Logistics order average weight pricing method, device and equipment and storage medium

Abstract

This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for quoting average weight for logistics orders. It calculates the estimated total weight of multiple shipments based on a preset historical database, and estimates the total weight of existing logistics orders using past shipment quantity and weight data. This allows customers to estimate shipment costs, facilitating bulk order shipments for large customers. By separately acquiring settlement weight information corresponding to individual average weight settlement groups and multiple shared average weight settlement groups, and quoting average weight based on this information, it eliminates the need to weigh each shipment individually, improving the speed of weight information acquisition, increasing quotation efficiency, thereby improving logistics order shipment efficiency, shortening the shipment cycle, and enhancing user experience.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, equipment, and storage medium for quoting average weight for logistics orders. Background Technology

[0002] In the e-commerce sector, logistics order quotations are usually calculated based on the weight of the package. Large customers have a huge number of logistics orders during peak periods. It is difficult to weigh and quote logistics orders within a specified time, which is inconvenient for large customers to send packages during special periods, reduces quotation processing efficiency, prolongs the delivery cycle, and reduces user experience. In addition, large customers' logistics orders contain multiple packages of different styles, sizes and weights. Users cannot estimate the total weight of the package before sending it, and therefore cannot estimate the delivery cost. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, the present invention aims to provide a method, apparatus, equipment, and storage medium for estimating the total weight of existing logistics orders by utilizing past shipment quantity and weight data, thereby facilitating customers to estimate the cost of shipment, enabling large customers to perform bulk order shipment operations, improving the speed of obtaining weight information, improving quotation efficiency, enhancing the efficiency of logistics order shipment, shortening the shipment cycle, and improving the user experience.

[0004] The first aspect of this invention provides a method for quoting average weight for logistics orders, comprising: acquiring customer logistics order information, parsing the customer logistics order information to obtain multiple shipment information, calculating the estimated total weight of the multiple shipment information based on a preset historical database; identifying destination information in each shipment information, and grouping the multiple shipments according to the destination information to obtain multiple groups of settlement objects to be classified; classifying the multiple groups of settlement objects to be classified according to a preset all-region average weight calculation participation model to obtain individual average weight settlement object groups and multiple common average weight settlement object groups; acquiring settlement weight information corresponding to the individual average weight settlement object groups and the multiple common average weight settlement object groups respectively, calculating the individual average weight settlement object groups according to the settlement weight information to obtain a first calculation result, calculating the common average weight settlement object groups according to the settlement weight information to obtain a second calculation result; integrating the first calculation result and the second calculation result to obtain an integrated result, and generating a final average weight quotation table according to the integrated result.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the step of obtaining customer logistics order information, parsing the customer logistics order information to obtain multiple ticket information, and calculating the estimated total weight based on a preset historical database includes: obtaining customer logistics order information, parsing the customer logistics order information to obtain total ticket quantity information, multiple ticket information, current item type information corresponding to each ticket information, and first category ticket quantity information for each type; obtaining historical item information corresponding to the current item type information from a preset historical database, and identifying the historical item quantity and historical item weight in the historical item information; calculating a reference package weight per unit number of items based on the historical item quantity and historical item weight; calculating the estimated weight for the current category based on the first category ticket quantity information and the reference package weight; summing all the estimated weights for the current category to obtain the estimated total weight; and sending the estimated total weight to the user terminal so that the user terminal generates and displays a total weight estimation page based on the estimated total weight.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the step of identifying the destination information in each ticket information and grouping multiple tickets according to the destination information to obtain multiple groups of objects to be classified and settled includes: extracting text information from each ticket information and identifying the destination information in the text information; performing word segmentation on the destination information to obtain multiple words and extracting character features of each word; identifying the character features through a thesaurus with different regional classifications; if the character features match the words in the thesaurus, then classifying the ticket corresponding to the character features into the thesaurus of this region, and grouping all tickets in the same thesaurus into one group to obtain multiple groups of objects to be classified and settled.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the step of classifying multiple groups of settlement objects to be classified according to a preset regional average weight calculation participation model to obtain individual average weight settlement object groups and multiple common average weight settlement object groups includes: creating a custom model based on multiple regions participating in the average weight calculation to obtain a regional average weight calculation participation model; filtering multiple groups of settlement objects to be classified with different destination information according to the regional average weight calculation participation model; classifying the groups of settlement objects to be classified that meet the filtering conditions as common average weight settlement object groups, and classifying the groups of settlement objects to be classified that do not meet the filtering conditions as individual average weight settlement object groups, thereby obtaining individual average weight settlement object groups and multiple common average weight settlement object groups after the filtering is completed.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the step of obtaining settlement weight information corresponding to the individual average weight settlement object group and the plurality of common average weight settlement object groups, calculating the individual average weight settlement object group based on the settlement weight information to obtain a first calculation result, and calculating the common average weight settlement object group based on the settlement weight information to obtain a second calculation result, includes: obtaining first total weight information of the individual average weight settlement object group and second total weight information of the plurality of common average weight settlement object groups, and obtaining second category ticket quantity information of the plurality of common average weight settlement object groups; identifying the quantity proportion of each common average weight settlement object group in the second category ticket quantity information, and obtaining the proportion weight of each common average weight settlement object group in the second total weight information based on the quantity proportion; obtaining a set of price groups, calculating the individual average weight settlement object group based on the set of price groups and the first total weight information to obtain a first calculation result, and calculating the common average weight settlement object group based on the set of price groups and the proportion weight to obtain a second calculation result.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the step of obtaining the set of quotation groups, calculating the individual average weight settlement object group based on the set of quotation groups and the first total weight information to obtain a first calculation result, and calculating each of the common average weight settlement object groups based on the set of quotation groups and the percentage weight to obtain a second calculation result, includes: identifying the origin information in the customer logistics order information; obtaining the set of quotation groups based on the origin information, and parsing the set of quotation groups to obtain multiple sub-quotation groups, each of the sub-quotation groups including regional information and billing rule information corresponding to the regional information; matching the multiple sub-quotation groups with the individual average weight settlement object group and each of the common average weight settlement object groups based on the regional information, each of the destination information corresponding to one of the regional information; calculating the individual average weight settlement object group based on the first total weight information and the billing rule information in the matched sub-quotation groups to obtain a first calculation result; and calculating each of the common average weight settlement object groups based on the percentage weight and the billing rule information in the matched sub-quotation groups to obtain a second calculation result.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the step of integrating the first calculation result and the second calculation result to obtain an integrated result, and generating a final average weight price table based on the integrated result, includes: accumulating multiple second calculation results to obtain an accumulated result; adding the accumulated result to the first calculation result to obtain an integrated result; generating a final average weight price table based on the integrated result; and sending the final average weight price table to the user terminal so that the user terminal generates and displays an average weight price page based on the final average weight price table.

