Method and system for obtaining acceptance point setting based on different logistics dimensions

Abstract

The invention provides a setting method and system for obtaining acceptance points based on different logistics dimensions. The setting method comprises the following steps: S1, constructing an acceptance point model based on an acceptance point matching data input condition and an acceptance point matching data output condition; wherein the input conditions comprise subjective factors and objective factors; the output condition comprises an expected acceptance point; s2, on the basis of the acceptance point model, obtaining recommended acceptance points according to subjective factors and objective factors in actual requirements, and on the basis of the currently obtained recommended acceptance points, further screening the obtained recommended acceptance points according to accident factors to obtain final matched recommended acceptance points; and S3, when the final matched recommended acceptance point is not obtained through further screening based on the accident factors, obtaining the recommended acceptance point by using an approximate point recommendation strategy. By constructing the acceptance point model, the problems of wrong acceptance point setting and low acceptance point setting efficiency in the complex informatization era are solved.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a method and system for setting acceptance points based on different logistics dimensions. Background Art

[0002] Logistics acceptance refers to the process of checking and confirming the quantity, quality, packaging, etc. of goods during logistics transportation. Correct logistics acceptance methods and steps can ensure the integrity and accuracy of goods and improve the efficiency of logistics operations.

[0003] With the rapid development of the logistics industry, especially under the premise of complex and multi-category industrial logistics and refined management by manufacturers and cargo owners, the setting of acceptance points has brought tremendous pressure. In the existing technology, the setting of acceptance points is usually determined based on the subjective judgment of business personnel. Inaccurate setting of acceptance points leads to empty runs by drivers and users, unnecessary traffic congestion, waste of pre-factory management resources and many other problems. Summary of the invention

[0004] In view of the defects in the prior art, the purpose of the present invention is to provide a method and system for setting acceptance points based on different logistics dimensions.

[0005] According to the present invention, a method for setting acceptance points based on different logistics dimensions includes:

[0006] Step S1: constructing an acceptance point model based on the acceptance point matching data input conditions and output conditions; wherein the input conditions include: subjective factors and objective factors; and the output conditions include the expected acceptance point;

[0007] Step S2: Based on the acceptance point model, obtain recommended acceptance points according to subjective factors and objective factors in actual needs, and further filter the recommended acceptance points obtained according to unexpected factors based on the currently obtained recommended acceptance points to obtain the final matching recommended acceptance points;

[0008] Step S3: In the case where the final matching recommended acceptance points are not obtained after further screening based on unexpected factors, the recommended acceptance points are obtained using the approximate point recommendation strategy.

[0009] Preferably, the step S1 comprises: based on subjective factors and objective factors;

[0010] Subjective factors are the requirements of upstream customers, including: unique attributes of items, designated warehouse areas, storage conditions, and special transportation requirements;

[0011] The objective factors are acceptance point conditions, including: technical personnel, storage conditions and space.

[0012] Preferably, in the acceptance point model, weighted values ​​are configured for each of the subjective factors and the objective factors, and the acceptance elements are prioritized based on the configured weighted values, and preferential matching is performed according to the priority order.

[0013] Preferably, step S2 comprises:

[0014] Step S2.1: Taking the subjective factors in the actual requirements as input conditions, preliminarily screen the matching expected acceptance points based on the acceptance point model;

[0015] Step S2.3: Record the expected acceptance points that meet the subjective factor conditions into the hit strategy flow table, and then determine whether objective factors are required to participate in the matching based on the matching mode of the responsibility chain. If necessary, trigger step S2.4, otherwise return the record result;

[0016] Step S2.4: Taking the objective factors in the actual demand as input conditions, on the basis of preliminary screening of the matching expected acceptance points, further screening the matching expected acceptance points using the acceptance point model;

[0017] Step S2.5: Record the expected acceptance points that meet the objective factor conditions into the hit strategy flow table;

[0018] Step S2.6: Integrate unexpected factors to comprehensively determine whether the selected expected acceptance points ultimately meet the acceptance points that meet the requirements, and record the acceptance point information; if all are missed, record the information that caused the missed acceptance points;

[0019] The unexpected factors are sudden conditions of variability and uncertainty, customer demands, and unpredictable conditions, including: rejection at the acceptance point, changes in acceptance conditions, and blacklists and whitelists.

