process of the system relies on static rules and cannot be adjusted flexibly according to real-time order status, thereby affecting the overall logistics efficiency and order response speed. SUMMARY

[0005] To this end, the application provides a logistics warehouse management system based on intelligent algorithm, which introduces a multi-layer screening and dynamic adjustment mechanism, combines real-time data acquisition and intelligent algorithm to overcome the problem of high route congestion rate and low response speed when facing complex and variable scenes due to excessive reliance on static threshold judgment and artificial decision in the prior art.

[0006] To achieve the above-mentioned purpose, the application provides a logistics warehouse management system based on intelligent algorithm, comprising:

[0007] The acquisition module is used to acquire in real time the loading and unloading complexity of each candidate storage location in the cargo handling process, the order urgency, the channel congestion rate of the candidate transportation channel between each candidate storage location and each delivery port, the obstacle avoidance frequency and the deceleration frequency of the cargo handling robot running at a preset driving speed in the candidate transportation channel;

[0008] The preliminary screening module is connected with the acquisition module and is used to determine a plurality of first storage locations according to the order urgency;

[0009] The first screening module is connected with the acquisition module and the preliminary screening module respectively, and is used to determine the comprehensive evaluation index of the candidate transportation channel according to the loading and unloading complexity, the channel congestion rate and the obstacle avoidance frequency of each first storage location, and to determine a plurality of first transportation channels according to the size relationship between the comprehensive evaluation index and a preset transportation selection threshold;

[0010] The second screening module is connected with the acquisition module and the first screening module respectively, and is used to determine a plurality of second transportation channels according to the overlap rate and the deceleration frequency of each first transportation channel within a preset selection time;

[0011] The final screening module is connected with the acquisition module and the second screening module respectively, and is used to determine a final transportation channel according to the overlap rate and the obstacle avoidance frequency of the second transportation channel of any first storage location and the remaining first storage locations within a preset screening range of the first storage location;

[0012] The adjustment module is connected with the acquisition module and the final screening module respectively, and is used to adjust the preset transportation selection threshold or the preset driving speed according to the channel congestion rate and the deceleration frequency within a preset transportation time after determining the final transportation channel.

[0013] Further, the first storage location is determined by the preliminary screening module when the order urgency is greater than the preset urgency threshold.

[0014] Further, the first screening module comprises:

[0015] The first normalization processing unit is configured to normalize the loading and unloading complexity, the channel blockage rate, and the obstacle avoidance frequency of each of the first storage locations within the preset first screening duration, to obtain a complexity normalization data set, a blockage rate normalization data set, and an obstacle avoidance frequency normalization data set.

[0016] The first determination unit is connected with the first normalization processing unit and configured to determine a plurality of first transport channels according to the loading and unloading complexity normalization data set, the channel blockage rate normalization data set, the obstacle avoidance frequency normalization data set, and the preset transport selection threshold.

[0017] Further, the first determination unit comprises:

[0018] The first index calculation sub-unit is configured to perform weighted summation on the average value of the loading and unloading complexity normalization data set, the average value of the channel blockage rate normalization data set, and the average value of the obstacle avoidance frequency normalization data set, to form a comprehensive evaluation index of each of the candidate transport channels.

[0019] The first determination sub-unit is connected with the first index calculation sub-unit and configured to determine the candidate transport channel as a first transport channel when the comprehensive evaluation index is greater than the preset transport selection threshold, to determine a plurality of first transport channels.

[0020] Further, the second screening module comprises:

[0021] The overlap rate fluctuation value calculation unit is configured to calculate the standard deviation of the overlap rate at each time point within the preset selection duration from the initial time point, to obtain a plurality of overlap rate fluctuation values.

[0022] The deceleration frequency fluctuation value calculation unit is configured to calculate the standard deviation of the deceleration frequency at each time point within the preset selection duration from the initial time point, to obtain a plurality of deceleration frequency fluctuation values.

[0023] The second normalization processing unit is connected with the overlap rate fluctuation value calculation unit and the deceleration frequency fluctuation value calculation unit, respectively, and configured to normalize the plurality of overlap rate fluctuation values and the plurality of deceleration frequency fluctuation values, respectively, to obtain an overlap fluctuation normalization data set and a deceleration fluctuation normalization data set.

[0024] The second index calculation unit is connected with the second normalization processing unit and configured to perform weighted summation on the average value of the overlap rate fluctuation normalization data set and the average value of the deceleration frequency fluctuation normalization data set, to obtain a fluctuation screening index of each of the first transport channels.

[0025] The determination unit is connected with the second index calculation unit and configured to determine a plurality of second transport channels according to the fluctuation screening index of all the first transport channels.

[0026] Further, the determining unit is configured to determine the first transport channel as the second transport channel if the fluctuation screening index is less than a preset screening index threshold, so as to determine a plurality of second transport channels.

[0027] Further, the final screening module comprises:

[0028] A candidate unit is configured to determine the second transport channel as a temporary channel if the coincidence rate is less than a preset coincidence rate threshold, so as to determine a plurality of temporary channels;

[0029] A first final determining unit, connected with the candidate unit, is configured to determine the temporary channel corresponding to the minimum value of the obstacle avoidance frequency as a final transport channel if the average value of the obstacle avoidance frequency within the preset reselection time length is less than a preset obstacle avoidance frequency threshold, so as to determine a final transport channel;

[0030] A second final determining unit, connected with the candidate unit, is configured to determine the final transport channel according to the transport completion time length of the second transport channel within a previous preset historical reselection time length and the obstacle avoidance frequency within a next preset reselection time length if the coincidence rate is greater than the preset coincidence rate threshold.

[0031] Further, the second final determining unit comprises:

[0032] A frequency screening sub-unit is configured to determine the second transport channel as a first reselection channel if the average value of the obstacle avoidance frequency within the preset reselection time length is less than a preset obstacle avoidance frequency threshold, so as to determine a plurality of first reselection channels;

[0033] A time screening sub-unit, connected with the frequency screening sub-unit, is configured to determine the final transport channel according to the transport completion time length of each of the first reselection channels, so as to determine a final transport channel.

[0034] Further, the adjustment module comprises:

[0035] A deviation calculating unit is configured to calculate the relative deviation of each of the congestion rates within the preset transport time length and a preset congestion rate threshold to obtain a congestion rate deviation set, and calculate the relative deviation of each of the deceleration frequencies within the preset transport time length and a preset deceleration frequency threshold to obtain a deceleration frequency deviation set;

[0036] An adjustment unit, connected with the deviation calculating unit, is configured to adjust the preset transport selection threshold or adjust the preset driving speed according to the congestion rate deviation set and the deceleration frequency deviation set.

