Collaborative configuration processing method and system for smart grain storage logistics platform

Abstract

The invention provides an intelligent grain storage logistics platform cooperative configuration processing method and system, and belongs to the technical field of logistics management, and the method comprises the steps: determining the transportation resource configuration priority corresponding to each cargo based on the historical logistics information of all cargoes in a grain storage logistics platform; and determining a cargo information processing priority corresponding to each cargo based on the historical retrieval information of all the cargoes in the grain storage logistics platform. And in response to the change of the logistics information of the target cargo, carrying out transportation resource scheduling based on the cargo type of the target cargo and the transportation resource configuration priority corresponding to the target cargo. The target goods are goods to be processed in the grain storage logistics platform. And in response to the change of the cargo information of the target cargo, performing cargo information management based on the cargo information processing priority corresponding to the target cargo. According to the smart grain storage logistics platform collaborative configuration processing method and system provided by the invention, the collaborative configuration efficiency of the logistics platform can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of logistics management technology, and more specifically, to a method and system for collaborative configuration processing of a smart grain storage logistics platform. Background Art

[0002] With the development of the logistics industry, the scale of grain storage logistics continues to expand, and the types of goods and transportation needs are becoming increasingly complex. The traditional way of relying on manual experience to allocate transportation resources and manage cargo information is no longer adaptable. In addition, past systems often focus on a single link and cannot achieve overall collaborative configuration, which restricts the efficient operation of grain storage logistics. Today, technologies such as big data and artificial intelligence are constantly maturing, and can collect and analyze massive amounts of historical logistics information and retrieval information, providing technical support for intelligent resource scheduling and information management, and promoting the application and development of collaborative configuration processing methods and systems for smart grain storage logistics platforms.

[0003] However, the existing logistics platform collaborative configuration processing technology still has the problem of low efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a collaborative configuration processing method and system for a smart grain storage logistics platform to improve the collaborative configuration efficiency of the logistics platform.

[0005] A first aspect of the disclosed embodiment provides a method for collaborative configuration processing of a smart grain storage logistics platform, which is applied to the grain storage logistics platform, and includes: Based on the historical logistics information of all goods on the grain storage logistics platform, the transportation resource allocation priority corresponding to each kind of goods is determined. Based on the historical retrieval information of all goods on the grain storage logistics platform, the cargo information processing priority corresponding to each kind of goods is determined.

[0006] In response to changes in the logistics information of the target goods, transportation resource scheduling is performed based on the cargo type of the target goods and the transportation resource configuration priority corresponding to the target goods. The target goods are goods to be processed in the grain storage logistics platform.

[0007] In response to changes in cargo information of the target cargo, cargo information management is performed based on a cargo information processing priority corresponding to the target cargo.

[0008] A second aspect of the disclosed embodiment provides a smart grain storage logistics platform collaborative configuration processing system, which is applied to the grain storage logistics platform, including: The priority determination module is used to determine the transportation resource configuration priority corresponding to each cargo based on the historical logistics information of all cargoes in the grain storage logistics platform. The cargo information processing priority corresponding to each cargo is determined based on the historical search information of all cargoes in the grain storage logistics platform.

[0009] The first response module is used to respond to changes in the logistics information of the target goods and perform transportation resource scheduling based on the cargo type of the target goods and the transportation resource configuration priority corresponding to the target goods. The target goods are goods to be processed in the grain storage logistics platform.

[0010] The second response module is used to manage the cargo information based on the cargo information processing priority corresponding to the target cargo in response to the cargo information of the target cargo changing.

[0011] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned smart grain storage and logistics platform collaborative configuration processing method are implemented.

[0012] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned smart grain storage and logistics platform collaborative configuration processing method are implemented.

[0013] The beneficial effects of the smart grain storage logistics platform collaborative configuration processing method and system provided by the embodiments of the present disclosure are: Determine the priority of transportation resource allocation based on historical logistics information, and accurately determine the transportation demand characteristics of different goods. For products with rapid order growth and high transportation timeliness requirements, prioritize high-quality transportation capacity and fast routes to reduce transportation delays, reduce costs, and improve overall logistics efficiency.

[0014] Based on historical retrieval information, the priority of cargo information processing is clarified to ensure that high-profile cargo information is updated and pushed in a timely manner. When the logistics or information of the target cargo changes, resources are quickly dispatched and information is managed according to the corresponding priority, so that the platform has flexible adaptability, adapts to complex and changing grain storage logistics scenarios, optimizes the operation process in all aspects, and improves customer satisfaction and industry competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of a process flow of a collaborative configuration processing method for a smart grain storage logistics platform provided in one embodiment of the present disclosure; Figure 2A structural block diagram of a collaborative configuration processing system for a smart grain storage logistics platform provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0018] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0019] Please refer to Figure 1 , Figure 1 A flow chart of a collaborative configuration processing method for a smart grain storage logistics platform provided in one embodiment of the present disclosure, the method may include S101 to S103.