[0020] Preferably, the combination strategy selection of the expected acceptance point that meets the subjective factor conditions or the expected acceptance point that meets the objective factor conditions includes: successful hit of all conditions and partial hit of conditions;

[0021] The partial condition hits include: matching with higher weighted values ​​first.

[0022] Preferably, step S3 comprises:

[0023] x={all / n|all=SUM(exp(S(i)))+SUM(exp(O(j)))+SUM(exp(A(k)),n=max(i)+max(j)+max(k)};

[0024] Among them, S(i) represents subjective factors; O(j) represents objective factors; A(k) represents unexpected factors; exp(factor) represents the expected value of the factor;

[0025] The acceptance point strategy expectation value that is closer to the x value is the recommended acceptance point.

[0026] Preferably, the method further comprises: using machine learning to comprehensively evaluate the hit accuracy of the recommended acceptance points, and optimizing the acceptance point model based on the evaluation results.

[0027] According to the present invention, a system for setting acceptance points based on different logistics dimensions is provided, comprising:

[0028] Module M1: constructing an acceptance point model based on the acceptance point matching data input conditions and output conditions; wherein the input conditions include: subjective factors and objective factors; the output conditions include the expected acceptance point;

[0029] Module M2: Based on the acceptance point model, obtain recommended acceptance points according to subjective factors and objective factors in actual needs, and further filter the recommended acceptance points based on the currently obtained recommended acceptance points according to unexpected factors to obtain the final matching recommended acceptance points;

[0030] Module M3: When the recommended acceptance points that are not finally matched are further screened based on unexpected factors, the recommended acceptance points are obtained using the approximate point recommendation strategy.

[0031] Preferably, the module M1 comprises: based on subjective factors and objective factors;

[0032] Subjective factors are the requirements of upstream customers, including: unique attributes of items, designated warehouse areas, storage conditions, and special transportation requirements;

[0033] The objective factors are acceptance point conditions, including: technical personnel, storage conditions and space;

[0034] In the acceptance point model, weighted values ​​are configured for each of the subjective factors and the objective factors, and the acceptance elements are prioritized based on the configured weighted values, and preferential matching is performed according to the priority order.

[0035] Preferably, the module M2 comprises:

[0036] Module M2.1: Taking the subjective factors in the actual requirements as input conditions, preliminarily screen the matching expected acceptance points based on the acceptance point model;

[0037] Module M2.3: Record the expected acceptance points that meet the subjective factor conditions into the hit strategy flow table, and then determine whether objective factors are required to participate in the matching based on the matching mode of the responsibility chain. If necessary, trigger module M2.4, otherwise return the record result;

[0038] Module M2.4: Taking the objective factors in the actual requirements as input conditions, on the basis of preliminary screening of matching expected acceptance points, further screening matching expected acceptance points using the acceptance point model;

[0039] Module M2.5: Record the expected acceptance points that meet the objective factors into the hit strategy flow table;

[0040] Module M2.6: Integrate unexpected factors to comprehensively determine whether the selected expected acceptance points ultimately meet the acceptance points that meet the requirements, and record the acceptance point information; if all are missed, record the information that caused the missed acceptance points;

[0041] The unexpected factors are sudden conditions and customer demands of variability and uncertainty and unpredictable conditions, including: acceptance point rejection, change of acceptance conditions and blacklist and whitelist;

[0042] The combination strategy selection of the expected acceptance point that meets the subjective factor conditions or the expected acceptance point that meets the objective factor conditions includes: successful hit of all conditions and hit of some conditions;

[0043] The partial condition hits include: matching with a higher weighted value first;

[0044] The module M3 comprises:

[0045] x={all / n|all=SUM(exp(S(i)))+SUM(exp(O(j)))+SUM(exp(A(k)),n=max(i)+max(j)+max(k)};

[0046] Among them, S(i) represents subjective factors; O(j) represents objective factors; A(k) represents unexpected factors; exp(factor) represents the expected value of the factor;

[0047] The acceptance point strategy expectation value that is closer to the x value is the recommended acceptance point.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention solves the problems of incorrect acceptance point settings and slow acceptance point setting efficiency in the complex information age by constructing an acceptance point model.

