Full Product Data Optimization Method, Electronic Device and Storage Medium of a Logistics System

Abstract

The present invention relates to the technical field of data processing, and particularly to a method for optimizing all product data of a logistics system, an electronic device, and a storage medium. The method includes: receiving a set of product logistics request information sent by each product service platform within a preset time period, parsing each product logistics request information to obtain a number of product logistics features corresponding to each product logistics request information, inputting the product logistics feature set of the product and the optimized target feature weights corresponding to each product logistics feature into a trained given classification model, and allocating a corresponding target logistics instruction for the product; by optimizing the feature weights of each product logistics feature, the logistics instructions allocated for each product are made more reasonable, thereby improving the logistics sending efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a full-product data optimization method, electronic equipment and storage medium for a logistics system. Background Art

[0002] At present, users' expectations for logistics services are no longer limited to the safe delivery of goods, but they hope to receive goods faster. However, due to the large number of product service platforms, such as a large number of shopping apps and shopping mini-programs, the total number of orders will be large. When users place orders on different product service platforms, due to the different properties of different product service platforms, for example, the order is placed early but the delivery time to the logistics system is late due to the large sending interval. When the logistics system integrates the received order information, it will lead to unreasonable logistics distribution and low logistics efficiency, affecting user satisfaction. Summary of the invention

[0003] In response to the above technical problems, the present invention provides a full-product data optimization method, electronic device and storage medium for a logistics system. By optimizing the feature weights of each product's logistics features, the logistics instructions assigned to each product are more reasonable, thereby improving logistics delivery efficiency.

[0004] According to a first aspect of the present invention, a method for optimizing full product data of a logistics system is provided, comprising the following steps:

[0005] S100, receiving a product logistics request information set sent by each product service platform within a preset time period , where A i is the i-th product logistics request information, i=1, 2, …, m, and m is the number of product logistics requests sent by any product service platform.

[0006] S200, for A i Analyze and obtain A i Corresponding product logistics feature set ,in, A i The corresponding j-th product logistics characteristics, j = 1, 2, ..., n, n is A i The number of corresponding product logistics characteristics.

[0007] S300, A i The corresponding product logistics feature set B i and each The corresponding optimized target feature weights are input into the trained given classification model, and the output result of the given classification model is B iThe corresponding product is allocated a corresponding target logistics instruction to update the logistics information of the corresponding product according to the target logistics instruction for A. i The logistics information of the corresponding product is updated.

[0008] According to a second aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, and the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the full product data optimization method of the above-mentioned logistics system.

[0009] According to a third aspect of the present invention, there is provided an electronic device including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0010] The present invention has at least the following beneficial effects:

[0011] The present invention provides a full product data optimization method for a logistics system, which receives a set of product logistics request information sent by each product service platform within a preset time period, parses each product logistics request information to obtain several product logistics features corresponding to each product logistics request information, inputs the product logistics feature set of the product and the optimized target feature weights corresponding to each product logistics feature into a trained given classification model, allocates a corresponding target logistics instruction for the product, and optimizes the data of the feature weights of each product logistics feature, making the logistics instructions allocated for each product more reasonable, thereby improving the logistics sending efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0013] Figure 1 It is a flowchart of the full product data optimization method for the logistics system provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0015] The embodiment of the present invention provides a full product data optimization method for a logistics system, as Figure 1As shown, the method includes the following steps:

[0016] S100, receiving the set of product logistics request information sent by each product service platform within a preset time period , where A i is the i-th product logistics request information, i = 1, 2, ……, m, and m is the number of product logistics requests sent by any product service platform. In a specific implementation, the number of product logistics request information sent by different product service platforms within the preset time period is different, which is related to the actual number sent within the preset time period.

[0017] Specifically, the preset time period is any time period divided according to a preset time cycle. For example, the duration of the preset time period can be 10s.

[0018] Furthermore, the product service platform refers to a platform that provides alternative products for users. For example, a shopping app or a shopping mini-program, etc.

[0019] S200, parsing A i to obtain the corresponding product logistics feature set of A i , where is the j-th product logistics feature corresponding to A i , j = 1, 2, ……, n, and n is the number of product logistics features corresponding to A i .