[0020] S101: Determine the transportation resource allocation priority corresponding to each cargo based on the historical logistics information of all cargoes in the grain storage logistics platform. Determine the cargo information processing priority corresponding to each cargo based on the historical search information of all cargoes in the grain storage logistics platform.

[0021] In this embodiment, the historical logistics information includes data on various links of grain storage logistics in the past period of time, such as order data, transportation data, resource scheduling data, etc. Order information may include order quantity, order time, and customer information; transportation information may include route, duration, and tool type; resource scheduling information may include vehicle allocation, storage occupancy, use of loading and unloading equipment, and personnel scheduling. The priority of transportation resource allocation refers to the order in which different goods obtain transportation resources to ensure the efficient transportation of key goods. The priority of transportation resource allocation may include multiple priority levels.

[0022] Historical search information refers to the records of cargo information inquiries by platform users in the past. Historical search information may include search frequency, search timeliness, search role association, etc. Cargo information processing priority refers to the order of cargo information processing and the degree of resource investment. Cargo information processing priority may include multiple priority levels. Different cargo information processing priorities correspond to different information update frequencies, information push strategies, and storage areas.

[0023] For example, historical logistics information comprehensively reflects the past transportation status of goods, from the increase or decrease of orders, the difficulty of transportation routes to the utilization rate of resource allocation, and analyzes the differences in the demand for transportation resources of goods. For example, goods with continuously rising order volumes and often congested transportation routes are in more urgent need of priority guarantee of transportation capacity. Historical retrieval information can be combined with the attention paid by internal platform staff and users to goods information. Frequently retrieved goods with high timeliness requirements mean that their information needs to be processed quickly and accurately. Combining the two, the priority of transportation resource allocation and goods information processing can be accurately matched, so that logistics operations and information services are closely aligned with actual needs.

[0024] S102: In response to changes in the logistics information of the target goods, transport resource scheduling is performed based on the cargo type of the target goods and the transport resource configuration priority corresponding to the target goods. The target goods are goods to be processed in the grain storage logistics platform.

[0025] In this embodiment, the target goods refer to specific grain goods that are currently in the circulation link of the grain storage logistics platform, whose transportation demand or status changes dynamically and need to be allocated and managed in a targeted manner. The relevant parameters of the target goods may include the name of the goods, the specifications of the goods, the code of the goods, the current location of the goods, the batch to which the goods belong, etc. These relevant parameters are helpful for accurate positioning and processing.

[0026] Changes in logistics information refer to changes in key data of the target goods during the warehousing and transportation process, triggering the subsequent resource scheduling adjustment mechanism. The relevant parameters when logistics information changes may include the increase or decrease in order quantity, details of transportation route changes, adjustment of estimated delivery time, status of transportation tools, changes in storage conditions, etc., providing a basis for scheduling decisions. Cargo type refers to the category of goods classified according to cargo classification standards, storage characteristics, transportation requirements, etc.

[0027] For example, when the target cargo logistics information is monitored to change, the cargo type of the target cargo is first located, and the required transportation resource adjustment direction and intensity are evaluated in combination with its transportation resource allocation priority. For example, if the order volume of high-priority refrigerated cargo increases suddenly, refrigerated trucks are allocated first and fast routes are planned; if low-priority ordinary grain products encounter transportation tool failures, backup resources are coordinated according to the conventional process. Smooth logistics is ensured based on the characteristics and priority of the cargo.

[0028] S103: In response to a change in cargo information of the target cargo, cargo information management is performed based on a cargo information processing priority corresponding to the target cargo.

[0029] In this embodiment, changes in cargo information include updates of various information of the target cargo, prompting the initiation of the information management process, such as product quality inspection updates, inventory quantity increases and decreases, price fluctuations, shelf life adjustments, packaging specification revisions, etc. Cargo information management may include cargo information updates and cargo information storage.

[0030] For example, when the target cargo information changes, the corresponding resources can be deployed based on the cargo information processing priority. High-priority cargo information is stored in the high-performance storage area in advance to ensure fast reading, for fast update, and pushed to related users through multiple channels in real time; low-priority cargo information is stored in the general storage area to reasonably allocate information management resources to adapt to the actual needs of the cargo.

[0031] From the above, it can be concluded that this embodiment determines the priority of transport resource allocation based on historical logistics information, and can accurately determine the transport demand characteristics of different goods. For products with rapid order growth and high transport timeliness requirements, high-quality transport capacity and fast routes are allocated first to reduce transport delays, reduce costs, and improve overall logistics efficiency.

