Logistics supply chain collaborative optimization method and system based on AI large model

Abstract

The invention provides a logistics supply chain collaborative optimization method and system based on an AI large model, and relates to the technical field of data processing. The method comprises the following steps: collecting logistics data of each circulation dimension in each logistics link on a logistics supply chain of a target product; determining a corresponding circulation dimension abrupt change degree based on the logistics data under each circulation dimension, and determining the circulation dimension abrupt change degree of a target circulation dimension in the circulation dimensions as the user demand abrupt change degree of the target product; determining an initial link impact degree of each logistics link based on the circulation dimension abrupt change degree and the user demand abrupt change degree; correcting the initial link impact degree based on the position information of each logistics link on the logistics supply chain to obtain a target link impact degree; identifying a high-voltage link, and correcting logistics data of an associated link based on the impact degree of a target link; and inputting the corrected logistics data into the logistics large model so as to realize optimization of the logistics supply chain. According to the invention, collaborative optimization of each link of the logistics supply chain is realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a logistics supply chain collaborative optimization method and system based on an AI large model. Background Art

[0002] The logistics supply chain refers to a full-chain network structure system starting from the procurement of raw materials, through production and manufacturing, warehousing and transportation, distribution and retail, and finally delivering products to consumers. Through the coordination and integration of logistics, information flow and capital flow, seamless connection of various links in the supply chain is achieved to meet customer needs and improve overall efficiency and competitiveness.

[0003] In related technologies, when new products are delivered to users through the above-mentioned logistics supply chain, since the relevant companies in the logistics supply chain do not understand the actual supply and demand relationship of the new products, artificial intelligence (AI) technology is usually used to build a large model, and the supply and demand relationship of the new products is predicted through the large model, and the logistics supply chain is collaboratively optimized accordingly.

[0004] However, due to the lack of prior market experience with new products, when the supply and demand relationship of new products is predicted using the above method, the matching degree between the logistics supply chain and the new products will be insufficient. As a result, when the upstream and downstream supply and demand relationship of the new product logistics supply chain changes, the logistics between the various links of the logistics supply chain cannot respond quickly and cannot cope with the sudden change of the new product, and thus cannot coordinately optimize the entire logistics supply chain according to real-time demand. Summary of the Invention

[0005] In view of this, the embodiments of the present disclosure propose a logistics supply chain collaborative optimization method and system based on an AI large model to solve the problem existing in related technologies that due to the lack of prior market experience about new products, when predicting the supply and demand relationship of new products, the logistics supply chain will not match the new products sufficiently. As a result, when the upstream and downstream supply and demand relationship of the new product logistics supply chain changes, the logistics between the various links of the logistics supply chain cannot respond quickly, and cannot cope with the sudden change state of the new product, and thus cannot collaboratively optimize the entire logistics supply chain according to real-time needs.

[0006] According to the first aspect of the present disclosure, a method for collaborative optimization of a logistics supply chain based on an AI large model is provided. The technical solutions adopted are as follows: Collect logistics data of each circulation dimension in each logistics link of the target product's logistics supply chain; Determining the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determining a target circulation dimension in the circulation dimension, and determining the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product; Determining the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree; Correcting the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link; Identifying a high-pressure link in the logistics link, and correcting the logistics data of a link associated with the high-pressure link based on the target link impact degree of the high-pressure link; The corrected logistics data is input into the logistics big model to optimize the logistics supply chain through the logistics big model.

[0007] Exemplarily, the circulation dimension mutation degree of the corresponding circulation dimension is determined based on the logistics data under each circulation dimension, the target circulation dimension in the circulation dimension is determined, and the circulation dimension mutation degree of the target circulation dimension is determined as the user demand mutation degree of the target product, including: obtaining the change amount of the logistics data of the current cycle and the previous cycle under each circulation dimension, calculating the difference between the change amount of the logistics data of the current cycle and the change amount of the logistics data of the previous cycle, and recording it as the circulation dimension mutation degree corresponding to the circulation dimension; determining the product inventory of the target product in the circulation dimension as the target circulation dimension, and determining the circulation dimension mutation degree corresponding to the product inventory as the user demand mutation degree.

[0008] Exemplarily, the determining of the initial link impact of each of the logistics links based on the circulation dimension mutation degree and the user demand mutation degree includes: for each of the logistics links, determining the link supply circulation rate of the logistics link based on the logistics data under each of the circulation dimensions corresponding to the logistics link; determining the user mutation association strength of each of the circulation dimensions corresponding to the logistics link based on the circulation dimension mutation degree, the link supply circulation rate and the user demand mutation degree of each of the circulation dimensions corresponding to the logistics link; determining the initial link impact of each of the logistics links based on the user mutation association strength and the circulation dimension mutation degree of each of the circulation dimensions corresponding to each of the logistics links; wherein, the initial link impact is used to describe the degree of impact of the current logistics link on the next logistics link under the user demand mutation degree.

[0009] Exemplarily, the user mutation association strength of each circulation dimension corresponding to the logistics link is determined based on the circulation dimension mutation degree of each circulation dimension corresponding to the logistics link, the link supply circulation rate and the user demand mutation degree, including: calculating the sum of the circulation dimension mutation degrees of each circulation dimension corresponding to the logistics link; for each circulation dimension, calculating the ratio of the circulation dimension mutation degree of the circulation dimension to the sum of the circulation dimension mutation degrees, and calculating the product of the circulation dimension mutation degree with the link supply circulation rate and the user demand mutation degree to obtain the user mutation association strength of the circulation dimension.

[0010] Exemplarily, the initial link impact degree of each logistics link is determined based on the user mutation association strength and the circulation dimension mutation degree of each circulation dimension corresponding to each logistics link, including: for each logistics link, recording the sequence composed of the user mutation association strength of each circulation dimension corresponding to the logistics link as the first sequence; for the next logistics link of the logistics link, recording the sequence composed of the circulation dimension mutation degree of each circulation dimension corresponding to the next logistics link as the second sequence; matching the user mutation association strength in the first sequence and the circulation dimension mutation degree in the second sequence, and determining the impact association influence strength between the user mutation association strength and the corresponding circulation dimension mutation degree; for each circulation dimension of the logistics link, determining the dimensional impact strength of the circulation dimension based on the user mutation association strength of the circulation dimension and the corresponding impact association influence strength; calculating the average value of the dimensional impact strength of each circulation dimension in the logistics link, and recording it as the initial link impact degree of the logistics link.

[0011] Exemplarily, the initial link impact degree is corrected based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link, including: determining the first core link in the sequential direction of the logistics supply chain and the second core link in the reverse direction; for each logistics link, determining the first link interval number between the logistics link and the first core link, and the second link interval number between the logistics link and the second core link; determining the first distance influence degree based on the first link interval number and the total number of logistics links in the logistics supply chain; determining the second distance influence degree based on the second link interval number and the total number of logistics links in the logistics supply chain; and determining the target link impact degree based on the first distance influence degree, the second distance influence degree and the initial link impact degree.