[0050] 2. The present invention can quickly respond to changes caused by changes in customer demand or emergencies by integrating and judging unexpected factors. This flexibility makes the acceptance strategy in the logistics process more adaptable and can effectively deal with uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0052] Figure 1 A flow chart of the method for setting acceptance points based on different logistics dimensions. DETAILED DESCRIPTION

[0053] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0054] Example 1

[0055] The purpose of logistics is to transport items safely and efficiently to a reasonable storage point. The item may be physical or virtual; it may be large or small; it may be square or round; it may be flammable or explosive. Listing so many item attributes here is intended to highlight the complexity and diversity of transported items. The acceptance storage corresponding to different items is also different. The present invention provides a method and system for setting acceptance points based on different logistics dimensions, which is used to solve the problem of how to reasonably allocate items with different attributes to appropriate acceptance storage points.

[0056] To meet business needs, it mainly depends on several factors, including: subjective factors, objective factors and unexpected factors;

[0057] Among them, the subjective factors (S(i)) are the requirements of upstream customers, such as the unique properties of items (flammable and explosive), designated storage areas, storage conditions (waterproof, moisture-proof, etc.), special transportation requirements (such as food-specific), etc.;

[0058] The objective factors (O(j)) are acceptance point conditions, such as technical personnel (technical level), storage conditions (waterproof, explosive, etc.), space, etc.;

[0059] The unexpected factors (A(k)) are mainly for sudden conditions and customer demands and unexpected conditions with variability and uncertainty, such as acceptance point rejection, change of acceptance conditions, blacklist and whitelist, etc.

[0060] Among them, subjective factors mainly highlight human factors such as customer wishes and requirements of acceptance work points; objective factors mainly consider the physical properties of acceptance items and acceptance points, which are objective and not subject to human will, in order to extract commonalities for training. Unexpected factors focus on sudden acceptance needs, such as sudden changes in policies; provide a better classification and aggregation method. Different from the traditional acceptance method, all acceptance settings are classified into one category. The reason why it is summarized into these three factors is that this method optimizes the selection of acceptance points from three dimensions, that is, different angles, and realizes and solves the problem of item zoning acceptance from multiple dimensions.

[0061] The acceptance point setting method based on different logistics dimensions is as follows: Figure 1 As shown, including:

[0062] Step 1: Carry out model training based on the existing acceptance point matching data input conditions (subjective factors, objective factors, unexpected factors) and output conditions (expected acceptance points) to form a large model data for acceptance point storage;

[0063] Step 2: First, input factors based on subjective factors, such as whether to specify the storage area, storage space, site size, cargo owner storage contract, etc. S(n), where S(n) represents the nth subjective factor, and preliminarily screen the matching strategy;

[0064] Step 3: Select the combination strategy for missed hits under subjective factors, which is mainly divided into the mode of judging success when all conditions are successfully hit and judging successful match when some conditions are hit;

[0065] Step 4: In the scenario where the subjective factor hits, record the hit subjective factor conditions to the hit strategy flow table, and judge whether the subsequent objective factors need to participate based on the matching mode of the responsibility chain. If the scenario needs to be continued, proceed to step 5, otherwise, complete the strategy and return the record result; wherein, the matching mode of the responsibility chain is a step-by-step matching mode from parent to child, from large category to small category, and from high weight to low weight. The matching mode is progressive, which can quickly exclude invalid options under the same-level matching rules, and as the granularity of the matching chain is refined and extended, the matching results are progressive and more accurate;

[0066] Step 5: For the objective factor missed strategy, record the missed objective condition factors and missed result information to the missed strategy flow table;