[0032] Based on historical retrieval information, the priority of cargo information processing is clarified to ensure that high-profile cargo information is updated and pushed in a timely manner. When the logistics or information of the target cargo changes, resources are quickly dispatched and information is managed according to the corresponding priority, so that the platform has flexible adaptability, adapts to complex and changing grain storage logistics scenarios, optimizes the operation process in all aspects, and improves customer satisfaction and industry competitiveness.

[0033] In one embodiment of the present disclosure, the historical logistics information includes order information, transportation information and resource scheduling information.

[0034] Based on the historical logistics information of all goods in the grain storage logistics platform, the transportation resource allocation priority corresponding to each type of goods is determined, including: Based on the order information of all goods in the grain storage logistics platform, the order quantity change trend characteristics and order timing characteristics of each goods are extracted.

[0035] Based on the transportation information of all goods in the grain storage logistics platform, the transportation route characteristics and transportation time characteristics of each kind of goods are extracted.

[0036] Based on the resource scheduling information of all goods in the grain storage logistics platform, the resource allocation characteristics of each kind of goods are extracted.

[0037] All goods are clustered based on the order volume change trend characteristics, time sequence characteristics, transportation route characteristics, transportation time characteristics and resource allocation characteristics, and the transportation resource allocation priority corresponding to each type of goods is determined based on the clustering results.

[0038] In this embodiment, the clustering result includes multiple clusters, each of which contains feature data corresponding to multiple types of goods.

[0039] Based on the clustering results, the transportation resource allocation priority for each type of cargo is determined, including: A goods list corresponding to each cluster is determined based on the feature data contained in the cluster. The goods list includes multiple goods.

[0040] The average characteristic data of each cluster is calculated based on the cargo list corresponding to the cluster, and the transportation resource configuration priority corresponding to the cargo list is determined based on the average characteristic data.

[0041] In this embodiment, the order volume change trend characteristics can reflect the increase or decrease trend of the number of goods orders over time. The order volume change trend characteristics may include the month-on-month data and year-on-year growth rate of the order volume in a specific time period. The order timing characteristics may include the time data of order generation, such as the order concentration month and quarter, the daily order peak period, the order proportion of special holidays and seasons, etc. The transportation route characteristics may include the route length, the complexity of the road conditions, the geographical area passed through, the number of transfers, etc. The transportation time characteristics may include the average transportation time, the standard deviation of the time, the longest / shortest transportation time and the corresponding conditions. The resource allocation characteristics may include the resource sufficiency rate, the resource idle rate, the resource adjustment frequency, etc.

[0042] In this embodiment, the average characteristic data includes average order volume growth rate, average transportation time, resource utilization efficiency, etc.

[0043] In this embodiment, by analyzing the changes in the number of orders in different time periods, the order volume change trend characteristics and the time period when the orders are concentrated are extracted to understand the dynamic situation of the demand for goods. Combined with the road conditions, places passed by and other factors of the transportation route, the characteristics of the transportation route are extracted, and combined with the time spent on each transportation, the characteristics of the transportation duration are extracted to measure the difficulty and efficiency of transportation. Based on the resource scheduling information, the actual situation of the past resource allocation to each product is analyzed to extract the resource allocation characteristics.

[0044] Using these multi-dimensional features as data basis, clustering algorithms are used to divide many goods into multiple clusters according to feature similarities. In each cluster, the corresponding list of goods is determined based on the feature data of the goods contained. The average order volume growth rate of all goods in each cluster is calculated to determine the growth trend of market demand for such goods; the average transportation time is calculated to determine the timeliness of logistics; the resource utilization efficiency is calculated to evaluate the resource input-output benefits. Based on these average feature data, a reasonable priority for transportation resource allocation is determined for each cluster's list of goods to ensure that key goods have priority access to high-quality resources.

[0045] For example, the priority of transport resource allocation is divided into three levels, and different weights are set for the average order volume growth rate, average transport time and resource utilization efficiency according to platform needs. For example, in the critical period of market expansion, the weight of the average order volume growth rate can be appropriately increased, such as set to 0.4; if the customer has strict requirements on timeliness, the weight of the average transport time can be set to 0.4; the weight of resource utilization efficiency is set to 0.2 accordingly. The weight setting needs to be dynamically adjusted in combination with business priorities.

[0046] For example, for the average order volume growth rate, if the average order volume growth rate exceeds 20% for multiple consecutive periods, it is preliminarily judged to have high priority potential, and the priority of transportation resource allocation is determined to be level 1; if it is between 10%-20%, the priority of transportation resource allocation is determined to be level 2; if it is lower than 10%, the priority of transportation resource allocation is determined to be level 3.