[0037] Further, the adjustment unit comprises:

[0038] The correlation calculation subunit is used to calculate the Pearson correlation coefficient between the congestion rate deviation set and the deceleration frequency deviation set to obtain the set correlation.

[0039] The first adjustment subunit, which is connected to the relevance calculation subunit, is used to adjust the preset transportation selection threshold according to the set relevance and the preset relevance threshold when the set relevance is positive and the set relevance is greater than the preset relevance threshold.

[0040] The second adjustment subunit, which is connected to the correlation calculation subunit, is used to adjust the preset driving speed according to the absolute value of the set correlation and the preset correlation threshold when the set correlation is negative and the absolute value of the set correlation is greater than the preset correlation threshold.

[0041] Compared with existing technologies, the advantages of this invention lie in its ability to achieve efficient selection and optimization of warehousing and transportation routes through multi-layered screening and dynamic adjustment. It comprehensively considers multiple key parameters such as loading and unloading complexity, order urgency, channel congestion rate, obstacle avoidance frequency, and deceleration frequency, and performs joint analysis based on their temporal and spatial variation characteristics. The changes in these parameters are interrelated; for example, congestion rate and deceleration frequency often fluctuate synchronously, loading and unloading complexity and obstacle avoidance frequency have a synergistic effect, and order urgency prioritizes the route selection order. By comprehensively judging these interrelated characteristics, the system not only improves the accuracy of transportation channel selection but also adjusts thresholds and driving speeds in real time during transportation, thereby effectively improving overall warehousing and transportation efficiency, reducing delay risks, and optimizing resource utilization. This effectively solves the problems of high route congestion rates and low response speeds in complex and ever-changing scenarios caused by over-reliance on static threshold judgments and manual decision-making.

[0042] Furthermore, by comparing the urgency of orders with preset urgency thresholds, priority storage locations can be effectively selected. This selection mechanism enables the system to prioritize time-sensitive tasks with limited handling resources, rationally allocate channels and robot scheduling, thereby improving overall transportation efficiency and reducing resource waste caused by delays. At the same time, it ensures the timely completion of high-priority orders and enhances the responsiveness and stability of the warehousing system.

[0043] Further, by uniformly normalizing the loading and unloading complexity, channel congestion rate and obstacle avoidance frequency within the preset first screening duration, the differences in the dimensions and value ranges of different indicators are effectively eliminated, the comparability between indicators and the scientificity of weight allocation are improved; on this basis, the first determination unit combines the normalized data set with the preset transportation selection threshold for comprehensive determination, which can more accurately and quickly identify the first transportation channel that meets the requirements, thereby eliminating unsuitable channels at an early stage, reducing subsequent operation pressure, improving the stability and precision of channel selection, and ultimately realizing efficient integration and intelligent decision-making of the logistics and warehousing system for multi-factor and multi-dimensional information, significantly improving overall transportation efficiency and resource utilization.

[0044] Further, by the cooperation of the first index calculation subunit and the first determination subunit, the multi-dimensional indicators such as loading and unloading complexity, channel congestion rate and obstacle avoidance frequency are normalized and then weighted and summed to form a comprehensive evaluation index, and the preset transportation selection threshold is combined for automatic determination, which not only realizes the unified quantification and standardization of multi-source data, but also significantly improves the precision and real-time performance of transportation channel screening, avoiding the one-sidedness and lag caused by relying on single indicators or manual experience in traditional technologies; at the same time, the unit can dynamically adjust the weight and threshold according to the actual operation situation, thereby maintaining efficient and stable decision-making ability in a multi-pallet, multi-channel and complex warehousing environment, effectively reducing subsequent operation load and improving the intelligent level and execution efficiency of overall path optimization.

[0045] Further, by introducing the calculation, normalization and comprehensive evaluation of the overlap rate fluctuation value and the deceleration frequency fluctuation value, the selection of the transportation channel can be more comprehensively optimized. First, the overlap rate fluctuation value calculation unit and the deceleration frequency fluctuation value calculation unit calculate the standard deviation of the overlap rate and the standard deviation of the deceleration frequency at each time within the initial time to the preset selection duration, thereby obtaining a plurality of overlap rate fluctuation values and deceleration frequency fluctuation values, capturing the fluctuation characteristics of the transportation channel during operation. Then, the second normalization processing unit normalizes these fluctuation values, so that indicators of different dimensions are compared under a unified standard, providing a fair and comparable basis for subsequent screening. Through the second index calculation unit, the average values of the normalized overlap fluctuation data set and the deceleration fluctuation data set are weighted and summed to form a fluctuation screening index, which can comprehensively consider the stability and operation efficiency of the channel and preferentially select channels with smaller fluctuations and lower volatility. Finally, the determination unit intelligently determines and selects a plurality of second transportation channels based on the fluctuation screening index of all first transportation channels, thereby ensuring that the selected transportation channels have higher stability and reliability during operation. Through this method, the second screening module can effectively improve the intelligent level of channel selection, reduce path congestion and low efficiency caused by fluctuations, and ultimately improve the adaptability, stability and overall transportation efficiency of the logistics system in a dynamic environment.

[0046] Further, by determining the first transportation channel with a fluctuation screening index less than a preset screening index threshold as the second transportation channel, a transportation channel with higher stability and smaller fluctuation can be accurately screened in dynamic operation, effectively avoiding misjudgment caused by a single fluctuation index, and ensuring that the selected transportation channel is more reliable and sustainable in complex environments, significantly improving the intelligent judgment ability of the system for the transportation channel, reducing path conflicts or congestion caused by excessive fluctuation, and thus improving the transportation efficiency and resource utilization of the logistics and warehousing system.

[0047] Further, through the cooperation of the candidate unit and the second final determination unit, the selection of the transportation channel can be dynamically optimized according to the changes in the coincidence rate and the obstacle avoidance frequency. The candidate unit first compares the coincidence rate with a preset coincidence rate threshold to determine the second transportation channel with a lower coincidence rate as a temporary channel, and further screens the temporary channel in combination with the obstacle avoidance frequency to determine the final transportation channel. When the coincidence rate is high, the second final determination unit further determines whether the second transportation channel is still suitable as the final transportation channel according to the historical transportation completion time and the obstacle avoidance frequency of the next stage, so as to realize more accurate channel adjustment in a dynamically changing transportation environment, improve the adaptability and flexibility of the system in complex environments, timely adjust the transportation path, avoid congestion and efficiency loss caused by high coincidence rate channels, and thus improve the overall transportation efficiency and utilization of warehouse resources.