[0067] Step 6: For the scenarios where objective factors are encountered, record the objective factor conditions encountered and select the acceptance points that meet the conditions;

[0068] Step 7: Integrate unexpected factors to comprehensively determine whether the acceptance points are finally in line with the requirements, and record the acceptance point information; if all are missed, record the information that caused the missed acceptance points;

[0069] Step 8: Even for the missed scenario, enable the approximate point recommendation strategy with 3-point estimation:

[0070] x={all / n|all=SUM(exp(S(i)))+SUM(exp(O(j)))+SUM(exp(A(k))),n=max(i)+max(j+max(k)}, the expected value of the acceptance point strategy closer to the x value is the recommended point;

[0071] Step 9: Acceptance point recommendation model learning reinforcement, based on the hit or miss conditions recorded in steps 3 to 7, compare the recommended strategy in step 8, analyze and verify the accuracy of the acceptance model, mark abnormal data, and perform regression training on the hit strategy.

[0072] The present invention also provides a system for setting acceptance points based on different logistics dimensions. The system for setting acceptance points based on different logistics dimensions can be implemented by executing the process steps of the method for setting acceptance points based on different logistics dimensions, that is, those skilled in the art can understand the method for setting acceptance points based on different logistics dimensions as a preferred implementation of the system for setting acceptance points based on different logistics dimensions.

[0073] Example 2

[0074] Embodiment 2 is a preferred embodiment of Embodiment 1

[0075] According to the present invention, a method for setting acceptance points based on different logistics dimensions includes:

[0076] Constructing an acceptance point model based on the acceptance point matching data input conditions and output conditions; wherein the input conditions include: subjective factors and objective factors; the output conditions include expected acceptance points;

[0077] Among them, the formation of the model is based on the amount of basic data. The present invention is a strategy model formed by training based on 1,002,034 acceptance data, 101 acceptance points, and 1,002 suppliers in a real production environment as basic data, forming a total of 7,498 acceptance point hit strategy models;

[0078] Define the input subjective factors, such as specific supplier, corresponding consignor, required warehouse area, system source (pscs, shopping mall system), document type (delivery note, purchase receipt, sales return receipt), etc. For simple expression, the subjective factors are replaced by S(i) below;

[0079] Define the objective acceptance conditions. The input information mainly includes product categories (major categories, types, leaf categories), product storage conditions, hazardous chemical marking, product length, width, and height, receiving bases, warehouse information, etc. For simplicity of expression, the following uses O(j) to represent the objective elements;

[0080] Based on the above input elements, define the acceptance point model. To reduce the large amount of calculations caused by the combination of acceptance point conditions, it is necessary to assign a weighting value Wi to the recommendation factors. The significance of the weighting value lies in automatically sorting the priorities of the acceptance elements to improve the hit rate of high-priority elements;

[0081] Start factor weighted verification W(S(1)) > W(S(3)) > W(S(2)), where S(1), S(2), and S(3) represent subjective factor conditions. In this scenario, the subjective factor matching strategy will perform priority verification in the matching order of S(1), S(3), and S(2);

[0082] In the full-condition hit strategy matching mode, the formula that satisfies all conditions is fn(S(1)) ∧ fn(S(3)) ∧ fn(S(2)), SUM(exp(S(i))) < SUM(S(i)), where exp(factor) is the expected value calculation function, S(i) represents a certain condition, record that the condition S(i) < exp(S(i)) is marked as an unmet condition, record the met and unmet conditions in the match_result table, and mark the record result of all met conditions as true to enter the next objective condition verification;

[0083] In the partial-condition hit strategy mode, the strategy calculation formula that is satisfied is fn(S(1)) ∨ fn(S(3)) ∨ fn(S(2)). The comparison order is still to match the one with the higher weighting value first to improve the hit rate and reduce the number of batches. Record that the condition S(i) < exp(S(i)) is marked as an unmet condition; S(i) ≥ exp(S(i)) is marked as a met condition; record the matching result in the match_result result set;