[0047] For the average transportation time, if the average transportation time is 20% lower than the industry average, the priority of transportation resource allocation for time-sensitive goods can be determined as level 1; if the average transportation time exceeds the average level by 20%, the priority of transportation resource allocation can be determined as level 3; if the difference between the average transportation time and the industry average level is between -20% and 20%, the priority of transportation resource allocation can be determined as level 2.

[0048] This embodiment takes into account changes in order volume, transportation time, resource utilization efficiency, etc., so that high-demand, time-sensitive goods are given priority access to high-quality transportation capacity and routes, avoiding resource mismatch and improving transportation efficiency. The weights and grading standards are flexibly adjusted in combination with market expansion and changes in customer demand, so that the priority is always in line with reality. This embodiment can reasonably allocate resources and reduce unnecessary expedited transportation and idle waste.

[0049] In one embodiment of the present disclosure, all goods are clustered based on order quantity change trend characteristics, time sequence characteristics, transportation route characteristics, transportation time characteristics, and resource allocation characteristics, including: A correlation analysis is performed on the order volume change trend characteristics, time sequence characteristics, transportation route characteristics, transportation time characteristics and resource allocation characteristics to obtain a related feature group.

[0050] Feature fusion is performed based on the correlation coefficient between features in the associated feature group.

[0051] All goods are clustered based on the associated feature groups after feature fusion.

[0052] In this embodiment, all goods are clustered based on the associated feature groups after feature fusion, including: Generate an associated feature vector based on the features in the associated feature group after feature fusion.

[0053] Based on the clustering algorithm, the associated feature vectors are clustered to obtain multiple clusters. Each cluster contains multiple associated feature vectors, and each associated feature vector corresponds to a type of goods.

[0054] Based on the goods corresponding to each associated feature vector, determine the cluster to which all goods belong.

[0055] In this embodiment, the associated feature group includes order quantity change trend features, time series features, transportation route features, transportation time features and resource allocation features, as well as the correlation coefficients between these multi-dimensional features. Based on the correlation coefficients between the multi-dimensional features, a mapping relationship between features of different dimensions can be established. For example, the correlation coefficient between the order quantity change trend feature and the time series feature is higher than the preset threshold, and the transportation route feature and the transportation time feature are higher than the preset threshold. These correlation coefficients provide a weight basis for subsequent feature fusion.

[0056] The associated feature vector is a group of associated features after feature fusion, presented in a structured, digital vector form. The associated feature vector contains the comprehensive feature performance of a cargo in multiple key logistics dimensions, and becomes a standardized input unit that can be directly processed by the subsequent clustering algorithm, making the comparison and classification of different cargoes based on these key features more convenient and efficient. The relevant parameters of the associated feature vector can include the vector dimension and the specific value of each dimension.

[0057] The generated associated feature vectors are stored in a special data set or cache area, waiting for the clustering algorithm to call. When the clustering algorithm is started, these associated feature vectors are used as core input data. The clustering algorithm determines the similarity between different goods in key logistics characteristics based on the distance measurement between vectors, thereby achieving the goal of clustering similar goods into a cluster, providing a grouping basis for subsequent accurate transportation resource allocation.

[0058] Exemplarily, the clustering algorithm can be a K-Means algorithm. First determine the initial cluster center, which can be a few vectors randomly selected from the associated feature vector set as the initial cluster center, and each cluster center represents a potential cluster. For example, if you want to cluster into 3 clusters, randomly select 3 associated feature vectors as the initial center. Each associated feature vector will be divided into the cluster to which the cluster center closest to it belongs. After the division is completed, the center position of each cluster will be recalculated. This new center position is obtained by calculating the average value of all associated feature vectors in the cluster in each dimension. When the position change of the cluster center in two adjacent iterations is less than a preset threshold, or the preset maximum number of iterations is reached, clustering is stopped. At this time, the final multiple clusters are obtained. The associated feature vectors in each cluster have a high similarity in key logistics characteristics. Each associated feature vector corresponds to a type of goods, and the goods between different clusters have obvious differences in these characteristics, thereby realizing clustering based on associated feature vectors.

[0059] This embodiment clusters similar goods into one cluster and distinguishes different goods based on multi-dimensional feature clustering, which is beneficial to subsequent targeted resource allocation. At the same time, the construction of associated feature vectors improves classification efficiency.

[0060] In one embodiment of the present disclosure, the cargo information processing priority corresponding to each cargo is determined based on the historical search information of all cargoes in the grain storage logistics platform, including: Based on the historical retrieval information of all goods in the grain storage logistics platform, the retrieval frequency, retrieval timeliness data and retrieval role association data corresponding to each type of goods are determined.

[0061] The cargo information processing priority corresponding to each cargo is determined based on the retrieval frequency, retrieval timeliness data and retrieval role association data.