[0020] Furthermore, the product logistics feature is any one of the order placement time, delivery time limit, shipping address, and receiving address corresponding to the product. In a specific implementation, other product logistics features can also be added, such as the feature of whether it is free shipping.

[0021] As mentioned above, when obtaining the logistics allocation situation corresponding to each order product, several product logistics features corresponding to the order need to be considered, and the influence degree of each product logistics feature on the logistics allocation situation is different. Therefore, it is necessary to first parse and obtain several logistics features, and then generate subsequent logistics instructions according to the obtained logistics features.

[0022] S300, inputting the product logistics feature set B i corresponding to A i and the optimized target feature weights corresponding to each into the trained given classification model, and using the output result of the given classification model to allocate the corresponding target logistics instruction for the product corresponding to B i , so as to implement the corresponding operation on A according to the target logistics instruction iUpdate the logistics information of the corresponding product; it can be understood that the target logistics instruction refers to the instruction to allocate any product to any logistics vehicle trip, and the characteristics corresponding to the logistics vehicle trip are the departure address, the departure time point, and the vehicle type size.

[0023] Further, the given classification model is a trained target random forest model.

[0024] Specifically, the given classification model is trained through the following steps:

[0025] S10. Obtain the historical product logistics feature sets corresponding to a number of historical products and the preset logistics instructions corresponding to each historical product, and construct a sample data set. Among them, the preset logistics instruction corresponding to the historical product is a logistics instruction preset according to the actual situation, so that the historical product has a high logistics sending efficiency.

[0026] S20. For any decision tree in the preset random forest model, randomly and with replacement extract a number of samples from the sample data set as the sub-training set of the decision tree.

[0027] S30. Input the historical product logistics feature sets in each sub-training set into the corresponding decision tree in the preset random forest model to obtain the logistics instruction output result of each decision tree.

[0028] S40. Adjust the parameters of the decision tree according to the preset logistics instruction in each sub-training set and the logistics instruction output result of each decision tree to obtain the trained given classification model. Among them, the parameter adjustment process in the training process of the random forest model is the prior art, or those skilled in the art adjust the parameters of the model according to actual needs, and the specific process will not be elaborated here.

[0029] Preferably, obtain through the following steps The corresponding target feature weights:

[0030] S310. Obtain The corresponding number of target impact indicators; the target impact indicators are the indicators obtained in advance that affect The corresponding target feature weights.

[0031] Further, obtain through the following steps The corresponding number of target impact indicators:

[0032] S311. Obtain The corresponding number of preset impact indicators.

[0033] Specifically, the preset impact indicators are the indicators preset to affect The corresponding target feature weights.

[0034] Further, the preset impact indicators include, but are not limited to, indicators such as the stability of the API interface of the product service platform, network latency duration, the number of orders placed within a preset time period, the decompression duration of the decompressed package, and the correct rate of address resolution.

[0035] S312. Perform preprocessing and normalization on the historical data corresponding to each preset impact indicator to obtain a number of target data corresponding to each preset impact indicator.

[0036] Specifically, the preprocessing of the historical data corresponding to each preset impact indicator includes cleaning the historical data and performing one-hot encoding operations.

[0037] S313. Calculate the Pearson correlation coefficient between each preset impact indicator and the corresponding logistics instruction data. Among them, the logistics instruction data can be a vector combination obtained by performing one-hot encoding on several logistics information in the logistics instruction. For example, the logistics information can be the departure time of the train number, the size of the vehicle type, etc. Those skilled in the art are aware of the calculation formula and calculation process of the Pearson correlation coefficient, which will not be elaborated here.

[0038] S314. Screen out each preset impact indicator whose corresponding Pearson correlation coefficient is greater than the preset Pearson correlation coefficient threshold and use them as target impact indicators. Those skilled in the art set the preset Pearson correlation coefficient threshold according to actual needs. For example, 0.6.

[0039] As described above, by calculating the Pearson correlation coefficient between each preset impact indicator and the corresponding logistics instruction data, it is possible to screen out the preset impact indicators with a high degree of correlation with the logistics instruction data and a large impact on the logistics instruction data. By inputting the data corresponding to the screened preset impact indicators into the classification model, the allocation result of the logistics instruction is made more accurate and reasonable.