[0012] Exemplarily, the identifying of the high-pressure link in the logistics link and correcting the logistics data of the associated link of the high-pressure link based on the target link impact of the high-pressure link include: when the user demand mutation degree is greater than a first preset threshold, triggering the logistics optimization alarm; when the logistics optimization alarm is triggered, identifying the logistics link with the target link impact degree greater than a second preset threshold as the high-pressure link; calculating the difference between the target link impact degree of the high-pressure link and the second preset threshold, and adjusting the logistics data of the associated link until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset conditions.

[0013] Exemplarily, the logistics data of the associated link is adjusted until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset condition, including: when the high-pressure link is discontinuously distributed, iteratively adjusting the logistics data of the previous logistics link of the high-pressure link until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset condition; when the high-pressure link is continuously distributed, iteratively adjusting the logistics data of multiple logistics links before the continuous distribution in turn until the difference between the target link impact degree of the continuously distributed high-pressure link and the second preset threshold meets the preset condition.

[0014] According to a second aspect of the present disclosure, a logistics supply chain collaborative optimization system based on an AI large model is provided, comprising: The data collection module is used to collect logistics data of various circulation dimensions in each logistics link of the target product's logistics supply chain; a data processing module, configured to determine, based on the logistics data under each of the circulation dimensions, a circulation dimension mutation degree of the corresponding circulation dimension, determine a target circulation dimension among the circulation dimensions, and determine the circulation dimension mutation degree of the target circulation dimension as a user demand mutation degree of the target product; The data processing module is further configured to determine the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree; The data processing module is further configured to correct the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain a target link impact degree for each logistics link; a logistics optimization module, configured to identify high-pressure links in the logistics links, and to modify the logistics data of associated links based on the target link impact degree of the high-pressure links; The logistics optimization module is further used to input the corrected logistics data into the logistics big model to optimize the logistics supply chain through the logistics big model.

[0015] Exemplarily, the logistics supply chain collaborative optimization system based on the AI ​​big model implements any of the above-mentioned logistics supply chain collaborative optimization methods based on the AI ​​big model based on the agent workflow and the big language model; the agent workflow includes a planner, an executor, a memory and a reflector; wherein: the planner is used to generate an execution plan for determining the target link impact degree of each logistics link in the logistics supply chain; the executor is used to determine the target link impact degree of each logistics link by executing the execution plan; and, based on the target link impact degree of the high-pressure link, the logistics data of the associated link is corrected; the memory is used to store the target link impact degree of each logistics link, the various intermediate data generated when calculating the target link impact degree, and the adjustment records of the logistics data; the reflector is used to identify the high-pressure link in the logistics link; and, generate an adjustment strategy for the logistics data of the associated link of the high-pressure link.

[0016] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing a program, wherein the program comprises instructions that, when executed by the processor, enable the processor to execute the above-mentioned AI large model-based logistics supply chain collaborative optimization method.

[0017] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above-mentioned logistics supply chain collaborative optimization method based on the AI ​​big model.

[0018] The present invention may have some or all of the following beneficial effects: In the logistics supply chain collaborative optimization method based on the AI ​​big model provided by the present invention, by collecting the logistics data of the target product in each circulation dimension in its logistics supply chain, and determining the circulation dimension mutation degree and the user demand mutation degree based on the logistics data, the initial link impact degree of each logistics link can be determined according to the circulation dimension mutation degree and the user demand mutation degree, and the initial link impact degree can be corrected based on the position of each logistics link in the logistics supply chain; the target link impact degree obtained by the present invention through correction can capture the implicit correlation between each logistics link, so that when the user demand mutates, the impact transmission path can be identified in time, thereby improving the efficiency of the collaborative optimization of the logistics supply chain; further, after obtaining the above-mentioned target link impact degree, the present invention can also identify the high-pressure link based on the target link impact degree, and alleviate the pressure on the high-pressure link due to the sudden change of user demand by adjusting the logistics data of the associated links of the high-pressure link; in addition, the present invention can also use the pre-trained logistics big model to quickly adapt the logistics supply chain of new products of different categories by inputting the corrected logistics data into the pre-trained logistics big model.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a logistics supply chain collaborative optimization method based on an AI big model according to an exemplary embodiment of the present disclosure is shown; Figure 2 A schematic block diagram of a logistics supply chain collaborative optimization system based on an AI big model according to an exemplary embodiment of the present disclosure is shown; Figure 3 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0022] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation methods, structures, features and effects of the logistics supply chain collaborative optimization method and system based on the AI ​​large model proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The specific scheme of the logistics supply chain collaborative optimization method and system based on the AI ​​big model provided by the present invention is explained in detail below with reference to the accompanying drawings.

[0025] See also Figure 1 , which shows a method flow chart of a logistics supply chain collaborative optimization method based on an AI large model provided by an embodiment of the present invention, such as Figure 1 As shown in FIG, the collaborative optimization method for logistics supply chain based on the AI ​​large model specifically includes the following steps: S110: Collecting logistics data of each circulation dimension in each logistics link in the logistics supply chain of the target product; S120: Determine the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determine the target circulation dimension in the circulation dimension, and determine the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product; S130: Determine the initial impact of each logistics link based on the sudden change degree of the circulation dimension and the sudden change degree of user demand; S140: Correcting the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link; S150: Identify high-pressure links in the logistics process, and modify logistics data of related links of the high-pressure links based on the impact degree of the target links of the high-pressure links; S160: Input the corrected logistics data into the logistics big model to optimize the logistics supply chain through the logistics big model.

[0026] The present invention collects logistics data of the target product in each circulation dimension in its logistics supply chain, and determines the circulation dimension mutation degree and the user demand mutation degree based on the logistics data, so that the initial link impact degree of each logistics link can be determined according to the circulation dimension mutation degree and the user demand mutation degree, and the initial link impact degree can be corrected based on the position of each logistics link in the logistics supply chain; the target link impact degree obtained by the correction of the present invention can capture the implicit correlation between the logistics links, so that when the user demand mutates, the impact transmission path can be identified in time, thereby improving the efficiency of collaborative optimization of the logistics supply chain; further, after obtaining the above-mentioned target link impact degree, the present invention can also identify the high-pressure link based on the target link impact degree, and alleviate the pressure on the high-pressure link due to the mutation of user demand by adjusting the logistics data of the associated links of the high-pressure link; in addition, the present invention can also use the pre-trained logistics large model to quickly adapt the logistics supply chain of new products of different categories by inputting the corrected logistics data into the pre-trained logistics large model.