[0084] After the subjective factor matching is completed, judge whether subsequent objective factors need to be involved. If not, complete the strategy and return the record result. In the scenario that requires the objective strategy, continue the subsequent matching process according to the responsibility chain mode;

[0085] Objective factor matching mode, the present invention extracts relatively fixed attributes of goods and acceptance points. The judgment of attribute conditions is stable with little change. The logic here can form a long-term stable and effective matching mode; the objective factors also need to pay attention to the weighted factors. The key to weights is to improve the key to the main matching factors in different strategies. We focus on extracting some core elements of major categories (O(1)), medium categories (O(2)), page categories (O(3)), bases (O(4)), warehouses (O(5)), and acceptance points (O(6)) as matching objective factors.

[0086] Condition a: Match according to the major category. If the created policy does not match the major category, it will be returned directly. If the major category is not empty, it will determine whether the data major category and the policy major category match.

[0087] Condition b: Match according to the middle class. If the created policy does not reach the middle class, it will be returned directly. If the middle class is not empty, it will determine whether the middle class of the data matches the middle class of the policy.

[0088] Condition c: Match according to the leaf class. If the created policy does not reach the middle class, it will be returned directly. If the leaf class is not empty, it will determine whether the data leaf class and the policy leaf class match.

[0089] Condition d: According to the base matching, if the created strategy does not reach the base, it will return directly. If the base is not empty, it will determine whether the data base and the strategy base match;

[0090] Condition e: According to the warehouse matching, if the created policy does not reach the warehouse, it will be returned directly. If the warehouse is not empty, it will determine whether the data warehouse and the policy warehouse match;

[0091] Condition f: According to the acceptance point matching, if the created strategy does not reach the acceptance point, it will return directly. If the acceptance point is not empty, it will determine whether the data acceptance point matches the strategy acceptance point;

[0092] There is no fixed order for the above conditional af matching. The order is compared by the set weights to improve the flexibility of the strategy.

[0093] The objective conditions still follow the full condition matching: condition a ∧ condition b ∧ .... ∧ condition f. This scenario needs to meet all matching conditions to hit the policy. The unmatched conditions are recorded in the matching result record set match_result and marked as false. The matched record data is true.

[0094] Partial condition name matching strategy: condition a ∨ condition b ∨ .... ∨ condition f. In scenarios where a single or partial condition is met, the met conditions are recorded in the result record set match_result and marked as true, mainly to facilitate subsequent error troubleshooting and regression training correction.

[0095] After the acceptance point strategy matching affected by objective factors is completed, the return main task determines whether to follow the unexpected factor judgment process according to the strategy priority. If not, the acceptance point strategy is hit and the acceptance point information is returned.

[0096] Unexpected factors are often non-essential conditions unless they meet the customized decision requirements of certain specific acceptance points. Define unexpected factors, acceptance point blacklists (customers and hazardous chemicals prohibited from entering), acceptance point operation time periods, and customized requirements. For simple expression, the unexpected factors are replaced by A(k) below; as supplementary decision conditions for subjective and objective factors.

[0097] If some suppliers are blacklisted by some warehouse acceptance points due to some unexpected issues, such as quality and payment, they will be directly rejected by the acceptance point. For some high-quality customers, they can also be directly added to the whitelist, and the decision-making strategy can quickly match the acceptance point.

[0098] In addition, due to technical or personnel limitations of some acceptance points, it is necessary to set up prohibited items that cannot be selected at this acceptance point. The main manifestations are hazardous chemicals and warehouse storage areas that cannot be accepted due to excessive length and width.

[0099] The completion of the unexpected factor matching scenario is uniformly recorded in the result set match_result.

[0100] For all the above missed strategy data, the present invention will adopt a global recommendation strategy system, enable the approximate point recommendation strategy, and use 3-point estimation x = {all / n|all = SUM(exp(S(i)))+SUM(exp(O(j)))+SUM(exp(A(k))), n = max(i)+max(j)+max(k)}. The acceptance point strategy expected value closer to the x value is the recommended point. There may be more than one recommended point strategy hit, which belongs to an acceptance point recommendation list. Each acceptance point will get a score based on the above scores. The maximum match score is 10 points. Extracting the top 5 acceptance point list requires manual selection of records.