[0062] In this embodiment, the cargo information processing priority corresponding to each cargo is determined based on the search frequency, the search timeliness data, and the search role association data, including: Substitute the retrieval frequency, retrieval timeliness data and retrieval role association data into the information processing priority calculation function to obtain the cargo information processing priority corresponding to each cargo.

[0063] The information processing priority calculation function is: + +

[0064] in, Indicates the information processing priority of the target goods, They represent the weight coefficients corresponding to the search frequency, search timeliness data and search role association data, n represents the number of types of goods, m represents the number of categories of search keywords, k represents the index of the search keyword category, Indicates the retrieval frequency data, It means summing up the total number of searches for all n kinds of goods on the kth keyword. represents the weight of the k-th search keyword, p represents the total number of searches for the target product within the time interval T, Indicates the timestamp of retrieving target cargo information. represents the time interval between the jth retrieval of the target goods information and the current time, represents the timeliness weight function, = , represents the attenuation coefficient, q represents the number of types of user roles for retrieval operations, l represents the index of the user role, Retrieve role-related data. It means summing up the total number of searches for all n types of goods in the lth role. Indicates the weight of the lth user role.

[0065] In this embodiment, the search frequency indicates how frequently the goods information is queried, reflecting the market's attention to the goods. The search frequency can include the total number of times the goods are searched within a certain period of time (such as the past month or quarter), and can also be subdivided into the number of searches under different keywords to analyze the attention paid to different information dimensions. The search timeliness data indicates the interval between each search and the current time, reflecting the degree of demand for the timeliness of updating goods information. The search timeliness data can include the time from the most recent search to the current time, as well as the average and standard deviation of the time intervals between each search, to measure the density of the search time distribution.

[0066] The search role-related data may include the user role that performs the search operation and the statistical data on the search results of different goods. Different roles have different focuses, which can assist in determining the direction of information services; user role classification such as internal staff, suppliers, distributors, end customers, etc.; the proportion of the number of searches for each type of goods by each role to the total number of searches; the distribution of search keywords commonly used by different roles, etc.

[0067] For example, historical search information of all goods is collected from the search record database of the grain storage logistics platform, and the search frequency, search timeliness data and search role-related data corresponding to each type of goods are extracted through data statistical analysis methods. These data are substituted into the information processing priority calculation function. The function comprehensively considers various factors according to the pre-set weights and calculation rules. For example, goods with high search frequency, recent searches and important roles will be given higher weights. Finally, the cargo information processing priority corresponding to each type of goods is calculated to guide the subsequent platform's management operations on cargo information, so that resources are given priority to goods that are more concerned and more urgently need information processing.

[0068] This embodiment can accurately locate goods that are of concern and need to be processed urgently based on the comprehensive consideration of search frequency, timeliness and role-related data, so that resources can be invested first. Manage information according to priority to ensure that important goods information is updated and pushed in time to meet the needs of different roles. Flexibly adjust the weight coefficient according to market changes to make the priority fit the actual situation.

[0069] In one embodiment of the present disclosure, cargo information management includes cargo information update and cargo information storage.

[0070] Cargo information management is performed based on the cargo information processing priority corresponding to the target cargo, including: The cargo information update frequency and cargo information push strategy are determined based on the cargo information processing priority corresponding to the target cargo. The cargo information of the target cargo is updated based on the cargo information update frequency and cargo information push strategy.

[0071] The cargo information storage space is determined based on the cargo information processing priority corresponding to the target cargo, and the cargo information of the target cargo is stored based on the cargo information storage space.

[0072] In this embodiment, the cargo information processing priority is the first information priority or the second information priority.

[0073] The cargo information storage space is determined based on the cargo information processing priority corresponding to the target cargo, including: In response to the cargo information processing priority corresponding to the target cargo being the first information priority, the cargo information storage space corresponding to the target cargo is determined to be the first storage area.

[0074] In response to the cargo information processing priority corresponding to the target cargo being the second information priority, the cargo information storage space corresponding to the target cargo is determined to be the second storage area.

[0075] The storage space capacity of the first storage area is smaller than that of the second storage area, and the response performance of the first storage area is higher than that of the second storage area.

[0076] In this embodiment, the cargo information update frequency specifies how often the cargo information is updated, reflecting the speed of the information update. Different priority cargoes correspond to different update time intervals, for example, the first information priority cargo is updated every 2 hours, and the second information priority cargo is updated every 8 hours.

[0077] The cargo information push strategy includes how to deliver cargo information to relevant users, including the planning of the push method, object, timing, etc. Push methods include in-site messages, text messages, APP push, email, etc., and push objects and push cycles include real-time push and scheduled push. The first storage area and the second storage area are storage spaces divided by the platform for cargo information of different priorities. The two are different in capacity and response performance, and are adapted to the storage needs of cargo of different priorities. The relevant parameters of the first storage area and the second storage area may include storage space capacity, response performance, storage medium type, etc.