[0027] Below, each step of the above-mentioned AI-based large-scale model-based logistics supply chain collaborative optimization method is described in detail: In step S110 , logistics data of each circulation dimension in each logistics link in the logistics supply chain of the target product is collected.

[0028] In the embodiment of the present application, the above-mentioned logistics supply chain refers to a full-chain network structure system starting from the procurement of raw materials, through production and manufacturing, warehousing and transportation, distribution and retail, until the product is delivered to consumers.

[0029] In the embodiments of the present application, the target product is any product delivered to users through the logistics supply chain. For example, the target product may be a new product delivered to users through the logistics supply chain. The embodiments of the present application utilize the AI-based large-scale model-based logistics supply chain collaborative optimization method to collaboratively optimize various links in the logistics supply chain based on the supply-demand relationship between users and the new product.

[0030] In the embodiment of the present application, the logistics links are the logistics nodes that the logistics supply chain goes through during the process of delivering the target product. Specifically, in an actual logistics scenario, the logistics supply chain may include logistics links such as procurement, production, delivery, and sales.

[0031] In the embodiments of this application, the circulation dimension refers to a perspective used to analyze the circulation of the target product's commodities, services, funds, and information within the logistics supply chain. For example, the procurement process may include circulation dimensions such as raw material inventory and procurement lead time; the production process may include circulation dimensions such as product inventory and product yield rate; the delivery process may include circulation dimensions such as truck assembly volume and truck transportation time; and the sales process may include circulation dimensions such as product inventory and customer satisfaction rate.

[0032] In an embodiment of the present application, the above-mentioned collection of logistics data of each circulation dimension in each logistics link on the logistics supply chain of the target product can be performed periodically. For example, taking the collaborative optimization of the logistics supply chain of the target product with days as a cycle as an example, the above-mentioned collection of logistics data of each circulation dimension in each logistics link on the logistics supply chain of the target product can be implemented as follows: for each logistics link in the above-mentioned logistics supply chain, the corresponding single-day starting dimension logistics data is recorded once before the daily operation, and the corresponding single-day ending dimension logistics data is recorded once after the operation ends. Specifically, taking the product inventory in the sales link as an example, the starting product inventory is recorded before the start of sales every day, and the ending product inventory is recorded after the end of sales.

[0033] Furthermore, in order to facilitate subsequent processing, after collecting the logistics data of each circulation dimension, the embodiment of the present application can also standardize the collected data. The specific implementation is as follows: the dimensions of the collected logistics data of each circulation dimension are unified, and their values ​​are normalized to the range of [0,1]. Specifically, the range normalization can be used to normalize based on the maximum and minimum values ​​in each dimension. This is a technical means well known to those skilled in the art. Other normalization methods can also be selected, which will not be limited or elaborated here.

[0034] In step S120, the circulation dimension mutation degree of the corresponding circulation dimension is determined based on the logistics data under each circulation dimension, the target circulation dimension in the circulation dimension is determined, and the circulation dimension mutation degree of the target circulation dimension is determined as the user demand mutation degree of the target product.

[0035] In the embodiment of the present application, the user demand mutation degree is a quantitative indicator used to measure the degree of change in user demand for the target product. Specifically, since the sales link can more intuitively represent the user's demand for the target product than other logistics links, when the demand relationship between the user and the target product changes, the sales link will be directly affected by the change in demand relationship and produce obvious logistics data fluctuations. Therefore, the target circulation dimension can be the circulation dimension of the product inventory of the target product in the sales link. For example, the user demand mutation degree can be determined by the change in the product inventory of the target product in the sales link in the current cycle and the previous cycle adjacent to it. The larger the value, the more obvious the change in user demand for the target product.

[0036] In the embodiments of the present application, the circulation dimension mutation degree is a quantitative indicator used to measure the severity of changes in logistics data for a single circulation dimension at each logistics link in the logistics supply chain, reflecting the potential impact of changes in logistics data for the corresponding circulation dimension on the next logistics link. For example, the circulation dimension mutation degree can be determined by the change in logistics data for the corresponding circulation dimension between the current cycle and the previous adjacent cycle. A larger value indicates a more significant change in the information for the circulation dimension within the current cycle, and a greater impact on the next logistics link.

[0037] Exemplarily, the above-mentioned determination of the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determination of the target circulation dimension in the circulation dimension, and determination of the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product can be achieved as follows: obtaining the change in logistics data of the current cycle and the previous cycle under each circulation dimension, calculating the difference in change between the change in logistics data of the current cycle and the change in logistics data of the previous cycle, and recording it as the circulation dimension mutation degree of the corresponding circulation dimension; determining the product inventory of the target product in the circulation dimension as the target circulation dimension, and determining the circulation dimension mutation degree corresponding to the product inventory as the user demand mutation degree.

[0038] Specifically, taking each day as a cycle and calculating the circulation dimension mutation degree of circulation dimension i as an example, the circulation dimension mutation degree of circulation dimension i can be calculated by the following formula: in, is the circulation dimension mutation degree of circulation dimension i; The change in logistics data of the circulation dimension on that day, that is, the difference between the logistics data of the starting dimension and the logistics data of the ending dimension corresponding to the circulation dimension i on that day; The change in the logistics data of the circulation dimension on the day before the current day, that is, the difference between the logistics data of the starting dimension and the logistics data of the ending dimension of the previous day corresponding to the circulation dimension i; is the normalization function.

[0039] Accordingly, the above user demand mutation degree can be calculated by the following formula: in, The sudden change of user demand for the target product; The change in inventory of the target product in the sales phase on that day; The change in the inventory of the target product in the sales phase from the day before the current day; is the normalization function.

[0040] In step S130, the initial link impact degree of each logistics link is determined based on the circulation dimension mutation degree and the user demand mutation degree.

[0041] In the embodiment of the present application, the above-mentioned initial link impact degree refers to the degree of impact pressure caused by each logistics link in the above-mentioned logistics supply chain on the logistics supply status of a subsequent logistics link when responding to changes in user demand for the target product. The larger the value of the initial link impact degree, the stronger the impact of the corresponding logistics link on the next logistics link.

[0042] Exemplarily, the above-mentioned determination of the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree can be achieved as follows: for each logistics link, based on the logistics data under each circulation dimension corresponding to the logistics link, determine the link supply circulation rate of the logistics link; based on the circulation dimension mutation degree, link supply circulation rate and user demand mutation degree of each circulation dimension corresponding to the logistics link, determine the user mutation association strength of each circulation dimension corresponding to the logistics link; based on the user mutation association strength and circulation dimension mutation degree of each circulation dimension corresponding to each logistics link, determine the initial link impact degree of each logistics link; wherein, the above-mentioned initial link impact degree is used to describe the degree of impact of the current logistics link on its next logistics link under the user demand mutation degree.