[0048] Further, through the cooperative work of the frequency screening sub-unit and the time screening sub-unit, the optimal final transportation channel can be efficiently and accurately screened in a complex dynamic environment. First, the frequency screening sub-unit determines whether the average value of the obstacle avoidance frequency within a preset reselection time period is less than a preset obstacle avoidance frequency threshold, and if so, determines the second transportation channel as the first reselected channel, ensuring that the transportation channel can provide a relatively stable and smooth transportation path in the case of low obstacle avoidance frequency, avoiding excessive frequent obstacle avoidance interference. Next, the time screening sub-unit is connected with the frequency screening sub-unit and further screens the optimal final transportation channel according to the transportation completion time of each first reselected channel. In this process, the transportation completion time as a time parameter can effectively reflect the efficiency and reliability of the channel, thereby optimizing the transportation path and avoiding inefficient or time-consuming channels. Through this multi-dimensional screening method considering obstacle avoidance frequency and time, the second final determination unit provides flexible and accurate path adjustment capability in a complex logistics and warehousing environment, ensuring the balance between efficiency and stability of the transportation channel, and thus improving the overall efficiency and adaptability of the logistics transportation system.

[0049] Further, through the coordination of the deviation calculation unit and the adjustment unit, fine control of dynamic adjustment of the transportation system during operation is realized. First, the deviation calculation unit calculates the relative deviation between the jam rate and the preset jam rate threshold within the preset transportation time, obtaining a jam rate deviation set, and also calculates the relative deviation between the deceleration frequency and the preset deceleration frequency threshold, obtaining a deceleration frequency deviation set, real-time monitoring the jam condition and deceleration frequency in the transportation channel, capturing the deviation in system operation, and ensuring that the system can flexibly respond to environmental changes in actual operation. Next, the adjustment unit adjusts the preset transportation selection threshold or the preset driving speed according to these deviation sets, thereby optimizing the selection of the transportation channel and the driving speed of the robot, avoiding the decline in transportation efficiency caused by excessive jamming or deceleration. Through this dynamic adjustment method based on real-time feedback, the adjustment module not only enhances the adaptability of the logistics and warehousing system in complex environments, but also significantly improves the utilization rate of resources and transportation efficiency, ensuring that the system always maintains efficient and stable operation under various changing conditions.

[0050] Further, through the coordination of the correlation calculation sub-unit and the first and second adjustment sub-units, precise adjustment of the system operating state is realized. First, the correlation calculation sub-unit calculates the Pearson correlation coefficient between the jam rate deviation set and the deceleration frequency deviation set, thereby obtaining the set correlation degree, which can quantify the relationship between jamming and deceleration, providing a clear basis for subsequent adjustment. Next, the first adjustment sub-unit determines according to the sign and size of the set correlation degree. When the set correlation degree is positive and greater than the preset correlation degree threshold, it indicates that the jam and deceleration frequency have similar trends, at which time the preset transportation selection threshold is adjusted to ensure that the system can more flexibly respond to changes in the jam condition, thereby optimizing path selection; on the contrary, when the set correlation degree is negative and the absolute value is greater than the threshold, it indicates that there is an opposite trend between jamming and deceleration frequency, at which time the preset driving speed is adjusted by the second adjustment sub-unit to avoid low transportation efficiency due to excessive deceleration. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A schematic diagram of the logistics and warehousing management system based on intelligent algorithms of the present embodiment;

[0052] Figure 2 A determination logic diagram for the first screening module to determine the first storage location of the present embodiment;

[0053] Figure 3 A determination logic diagram for the first determination unit to determine the first transportation channel of the present embodiment;

[0054] Figure 4 A determination logic diagram for the adjustment unit to adjust the preset transportation selection threshold or the preset driving speed of the present embodiment. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present application clearer, the following further describes the present application with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0056] The preferred embodiments of the present application are described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and not to limit the protection scope of the present application.

[0057] Please refer to Figure 1 The present embodiment provides a logistics warehouse management system based on intelligent algorithm, which comprises:

[0058] The acquisition module is used to acquire in real time the loading and unloading complexity of each candidate storage location in the cargo carrying process, the order urgency, the channel blockage rate of the candidate transportation channel between each candidate storage location and each delivery port, the obstacle avoidance frequency and the deceleration frequency of the cargo carrying robot running at a preset driving speed in the candidate transportation channel;

[0059] The preliminary screening module is connected with the acquisition module and is used to determine a plurality of first storage locations according to the order urgency;

[0060] The first screening module is connected with the acquisition module and the preliminary screening module respectively, and is used to determine the comprehensive evaluation index of the candidate transportation channel according to the loading and unloading complexity, the channel blockage rate and the obstacle avoidance frequency of each first storage location, and to determine the size relationship between the comprehensive evaluation index and a preset transportation selection threshold value, so as to determine a plurality of first transportation channels;

[0061] The second screening module is connected with the acquisition module and the first screening module respectively, and is used to determine a plurality of second transportation channels according to the overlap rate and the deceleration frequency of each first transportation channel within a preset selection time length;

[0062] The final screening module is connected with the acquisition module and the second screening module respectively, and is used to determine the final transportation channel according to the overlap rate and the obstacle avoidance frequency of the second transportation channel of any first storage location and the remaining first storage locations within a preset screening range of the storage location;

[0063] The adjustment module is connected with the acquisition module and the final screening module respectively, and is used to adjust the preset transportation selection threshold value or the preset driving speed according to the channel blockage rate and the deceleration frequency within a preset transportation time length after determining the final transportation channel.

[0064] In this embodiment, the intelligent algorithm-based logistics and warehousing management system is applied to a large-scale intelligent logistics and warehousing center. The center has multiple independent handling channels and hundreds of storage locations. The handling robots run at high speed between multiple shipping outlets and dynamically select paths in real time based on order priority and channel status. The system continuously collects data such as channel congestion rate, obstacle avoidance frequency, deceleration frequency, and cargo loading and unloading complexity through a sensor network. Combined with preset thresholds and filtering rules, the system intelligently schedules robot paths and speeds to ensure efficient and safe completion of cargo handling tasks, while adapting to dynamic changes in the warehousing environment.