[0078] For example, when the target goods have information changes or reach a scheduled update node, the corresponding goods information update frequency and push strategy are determined according to the corresponding goods information processing priority (first information priority or second information priority). For the update frequency, the goods with the first information priority will be set with a shorter time interval to ensure the timeliness of the information; the push strategy will select the appropriate push method and timing for accurate push according to the focus of different user roles and the importance of information.

[0079] At the same time, the storage location of the cargo information is determined according to the cargo information processing priority. If it is the first information priority, the cargo information is stored in the first storage area with fast response speed but relatively small capacity, which is convenient for rapid response to query needs; if it is the second information priority, it is stored in the second storage area with large capacity but slightly slower response, which meets the needs of large amounts of information storage and makes rational use of storage resources. Through such differentiated management, efficient and reasonable control of cargo information is achieved, meeting the different needs of different cargoes in information services.

[0080] This embodiment can ensure the timeliness of information, set a short update interval for high-priority goods, and quickly update their information so that relevant parties can grasp the situation in time. Plan the push strategy according to the user role and the importance of information, accurately reach the demanders, and push more accurately. Differentiate storage areas by priority, take into account both response speed and storage capacity, improve overall efficiency, and optimize storage resources. Through differentiated management, adapt to different cargo information service needs, enhance the scientificity and practicality of platform information management, and meet diverse needs.

[0081] Corresponding to the collaborative configuration processing method of the smart grain storage logistics platform in the above embodiment, Figure 2This is a block diagram of the structure of the smart grain storage logistics platform collaborative configuration processing system provided by an embodiment of the present disclosure. For the sake of convenience, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The smart grain storage logistics platform collaborative configuration processing system 20 is applied to the grain storage logistics platform, including: a priority determination module 21, a first response module 22 and a second response module 23.

[0082] The priority determination module 21 is used to determine the transportation resource configuration priority corresponding to each cargo based on the historical logistics information of all cargoes in the grain storage logistics platform and to determine the cargo information processing priority corresponding to each cargo based on the historical search information of all cargoes in the grain storage logistics platform.

[0083] The first response module 22 is used to respond to changes in the logistics information of the target goods and perform transportation resource scheduling based on the cargo type of the target goods and the transportation resource configuration priority corresponding to the target goods. The target goods are goods to be processed in the grain storage logistics platform.

[0084] The second response module 23 is used to manage the cargo information based on the cargo information processing priority corresponding to the target cargo in response to the cargo information of the target cargo being changed.

[0085] In one embodiment of the present disclosure, the historical logistics information includes order information, transportation information and resource scheduling information. The priority determination module 21 is specifically used to extract the order quantity change trend characteristics and order timing characteristics of each type of goods based on the order information of all goods in the grain storage logistics platform.

[0086] Based on the transportation information of all goods in the grain storage logistics platform, the transportation route characteristics and transportation time characteristics of each kind of goods are extracted.

[0087] Based on the resource scheduling information of all goods in the grain storage logistics platform, the resource allocation characteristics of each kind of goods are extracted.

[0088] All goods are clustered based on the order volume change trend characteristics, time sequence characteristics, transportation route characteristics, transportation time characteristics and resource allocation characteristics, and the transportation resource allocation priority corresponding to each type of goods is determined based on the clustering results.

[0089] In one embodiment of the present disclosure, the clustering result includes multiple clusters. Each cluster contains feature data corresponding to multiple goods. The priority determination module 21 is further used to determine a list of goods corresponding to each cluster based on the feature data contained in the cluster. The list of goods includes multiple goods.

[0090] The average characteristic data of each cluster is calculated based on the cargo list corresponding to the cluster, and the transportation resource configuration priority corresponding to the cargo list is determined based on the average characteristic data.

[0091] In one embodiment of the present disclosure, the priority determination module 21 is further used to perform correlation analysis on order quantity change trend characteristics, time sequence characteristics, transportation route characteristics, transportation time characteristics and resource allocation characteristics to obtain a correlation feature group.

[0092] Feature fusion is performed based on the correlation coefficient between features in the associated feature group.

[0093] All goods are clustered based on the associated feature groups after feature fusion.

[0094] In an embodiment of the present disclosure, the priority determination module 21 is further configured to generate an associated feature vector based on features in the associated feature group after feature fusion.

[0095] Based on the clustering algorithm, the associated feature vectors are clustered to obtain multiple clusters. Each cluster contains multiple associated feature vectors, and each associated feature vector corresponds to a type of goods.

[0096] Based on the goods corresponding to each associated feature vector, determine the cluster to which all goods belong.