[0011] A second aspect of the present invention provides a logistics order average weight pricing device, comprising: a parsing and calculation module, configured to acquire customer logistics order information, parse the customer logistics order information to obtain multiple shipment information, and calculate the estimated total weight based on a preset historical database; a grouping and identification module, configured to identify destination information in each shipment information, and group the multiple shipments according to the destination information to obtain multiple groups of settlement objects to be classified; a classification module, configured to classify the multiple groups of settlement objects to be classified according to a preset all-area average weight calculation participation model to obtain individual average weight settlement object groups and multiple common average weight settlement object groups; an acquisition and calculation module, configured to acquire settlement weight information corresponding to the individual average weight settlement object groups and the multiple common average weight settlement object groups respectively, calculate the individual average weight settlement object groups according to the settlement weight information to obtain a first calculation result, and calculate the common average weight settlement object groups according to the settlement weight information to obtain a second calculation result; and an integration and generation module, configured to integrate the first calculation result and the second calculation result to obtain an integration result, and generate a final average weight pricing table according to the integration result.

[0012] Optionally, in a first implementation of the second aspect of the present invention, the parsing and calculation module includes: an acquisition and parsing unit, configured to acquire customer logistics order information and parse the customer logistics order information to obtain total quantity information of tickets, multiple ticket information, current item type information corresponding to each ticket information, and first category ticket quantity information for each type; an acquisition and identification unit, configured to acquire historical item information corresponding to the current item type information from a preset historical database, and identify the historical item quantity and historical item weight in the historical item information; a first calculation unit, configured to calculate the reference package weight per unit number of items based on the historical item quantity and historical item weight; a second calculation unit, configured to calculate the estimated weight of the current category based on the first category ticket quantity information and the reference package weight; a first accumulation unit, configured to accumulate all the estimated weights of the current category to obtain the estimated total weight; and a first sending unit, configured to send the estimated total weight to the user terminal so that the user terminal generates and displays a total weight estimation page based on the estimated total weight.

[0013] Optionally, in a second implementation of the second aspect of the present invention, the identification grouping module includes: an extraction and identification unit, used to extract text information from each ticket and identify destination information in the text information; a word segmentation and extraction unit, used to segment the destination information to obtain multiple words and extract character features of each word; and an identification and classification unit, used to identify the character features through a thesaurus with different regional classifications. If the character feature matches a word in the thesaurus, the ticket corresponding to the character feature is classified into the thesaurus of this region, and all tickets in the same thesaurus are grouped together to obtain multiple groups of objects to be classified and settled.

[0014] Optionally, in a third implementation of the second aspect of the present invention, the classification module includes: a custom unit, used to customize a model based on multiple regions participating in the average weight calculation to obtain a full-region average weight calculation participation model; a filtering unit, used to filter multiple groups of settlement objects to be classified that have different destination information based on the full-region average weight calculation participation model; and a classification unit, used to classify the groups of settlement objects to be classified that meet the filtering conditions into a common average weight settlement object group, and classify the groups of settlement objects to be classified that do not meet the filtering conditions into individual average weight settlement object groups, thereby obtaining individual average weight settlement object groups and multiple common average weight settlement object groups after the filtering is completed.

[0015] Optionally, in a fourth implementation of the second aspect of the present invention, the acquisition and calculation module includes: an acquisition unit, configured to acquire the first total weight information of the individual average weight settlement object group and the second total weight information of the multiple common average weight settlement object groups, and acquire the second category ticket quantity information of the multiple common average weight settlement object groups; an identification acquisition unit, configured to identify the quantity ratio of each common average weight settlement object group in the second category ticket quantity information, and acquire the percentage weight of each common average weight settlement object group in the second total weight information according to the quantity ratio; and an acquisition and calculation unit, configured to acquire a set of price groups, calculate the individual average weight settlement object group according to the set of price groups and the first total weight information to obtain a first calculation result, and calculate the common average weight settlement object group according to the set of price groups and the percentage weight to obtain a second calculation result.