[0065] In this embodiment, the data acquisition module specifically uses LiDAR and ultrasonic sensors installed at each candidate transport channel and each cargo location to scan and generate 3D point cloud data of the channels in real time to measure the channel congestion rate. A vision camera and deep learning algorithm are configured on the handling robot to identify the type and size of goods to calculate the loading and unloading complexity. The robot's built-in speed sensor, acceleration sensor, and obstacle avoidance sensor record the robot's speed changes, obstacle avoidance attempts, and deceleration frequency in real time. Furthermore, the module obtains order priorities in real time through the warehouse management system interface, thereby simultaneously collecting multiple key parameters such as loading and unloading complexity, order urgency, congestion rate, obstacle avoidance frequency, and deceleration frequency.

[0066] The preset travel speed is the basic operating speed of the handling robot, which depends on the aisle width, cargo type, and safety requirements. It is typically set between 0.5 m / s and 2.0 m / s; in this embodiment, it is set to 1.2 m / s to balance transportation efficiency and safety. The preset transportation selection threshold is a reference standard for judging the quality of a transportation aisle, depending on loading and unloading complexity, congestion rate, and obstacle avoidance frequency. It is typically set between 0.3 and 0.7; in this embodiment, it is set to 0.5 to effectively filter out the optimal transportation aisle. The preset selection duration is the time window used to calculate changes in aisle performance, depending on warehouse size and transportation frequency. The preset timeout is set between 5 and 15 minutes, and in this embodiment it is set to 10 minutes to balance data acquisition accuracy and response speed. The preset filtering range is a reference range for determining the final transportation channel, which depends on the density of cargo locations and the channel layout. It is usually set to 1 to 3 times the distance between adjacent cargo locations, and in this embodiment it is set to 2 times the distance to improve the accuracy of path selection. The preset transportation duration is a reference period for subsequent channel performance analysis, which depends on the length of the transportation task and the complexity of the channel. It is usually set between 15 and 30 minutes, and in this embodiment it is set to 20 minutes to effectively reflect the channel operation status and support dynamic adjustment.

[0067] Through multi-layered screening and dynamic adjustment, the system achieves efficient selection and optimization of warehousing and transportation routes. It comprehensively considers multiple key parameters, including loading and unloading complexity, order urgency, channel congestion rate, obstacle avoidance frequency, and deceleration frequency, and performs joint analysis based on their temporal and spatial variation characteristics. The changes in these parameters are interrelated; for example, congestion rate and deceleration frequency often fluctuate synchronously, loading and unloading complexity and obstacle avoidance frequency have a synergistic effect, and order urgency prioritizes the route selection order. By comprehensively judging these interrelated characteristics, the system not only improves the accuracy of transportation channel selection but also adjusts thresholds and travel speeds in real time during transportation. This effectively improves overall warehousing and transportation efficiency, reduces delay risks, and optimizes resource utilization. It effectively solves the problems of high route congestion rates and low response speeds caused by over-reliance on static threshold judgments and manual decision-making in complex and ever-changing scenarios.

[0068] Please see Figure 2 As shown, this is the determination logic diagram of the first storage location by the initial screening module in this embodiment. In this embodiment, the first storage location is determined by the initial screening module when the urgency of the order is greater than the preset urgency threshold.

[0069] The preset urgency threshold is a value used to determine whether the urgency of an order meets the priority processing standard. It depends on the system's requirements for order processing timeliness and resource allocation strategy, and is usually set between 0 and 1. In this embodiment, it is set to 0.6, which ensures that when the urgency of an order exceeds this threshold, the system automatically determines the corresponding storage location of the order as the first storage location, thereby prioritizing the allocation of transportation resources and improving overall operational efficiency and response speed.

[0070] By comparing the urgency of orders with preset urgency thresholds, priority storage locations are effectively selected. This selection mechanism enables the system to prioritize time-sensitive tasks with limited handling resources, rationally allocate channels and robot scheduling, thereby improving overall transportation efficiency and reducing resource waste caused by delays. At the same time, it ensures the timely completion of high-priority orders and enhances the responsiveness and stability of the warehousing system.

[0071] Specifically, the first screening module includes:

[0072] The first normalization processing unit is used to normalize the loading and unloading complexity, the channel congestion rate, and the obstacle avoidance frequency of each of the first cargo locations within a preset first screening time, so as to obtain a complexity normalized dataset, a congestion rate normalized dataset, and an obstacle avoidance frequency normalized dataset.

[0073] The first determination unit, which is connected to the first normalization processing unit, is used to determine a number of first transportation channels based on the loading and unloading complexity normalized dataset, the channel congestion rate normalized dataset, the obstacle avoidance frequency normalized dataset, and the preset transportation selection threshold.

[0074] The preset first screening time refers to a fixed time period in the logistics and warehousing management system used for the first screening module operation. It depends on the system's assessment of factors such as the complexity of loading and unloading at the storage locations, the congestion rate of transportation channels, and the frequency of obstacle avoidance, and is typically set between 5 minutes and 2 hours. In this embodiment, it is set to 30 minutes, which ensures that priority storage locations are effectively screened within this time frame and provides sufficient time for subsequent path planning and robot scheduling.

[0075] By uniformly normalizing the loading and unloading complexity, channel congestion rate, and obstacle avoidance frequency within a preset first screening period, the differences in the dimensions and value ranges of different indicators are effectively eliminated, improving the comparability between indicators and the scientific nature of weight allocation. On this basis, the first judgment unit combines the normalized dataset with the preset transportation selection threshold to make a comprehensive judgment, which can more accurately and quickly identify the first transportation channel that meets the requirements. This allows unsuitable channels to be eliminated in the early stages, reducing the subsequent computational pressure and improving the stability and accuracy of channel selection. Ultimately, this achieves efficient integration and intelligent decision-making of multi-factor and multi-dimensional information in the logistics and warehousing system, significantly improving overall transportation efficiency and resource utilization.

[0076] Please see Figure 3 As shown, this is the determination logic diagram of the first determination unit in this embodiment determining the first transportation channel. The first determination unit includes:

[0077] The first index calculation subunit is used to perform a weighted summation of the average value of the normalized loading and unloading complexity dataset, the average value of the normalized channel congestion rate dataset, and the average value of the normalized obstacle avoidance frequency dataset to form a comprehensive evaluation index for each of the candidate transportation channels, Q = a × A + b × B + h × H, where Q is the comprehensive evaluation index, a is the preset loading and unloading complexity weight, A is the average value of the normalized loading and unloading complexity dataset, b is the preset channel congestion rate weight, B is the average value of the normalized channel congestion rate dataset, H is the preset obstacle avoidance frequency weight, and H is the average value of the normalized obstacle avoidance frequency dataset.