[0097] In one embodiment of the present disclosure, the priority determination module 21 is specifically used to determine the search frequency, search timeliness data and search role association data corresponding to each type of goods based on the historical search information of all goods in the grain storage logistics platform.

[0098] The cargo information processing priority corresponding to each cargo is determined based on the retrieval frequency, retrieval timeliness data and retrieval role association data.

[0099] In one embodiment of the present disclosure, the priority determination module 21 is further used to substitute the retrieval frequency, retrieval timeliness data and retrieval role association data into the information processing priority calculation function to obtain the cargo information processing priority corresponding to each cargo.

[0100] The information processing priority calculation function is: + +

[0101] in, Indicates the information processing priority of the target goods, They represent the weight coefficients corresponding to the search frequency, search timeliness data and search role association data, n represents the number of types of goods, m represents the number of categories of search keywords, k represents the index of the search keyword category, Indicates the retrieval frequency data, It means summing up the total number of searches for all n kinds of goods on the kth keyword. represents the weight of the k-th search keyword, p represents the total number of searches for the target product within the time interval T, Indicates the timestamp of retrieving target cargo information. represents the time interval between the jth retrieval of the target goods information and the current time, represents the timeliness weight function, = , represents the attenuation coefficient, q represents the number of types of user roles for retrieval operations, l represents the index of the user role, Retrieve role-related data. It means summing up the total number of searches for all n types of goods in the lth role. Indicates the weight of the lth user role.

[0102] In one embodiment of the present disclosure, cargo information management includes cargo information update and cargo information storage. The second response module 23 is specifically used to determine the cargo information update frequency and cargo information push strategy based on the cargo information processing priority corresponding to the target cargo. The cargo information of the target cargo is updated based on the cargo information update frequency and cargo information push strategy.

[0103] The cargo information storage space is determined based on the cargo information processing priority corresponding to the target cargo, and the cargo information of the target cargo is stored based on the cargo information storage space.

[0104] In one embodiment of the present disclosure, the cargo information processing priority is the first information priority or the second information priority. The second response module 23 is further configured to determine that the cargo information storage space corresponding to the target cargo is the first storage area in response to the cargo information processing priority corresponding to the target cargo being the first information priority.

[0105] In response to the cargo information processing priority corresponding to the target cargo being the second information priority, the cargo information storage space corresponding to the target cargo is determined to be the second storage area.

[0106] The storage space capacity of the first storage area is smaller than that of the second storage area, and the response performance of the first storage area is higher than that of the second storage area.

[0107] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0108] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0109] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0110] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0111] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the collaborative configuration processing method of the smart grain storage and logistics platform provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0112] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0113] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0114] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0118] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for collaborative configuration and processing of a smart grain storage logistics platform, characterized in that: Applied to grain storage logistics platform, including: Determine the transportation resource allocation priority corresponding to each cargo based on the historical logistics information of all cargoes on the grain storage logistics platform; determine the cargo information processing priority corresponding to each cargo based on the historical search information of all cargoes on the grain storage logistics platform; In response to changes in the logistics information of the target goods, transport resource scheduling is performed based on the cargo type of the target goods and the transport resource configuration priority corresponding to the target goods; the target goods are goods to be processed in the grain storage logistics platform; In response to a change in cargo information of a target cargo, cargo information management is performed based on a cargo information processing priority corresponding to the target cargo.

2. The method for collaborative configuration of a smart grain storage logistics platform according to claim 1, characterized in that: The historical logistics information includes order information, transportation information and resource scheduling information; The method of determining the transportation resource allocation priority corresponding to each cargo based on the historical logistics information of all cargoes in the grain storage logistics platform includes: Based on the order information of all goods in the grain storage logistics platform, the order quantity change trend characteristics and order timing characteristics of each kind of goods are extracted; Based on the transportation information of all goods in the grain storage logistics platform, the transportation route characteristics and transportation time characteristics of each kind of goods are extracted; Based on the resource scheduling information of all goods in the grain storage logistics platform, the resource allocation characteristics of each kind of goods are extracted; All goods are clustered based on the order quantity change trend characteristics, the time sequence characteristics, the transportation route characteristics, the transportation time characteristics and the resource allocation characteristics, and the transportation resource allocation priority corresponding to each type of goods is determined based on the clustering results.

3. The method for collaborative configuration of a smart grain storage logistics platform according to claim 2, characterized in that: The clustering result includes multiple clusters; each cluster contains feature data corresponding to multiple goods; The step of determining the transportation resource allocation priority corresponding to each type of cargo based on the clustering results includes: Determine a goods list corresponding to each cluster based on the characteristic data contained in the cluster; the goods list includes multiple goods; The average characteristic data of each cluster is calculated based on the cargo list corresponding to the cluster, and the transportation resource configuration priority corresponding to the cargo list is determined based on the average characteristic data.