[0016] Optionally, in a fifth implementation of the second aspect of the present invention, the acquisition and calculation unit is specifically used to identify the origin information in the customer logistics order information; obtain a set of quotation groups based on the origin information, and parse the set of quotation groups to obtain multiple sub-quotation groups, each of the sub-quotation groups including regional information and billing rule information corresponding to the regional information; match the multiple sub-quotation groups with the individual average weight settlement object group and each of the common average weight settlement object groups based on the regional information, each of the destination information corresponding to one of the regional information; calculate the individual average weight settlement object group based on the first total weight information and the billing rule information in the matched sub-quotation groups to obtain a first calculation result; calculate each common average weight settlement object group based on the percentage weight and the billing rule information in the matched sub-quotation groups to obtain a second calculation result.

[0017] Optionally, in a sixth implementation of the second aspect of the present invention, the integration generation module includes: a second accumulation unit, configured to accumulate multiple second calculation results to obtain an accumulation result; an addition unit, configured to add the accumulation result to the first calculation result to obtain an integration result; a generation unit, configured to generate a final average weight price table based on the integration result; and a second sending unit, configured to send the final average weight price table to a user terminal, so that the user terminal generates and displays an average weight price page based on the final average weight price table.

[0018] A third aspect of the present invention provides a logistics order average weight pricing device, the logistics order average weight pricing device comprising: a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to cause the logistics order average weight pricing device to execute each step of the logistics order average weight pricing method described above.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement each step of the above-described method for quoting the average weight of logistics orders.

[0020] In the technical solution of this invention, multiple shipment information is calculated based on a preset historical database to obtain the estimated total weight. The total weight of existing logistics orders is estimated using past shipment quantity and weight data, thus facilitating customers' estimation of shipment costs. This makes it easier for large customers to perform bulk order shipment operations. By separately obtaining settlement weight information corresponding to individual average weight settlement object groups and multiple common average weight settlement object groups, average weight pricing is performed based on the settlement weight information. This eliminates the need to weigh each shipment individually, improving the speed of weight information acquisition, increasing pricing efficiency, thereby improving logistics order shipment efficiency, shortening the shipment cycle, and enhancing the user experience. Attached Figure Description

[0021] Figure 1 This is a first flowchart of the logistics order average weight pricing method provided in an embodiment of the present invention;

[0022] Figure 2 This is a second flowchart of the logistics order average weight pricing method provided in an embodiment of the present invention;

[0023] Figure 3 This is a third flowchart of the logistics order average weight pricing method provided in an embodiment of the present invention;

[0024] Figure 4 This is a fourth flowchart of the logistics order average weight pricing method provided in this embodiment of the invention;

[0025] Figure 5 A schematic diagram of a logistics order weight quotation device provided in an embodiment of the present invention;

[0026] Figure 6 This is another structural schematic diagram of the logistics order weight quotation device provided in an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of the logistics order weight quotation device provided in an embodiment of the present invention. Detailed Implementation

[0028] This invention provides a method, apparatus, device, and storage medium for quoting average weight for logistics orders. It calculates the estimated total weight based on information from multiple shipments using a pre-set historical database. By utilizing past shipment quantity and weight data, it estimates the total shipment weight of existing logistics orders, thus facilitating customers' estimation of shipment costs. This makes it easier for large customers to perform bulk order shipment operations. By separately acquiring settlement weight information corresponding to individual average weight settlement object groups and multiple shared average weight settlement object groups, it performs average weight quotations based on the settlement weight information, eliminating the need to weigh each shipment individually. This improves the speed of weight information acquisition, increases quotation efficiency, and ultimately enhances logistics order shipment efficiency, shortens the shipment cycle, and improves user experience.

[0029] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the logistics order average weight pricing method in this invention includes:

[0031] 101. Obtain customer logistics order information, parse the customer logistics order information to obtain multiple shipment information, calculate the estimated total weight based on the preset historical database;

[0032] In this embodiment, customer logistics order information is obtained. Each customer logistics order information includes multiple waybills. Each shipment information refers to the cargo information corresponding to a waybill, which is a single cargo unit in a transportation task. The customer logistics order information is parsed to obtain multiple shipment information. Each shipment information includes the type of cargo. The multiple shipment information is calculated based on a preset historical database to identify the historical weight of each type of shipment. The historical weights corresponding to all shipment information are added together to obtain the estimated total weight.

[0033] 102. Identify the destination information in each ticket and group multiple tickets according to the destination information to obtain multiple groups of objects to be classified and settled;

[0034] In this embodiment, the destination information in each ticket is identified. The ticket information includes the destination address, destination postal code, destination city, etc. Multiple tickets are grouped according to the destination information. By comparing the destination information of each ticket, ticket groups belonging to the same destination can be determined and placed in the same category settlement object group. For example, if multiple tickets have Beijing as their destination, these tickets can be divided into a category settlement object group. For each category settlement object group, settlement can be performed according to different billing methods.

[0035] 103. Based on the preset all-area average weight calculation participation model, classify multiple groups of settlement objects to be classified, and obtain individual average weight settlement object groups and multiple common average weight settlement object groups.

[0036] In this embodiment, each group of objects to be classified and settled has its own corresponding destination city. The preset regional average weight calculation participation model is a custom model, and the cities participating in the average weight calculation are customized. When the destination city corresponding to the group of objects to be classified and settled belongs to the cities participating in the average weight calculation, the group of objects to be classified and settled is classified into the common average weight settlement object group. When the destination city corresponding to the group of objects to be classified and settled does not belong to the cities participating in the average weight calculation, the group of objects to be classified and settled is classified into the separate average weight settlement object group.