[0040] S320. Perform dimensionless processing on the data corresponding to each target impact indicator, and obtain the index weight corresponding to each target impact indicator according to the processed data; it can be understood that: before the dimensionless processing, different types of data are first unified into the same type of data, for example, all are converted into extremely large data or extremely small data. The data conversion process is an existing technology and will not be elaborated here.

[0041] Specifically, step S320 includes the following steps:

[0042] S321. Perform dimensionless processing on the data corresponding to each target impact indicator to obtain the corresponding dimensionless index data set , where is the corresponding The dimensionless data of each target impact index , where f is The number of corresponding target impact indices.

[0043] Furthermore, when performing dimensionless processing on the data corresponding to each target impact index, the initial value method or the mean value method can be used to eliminate the differences in the dimensions and orders of magnitude of different indices. Those skilled in the art are aware of the specific processes of using the initial value method and the mean value method for dimensionless processing, and will not be described in detail here.

[0044] S322. Calculate the difference between each and The corresponding preset reference value; it can be understood that: The corresponding preset reference value is The preset optimal value of the th target impact index.

[0045] S323. According to the maximum difference and the minimum difference obtained from several differences, and the difference between each and The corresponding preset reference value, calculate the correlation coefficient between each target impact index and The corresponding target feature weight; it can be understood that: the several differences refer to the differences corresponding to all the dimensionless data, that is, there are f×n differences in total.

[0046] Specifically, the correlation coefficient between the target impact index and The corresponding target feature weight meets the following conditions:

[0047] , where Represents The th target impact index corresponding to The correlation coefficient with the corresponding target feature weight, d is the minimum difference, D is the maximum difference, Is And The difference between the corresponding preset reference value, ρ is the preset resolution coefficient, where ρ ∈ [0, 1]. For example, the value of ρ is 0.5.

[0048] S324. According to the correlation coefficient between each target impact index and The corresponding target feature weight, obtain the index weight corresponding to each target impact index.

[0049] Specifically, the index weight corresponding to the target impact index meets the following conditions:

[0050] , where denote the index weight of the corresponding denote the index of the corresponding correlation coefficient between the target impact index and the corresponding target feature weight, refers to the i g-th product logistics feature corresponding to A, where the value range of g is from 1 to n.

[0051] Above, considering different product service platforms, due to different attributes, their corresponding feature weights are also different. The attributes include several target impact indexes, and different target impact indexes have different degrees of influence on the feature weights. Through the above process, the correlation coefficient between each target impact index and the corresponding target feature weight is calculated, and the index weight corresponding to the target impact index is obtained according to the correlation coefficient, making the setting of the index weight more accurate and reliable.

[0052] S330. According to the values of several target impact indexes and the index weight of each target impact index, calculate the weighted sum to obtain the corresponding target impact factor.

[0053] S340. Multiply the target impact factor by the corresponding initial feature weight as the corresponding target feature weight; it can be understood that: the corresponding initial feature weight is the pre-set weight. In specific implementation, the normalization process can also be performed on several target feature weights, and all are normalized to between 0 and 1.

[0054] Above, since the feature weight of each product logistics feature is affected by multiple factors, by analyzing multiple influencing factors and determining the influence degree of each influencing factor on the product logistics feature, the obtained feature weight of the product logistics feature is more accurate and reliable, realizing the optimization of product data. Through the optimized data, the logistics instructions assigned to each product by the classification model are more reasonable, thus improving the logistics sending efficiency.

[0055] In summary, the embodiments of the present invention provide an optimization method for all product data of a logistics system, which receives a set of product logistics request information sent by each product service platform within a preset time period, parses each product logistics request information to obtain several product logistics features corresponding to each product logistics request information, inputs the product logistics feature set of the product and the optimized target feature weights corresponding to each product logistics feature into a trained given classification model, assigns a corresponding target logistics instruction to the product, and optimizes the data of the feature weights of each product logistics feature, so that the logistics instructions assigned to each product are more reasonable, thereby improving the logistics sending efficiency.