[0101] The manually selected acceptance point information will eventually be recorded in the unified result set match_result.

[0102] The result set match_result is trained and developed for regression, and the manual comparison and correction of match_result results are carried out regularly. According to the machine learning comprehensive scoring acceptance point strategy hit accuracy, the problem of the hit strategy with too low success rate is checked and corrected, which is an important part of this method.

[0103] Example 3

[0104] Embodiment 3 is a preferred embodiment of Embodiment 1 or Embodiment 2

[0105] According to the present invention, a method for setting acceptance points based on different logistics dimensions includes:

[0106] Suppliers from the mall need to deliver the purchase commodity A entry orders of major category C1, medium category C11, and leaf category C111 to the acceptance point warehouse in Chongqing. The customer classification is marked as hazardous chemicals, waterproof, weight 20 tons, length, width and height (4m*2m*1m). In order to reduce resource scheduling and acceptance checkpoint problems. The system strategy adjusts the unexpected factor strategy to the highest priority. The system prioritizes checking whether there are warehouses in the acceptance point in Chongqing that refuse to accept the customer or the customer chooses the fleet, to avoid the problem of customers being unable to enter the goods. A preliminary acceptance diagnosis is given before the goods are transported, and the reasons for possible rejection are given, which are recorded in the user recommendation feedback table, making it convenient for suppliers to anticipate problems and deal with them in advance.

[0107] In view of the variability of real-world scenario requirements, the method increases scalability. In the scenario where the system is stuck due to unexpected factors and the goods need to be delivered urgently, with the consent of the business party, the input parameters of the unexpected factor conditions can be temporarily or permanently changed to lower the score weight of the customer or carrier in the unexpected factor:

[0108] |SUM(exp(A(j)))*weighted value (adjusted to 0.01)-SUM(A(j)))|≤deviation value

[0109] This method sets the factor score within the deviation value, which can be measured and evaluated through the method, aiming to improve the compatibility of the system, configure more business scenarios, and pass different acceptance strategies with lower requirements.

[0110] When commodity A passes the unexpected factor acceptance strategy, at this time, according to the order elements, the acceptance point recommendation strategy is selected according to the scenario where the subjective factors are higher than the customer factors. Obtain the supplier's subjective factors (some are listed to illustrate the case): Chongqing base, waterproofing, and hazardous chemicals. In the case of subjective factors selecting a parallel recommendation strategy, in the scenario where the weights of each factor are equal, the algorithm needs to meet all subjective conditions fn (Chongqing base) ∧ fn (waterproofing) ∧ fn (hazardous chemicals); in some scenarios, the subjective factors are not parallel, but have priority requirements. Here, you can enable high-priority condition scoring and enable sequential matching strategies. For example, only one of them needs to be met, fn (Chongqing base) ∨ fn (waterproofing) ∨ fn (hazardous chemicals). When the base is Chongqing, other conditions are no longer involved in the judgment. If the first condition is not met, continue to judge the second condition downward until some scenarios are met and it can be determined to be passed;

[0111] For scenarios where all conditions are not met, we will record the unmet information and enable the global recommendation algorithm strategy (described in the subsequent steps).

[0112] In the scenario that meets the subjective factors of the scene, product A enters the objective factor decision-making stage: major category C1, medium category C11, leaf category C111, hazardous chemicals, length, width and height (4m*2m*1m).

[0113] Check whether the category has passed: The judgment and acceptance strategy for large and medium-sized categories is relatively fixed, and the global recommended strategy will be used for unsuccessful records.

[0114] Directly check whether the acceptance points in Chongqing area are met, and follow the global recommended strategy without going through the records.

[0115] For special types of goods, some acceptance points do not meet the acceptance conditions. For example, some ingredients of the goods cannot exceed the standard and special technical means are required to identify the grade. The acceptance points are screened to see if they meet the acceptance requirements. If not, the global recommendation algorithm strategy is enabled.