[0037] 104. Obtain the settlement weight information corresponding to the individual average weight settlement object group and the multiple common average weight settlement object groups respectively. Calculate the individual average weight settlement object group based on the settlement weight information to obtain the first calculation result. Calculate the common average weight settlement object group based on the settlement weight information to obtain the second calculation result.

[0038] In this embodiment, for each individual average weight settlement object group, all tickets corresponding to it are weighed uniformly to obtain the total weight information of the corresponding tickets as the settlement weight information. It is not necessary to weigh each ticket individually, which improves the speed of obtaining weight information. The first calculation result is obtained by calculating based on the settlement weight information and the destination city billing standard of the individual average weight settlement object group. For multiple common average weight settlement object groups, all tickets corresponding to them are weighed uniformly to obtain the total weight information of the corresponding tickets as the settlement weight information. It is not necessary to weigh each ticket individually, which improves the speed of obtaining weight information. The second calculation result is obtained by calculating based on the destination city billing standard of the common average weight settlement object group and the proportion of tickets in this destination city. Multiple common average weight settlement object groups correspond to multiple second calculation results.

[0039] 105. Integrate the first calculation result and the second calculation result to obtain the integrated result, and generate the final average weight quotation table based on the integrated result;

[0040] In this embodiment, for each individual weight settlement object group, the first calculation result is used as its individual settlement amount. For multiple common weight settlement object groups, the corresponding second calculation results are accumulated to obtain their common settlement amount. The individual settlement amount and the common settlement amount are added together to obtain the final quotation amount. The final weight quotation table is generated based on the final quotation amount.

[0041] In this embodiment of the invention, the estimated total weight is obtained by calculating the information of multiple shipments based on a preset historical database. The total weight of existing logistics orders is estimated using past shipment quantity and weight data, which helps customers estimate the cost of shipments. This facilitates bulk order shipment operations for large customers. By obtaining the settlement weight information corresponding to individual average weight settlement object groups and multiple common average weight settlement object groups, the average weight quotation is made based on the settlement weight information. There is no need to weigh each shipment individually, which improves the speed of obtaining weight information, improves the efficiency of quotation, and thus improves the efficiency of logistics order shipments, shortens the shipment cycle, and improves the user experience.

[0042] Please see Figure 2 The second embodiment of the logistics order average weight pricing method in this invention includes:

[0043] 201. Obtain customer logistics order information, and parse the customer logistics order information to obtain the total number of tickets, multiple ticket information, the current item type information corresponding to each ticket information, and the number of tickets in the first category of each type;

[0044] In this embodiment, customer logistics order information is acquired and parsed to obtain total ticket quantity information, multiple ticket information, current item type information corresponding to each ticket information, and first category ticket quantity information for each type. Different ticket information can be distinguished and tracked based on identifiers such as ticket number or package number. For each ticket information, its current item type information is obtained, such as clothing, shoes, electronic products, etc. Tickets of the same type are classified and summarized, and the summarized quantity information is the first category ticket quantity information, which provides basic data for subsequent data analysis and decision-making.

[0045] 202. Retrieve historical item information corresponding to the current item type information from the preset historical database, and identify the quantity and weight of historical items in the historical item information;

[0046] In this embodiment, historical item information corresponding to the current item type information is obtained from a preset historical database. The quantity and weight of historical items in the historical item information are identified. For example, if the item corresponding to the current item type information is a television set, then historical item information of type television set is obtained from the preset historical database. The historical quantity and total historical weight of television sets are identified. For example, the historical quantity of television sets is 3 units, and the historical total weight is 30 kg.

[0047] 203. Calculate the reference package weight per unit number of items based on the quantity and weight of historical items;

[0048] In this embodiment, the reference package weight per unit number of items is calculated based on the number and weight of historical items. The reference package weight is obtained by dividing the weight of historical items by the number of historical items. For example, if the number of historical items is 3 and the weight of historical items is 30kg, then the reference package weight per unit number of items is calculated to be 10kg.

[0049] 204. Calculate the estimated weight of the current category based on the number of tickets in the first category and the reference package weight;

[0050] In this embodiment, the estimated weight of the current category is calculated based on the number of tickets in the first category and the weight of the reference package. For example, if the number of tickets in the first category is 20 and the weight of the reference package is 10kg, the estimated weight of the current category is calculated to be 200kg.

[0051] 205. Sum the estimated weights of all current categories to obtain the total estimated weight;

[0052] In this embodiment, the estimated weights of all current categories are summed to obtain the estimated total weight. For example, there is ticket quantity information for the first category, which has three categories. The estimated weight of the first category, televisions, is 200kg, the estimated weight of the second category, air conditioners, is 300kg, and the estimated weight of the third category, refrigerators, is 500kg. The estimated weight of all current categories is summed to obtain 1000kg as the estimated total weight.

[0053] 206. Send the estimated total weight to the user's terminal so that the user's terminal can generate and display a total weight estimation page based on the estimated total weight;

[0054] In this embodiment, the estimated total weight is sent to the user terminal, for example, 1000kg is sent to the user terminal, and a display page with the words 1000kg is generated on the user terminal.

[0055] In this embodiment of the invention, multiple shipment information are calculated based on a preset historical database to obtain the estimated total weight. The total weight of existing logistics orders is estimated using past shipment quantity and weight data, thereby facilitating customers to estimate the cost of shipment and making it easier for large customers to perform bulk order shipment operations.