[0043] In the embodiments of the present application, the link supply circulation rate is a quantitative indicator used to measure the efficiency of goods circulation at each logistics link in the logistics supply chain during the current cycle. This link supply circulation rate can be determined by integrating the changes in logistics data under multiple circulation dimensions for the corresponding logistics link, reflecting the goods circulation characteristics of the corresponding logistics link itself.

[0044] Specifically, taking any logistics link in the logistics supply chain as an example, the link supply circulation rate of the logistics link in the current cycle can be calculated using the following formula: in, Supply circulation rate for the above logistics links in the current cycle; Indicates the number of circulation dimensions contained in the above logistics links; For the logistics link above The starting dimension data of each circulation dimension in the current cycle; For the logistics link above The circulation dimension is the dimension data at the end of the current cycle; the supply circulation rate of the above links The larger the value is, the better the cargo circulation in the above logistics link is in the current cycle.

[0045] In an embodiment of the present application, the above-mentioned user mutation correlation strength refers to the degree of correlation between the change in the corresponding circulation dimension in any logistics link in the logistics supply chain and the change in user demand when user demand changes, and is used to measure the impact intensity of the circulation dimension on the next logistics link when responding to changes in user demand.

[0046] Exemplarily, the above-mentioned determination of the user mutation association strength of each circulation dimension corresponding to the logistics link based on the circulation dimension mutation degree of each circulation dimension corresponding to the logistics link, the link supply circulation rate and the user demand mutation degree can be achieved as follows: calculate the sum of the circulation dimension mutation degrees of each circulation dimension corresponding to the logistics link; for each circulation dimension, calculate the ratio of the circulation dimension mutation degree of the circulation dimension to the sum of the circulation dimension mutation degrees, and calculate its product with the link supply circulation rate and the user demand mutation degree to obtain the user mutation association strength of the circulation dimension.

[0047] Specifically, taking any logistics link in the above logistics supply chain as an example, the user mutation correlation strength corresponding to any circulation dimension in the logistics link can be calculated by the following formula: in, The correlation strength of user mutations corresponding to the circulation dimension in the above logistics link in the current cycle; is the circulation dimension mutation degree corresponding to the circulation dimension; Indicates the number of circulation dimensions contained in the above logistics links; It is the sum of the circulation dimension mutation degrees corresponding to all circulation dimensions included in the current logistics link; It is the ratio of the mutation degree of the circulation dimension corresponding to the above circulation dimension to the sum of the mutation degrees of all dimensions in the above logistics link to which it belongs, reflecting the relative change intensity of the circulation dimension in the above logistics link to which it belongs; Supply circulation rate for the above logistics links; It is the sudden change of the circulation of the above logistics link in the current cycle. The larger the value, the more obvious the change of the characteristics of the logistics link itself. is the user demand mutation degree of the change in user demand for the target product; in addition, the The larger the value is, the more obvious the change in the unique characteristics of the logistics link represented by the corresponding circulation dimension when user demand changes, reflecting that the impact intensity of the circulation dimension on the subsequent logistics link is greater.

[0048] In an embodiment of the present application, when determining the link supply circulation rate and user mutation correlation strength through the above process, illustratively, the above-mentioned user mutation correlation strength of each circulation dimension corresponding to the logistics link is determined based on the circulation dimension mutation degree of each circulation dimension corresponding to the logistics link, the link supply circulation rate, and the user demand mutation degree; based on the user mutation correlation strength and circulation dimension mutation degree of each circulation dimension corresponding to each logistics link, the initial link impact degree of each logistics link is determined as follows: for each logistics link, the sequence composed of the user mutation correlation strength of each circulation dimension corresponding to the logistics link is recorded as a first sequence; for the next logistics link of the logistics link, the sequence composed of the circulation dimension mutation degree of each circulation dimension corresponding to the next logistics link is recorded as a second sequence; the user mutation correlation strength in the first sequence and the circulation dimension mutation degree in the second sequence are matched, and the impact correlation influence strength between the user mutation correlation strength and the corresponding circulation dimension mutation degree is determined; for each circulation dimension of the logistics link, the dimension impact strength of the circulation dimension is determined based on the user mutation correlation strength of the circulation dimension and its corresponding impact correlation influence strength; the average value of the dimension impact strength of each circulation dimension in the logistics link is calculated and recorded as the initial link impact degree of the logistics link.

[0049] Specifically, according to the order of influence of the user's demand relationship for the target product on the logistics supply chain: sales link - delivery link - production link - procurement link, when the user's demand relationship for the target product suddenly changes, the previous logistics link gradually penetrates the next logistics link; taking the above-mentioned sales link as the current logistics link and the above-mentioned delivery link as the next logistics link as an example, the initial link impact degree of the sales link can be determined according to the above method as follows, which specifically includes the following steps: S1: Assuming that the above sales link includes two circulation dimensions, and the user mutation correlation strengths corresponding to these two circulation dimensions are D1 and D2 respectively, then the above first sequence is L1={D1, D2}.

[0050] S2: Assuming that the above-mentioned delivery link includes three circulation dimensions, and the circulation dimension mutation degrees corresponding to these three circulation dimensions are C1, C2 and C3 respectively, then the above-mentioned second sequence is L2={C1, C2, C3}.

[0051] S3: The above matching of the user mutation association strength in the first sequence and the circulation dimension mutation degree in the second sequence can be achieved based on the Dynamic Time Warping Matching (DTW) algorithm.

[0052] Specifically, the first sequence of the sales link and the second sequence of the delivery link can be dynamically matched through the above-mentioned DTW matching to determine the optimal correspondence between the elements in the first and second sequences. For example, for the time series of logistics data of a certain circulation dimension in the first sequence and the time series of logistics data corresponding to the circulation dimension in the second sequence, the two elements closest to each other in the two time series can be used as the optimal correspondence, so that the impact transmission effect between the circulation dimension in the first sequence and the circulation dimension matched with it in the second sequence can be quantified based on the minimum distance value, that is, the impact correlation influence intensity corresponding to the circulation dimension in the first sequence. Specifically, the impact correlation influence intensity can be taken as the inverse proportional value of the above-mentioned minimum distance.

[0053] It should be noted that the matching relationship between the circulation dimensions corresponding to the first sequence and the circulation dimensions corresponding to the second sequence can be determined based on actual scenarios, and a circulation dimension corresponding to the first sequence may have multiple matching circulation dimensions in the second sequence. For example, in actual logistics scenarios, the circulation dimension of product inventory at the sales stage may affect both truck assembly volume and truck transportation time at the delivery stage. Therefore, when determining the matching relationship, a one-to-many correspondence between {product inventory, truck assembly volume} and {product inventory, truck transportation time} can be determined.