[0078] The first determination subunit, which is connected to the first index calculation subunit, is used to determine the candidate transportation channel as the first transportation channel when the comprehensive evaluation index is greater than the preset transportation selection threshold, so as to determine a plurality of first transportation channels.

[0079] The preset loading and unloading complexity weight refers to the importance coefficient of loading and unloading complexity in the comprehensive evaluation of a logistics and warehousing management system. It depends on the system's assessment of factors such as cargo characteristics and loading / unloading difficulty, and is typically set between 0 and 1. In this embodiment, it is set to 0.4, ensuring that the impact of loading and unloading complexity on transportation channel selection is fully reflected in the comprehensive evaluation. The preset channel congestion rate weight refers to the importance coefficient of channel congestion rate in the comprehensive evaluation of a logistics and warehousing management system. It depends on the system's emphasis on transportation channel accessibility, and is typically set between 0 and 1. In this embodiment, it is set to 0.3, reasonably incorporating the impact of channel congestion on path selection into the comprehensive evaluation. The preset obstacle avoidance frequency weight refers to the importance coefficient of obstacle avoidance frequency in the comprehensive evaluation of a logistics and warehousing management system. It depends on the system's consideration of the obstacle avoidance efficiency and safety of the handling robot, and is typically set between 0 and 1. In this embodiment, it is set to 0.3, reasonably considering the impact of obstacle avoidance frequency on transportation path selection in the comprehensive evaluation, ensuring the smooth operation of the handling robot.

[0080] By coordinating the first index calculation subunit and the first judgment subunit, the multi-dimensional indicators such as loading and unloading complexity, channel congestion rate, and obstacle avoidance frequency are normalized and then weighted and summed to form a comprehensive evaluation index. Combined with preset transportation selection thresholds, the index is automatically judged. This not only achieves unified quantification and standardization of multi-source data, but also significantly improves the accuracy and real-time performance of transportation channel selection. It avoids the one-sidedness and lag caused by relying on a single indicator or human experience in traditional technologies. At the same time, the unit can dynamically adjust the weights and thresholds according to the actual operation, thereby maintaining efficient and stable decision-making capabilities in multi-position, multi-channel, and complex warehousing environments. This effectively reduces the subsequent computational load and improves the intelligence level and execution efficiency of the overall path optimization.

[0081] Specifically, the second filtering module includes:

[0082] The overlap rate fluctuation value calculation unit is used to calculate the standard deviation of the overlap rate at each time from the initial time to the preset selected time period, and obtain several overlap rate fluctuation values.

[0083] The deceleration frequency fluctuation value calculation unit is used to calculate the standard deviation of the deceleration frequency at each time from the initial time to the preset selected time period, and obtain several deceleration frequency fluctuation values.

[0084] The second normalization processing unit is connected to the overlap rate fluctuation value calculation unit and the deceleration frequency fluctuation value calculation unit respectively, and is used to normalize the several overlap rate fluctuation values ​​and the deceleration frequency fluctuation values ​​respectively to obtain the overlap fluctuation normalized dataset and the deceleration fluctuation normalized dataset.

[0085] The second index calculation unit, connected to the second normalization processing unit, is used to perform a weighted summation of the average value of the normalized dataset of overlap rate fluctuation values ​​and the average value of the normalized dataset of deceleration frequency fluctuation values ​​to obtain the fluctuation screening index of each of the first transportation channels, P=d×D+e×E, where P is the fluctuation screening index, d is the preset overlap rate weight, D is the average value of the normalized dataset of overlap rate fluctuation values, e is the preset deceleration frequency weight, and E is the average value of the normalized dataset of deceleration frequency fluctuation values.

[0086] A determination unit, connected to the second index calculation unit, is used to determine several second transportation channels based on the fluctuation screening index of all first transportation channels.

[0087] Overlap rate, in a logistics and warehousing management system, refers to the degree of overlap between different first transport channels. Specifically, the overlap rate reflects whether different first transport channels share the same path or resources within a preset selection time. Overlap rate = length of the shared first transport channel ÷ total length of the first transport channels.

[0088] A higher overlap rate indicates more resource sharing between different primary transport routes, which may lead to route congestion or other transport delays. Generally, a lower overlap rate indicates more optimized route selection, reducing conflicts between different tasks.

[0089] By introducing the calculation, normalization, and comprehensive evaluation of overlap rate fluctuation values ​​and deceleration frequency fluctuation values, the selection of transportation corridors can be optimized more comprehensively. First, the overlap rate fluctuation value calculation unit and the deceleration frequency fluctuation value calculation unit calculate the standard deviation of the overlap rate and the standard deviation of the deceleration frequency at each moment within the preset selection period from the initial time, respectively, thus obtaining several overlap rate fluctuation values ​​and deceleration frequency fluctuation values ​​to capture the fluctuation characteristics of the transportation corridor during operation. Next, the second normalization processing unit normalizes these fluctuation values, allowing indicators of different dimensions to be compared under a unified standard, providing a fair and comparable basis for subsequent screening. Through the second index calculation unit, the weighted sum of the average values ​​of the normalized overlap fluctuation dataset and the deceleration fluctuation dataset forms a fluctuation screening index. This process comprehensively considers the stability and operational efficiency of the corridor and prioritizes corridors with smaller fluctuations and lower volatility. Finally, the determination unit intelligently determines and screens several second transportation corridors based on the fluctuation screening indices of all first transportation corridors, thereby ensuring that the selected transportation corridors have higher stability and reliability during operation. In this way, the second screening module can effectively improve the intelligence level of channel selection, reduce path congestion and inefficiency caused by fluctuations, and ultimately improve the adaptability, stability and overall transportation efficiency of the logistics system in dynamic environments.

[0090] Specifically, the determining unit is used to determine the first transportation channel whose fluctuation screening index is less than a preset screening index threshold as the second transportation channel, so as to determine a number of second transportation channels.