[0056] Please see Figure 3 The third embodiment of the logistics order average weight pricing method in this invention includes:

[0057] 301. Extract the text information from each ticket and identify the destination information within the text information;

[0058] In this embodiment, regular expression text processing technology is used to process the text information of each ticket and extract the text information to be filled in each ticket. For example, keywords such as "name", "address", and "phone number" can be searched to locate the filling position, and the surrounding text is extracted as the filling information. After extraction, the destination information in the text information is identified. For example, if "address: Tianhe District, Guangzhou City, Guangdong Province" is extracted, the corresponding destination information is identified as "Tianhe District, Guangzhou City, Guangdong Province".

[0059] 302. Perform word segmentation on the destination information to obtain multiple words, and extract the character features of each word;

[0060] In this embodiment, the destination information is segmented into multiple words. For example, the destination information "Guangdong Province, Guangzhou City, Tianhe District, XXXX" is segmented using the THULAC Chinese word segmentation tool to obtain "Guangdong Province", "Guangzhou City", "Tianhe District", "XXX", and "XX". The character features of each word, such as length, first letter, last letter, whether it contains numbers and special symbols, are extracted by regular expressions or string truncation. Natural language processing technology is then used for analysis and understanding.

[0061] Alternatively, deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can be used to vectorize each word after word segmentation. For the character features of each word, they can be converted into corresponding vector representations. For example, a word of length L can be represented as an L-dimensional vector, where each element represents the character encoding at that position.

[0062] 303. Character features are identified by using word libraries with different regional classifications. If a character feature matches a word in the word library, the ticket corresponding to the character feature is classified into the word library of that region. All tickets in the same word library are grouped together to obtain multiple groups of objects to be classified and settled.

[0063] In this embodiment, character features are identified using thesauruses with different regional classifications. For example, Named Entity Recognition (NER) technology is used to automatically identify the place name entities corresponding to the character features. Different regional classifications correspond to different place name entities. Finally, if the character feature matches a word in the thesaurus, the ticket corresponding to the character feature is classified into the thesaurus of that region. All tickets in the same thesaurus are grouped together to obtain multiple groups of objects to be classified and settled. The destination city is used as the classification basis to obtain more reasonable and accurate billing results. For example, if the place name entity is "Guangzhou City", this ticket is classified into the thesaurus with the region classification of Guangzhou City. All tickets in the thesaurus with the region classification of Guangzhou City are grouped together. All tickets in the thesaurus of each regional classification are grouped together to obtain multiple groups of objects to be classified and settled in multiple regions.

[0064] 304. Custom models are created based on multiple regions participating in the average weight calculation to obtain the average weight calculation participation model for the entire region;

[0065] In this embodiment, a list of cities participating in the average weight calculation is predefined, and a regional average weight calculation participation model is generated based on the list of cities.

[0066] 305. Based on the regional average weight calculation participation model, filter multiple groups of settlement objects with different destination information;

[0067] In this embodiment, for each group of objects to be classified and settled, the corresponding destination city information is obtained. If the destination city belongs to the list of cities participating in the average weight calculation, the group of objects to be classified and settled is classified into a common average weight settlement group; otherwise, it is classified into a separate average weight settlement group. For example, Changsha and Guangzhou are defined as cities participating in the average weight calculation, while Hong Kong is defined as a city not participating in the average weight calculation. In this way, the groups of objects to be classified and settled can be further subdivided according to the city information, and the billing standard for each group of objects can be determined.

[0068] 306. Classify the groups of settlement objects that meet the screening criteria into common weight settlement object groups, and classify the groups of settlement objects that do not meet the screening criteria into individual weight settlement object groups. After screening, you will get individual weight settlement object groups and multiple common weight settlement object groups.

[0069] In this embodiment, if the destination city is among the cities participating in the average weight calculation, the group of objects to be classified and settled is assigned to the common average weight settlement object group; otherwise, it is assigned to the individual average weight settlement object group. After the filtering is completed, the individual average weight settlement object group that does not participate in the average weight calculation and the multiple common average weight settlement object groups that participate in the average weight calculation are obtained.

[0070] In this embodiment of the invention, the settlement objects that participate in the average weight calculation and those that do not participate in the average weight calculation are automatically distinguished according to the preset billing rules, so that the quotation is more accurate and reasonable.

[0071] Please see Figure 4 The fourth embodiment of the logistics order average weight pricing method in this invention includes:

[0072] 401. Obtain the first total weight information of a single average weight settlement object group and the second total weight information of multiple common average weight settlement object groups, and obtain the second category ticket quantity information of multiple common average weight settlement object groups;

[0073] In this embodiment, for each individual weight-based settlement object group, all tickets corresponding to it are weighed uniformly to obtain the total weight information of the corresponding tickets as the first total weight information. It is not necessary to weigh each ticket individually, which improves the speed of obtaining weight information. For multiple common weight-based settlement object groups, all tickets corresponding to them are weighed uniformly to obtain the total weight information of the corresponding tickets as the second total weight information. It is not necessary to weigh each ticket individually, which improves the speed of obtaining weight information. The second category of ticket quantity information of multiple common weight-based settlement object groups is obtained. The second category of ticket quantity information is the total quantity information of tickets in all common weight-based settlement object groups.

[0074] 402. Identify the proportion of each common weight settlement object group in the second category ticket quantity information, and obtain the proportion weight of each common weight settlement object group in the second total weight information based on the proportion of quantity.

[0075] In this embodiment, firstly, the total number of tickets in all common weight settlement object groups is calculated as N, that is, the number of tickets in the second category is N, and the total weight information is W. Then, for each common weight settlement object group, the number of tickets it contains is counted as n, and the proportion of each common weight settlement object group in the number of tickets in the second category is calculated as p = n / N. Finally, the proportion weight of each common weight settlement object group is calculated as w = W × p.