[0054] S4: In the current cycle, taking any circulation dimension in the above sales link as an example, after determining the impact correlation strength between the circulation dimension and the circulation dimension with a matching relationship in the delivery link, the dimensional impact strength of the circulation dimension can be calculated using the following formula: : in, The comprehensive impact intensity of any circulation dimension in the current logistics link on the corresponding circulation dimension in the next logistics link when the user's demand for the target product changes; The number of circulation dimensions that have matching relationships with this circulation dimension in the next logistics link; For this circulation dimension to the next logistics link The impact correlation influence intensity of the impact correlation dimension; in the above formula, the impact intensity of the above dimension The larger the value is, the greater the extent to which this circulation dimension affects the circulation dimension in the next logistics link when responding to a sudden change in user demand. This reflects that when user demand has an impact on this logistics link, this circulation dimension acts as the main link, transmitting the impact of user demand in this logistics link to the corresponding dimension in the next logistics link.

[0055] S5: Calculate the average value of the dimensional impact intensity corresponding to all circulation dimensions in the current logistics link as the initial link impact intensity of the current logistics link on the next logistics link The impact of this initial stage The larger the value is, the greater the impact pressure on the logistics supply status of the next logistics link when the current logistics link responds to a sudden change in user demand.

[0056] In step S140, the initial link impact degree is corrected based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link.

[0057] Since changes in user demand for target products will directly affect the sales link in the logistics supply chain, when performing collaborative optimization of the logistics supply chain, the embodiment of the present application usually uses the order of sales link - delivery link - production link - procurement link to determine the initial link impact of the previous logistics link on the next logistics link in this order through the above steps S110 to S130.

[0058] However, in actual logistics scenarios, when user demand for target products changes, in addition to gradually transferring the impact on the logistics status from the previous logistics link to the next logistics link in the above order, the above logistics supply chain also simultaneously transfers the impact on the logistics status from the previous logistics link to the next logistics link in the logistics supply link sequence (procurement link - production link - delivery link - sales link). Therefore, after obtaining the initial link impact degree of the sales link - delivery link - production link - procurement link through the above process, the initial link impact degree can also be corrected based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link.

[0059] Exemplarily, the above-mentioned correction of the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link can be achieved as follows: determine the first core link in the sequential direction of the logistics supply chain and the second core link in the reverse direction; for each logistics link, determine the number of first link intervals between the logistics link and the first core link, and the number of second link intervals between the logistics link and the second core link; determine the first distance influence degree based on the number of first link intervals and the total number of logistics links in the logistics supply chain; determine the second distance influence degree based on the number of second link intervals and the total number of logistics links in the logistics supply chain; determine the target link impact degree based on the first distance influence degree, the second distance influence degree and the initial link impact degree.

[0060] In a specific embodiment of the present application, after determining the initial link impact of each logistics link on the next logistics link in the order of sales link, delivery link, production link, and procurement link in the logistics supply chain, the above-mentioned determination of the target link impact can be specifically implemented as follows: S1: The procurement link plays a core role in the sequential direction of the logistics supply chain (procurement link - production link - delivery link - sales link), and the sales link plays a core role in the reverse direction of the logistics supply chain (sales link - delivery link - production link - procurement link). Therefore, in this step, the procurement link in the sequential direction of the logistics supply chain is determined as the first core link mentioned above; the sales link in the reverse direction of the logistics supply chain is determined as the second core link.

[0061] S2: Take any one of the logistics links in the sales link, delivery link, production link, and procurement link as an example, and calculate the impact of the distance between the logistics link and the sales link / procurement link. ;in, Indicates the number of links between the logistics link and the sales / purchasing link; Represents the total number of links in the logistics supply chain.

[0062] S3: Determine the first distance impact between the logistics link and the sales link , and the second distance impact of the above-mentioned logistics link and procurement link Finally, the target link impact degree of the logistics link can be calculated by the following formula: in, The impact degree of this logistics link on the target link of the next logistics link; The first distance impact degree between the logistics link and the sales link (i.e. the first core link mentioned above); Indicates the second distance influence degree between the logistics link and the procurement link (that is, the second core link mentioned above); In addition, in the above formula, The larger the value of , the weaker the impact of the logistics supply chain on the supply status of the logistics link in the logistics supply sequence (procurement link - production link - delivery link - sales link), and the stronger the impact of the impact effect sequence (sales link - delivery link - production link - procurement link). is the initial impact degree of the logistics link; is the normalization function.

[0063] In step S150, the high-pressure link in the logistics link is identified, and the logistics data of the associated links of the high-pressure link are corrected based on the target link impact degree of the high-pressure link.

[0064] In this embodiment of the present application, the aforementioned high-pressure link refers to a logistics link in the logistics supply chain that cannot withstand sudden changes in pressure and needs to adjust its logistics status when user demand for the target product changes. For example, this embodiment of the present application may identify a logistics link as a high-pressure link if the impact of the target link exceeds a preset threshold.

[0065] In an embodiment of the present application, the associated link of the above-mentioned high-pressure link is a logistics link that affects the logistics state of the high-pressure link. The logistics pressure of the high-pressure link can be reduced by adjusting the logistics state of the associated link, thereby adjusting the logistics pressure of the high-pressure link to an acceptable range.

[0066] Exemplarily, the above-mentioned identification of high-pressure links in the logistics links and correction of the logistics data of the associated links of the high-pressure links based on the target link impact of the high-pressure links can be achieved as follows: when the user demand mutation degree is greater than the first preset threshold, the logistics optimization alarm is triggered; when the logistics optimization alarm is triggered, the logistics link with the target link impact greater than the second preset threshold is identified as a high-pressure link; the difference between the target link impact of the high-pressure link and the second preset threshold is calculated, and the logistics data of the associated links are adjusted until the difference between the target link impact of the high-pressure link and the second preset threshold meets the preset conditions.

[0067] Among them, according to the different distribution of high-pressure links, the above-mentioned adjustment of the logistics data of the associated links until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset conditions can be achieved as follows: when the high-pressure link is discontinuously distributed, the logistics data of the previous logistics link of the high-pressure link is iteratively adjusted until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset conditions; when the high-pressure link is continuously distributed, the logistics data of the logistics links before the continuous distribution are iteratively adjusted in turn until the difference between the target link impact degree of the continuously distributed high-pressure link and the second preset threshold meets the preset conditions.