[0091] The preset screening index threshold is a fixed threshold used in a logistics and warehousing management system to determine whether to include the first transportation channel in the screening range of the second transportation channel. It depends on the system's comprehensive evaluation criteria for transportation channels and is typically set between 0.1 and 1.0. In this embodiment, it is set to 0.5 to ensure that the first transportation channel is only identified as the second transportation channel when the fluctuation screening index is less than this threshold, thus optimizing the channel screening process and improving the system's selection efficiency and accuracy.

[0092] By identifying the first transportation channel with a fluctuation screening index lower than a preset screening index threshold as the second transportation channel, the system can accurately select transportation channels with higher stability and lower fluctuations during dynamic operation. This effectively avoids misjudgment caused by a single fluctuation indicator. Furthermore, by comprehensively evaluating fluctuation characteristics, the system ensures that the selected transportation channels are more reliable and sustainable in complex environments. This significantly improves the system's intelligent judgment ability on transportation channels, reduces path conflicts or congestion caused by excessive fluctuations, and thus improves the transportation efficiency and resource utilization of the logistics warehousing system.

[0093] Specifically, the final filtering module includes:

[0094] The candidate unit is used to determine that the second transportation channel is a temporary channel when the overlap rate is less than a preset overlap rate threshold, so as to identify a number of temporary channels;

[0095] The first final determination unit is connected to the candidate unit and is used to determine the temporary channel corresponding to the minimum value of the obstacle avoidance frequency as the final transportation channel when the average value of the obstacle avoidance frequency within the preset reselection time is less than the preset obstacle avoidance frequency threshold.

[0096] The second final determination unit, which is connected to the candidate unit, is used to determine the final transportation channel based on the transportation completion time of the previous preset historical reselection time of the second transportation channel and the obstacle avoidance frequency in the next preset reselection time when the overlap rate is greater than the preset overlap rate threshold.

[0097] Overlap rate refers to the proportion in a logistics and warehouse management system where the second transport channels corresponding to different storage locations use the same route. R F,G =|C F ∩C G | / |C F ∪C G |, where C FC is the set of second transport channels for the first cargo location F. G R is the set of second transport channels for the first cargo location G. F,G Let R be the overlap rate of the second transport channel between the two cargo locations. F,G The value range is [0,1]. The larger the value, the higher the degree of sharing of the second transportation channel between the two cargo locations.

[0098] When the overlap rate is low, it means that the transportation channels of each cargo location are independent of each other and the probability of overlap is small; when the overlap rate is high, it means that the transportation channels of multiple cargo locations have a high probability of overlap, which may lead to traffic congestion or a decrease in path efficiency.

[0099] The preset overlap rate threshold is a critical value for determining whether a transportation channel needs to be reselected. It depends on the degree of overlap of transportation channels corresponding to multiple cargo locations and is usually set between 0 and 1. In this embodiment, it is set to 0.75, which can trigger a reselection operation when the overlap rate is higher than this value, thereby avoiding excessive congestion caused by multiple cargo locations on the same path and thus optimizing logistics path selection and transportation efficiency.

[0100] The preset obstacle avoidance frequency threshold is the standard for the logistics and warehousing management system to judge the obstacle avoidance capability of temporary channels. It depends on factors such as the number of handling robots, channel width, and obstacle density in the warehousing environment. It is usually set between 0.3 times / minute and 1.5 times / minute. In this embodiment, it is set to 0.8 times / minute, which can quickly identify transportation channels with low obstacle avoidance frequency and more stable operation within the preset reselection time, thereby determining the final reselection channel and ensuring the efficiency and reliability of the final transportation channel.

[0101] The preset reselection time refers to the time range that the system presets when performing reselection. It depends on factors such as the complexity of the channel transportation, the amount of goods, and the response speed of the handling robot. It is usually set between 30 seconds and 5 minutes. In this embodiment, it is set to 2 minutes, which can ensure that the obstacle avoidance frequency and transportation completion time are evaluated within a reasonable time, and the transportation channel is adjusted in time to optimize the overall efficiency of the warehouse management system.

[0102] Through the collaboration of the candidate unit and the second final determination unit, the selection of transportation channels can be dynamically optimized based on changes in overlap rate and obstacle avoidance frequency. The candidate unit first compares the overlap rate with a preset overlap rate threshold, identifying the second transportation channel with a lower overlap rate as a temporary channel. This temporary channel is then further filtered based on the obstacle avoidance frequency to determine the final transportation channel. When the overlap rate is high, the second final determination unit further assesses whether the second transportation channel is still suitable as the final transportation channel based on historical transportation completion times and the obstacle avoidance frequency for the next stage. This allows for more precise channel adjustments in a dynamically changing transportation environment, improving the system's adaptability and flexibility in complex environments, timely adjusting transportation routes, avoiding congestion and efficiency losses caused by high overlap rate channels, and ultimately improving overall transportation efficiency and the utilization rate of warehousing resources.

[0103] Specifically, the second final determination unit includes:

[0104] A frequency filtering subunit is used to determine the second transport channel as the first reselection channel when the average value of the obstacle avoidance frequency within the preset reselection time is less than a preset obstacle avoidance frequency threshold, thereby determining a plurality of first reselection channels;

[0105] A time-filtering subunit, connected to the frequency-filtering subunit, is used to determine the final transportation channel based on the transportation completion time of each of the first reselection channels.

[0106] By working collaboratively with the frequency selection subunit and the time selection subunit, the optimal final transportation channel can be efficiently and accurately selected in complex dynamic environments. First, the frequency selection subunit judges the optimal channel based on the average obstacle avoidance frequency within a preset reselection time. If this value is less than a preset obstacle avoidance frequency threshold, the second transportation channel is designated as the first reselection channel, ensuring a relatively stable and smooth transportation path under low obstacle avoidance frequency conditions, avoiding excessively frequent obstacle avoidance interference. Next, the time selection subunit, connected to the frequency selection subunit, further selects the optimal final transportation channel based on the transportation completion time of each first reselection channel. In this process, the transportation completion time, as a time parameter, effectively reflects the channel's utilization efficiency and reliability, thereby optimizing the transportation path and preventing inefficient or excessively time-consuming channels from being selected. Through this multi-dimensional selection method that comprehensively considers obstacle avoidance frequency and time, the second final determination unit provides flexible and precise path adjustment capabilities in complex logistics and warehousing environments, ensuring a balance between efficiency and stability in transportation channels, thereby improving the overall efficiency and adaptability of the logistics transportation system.