[0076] 403. Identify the origin information in the customer's logistics order information;

[0077] In this embodiment, firstly, the field or location of the origin information is determined. Generally, the origin information is listed separately or placed in a field together with other address information. For example, if the order data is in spreadsheet format, there may be a dedicated "origin" column. If the order data is in text format, the address information needs to be extracted using methods such as regular expressions. Then, after determining the field or location of the origin information, corresponding identification and extraction are required for different formats. For example, if the origin information is a city name, it can be identified and extracted using methods such as Natural Language Processing (NLP) and pattern matching. If the origin information is a postal code, it can be directly identified and extracted using methods such as regular expressions.

[0078] 404. Obtain a set of price groups based on the origin information, and parse the set of price groups to obtain multiple sub-price groups. Each sub-price group includes regional information and billing rule information corresponding to the regional information.

[0079] In this embodiment, the set of price quotation groups is stored in a data table or file. Price quotation groups that meet the conditions can be filtered based on the origin information. For example, if the origin information is a city name, such as Shanghai, then the corresponding price quotation group can be matched based on that city. If the origin information is a postal code, then the postal code can be used directly as a condition to filter the price quotation group. The price quotation group is parsed to obtain multiple sub-price quotation groups, such as a sub-price quotation group from Shanghai to Guangzhou, a sub-price quotation group from Shanghai to Changsha, and a sub-price quotation group from Shanghai to Hong Kong. Each sub-price quotation group includes regional information and billing rule information corresponding to the regional information. For example, the regional information of the sub-price quotation group from Shanghai to Guangzhou is "from Shanghai to Guangzhou".

[0080] 405. Match multiple sub-quote groups with individual average weight settlement object groups and each common average weight settlement object group based on regional information. Each destination information corresponds to one regional information.

[0081] In this embodiment, multiple sub-quote groups are matched with individual average weight settlement object groups and each common average weight settlement object group based on regional information. Each destination information corresponds to a regional information. For example, the destination information of an individual average weight settlement object group is "Hong Kong Special Administrative Region XXXXX", which is matched with the sub-quote group from Shanghai to Hong Kong. The destination information of one common average weight settlement object group is "Guangzhou City, Tianhe District, Guangdong Province XXXXX", which is matched with the sub-quote group from Shanghai to Guangzhou. The destination information of another common average weight settlement object group is "Changsha City, Furong District, Hunan Province XXXXX", which is matched with the sub-quote group from Shanghai to Changsha. The billing standard for each settlement object group is determined in this way.

[0082] 406. Calculate the individual average weight settlement object group based on the first total weight information and the billing rule information in the matched sub-quote group to obtain the first calculation result;

[0083] In this embodiment, for each individual average weight settlement object group, all tickets corresponding to it are weighed uniformly to obtain the total weight information of the corresponding tickets as the first total weight information. It is not necessary to weigh each ticket individually, which improves the speed of obtaining weight information. The individual average weight settlement object group is calculated based on the first total weight information and the billing rule information in the matched sub-quote group to obtain the first calculation result. For example, if the matched sub-quote group is the Shanghai to Hong Kong sub-quote group, the individual average weight settlement object group is calculated based on the first total weight information and the billing rule information in the Shanghai to Hong Kong sub-quote group to obtain the first calculation result, which is X yuan.

[0084] 407. Calculate the billing rules for each common weight settlement object group based on the weight percentage and the billing rules information in the matched sub-quote groups to obtain the second calculation result;

[0085] In this embodiment, after calculating the percentage weight of each common average weight settlement object group as w = W × p, the second calculation result is obtained by calculating each common average weight settlement object group based on the percentage weight and the billing rule information in the matched sub-quote group. Multiple common average weight settlement object groups correspond to multiple second calculation results. For example, if the destination information of one common average weight settlement object group is "XXXXX, Tianhe District, Guangzhou City, Guangdong Province", it is matched with the sub-quote group from Shanghai to Guangzhou City. The percentage weight of this common average weight settlement object group is w1. Based on the percentage weight of w1 and the billing rule information in the matched sub-quote group from Shanghai to Guangzhou City, the second calculation result is obtained. The billing rules for the Guangzhou sub-quote group are used to calculate the cost of this common weight settlement object group, resulting in a first second calculation result of C1 yuan. The destination information for another common weight settlement object group is "XXXXX, Furong District, Changsha City, Hunan Province". This is matched with the Shanghai to Changsha sub-quote group. The weight percentage of this common weight settlement object group is w2. Based on the weight percentage of w2 and the billing rules for the Shanghai to Changsha sub-quote group, the cost of this common weight settlement object group is calculated, resulting in a second second calculation result of C2 yuan.

[0086] 408. Summate the multiple second calculation results to obtain the summation result;

[0087] In this embodiment, multiple second calculation results are summed to obtain a cumulative result. For example, the first second calculation result and the second second calculation result are summed to obtain a cumulative result, which is R' = C1 + C2.

[0088] 409. Add the accumulated result to the first calculation result to obtain the integrated result;

[0089] In this embodiment, the accumulated result is added to the first calculation result to obtain the integrated result, which is R = X + R' yuan.

[0090] 410. Generate the final average weight quotation table based on the integration results;

[0091] In this embodiment, a final average weight price list is generated based on the integration results.

[0092] 411. Send the final average weight quotation to the user terminal so that the user terminal can generate and display the average weight quotation page based on the final average weight quotation.