[0056] The embodiments of the present invention also provide a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to a method for implementing a method in the method embodiments, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0057] The embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0058] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for illustration and not for limiting the scope of the present invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for optimizing all product data of a logistics system, characterized in that: The method comprises the following steps: S100, receiving a product logistics request information set sent by each product service platform within a preset time period , Among them, A i is the i-th product logistics request information, i=1, 2, ..., m, m is the number of product logistics requests sent by any product service platform; S200, for A i Analyze and obtain A i Corresponding product logistics feature set , in, A i The corresponding j-th product logistics characteristics, j = 1, 2, ..., n, n is A i the number of corresponding product logistics characteristics; S300, A i The corresponding product logistics feature set B i and each The corresponding optimized target feature weights are input into the trained given classification model, and the output result of the given classification model is B i The corresponding products are assigned corresponding target logistics instructions to achieve A according to the target logistics instructions. i Update the logistics information of the corresponding products; The method trains a given classification model through the following steps: S10, obtaining a historical product logistics feature set corresponding to a number of historical products and a preset logistics instruction corresponding to each historical product, and constructing a sample data set; S20, for any decision tree in the preset random forest model, a number of samples are randomly extracted with replacement from the sample data set as a sub-training set of the decision tree; S30, inputting a number of historical product logistics feature sets in each sub-training set into a corresponding decision tree in a preset random forest model to obtain a logistics instruction output result of each decision tree; S40, adjusting the parameters of the decision tree according to the preset logistics instructions in each sub-training set and the logistics instruction output results of each decision tree to obtain a trained given classification model; The method obtains the following steps The corresponding target feature weights are: S310, obtain Corresponding to several target impact indicators; the target impact indicators are pre-acquired impact The corresponding target feature weight indicator; S320, performing dimensionless processing on the data corresponding to each target impact indicator, and obtaining the indicator weight corresponding to each target impact indicator according to the processed data; S330, calculating a weighted sum based on the values ​​of the plurality of target impact indicators and the indicator weight of each target impact indicator to obtain The corresponding target impact factor; S340, the target impact factor and The product of the corresponding initial feature weights is The corresponding target feature weight.

2. The method for optimizing the full product data of a logistics system according to claim 1, characterized in that: Obtained through the following steps Corresponding target impact indicators: S311, obtain Corresponding several preset impact indicators; S312, preprocessing and normalizing the collected historical data corresponding to each preset impact indicator to obtain a number of target data corresponding to each preset impact indicator; S313, calculating the Pearson correlation coefficient between each preset influencing indicator and the corresponding logistics instruction data according to a number of target data corresponding to each preset influencing indicator; S314, each preset influence indicator whose corresponding Pearson correlation coefficient is greater than a preset Pearson correlation coefficient threshold is selected and used as a target influence indicator.

3. The method for optimizing the full product data of a logistics system according to claim 1, characterized in that: Step S320 includes the following steps: S321, dimensionless processing is performed on the data corresponding to each target impact indicator to obtain Corresponding dimensionless indicator dataset , in, for The corresponding dimensionless data of the target impact indicator, , f is The number of corresponding target impact indicators; S322, calculate each and The difference between the corresponding preset reference values; S323, according to the maximum difference and the minimum difference obtained from the plurality of differences, and each and The difference between the corresponding preset reference values ​​is calculated to calculate the impact index of each target and The correlation coefficient of the corresponding target feature weight; Among them, the target impact index and The correlation coefficient of the corresponding target feature weight meets the following conditions: ， in, express The corresponding Target impact indicators and The corresponding correlation coefficient of the target feature weight, d is the minimum difference, D is the maximum difference, for and The difference between the corresponding preset reference values, ρ is the preset resolution coefficient; S324, according to each target impact indicator and The correlation coefficient of the corresponding target feature weight is used to obtain the indicator weight corresponding to each target influencing indicator; Among them, the indicator weights corresponding to the target impact indicators meet the following conditions: ， in, express The corresponding The indicator weight of each target impact indicator, express The corresponding Target impact indicators and The corresponding correlation coefficient of the target feature weight, A i The corresponding g-th product logistics characteristic, the value of g ranges from 1 to n.

4. The method for optimizing the full product data of a logistics system according to claim 2, characterized in that: The preset influencing indicators include the API interface stability of the product service platform, network delay duration, number of orders placed within a preset time period, decompression time of the decompressed package, and address resolution accuracy.

5. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the full product data optimization method of the logistics system as described in any one of claims 1-4.

6. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 5.

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