[0107] Specifically, the adjustment module includes:

[0108] The deviation calculation unit is used to calculate the relative deviation between each of the congestion rates and the preset congestion rate threshold within the preset transportation time to obtain a congestion rate deviation set, and to calculate the relative deviation between each of the deceleration frequencies and the preset deceleration frequency threshold within the preset transportation time to obtain a deceleration frequency deviation set.

[0109] An adjustment unit, connected to the deviation calculation unit, is used to adjust the preset transportation selection threshold or the preset driving speed based on the congestion rate deviation set and the deceleration frequency deviation set.

[0110] The preset congestion rate threshold is a standard value used to determine whether a transportation channel is in a high state of congestion. It depends on the system's tolerance for congestion in the transportation channel and is usually set between 0 and 1. In this embodiment, it is set to 0.8, which can ensure that when the congestion rate of the transportation channel exceeds this threshold, the system can dynamically adjust the transportation selection threshold or driving speed according to the change in the congestion rate, thereby avoiding excessive congestion and improving transportation efficiency.

[0111] The preset deceleration frequency threshold is a standard value used to determine whether the handling robot in the transport channel needs to decelerate frequently. It depends on the system's tolerance for deceleration behavior and the requirements for transport efficiency. It is usually set between 0 and 1. In this embodiment, it is set to 0.5, which can ensure that when the deceleration frequency exceeds this threshold, the system can dynamically adjust the driving speed or transport selection threshold according to the change of deceleration frequency, thereby optimizing the transport path and improving the overall transport efficiency.

[0112] Through the coordinated operation of the deviation calculation unit and the adjustment unit, refined control of the transportation system's dynamic adjustment during operation is achieved. First, the deviation calculation unit calculates the relative deviation between the congestion rate within a preset transportation time and a preset congestion rate threshold, obtaining a congestion rate deviation set. Similarly, it calculates the relative deviation between the deceleration frequency and a preset deceleration frequency threshold, obtaining a deceleration frequency deviation set. This allows for real-time monitoring of congestion status and deceleration frequency in the transportation channels, capturing deviations in system operation and ensuring the system can flexibly respond to environmental changes during actual operation. Next, the adjustment unit adjusts preset transportation selection thresholds or preset travel speeds based on these deviation sets, thereby optimizing the selection of transportation channels and the robot's travel speed, avoiding a decrease in transportation efficiency due to congestion or excessive deceleration. Through this dynamic adjustment method based on real-time feedback, the adjustment module not only enhances the adaptability of the logistics and warehousing system in complex environments but also significantly improves resource utilization and transportation efficiency, ensuring the system maintains efficient and stable operation under various changing conditions.

[0113] Please see Figure 4 As shown, it is the logic diagram of the adjustment unit in this embodiment determining whether to adjust the preset transportation selection threshold or the preset driving speed.

[0114] The adjustment unit includes:

[0115] The correlation calculation subunit is used to calculate the Pearson correlation coefficient between the congestion rate deviation set and the deceleration frequency deviation set to obtain the set correlation.

[0116] The first adjustment subunit, which is connected to the correlation calculation subunit, is used to increase the preset transportation selection threshold according to the set correlation and the preset correlation threshold when the set correlation is positive and the set correlation is greater than the preset correlation threshold. U'=U×[1+s×(Y-Y0) / Y0], where s is the first preset threshold adjustment coefficient, U' is the adjusted preset transportation selection threshold, U is the original preset transportation selection threshold, Y is the set correlation, and Y0 is the preset correlation threshold.

[0117] The second adjustment subunit, which is connected to the correlation calculation subunit, is used to reduce the preset driving speed according to the absolute value of the set correlation and the preset correlation threshold when the set correlation is negative and the absolute value of the set correlation is greater than the preset correlation threshold. M'=M×[1-n×(︱Y︱-Y0) / Y0], where n is the second preset threshold adjustment coefficient, M' is the preset driving speed after adjustment, and M is the preset driving speed before adjustment.

[0118] The preset correlation threshold is a standard value used to judge the strength of the correlation between congestion rate deviation and deceleration frequency deviation. It depends on the system's tolerance for the correlation between the two and is usually set between 0 and 1. In this embodiment, it is set to 0.8, which ensures that when the correlation between the two is high, the system can take appropriate dynamic adjustment measures to optimize the transportation route and improve efficiency. The first preset threshold adjustment coefficient is a coefficient used to increase the preset transportation selection threshold when the set correlation is positive and greater than the preset correlation threshold. It depends on the sensitivity of the set correlation to transportation channel selection and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can quickly increase the preset transportation selection threshold and thus select better transportation channels. The second preset threshold adjustment coefficient is a coefficient used to decrease the preset driving speed when the set correlation is negative and its absolute value is greater than the preset correlation threshold. It depends on the sensitivity of the absolute value of the set correlation to driving speed adjustment and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.2, which can quickly reduce the preset driving speed to ensure transportation safety and system stability.

[0119] Through the coordinated operation of the correlation calculation subunit and the first and second adjustment subunits, precise adjustment of the system's operating status is achieved. First, the correlation calculation subunit calculates the Pearson correlation coefficient between the congestion rate deviation set and the deceleration frequency deviation set, thus obtaining the set correlation, which quantifies the relationship between congestion and deceleration, providing a clear basis for subsequent adjustments. Next, the first adjustment subunit makes a judgment based on the sign and magnitude of the set correlation. When the set correlation is positive and greater than the preset correlation threshold, it indicates that congestion and deceleration frequency have similar trends. In this case, the preset transport selection threshold is adjusted to ensure the system can more flexibly respond to changes in congestion conditions, thereby optimizing route selection. Conversely, when the set correlation is negative and its absolute value is greater than the threshold, it indicates that congestion and deceleration frequency have opposite trends. In this case, the second adjustment subunit adjusts the preset driving speed to avoid low transport efficiency due to excessive deceleration.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A logistics and warehousing management system based on intelligent algorithms, characterized in that, include: The data acquisition module is used to acquire in real time the loading and unloading complexity of each candidate storage location, the urgency of the order, the channel congestion rate of each candidate storage location and each shipping outlet, and the obstacle avoidance frequency and deceleration frequency of the handling robot running at a preset speed in the candidate transportation channel. A preliminary screening module, which is connected to the acquisition module, is used to determine several first storage locations based on the urgency of the orders; The first screening module is connected to the acquisition module and the initial screening module respectively. It is used to determine the comprehensive evaluation index of the candidate transportation channel based on the loading and unloading complexity, the channel blockage rate and the obstacle avoidance frequency of each first cargo location, and to determine the relationship between the comprehensive evaluation index and the preset transportation selection threshold in order to determine a number of first transportation channels. The second screening module is connected to the acquisition module and the first screening module respectively, and is used to determine a number of second transportation channels based on the overlap rate of each of the first transportation channels within a preset selection time and the deceleration frequency. The final screening module is connected to the acquisition module and the second screening module respectively, and is used to determine the final transportation channel based on the overlap rate of the second transportation channel of any first cargo location and the remaining first cargo locations within the preset screening range of the cargo location and the obstacle avoidance frequency. An adjustment module, which is connected to the acquisition module and the final filtering module respectively, is used to adjust the preset transportation selection threshold or adjust the preset driving speed based on the channel congestion rate and the deceleration frequency within a preset transportation time after the final transportation channel is determined.