[0068] In a specific embodiment, the process of identifying the high-pressure link in the logistics link and correcting the logistics data of the associated links of the high-pressure link based on the target link impact of the high-pressure link is described in detail below: In this specific embodiment, when the sudden change degree of user demand for the target product in the current cycle is greater than 0.5 (i.e., the value of the first preset threshold is 0.5), the logistics link with a target link impact degree greater than 0.9 (i.e., the value of the second preset threshold is 0.9) is set as the high-pressure logistics supply link in the current cycle; if there is no high-pressure logistics supply link in the current cycle, it proves that the logistics supply chain can operate normally and there is no need to perform logistics collaborative optimization on the logistics link; if there is a high-pressure logistics supply link in the current cycle, it is further determined whether the high-pressure logistics supply link in the current cycle is a continuously distributed link in the logistics supply chain: If the high-pressure logistics supply links are not adjacent and continuously distributed in the logistics supply chain, that is, there is at least one normal logistics link before the high-pressure logistics supply link, in this case, this specific embodiment improves the logistics supply capacity of a normal logistics link before the high-pressure logistics supply link, so that local pressure can be diverted in the logistics supply chain, thereby alleviating the pressure caused by a sudden change in user demand for new products. The specific implementation is as follows: calculate the difference ∆F1 between the target link impact degree of the high-pressure logistics supply link and the above-mentioned second preset threshold value. The larger the value, the greater the degree to which the high-pressure logistics supply link requires adjustment from the previous logistics link; modify the logistics information data of the normal logistics link before the high-pressure link, and cumulatively calculate the target link impact degree of the high-pressure logistics supply link to gradually reduce the target link impact degree; repeat the above process until the value of ∆F1 is 0.

[0069] If the high-pressure logistics supply links are distributed adjacently and continuously in the logistics supply chain, for example, the distribution of the high-pressure logistics supply links in the logistics supply chain is as follows: normal logistics link 1—normal logistics link 2—logistics supply high-pressure link 3—logistics supply high-pressure link 4, then the normal logistics link (normal logistics link 2) before the continuously distributed high-pressure logistics supply link will have to bear greater pressure. Therefore, multiple normal logistics links need to be adjusted synchronously to share the pressure of the high-pressure logistics supply links. The specific implementation is as follows: starting from a normal logistics link (normal logistics link 2) before the continuously distributed high-pressure logistics supply link, the logistics information data of the normal logistics link is corrected, and the target link impact degree of the high-pressure logistics supply link is cumulatively calculated to gradually reduce the target link impact degree of the high-pressure logistics supply link until the difference between the high-pressure logistics supply link and the second preset threshold is 0; if the above target cannot be achieved through the current normal logistics link (normal logistics link 2), the above process is executed for the normal logistics links before the current normal logistics link (for example, the above-mentioned normal logistics link 1), and so on, until the difference between the high-pressure logistics supply link and the second preset threshold is 0.

[0070] In step S160, the corrected logistics data is input into the logistics big model to optimize the logistics supply chain through the logistics big model.

[0071] In an embodiment of the present application, after the logistics data of the associated links of the high-pressure link are adjusted through the above process to reduce the pressure of the high-pressure link to an acceptable range, the adjusted logistics data can also be input into a pre-trained logistics model, and the coordinated optimization of each logistics link in the logistics supply chain can be achieved through the logistics model.

[0072] Specifically, the logistics supply information data of each logistics link determined through the above process can be input into the above logistics big model as standard data, so as to determine the logistics supply preparation corresponding to each logistics link in the next cycle of the logistics supply chain through the logistics big model, so that the logistics supply status of each logistics link in the next cycle meets the requirements of the above logistics supply information data.

[0073] The present invention collects logistics data of the target product in each circulation dimension in its logistics supply chain, and determines the circulation dimension mutation degree and the user demand mutation degree based on the logistics data, so that the initial link impact degree of each logistics link can be determined according to the circulation dimension mutation degree and the user demand mutation degree, and the initial link impact degree can be corrected based on the position of each logistics link in the logistics supply chain; the target link impact degree obtained by the correction of the present invention can capture the implicit correlation between the logistics links, so that when the user demand mutates, the impact transmission path can be identified in time, thereby improving the efficiency of collaborative optimization of the logistics supply chain; further, after obtaining the above-mentioned target link impact degree, the present invention can also identify the high-pressure link based on the target link impact degree, and alleviate the pressure on the high-pressure link due to the mutation of user demand by adjusting the logistics data of the associated links of the high-pressure link; in addition, the present invention can also use the pre-trained logistics large model to quickly adapt the logistics supply chain of new products of different categories by inputting the corrected logistics data into the pre-trained logistics large model.

[0074] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware 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 exceed the scope of the present invention.

[0075] Correspondingly, the embodiment of the present disclosure also provides a logistics supply chain collaborative optimization system based on AI big model, referring to Figure 2 As shown, the AI ​​large model-based logistics supply chain collaborative optimization system 200 may include a data acquisition module 210, a data processing module 220, and a logistics optimization module 230, wherein: The data collection module 210 is used to collect logistics data of various circulation dimensions in various logistics links in the logistics supply chain of the target product; The data processing module 220 is configured to determine the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determine the target circulation dimension in the circulation dimension, and determine the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product; The data processing module 220 is further used to determine the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree; The data processing module 220 is further configured to modify the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link; The logistics optimization module 230 is used to identify high-pressure links in the logistics process and modify the logistics data of the associated links based on the target link impact of the high-pressure links; The logistics optimization module 230 is also used to input the corrected logistics data into the logistics big model to optimize the logistics supply chain through the logistics big model.

[0076] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: obtain the change in logistics data of the current cycle and the previous cycle under each circulation dimension, calculate the difference in change between the change in logistics data of the current cycle and the change in logistics data of the previous cycle, and record it as the circulation dimension mutation degree of the corresponding circulation dimension; determine the product inventory of the target product in the circulation dimension as the target circulation dimension, and determine the circulation dimension mutation degree corresponding to the product inventory as the user demand mutation degree.

[0077] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: for each logistics link, determine the link supply circulation rate of the logistics link based on the logistics data under each circulation dimension corresponding to the logistics link; determine the user mutation association strength of each circulation dimension corresponding to the logistics link based on the circulation dimension mutation degree, link supply circulation rate and user demand mutation degree of each circulation dimension corresponding to the logistics link; determine the initial link impact degree of each logistics link based on the user mutation association strength and circulation dimension mutation degree of each circulation dimension corresponding to each logistics link; wherein, the initial link impact degree is used to describe the impact degree of the current logistics link on its next logistics link under the user demand mutation degree.

[0078] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: calculate the sum of the circulation dimension mutation degrees of each circulation dimension corresponding to the logistics link; for each circulation dimension, calculate the ratio of the circulation dimension mutation degree of the circulation dimension to the sum of the above-mentioned circulation dimension mutation degrees, and calculate its product with the link supply circulation rate and the user demand mutation degree to obtain the user mutation association strength of the circulation dimension.