[0093] In this embodiment, the final average weight price list is sent to the user terminal. For example, R yuan is sent to the user terminal, and a display page with the words "R yuan" is generated on the user terminal.

[0094] In this embodiment of the invention, by obtaining the settlement weight information corresponding to individual average weight settlement object groups and multiple common average weight settlement object groups respectively, and making average weight quotations based on the settlement weight information, it is not necessary to weigh each shipment individually, thereby improving the speed of obtaining weight information, improving quoting efficiency, and thus improving the efficiency of logistics order delivery, shortening the delivery cycle, and improving the user experience.

[0095] The above describes the average weight pricing method for logistics orders in embodiments of the present invention. The following describes the average weight pricing device for logistics orders in embodiments of the present invention. Please refer to [link / reference]. Figure 5 One embodiment of the logistics order weight quotation device in this invention includes:

[0096] The parsing and calculation module 501 is used to obtain customer logistics order information, parse the customer logistics order information to obtain multiple shipment information, and calculate the estimated total weight based on the multiple shipment information according to the preset historical database.

[0097] The grouping identification module 502 is used to identify the destination information in each ticket information and group multiple tickets according to the destination information to obtain multiple groups of objects to be classified and settled.

[0098] The classification module 503 is used to classify multiple groups of settlement objects to be classified according to a preset full-area average weight calculation participation model, so as to obtain a separate average weight settlement object group and multiple common average weight settlement object groups.

[0099] The calculation module 504 is used to obtain the settlement weight information corresponding to the individual average weight settlement object group and the multiple common average weight settlement object groups respectively, calculate the individual average weight settlement object group according to the settlement weight information to obtain the first calculation result, and calculate the common average weight settlement object group according to the settlement weight information to obtain the second calculation result.

[0100] The integration and generation module 505 is used to integrate the first calculation result and the second calculation result to obtain the integration result, and generate the final average weight quotation table based on the integration result.

[0101] In this embodiment, the estimated total weight is obtained by calculating the information of multiple shipments based on a preset historical database. The total weight of existing logistics orders is estimated using past shipment quantity and weight data, which helps customers estimate the cost of shipment. This facilitates bulk order shipment operations for large customers. By obtaining the settlement weight information corresponding to individual average weight settlement object groups and multiple common average weight settlement object groups, the average weight quotation is made based on the settlement weight information. There is no need to weigh each shipment individually, which improves the speed of obtaining weight information, improves the efficiency of quotation, and thus improves the efficiency of logistics order shipment, shortens the shipment cycle, and improves the user experience.

[0102] Please see Figure 6 Another embodiment of the logistics order weight quotation device in this invention includes:

[0103] The parsing and calculation module 501 is used to obtain customer logistics order information, parse the customer logistics order information to obtain multiple shipment information, and calculate the estimated total weight based on the multiple shipment information according to the preset historical database.

[0104] The grouping identification module 502 is used to identify the destination information in each ticket information and group multiple tickets according to the destination information to obtain multiple groups of objects to be classified and settled.

[0105] The classification module 503 is used to classify multiple groups of settlement objects to be classified according to a preset full-area average weight calculation participation model, so as to obtain a separate average weight settlement object group and multiple common average weight settlement object groups.

[0106] The calculation module 504 is used to obtain the settlement weight information corresponding to the individual average weight settlement object group and the multiple common average weight settlement object groups respectively, calculate the individual average weight settlement object group according to the settlement weight information to obtain the first calculation result, and calculate the common average weight settlement object group according to the settlement weight information to obtain the second calculation result.

[0107] The integration and generation module 505 is used to integrate the first calculation result and the second calculation result to obtain the integration result, and generate the final average weight quotation table based on the integration result;

[0108] In this embodiment, the parsing and calculation module 501 includes: an acquisition and parsing unit 5011, used to acquire customer logistics order information and parse the customer logistics order information to obtain total quantity information of tickets, multiple ticket information, current item type information corresponding to each ticket information, and first category ticket quantity information for each type; an acquisition and identification unit 5012, used to acquire historical item information corresponding to the current item type information from a preset historical database, and identify the historical item quantity and historical item weight in the historical item information; a first calculation unit 5013, used to calculate the reference package weight per unit number of items based on the historical item quantity and historical item weight; a second calculation unit 5014, used to calculate the estimated weight of the current category based on the first category ticket quantity information and the reference package weight; a first accumulation unit 5015, used to accumulate all the estimated weights of the current category to obtain the estimated total weight; and a first sending unit 5016, used to send the estimated total weight to the user terminal so that the user terminal can generate and display a total weight estimation page based on the estimated total weight.

[0109] In this embodiment, the identification and grouping module 502 includes: an extraction and identification unit 5021, used to extract the text information filled in each ticket and identify the destination information in the text information; a word segmentation and extraction unit 5022, used to perform word segmentation processing on the destination information to obtain multiple words and extract the character features of each word; and an identification and classification unit 5023, used to identify the character features through a thesaurus with different regional classifications. If the character feature matches a word in the thesaurus, the ticket corresponding to the character feature is classified into the thesaurus of this region, and all tickets in the same thesaurus are divided into a group to obtain multiple groups of objects to be classified and settled.