2. The logistics and warehousing management system based on intelligent algorithms according to claim 1, characterized in that, The first storage location is determined based on the initial screening module when the urgency of the order is greater than a preset urgency threshold.

3. The logistics and warehousing management system based on intelligent algorithms according to claim 2, characterized in that, The first filtering module includes: The first normalization processing unit is used to normalize the loading and unloading complexity, the channel congestion rate, and the obstacle avoidance frequency of each of the first cargo locations within a preset first screening time, so as to obtain a complexity normalized dataset, a congestion rate normalized dataset, and an obstacle avoidance frequency normalized dataset. The first determination unit, which is connected to the first normalization processing unit, is used to determine a number of first transportation channels based on the complexity normalized dataset, the congestion rate normalized dataset, the obstacle avoidance frequency normalized dataset, and the preset transportation selection threshold.

4. The logistics and warehousing management system based on intelligent algorithms according to claim 3, characterized in that, The first determination unit includes: The first index calculation subunit is used to perform a weighted summation of the average value of the normalized dataset of loading and unloading complexity, the average value of the normalized dataset of channel congestion rate, and the average value of the normalized dataset of obstacle avoidance frequency, to form a comprehensive evaluation index for each of the candidate transportation channels. The first determination subunit, which is connected to the first index calculation subunit, is used to determine the candidate transportation channel as the first transportation channel when the comprehensive evaluation index is greater than the preset transportation selection threshold, so as to determine a plurality of first transportation channels.

5. The logistics and warehousing management system based on intelligent algorithms according to claim 4, characterized in that, The second filtering module includes: The overlap rate fluctuation value calculation unit is used to calculate the standard deviation of the overlap rate at each time from the initial time to the preset selected time period, and obtain several overlap rate fluctuation values. The deceleration frequency fluctuation value calculation unit is used to calculate the standard deviation of the deceleration frequency at each time from the initial time to the preset selected time period, and obtain several deceleration frequency fluctuation values. The second normalization processing unit is connected to the overlap rate fluctuation value calculation unit and the deceleration frequency fluctuation value calculation unit respectively, and is used to normalize the several overlap rate fluctuation values ​​and the deceleration frequency fluctuation values ​​respectively to obtain the overlap fluctuation normalized dataset and the deceleration fluctuation normalized dataset. The second index calculation unit, which is connected to the second normalization processing unit, is used to perform a weighted summation of the average value of the overlapping fluctuation normalization dataset and the average value of the deceleration fluctuation normalization dataset to obtain the fluctuation screening index of each of the first transportation channels. A determination unit, connected to the second index calculation unit, is used to determine several second transportation channels based on the fluctuation screening index of all first transportation channels.

6. The logistics and warehousing management system based on intelligent algorithms according to claim 5, characterized in that, The determining unit is used to identify the first transportation channel whose fluctuation screening index is less than a preset screening index threshold as the second transportation channel, thereby determining a number of second transportation channels.

7. The logistics and warehousing management system based on intelligent algorithms according to claim 6, characterized in that, The final filtering module includes: The candidate unit is used to determine that the second transportation channel is a temporary channel when the overlap rate is less than a preset overlap rate threshold, so as to identify a number of temporary channels; The first final determination unit is connected to the candidate unit and is used to determine the temporary channel corresponding to the minimum value of the obstacle avoidance frequency as the final transportation channel when the average value of the obstacle avoidance frequency within the preset reselection time is less than the preset obstacle avoidance frequency threshold. The second final determination unit, which is connected to the candidate unit, is used to determine the final transportation channel based on the transportation completion time of the previous preset historical reselection time of the second transportation channel and the obstacle avoidance frequency in the next preset reselection time when the overlap rate is greater than the preset overlap rate threshold.

8. The logistics and warehousing management system based on intelligent algorithms according to claim 7, characterized in that, The second final determination unit includes: A frequency filtering subunit is used to determine the second transport channel as the first reselection channel when the average value of the obstacle avoidance frequency within the preset reselection time is less than a preset obstacle avoidance frequency threshold, thereby determining a plurality of first reselection channels; A time-filtering subunit, connected to the frequency-filtering subunit, is used to determine the final transportation channel based on the transportation completion time of each of the first reselection channels.

9. The logistics and warehousing management system based on intelligent algorithms according to claim 8, characterized in that, The adjustment module includes: The deviation calculation unit is used to calculate the relative deviation between each of the congestion rates and the preset congestion rate threshold within the preset transportation time to obtain a congestion rate deviation set, and to calculate the relative deviation between each of the deceleration frequencies and the preset deceleration frequency threshold within the preset transportation time to obtain a deceleration frequency deviation set. An adjustment unit, connected to the deviation calculation unit, is used to adjust the preset transportation selection threshold or the preset driving speed based on the congestion rate deviation set and the deceleration frequency deviation set.

10. The logistics and warehousing management system based on intelligent algorithms according to claim 9, characterized in that, The adjustment unit includes: The correlation calculation subunit is used to calculate the Pearson correlation coefficient between the congestion rate deviation set and the deceleration frequency deviation set to obtain the set correlation. The first adjustment subunit, which is connected to the relevance calculation subunit, is used to adjust the preset transportation selection threshold according to the set relevance and the preset relevance threshold when the set relevance is positive and the set relevance is greater than the preset relevance threshold. The second adjustment subunit, which is connected to the correlation calculation subunit, is used to adjust the preset driving speed according to the absolute value of the set correlation and the preset correlation threshold when the set correlation is negative and the absolute value of the set correlation is greater than the preset correlation threshold.

Images