[0079] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: for each logistics link, record the sequence composed of the user mutation association strength of each circulation dimension corresponding to the logistics link as the first sequence; for the next logistics link of the logistics link, record the sequence composed of the circulation dimension mutation degree of each circulation dimension corresponding to the next logistics link as the second sequence; match the user mutation association strength in the first sequence and the circulation dimension mutation degree in the second sequence, and determine the impact correlation influence strength between the user mutation association strength and its corresponding circulation dimension mutation degree; for each circulation dimension of the logistics link, determine the dimensional impact strength of the circulation dimension based on the user mutation association strength of the circulation dimension and its corresponding impact correlation influence strength; calculate the average value of the dimensional impact strength of each circulation dimension in the logistics link, and record it as the initial link impact degree of the logistics link.

[0080] In an embodiment of the present application, the above-mentioned data processing module is specifically used to: determine the first core link in the sequential direction of the logistics supply chain and the second core link in the reverse direction; for each logistics link, determine the number of first link intervals between the logistics link and the first core link, and the number of second link intervals between the logistics link and the second core link; determine the first distance influence degree based on the first link interval number and the total number of logistics links in the logistics supply chain; determine the second distance influence degree based on the second link interval number and the total number of logistics links in the logistics supply chain; determine the target link impact degree based on the first distance influence degree, the second distance influence degree and the initial link impact degree.

[0081] In an embodiment of the present application, the above-mentioned logistics optimization module is specifically used to: trigger a logistics optimization alarm when the sudden change in user demand is greater than a first preset threshold; when the logistics optimization alarm is triggered, identify the logistics link whose target link impact is greater than a second preset threshold as a high-pressure link; calculate the difference between the target link impact of the high-pressure link and the second preset threshold, and adjust the logistics data of the associated link until the difference between the target link impact of the high-pressure link and the second preset threshold meets the preset conditions.

[0082] In an embodiment of the present application, the above-mentioned logistics optimization module is specifically used to: when the high-pressure link is discontinuously distributed, iteratively adjust the logistics data of the previous logistics link of the high-pressure link until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset conditions; when the high-pressure link is continuously distributed, iteratively adjust the logistics data of multiple logistics links before the continuous distribution in turn until the difference between the target link impact degree of the continuously distributed high-pressure link and the second preset threshold meets the preset conditions.

[0083] In a specific embodiment of the present application, the above-mentioned logistics supply chain collaborative optimization system based on the AI ​​big model can implement the above-mentioned logistics supply chain collaborative optimization method based on the AI ​​big model based on the agent workflow and the big language model; specifically, the above-mentioned agent workflow includes a planner, an executor, a memory and a reflector; wherein: the above-mentioned planner is used to generate an execution plan for determining the target link impact of each logistics link in the logistics supply chain; the above-mentioned executor is used to determine the target link impact of each logistics link by executing the above-mentioned execution plan; and, based on the target link impact of the high-pressure link, the logistics data of the associated link is corrected; the above-mentioned memory is used to store the target link impact of each logistics link, the various intermediate data generated when calculating the target link impact, and the adjustment records of the logistics data; the above-mentioned reflector is used to identify the high-pressure link in the logistics link; and, generate an adjustment strategy for the logistics data of the associated link of the high-pressure link.

[0084] In the specific scenario of the logistics supply chain collaborative optimization method and system based on the AI ​​big model provided in the embodiment of the present application, the processing flow of calculating the impact degree of the target link in steps S110 to S140 in the above-mentioned logistics supply chain collaborative optimization method based on the AI ​​big model is the execution plan of each sub-step that needs to be generated by the above-mentioned planner; after the execution plan including each sub-step is generated by the above-mentioned planner, the specific execution of the execution plan is realized by the above-mentioned executor, and the data and calculation results in the calculation process are stored in the above-mentioned memory; the calculated target link impact degree is input into the above-mentioned reflector; after receiving the above-mentioned target link impact degree, the reflector can identify the high-pressure link in the logistics link based on the target link impact degree, and generate an adjustment strategy for the logistics data of the associated links of the high-pressure link. The adjustment strategy has been elaborated in detail in the corresponding position of the above-mentioned logistics supply chain collaborative optimization method based on the AI ​​big model, so it will not be repeated here.

[0085] The specific implementation details of the above-mentioned logistics supply chain collaborative optimization system based on AI big model have been described in detail in the corresponding position of the logistics supply chain collaborative optimization method based on AI big model, so they will not be repeated here.

[0086] Figure 3 This is a schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 3 , which shows a structural diagram of an electronic device 300 suitable for implementing the embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0087] like Figure 3As shown, electronic device 300 may include a processing device (e.g., a central processing unit, graphics processing unit, etc.) 301, which can perform various appropriate actions and processes to implement the valve control method according to the program stored in read-only memory (ROM) 302 or the program loaded from storage device 308 into random access memory (RAM) 303 to implement the valve control method according to the embodiments of the present disclosure. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0088] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0089] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for executing the method shown in the flowchart, thereby implementing the valve control method described above. In such an embodiment, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0090] It should be noted that the computer-readable medium described above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0091] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0092] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0093] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: Collect logistics data of each circulation dimension in each logistics link of the target product's logistics supply chain; Determine the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determine the target circulation dimension in the circulation dimension, and determine the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product; Determine the initial impact of each logistics link based on the sudden change of circulation dimension and user demand; Based on the position information of each logistics link in the logistics supply chain, the initial link impact degree is corrected to obtain the target link impact degree of each logistics link; Identify high-pressure links in the logistics process, and modify the logistics data of the associated links of the high-pressure links based on the impact of the target links of the high-pressure links; The corrected logistics data is input into the logistics big model to optimize the logistics supply chain through the logistics big model.

[0094] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0095] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0097] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0098] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0099] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0100] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the scope of the above disclosure. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0101] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

Claims

1. A collaborative optimization method for logistics supply chain based on AI big model, characterized by: The method comprises: Collect logistics data of each circulation dimension in each logistics link of the target product's logistics supply chain; Determining the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determining a target circulation dimension in the circulation dimension, and determining the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product; Determining the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree; Correcting the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link; Identifying a high-pressure link in the logistics link, and correcting the logistics data of a link associated with the high-pressure link based on the target link impact degree of the high-pressure link; The corrected logistics data is input into the logistics big model to optimize the logistics supply chain through the logistics big model.

2. The collaborative optimization method for logistics supply chain based on AI big model according to claim 1 is characterized in that: Determining the circulation dimension mutation degree of the corresponding circulation dimension based on the logistics data under each circulation dimension, determining a target circulation dimension in the circulation dimension, and determining the circulation dimension mutation degree of the target circulation dimension as the user demand mutation degree of the target product, includes: Obtaining the change in the logistics data of the current cycle and the previous cycle under each circulation dimension, calculating the difference between the change in the logistics data of the current cycle and the change in the logistics data of the previous cycle, and recording it as the circulation dimension mutation degree corresponding to the circulation dimension; The product inventory of the target product in the circulation dimension is determined as the target circulation dimension, and the circulation dimension mutation degree corresponding to the product inventory is determined as the user demand mutation degree.