[0116] The area that matches the acceptance point, whether there are any height restrictions, width restrictions, volume restrictions, etc. for the length, width, and height, and the site storage conditions of the acceptance point, whether there are any width restrictions, height restrictions, and volume restrictions.

[0117] The scene acceptance points that all meet the subjective factors, objective factors, and unexpected factors can be determined as the acceptance points of product A, the acceptance information is recorded, and a list of recommended points is returned.

[0118] In the scenario where the acceptance recommendation strategy does not meet the acceptance point, the global recommendation algorithm strategy can be enabled to approximately match the recommended acceptance points, with expected value E(x) = sum(Ex(subjective factor score) + Ex(objective factor score) + Ex(unexpected factor score)) / total number of factors, actual value Avg(acceptance point 1) = sum(acceptance point 1 subjective factor score + acceptance point 1 objective factor score + acceptance point 1 unexpected factor score) / total number of factors, actual value Avg(acceptance point 2) = sum(acceptance point 2 subjective factor score + acceptance point 2 objective factor score + acceptance point 2 unexpected factor score) / total number of factors; take the absolute value of the difference between the score and the expected value of the recommended acceptance point, value1 = |Avg(acceptance point 1)-E(x)|, value2 = |Avg(acceptance point 2)-E(x)|, sort the values ​​value1 and value2 from small to large, the smaller the value, the closer it is to the expected acceptance point, and give the recommended acceptance point return mark.

[0119] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0120] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for setting acceptance points based on different logistics dimensions, characterized in that: include: Step S1: constructing an acceptance point model based on the acceptance point matching data input conditions and output conditions; wherein the input conditions include: subjective factors and objective factors; and the output conditions include the expected acceptance point; Step S2: Based on the acceptance point model, obtain recommended acceptance points according to subjective factors and objective factors in actual needs, and further filter the recommended acceptance points obtained according to unexpected factors based on the currently obtained recommended acceptance points to obtain the final matching recommended acceptance points; Step S3: In the case where the final matching recommended acceptance points are not obtained after further screening based on unexpected factors, the recommended acceptance points are obtained using the approximate point recommendation strategy.

2. The method for setting acceptance points based on different logistics dimensions according to claim 1 is characterized in that: The step S1 comprises: based on subjective factors and objective factors; Subjective factors are the requirements of upstream customers, including: unique attributes of items, designated warehouse areas, storage conditions, and special transportation requirements; The objective factors are acceptance point conditions, including: technical personnel, storage conditions and space.

3. The method for setting acceptance points based on different logistics dimensions according to claim 1 is characterized in that: In the acceptance point model, weighted values ​​are configured for each of the subjective factors and the objective factors, and the acceptance elements are prioritized based on the configured weighted values, and preferential matching is performed according to the priority order.

4. The method for setting acceptance points based on different logistics dimensions according to claim 3 is characterized in that: The step S2 comprises: Step S2.1: Taking the subjective factors in the actual requirements as input conditions, preliminarily screen the matching expected acceptance points based on the acceptance point model; Step S2.3: Record the expected acceptance points that meet the subjective factor conditions into the hit strategy flow table, and then determine whether objective factors are required to participate in the matching based on the matching mode of the responsibility chain. If necessary, trigger step S2.4, otherwise return the record result; Step S2.4: Taking the objective factors in the actual demand as input conditions, on the basis of preliminary screening of the matching expected acceptance points, further screening the matching expected acceptance points using the acceptance point model; Step S2.5: Record the expected acceptance points that meet the objective factor conditions into the hit strategy flow table; Step S2.6: Integrate unexpected factors to comprehensively determine whether the selected expected acceptance points ultimately meet the acceptance points that meet the requirements, and record the acceptance point information; if all are missed, record the information that caused the missed acceptance points; The unexpected factors are sudden conditions of variability and uncertainty, customer demands, and unpredictable conditions, including: rejection at the acceptance point, changes in acceptance conditions, and blacklists and whitelists.