4. The method for collaborative configuration of a smart grain storage logistics platform according to claim 2, characterized in that: The clustering of all goods based on the order quantity change trend feature, the time sequence feature, the transportation route feature, the transportation time feature and the resource allocation feature includes: Performing correlation analysis on the order quantity change trend feature, the time sequence feature, the transportation route feature, the transportation time feature, and the resource allocation feature to obtain a correlation feature group; Performing feature fusion based on the correlation coefficients between the features in the associated feature group; All goods are clustered based on the associated feature groups after feature fusion.

5. The method for collaborative configuration of a smart grain storage logistics platform according to claim 4, characterized in that: The clustering of all goods based on the associated feature groups after feature fusion includes: Generate an associated feature vector based on the features in the associated feature group after feature fusion; Clustering the associated feature vectors based on a clustering algorithm to obtain multiple clusters; each cluster contains multiple associated feature vectors, and each associated feature vector corresponds to a type of goods; Based on the goods corresponding to each associated feature vector, determine the cluster to which all goods belong.

6. The method for collaborative configuration of a smart grain storage logistics platform according to claim 1, characterized in that: The process of determining the cargo information processing priority corresponding to each cargo based on the historical search information of all cargoes in the grain storage logistics platform includes: Based on the historical search information of all goods on the grain storage logistics platform, the search frequency, search timeliness data and search role association data corresponding to each kind of goods are determined; The cargo information processing priority corresponding to each cargo is determined based on the retrieval frequency, the retrieval timeliness data and the retrieval role association data.

7. The method for collaborative configuration of a smart grain storage logistics platform according to claim 6, characterized in that: The determining of the cargo information processing priority corresponding to each cargo based on the retrieval frequency, the retrieval timeliness data and the retrieval role association data includes: Substituting the retrieval frequency, retrieval timeliness data and retrieval role association data into the information processing priority calculation function to obtain the cargo information processing priority corresponding to each cargo; The information processing priority calculation function is: + + in, Indicates the information processing priority of the target goods, They represent the weight coefficients corresponding to the search frequency, search timeliness data and search role association data, n represents the number of types of goods, m represents the number of categories of search keywords, k represents the index of the search keyword category, Indicates the retrieval frequency data, It means summing up the total number of searches for all n kinds of goods on the kth keyword. represents the weight of the k-th search keyword, p represents the total number of searches for the target product within the time interval T, Indicates the timestamp of retrieving target cargo information. represents the time interval between the jth retrieval of the target goods information and the current time, represents the timeliness weight function, = , represents the attenuation coefficient, q represents the number of types of user roles for retrieval operations, l represents the index of the user role, Retrieve role-related data. It means summing up the total number of searches for all n types of goods in the lth role. Indicates the weight of the lth user role.

8. The method for collaborative configuration of a smart grain storage logistics platform according to claim 1, characterized in that: The cargo information management includes cargo information update and cargo information storage; The managing cargo information based on the cargo information processing priority corresponding to the target cargo includes: Determine a cargo information update frequency and a cargo information push strategy based on the cargo information processing priority corresponding to the target cargo; update cargo information for the target cargo based on the cargo information update frequency and the cargo information push strategy; A cargo information storage space is determined based on the cargo information processing priority corresponding to the target cargo; and cargo information of the target cargo is stored based on the cargo information storage space.

9. The method for collaborative configuration of a smart grain storage logistics platform according to claim 8, characterized in that: The cargo information processing priority is the first information priority or the second information priority; The determining of the cargo information storage space based on the cargo information processing priority corresponding to the target cargo includes: In response to the cargo information processing priority corresponding to the target cargo being the first information priority, determining the cargo information storage space corresponding to the target cargo to be the first storage area; In response to the cargo information processing priority corresponding to the target cargo being the second information priority, determining the cargo information storage space corresponding to the target cargo to be the second storage area; The storage space capacity of the first storage area is smaller than that of the second storage area, and the response performance of the first storage area is higher than that of the second storage area.

10. A smart grain storage logistics platform collaborative configuration processing system, characterized in that: Applied to grain storage logistics platform, including: The priority determination module is used to determine the transportation resource allocation priority corresponding to each cargo based on the historical logistics information of all cargoes in the grain storage logistics platform; and to determine the cargo information processing priority corresponding to each cargo based on the historical search information of all cargoes in the grain storage logistics platform; A first response module is used to respond to changes in the logistics information of the target goods and perform transportation resource scheduling based on the cargo type of the target goods and the transportation resource configuration priority corresponding to the target goods; the target goods are goods to be processed in the grain storage logistics platform; The second response module is used to manage the cargo information based on the cargo information processing priority corresponding to the target cargo in response to the cargo information of the target cargo changing.

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