3. The collaborative optimization method for logistics supply chain based on AI large model according to claim 2 is characterized in that: The determining of the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree includes: For each of the logistics links, determining the link supply circulation rate of the logistics link based on the logistics data under each of the circulation dimensions corresponding to the logistics link; Determining the user mutation correlation strength of each circulation dimension corresponding to the logistics link based on the circulation dimension mutation degree of each circulation dimension corresponding to the logistics link, the link supply circulation rate, and the user demand mutation degree; Based on the user mutation association strength and the circulation dimension mutation degree of each logistics link corresponding to each circulation dimension, the initial link impact degree of each logistics link is determined; wherein, the initial link impact degree is used to describe the impact degree of the current logistics link on the next logistics link under the user demand mutation degree.

4. The collaborative optimization method for logistics supply chain based on AI large model according to claim 3 is characterized in that: Determining the user mutation correlation strength of each circulation dimension corresponding to the logistics link based on the circulation dimension mutation degree of each circulation dimension corresponding to the logistics link, the link supply circulation rate, and the user demand mutation degree includes: Calculating the sum of the circulation dimension mutation degrees of each circulation dimension corresponding to the logistics link; For each circulation dimension, the ratio of the circulation dimension mutation degree to the sum of the circulation dimension mutation degrees is calculated, and the product of the ratio and the link supply circulation rate and the user demand mutation degree is calculated to obtain the user mutation association strength of the circulation dimension.

5. The collaborative optimization method for logistics supply chain based on AI big model according to claim 3 is characterized in that: The determining of the initial link impact degree of each logistics link based on the user mutation association strength and the circulation dimension mutation degree of each logistics link includes: For each logistics link, a sequence consisting of the user mutation correlation strengths of each circulation dimension corresponding to the logistics link is recorded as a first sequence; For the next logistics link of the logistics link, a sequence consisting of the circulation dimension mutation degrees of each circulation dimension corresponding to the next logistics link is recorded as a second sequence; Matching the user mutation association strength in the first sequence and the circulation dimension mutation degree in the second sequence, and determining the impact association influence strength between the user mutation association strength and the corresponding circulation dimension mutation degree; For each circulation dimension of the logistics link, determining the dimension impact intensity of the circulation dimension based on the user mutation association intensity of the circulation dimension and the corresponding impact association influence intensity; Calculate the average value of the dimensional impact strength of each circulation dimension in the logistics link, and record it as the initial link impact degree of the logistics link.

6. The method for collaborative optimization of logistics supply chain based on AI large model according to claim 3 is characterized in that: The step of correcting the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain the target link impact degree of each logistics link includes: Determine the first core link in the sequential direction and the second core link in the reverse direction of the logistics supply chain; For each of the logistics links, determining a first link interval number between the logistics link and the first core link, and a second link interval number between the logistics link and the second core link; Determining a first distance impact degree based on the number of first link intervals and the total number of logistics links in the logistics supply chain; Determining a second distance impact degree based on the number of intervals between the second links and the total number of the logistics links in the logistics supply chain; The target link impact degree is determined based on the first distance impact degree, the second distance impact degree, and the initial link impact degree.

7. The collaborative optimization method for logistics supply chain based on AI big model according to claim 1 is characterized in that: The step of identifying a high-pressure link in the logistics link and correcting the logistics data of a link associated with the high-pressure link based on the target link impact degree of the high-pressure link includes: When the user demand mutation degree is greater than a first preset threshold, triggering the logistics optimization alarm; When the logistics optimization alarm is triggered, the logistics link whose impact degree of the target link is greater than a second preset threshold is identified as the high-pressure link; Calculate the difference between the target link impact degree of the high-pressure link and the second preset threshold, and adjust the logistics data of the associated link until the difference between the target link impact degree of the high-pressure link and the second preset threshold meets the preset conditions.

8. The collaborative optimization method for logistics supply chain based on AI big model according to claim 7 is characterized in that: The adjusting the logistics data of the associated link until the difference between the target link impact degree of the high-pressure link and the second preset threshold value meets a preset condition includes: When the high-pressure link is discontinuously distributed, iteratively adjusting the logistics data of the logistics link preceding the high-pressure link until the difference between the target link impact degree of the high-pressure link and the second preset threshold value meets the preset condition; When the high-pressure link is continuously distributed, the logistics data of the multiple logistics links before the continuous distribution are iteratively adjusted in sequence until the difference between the target link impact degree of the continuously distributed high-pressure link and the second preset threshold meets the preset condition.

9. A logistics supply chain collaborative optimization system based on AI big model, characterized by: The system comprises: The data collection module is used to collect logistics data of various circulation dimensions in each logistics link of the target product's logistics supply chain; a data processing module, configured to determine, based on the logistics data under each of the circulation dimensions, a circulation dimension mutation degree of the corresponding circulation dimension, determine a target circulation dimension among the circulation dimensions, and determine the circulation dimension mutation degree of the target circulation dimension as a user demand mutation degree of the target product; The data processing module is further configured to determine the initial link impact degree of each logistics link based on the circulation dimension mutation degree and the user demand mutation degree; The data processing module is further configured to correct the initial link impact degree based on the position information of each logistics link in the logistics supply chain to obtain a target link impact degree for each logistics link; a logistics optimization module, configured to identify high-pressure links in the logistics links, and to modify the logistics data of associated links based on the target link impact degree of the high-pressure links; The logistics optimization module is further used to input the corrected logistics data into the logistics big model to optimize the logistics supply chain through the logistics big model.

10. The AI ​​large model-based logistics supply chain collaborative optimization system according to claim 9 is characterized in that: The AI ​​large model-based logistics supply chain collaborative optimization system implements the AI ​​large model-based logistics supply chain collaborative optimization method according to any one of claims 1 to 8 in the form of an agent workflow and a large language model; the agent workflow includes a planner, an executor, a memory, and a reflector; wherein: The planner is used to generate an execution plan for determining the target link impact degree of each logistics link in the logistics supply chain; The executor is configured to determine the target link impact degree of each logistics link by executing the execution plan; and to modify the logistics data of the associated link based on the target link impact degree of the high-pressure link; The memory is used to store the target link impact degree of each logistics link, various intermediate data generated when calculating the target link impact degree, and adjustment records of the logistics data; The reflector is used to identify the high-pressure link in the logistics link; and generate an adjustment strategy for the logistics data of the associated link of the high-pressure link.

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