5. The method for setting acceptance points based on different logistics dimensions according to claim 4 is characterized in that: The combination strategy selection of the expected acceptance point that meets the subjective factor conditions or the expected acceptance point that meets the objective factor conditions includes: successful hit of all conditions and hit of some conditions; The partial condition hits include: matching with higher weighted values ​​first.

6. The method for setting acceptance points based on different logistics dimensions according to claim 1, characterized in that: The step S3 comprises: x={all / n|all=SUM(exp(S(i)))+SUM(exp(O(j)))+SUM(exp(A(k)),n=max(i)+max(j)+max(k)}; Among them, S(i) represents subjective factors; O(j) represents objective factors; A(k) represents unexpected factors; exp(factor) represents the expected value of the factor; The acceptance point strategy expectation value that is closer to the x value is the recommended acceptance point.

7. The method for setting acceptance points based on different logistics dimensions according to claim 1, characterized in that: The method also includes: using machine learning to comprehensively evaluate the hit accuracy of the recommended acceptance points, and optimizing the acceptance point model based on the evaluation results.

8. A system for setting acceptance points based on different logistics dimensions, characterized in that: include: Module M1: constructing an acceptance point model based on the acceptance point matching data input conditions and output conditions; wherein the input conditions include: subjective factors and objective factors; the output conditions include the expected acceptance point; Module M2: Based on the acceptance point model, obtain recommended acceptance points according to subjective factors and objective factors in actual needs, and further filter the recommended acceptance points based on the currently obtained recommended acceptance points according to unexpected factors to obtain the final matching recommended acceptance points; Module M3: When the recommended acceptance points that are not finally matched are further screened based on unexpected factors, the recommended acceptance points are obtained using the approximate point recommendation strategy.

9. The system for setting acceptance points based on different logistics dimensions according to claim 8, characterized in that: The module M1 includes: based on subjective factors and objective factors; Subjective factors are the requirements of upstream customers, including: unique attributes of items, designated warehouse areas, storage conditions, and special transportation requirements; The objective factors are acceptance point conditions, including: technical personnel, storage conditions and space; In the acceptance point model, weighted values ​​are configured for each of the subjective factors and the objective factors, and the acceptance elements are prioritized based on the configured weighted values, and preferential matching is performed according to the priority order.

10. The system for setting acceptance points based on different logistics dimensions according to claim 9, characterized in that: The module M2 comprises: Module M2.1: Taking the subjective factors in the actual requirements as input conditions, preliminarily screen the matching expected acceptance points based on the acceptance point model; Module M2.3: Record the expected acceptance points that meet the subjective factor conditions into the hit strategy flow table, and then determine whether objective factors are required to participate in the matching based on the matching mode of the responsibility chain. If necessary, trigger module M2.4, otherwise return the record result; Module M2.4: Taking the objective factors in the actual requirements as input conditions, on the basis of preliminary screening of matching expected acceptance points, further screening matching expected acceptance points using the acceptance point model; Module M2.5: Record the expected acceptance points that meet the objective factors into the hit strategy flow table; Module M2.6: Integrate unexpected factors to comprehensively determine whether the selected expected acceptance points ultimately meet the acceptance points that meet the requirements, and record the acceptance point information; if all are missed, record the information that caused the missed acceptance points; The unexpected factors are sudden conditions and customer demands of variability and uncertainty and unpredictable conditions, including: acceptance point rejection, change of acceptance conditions and blacklist and whitelist; The combination strategy selection of the expected acceptance point that meets the subjective factor conditions or the expected acceptance point that meets the objective factor conditions includes: successful hit of all conditions and hit of some conditions; The partial condition hits include: matching with a higher weighted value first; The module M3 comprises: x={all / n|all=SUM(exp(S(i)))+SUM(exp(O(j)))+SUM(exp(A(k)),n=max(i)+max(j)+max(k)}; Among them, S(i) represents subjective factors; O(j) represents objective factors; A(k) represents unexpected factors; exp(factor) represents the expected value of the factor; The acceptance point strategy expectation value that is closer to the x value is the recommended acceptance point.

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