[0110] In this embodiment, the classification module 503 includes: a custom unit 5031, used to customize a model based on multiple regions participating in the average weight calculation, to obtain a full-region average weight calculation participation model; a filtering unit 5032, used to filter multiple groups of settlement objects with different destination information based on the full-region average weight calculation participation model; and a classification unit 5033, used to classify the groups of settlement objects that meet the filtering conditions into a common average weight settlement object group, and classify the groups of settlement objects that do not meet the filtering conditions into individual average weight settlement object groups, thereby obtaining individual average weight settlement object groups and multiple common average weight settlement object groups after the filtering is completed.

[0111] In this embodiment, the acquisition calculation module 504 includes: an acquisition unit 5041, used to acquire the first total weight information of the individual average weight settlement object group and the second total weight information of multiple common average weight settlement object groups, and to acquire the second category ticket quantity information of the multiple common average weight settlement object groups; an identification acquisition unit 5042, used to identify the quantity ratio of each common average weight settlement object group in the second category ticket quantity information, and to acquire the percentage weight of each common average weight settlement object group in the second total weight information based on the quantity ratio; and an acquisition calculation unit 5043, used to acquire the price group set, calculate the individual average weight settlement object group based on the price group set and the first total weight information to obtain a first calculation result, and calculate the common average weight settlement object group based on the price group set and the percentage weight to obtain a second calculation result.

[0112] In this embodiment, the calculation unit 5043 is specifically used to identify the origin information in the customer's logistics order information; obtain a set of quotation groups based on the origin information, and parse the set of quotation groups to obtain multiple sub-quotation groups, each sub-quotation group including regional information and billing rule information corresponding to the regional information; match the multiple sub-quotation groups with the individual average weight settlement object group and each common average weight settlement object group according to the regional information, and each destination information corresponds to one regional information; calculate the individual average weight settlement object group according to the first total weight information and the billing rule information in the matched sub-quotation groups to obtain a first calculation result; calculate each common average weight settlement object group according to the percentage weight and the billing rule information in the matched sub-quotation groups to obtain a second calculation result.

[0113] In this embodiment, the integration generation module 505 includes: a second accumulation unit 5051, used to accumulate multiple second calculation results to obtain an accumulation result; an addition unit 5052, used to add the accumulation result to the first calculation result to obtain an integration result; a generation unit 5053, used to generate a final average weight price table based on the integration result; and a second sending unit 5054, used to send the final average weight price table to the user terminal so that the user terminal can generate and display an average weight price page based on the final average weight price table.

[0114] above Figure 5 and Figure 6 The logistics order weight quoting device in this embodiment of the invention will be described in detail from the shooting direction of the modular functional entity. The logistics order weight quoting device in this embodiment of the invention will be described in detail from the shooting direction of hardware processing.

[0115] Figure 7This is a schematic diagram of a logistics order weight quoting device 600 provided in an embodiment of the present invention. The logistics order weight quoting device 600 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the logistics order weight quoting device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 630 on the logistics order weight quoting device 600 to implement the steps of the logistics order weight quoting method provided in the above-described method embodiments.

[0116] The logistics order average weight quotation device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 7 The illustrated structure of the logistics order average weight pricing equipment does not constitute a limitation on the logistics order average weight pricing equipment, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0117] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the logistics order weight quotation method.

[0118] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0119] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for quoting prices based on average weight in logistics orders, characterized in that, include: Obtain customer logistics order information, parse the customer logistics order information to obtain multiple ticket information, and calculate the estimated total weight based on the multiple ticket information according to the preset historical database; Identify the destination information in each ticket information, and group the multiple tickets according to the destination information to obtain multiple groups of objects to be classified and settled; According to the preset full-area average weight calculation participation model, the multiple groups of settlement objects to be classified are classified to obtain individual average weight settlement object groups and multiple common average weight settlement object groups. The settlement weight information corresponding to the individual average weight settlement object group and the multiple common average weight settlement object groups are obtained respectively. The individual average weight settlement object group is calculated based on the settlement weight information to obtain a first calculation result. The common average weight settlement object group is calculated based on the settlement weight information to obtain a second calculation result. The first calculation result and the second calculation result are integrated to obtain an integrated result, and a final average weight quotation table is generated based on the integrated result.

2. The logistics order average weight pricing method according to claim 1, characterized in that, The process of acquiring customer logistics order information, parsing the customer logistics order information to obtain multiple shipment information, and calculating the estimated total weight based on the multiple shipment information according to a preset historical database includes: Obtain customer logistics order information, and parse the customer logistics order information to obtain total number of tickets, multiple ticket information, current item type information corresponding to each ticket information, and number of tickets in the first category of each type; Retrieve historical item information corresponding to the current item type information from a preset historical database, and identify the quantity and weight of historical items in the historical item information; The reference package weight per unit number of items is calculated based on the quantity and weight of the historical items. The estimated weight of the current category is calculated based on the number of tickets in the first category and the weight of the reference package. The estimated total weight is obtained by summing up all the estimated weights for the current categories. The estimated total weight is sent to the user's terminal so that the user's terminal can generate and display a total weight estimation page based on the estimated total weight.

3. The logistics order average weight pricing method according to claim 1, characterized in that, The process involves identifying the destination information in each ticket and grouping multiple tickets based on the destination information to obtain multiple groups of objects to be classified and settled, including: Extract the text information from each ticket and identify the destination information within the text information; The destination information is segmented into multiple words, and the character features of each word are extracted. The character features are identified by using a thesaurus with different regional classifications. If the character feature matches a word in the thesaurus, the ticket corresponding to the character feature is classified into the thesaurus of that region. All tickets in the same thesaurus are grouped together to obtain multiple groups of objects to be classified and settled.

4. The logistics order average weight pricing method according to claim 1, characterized in that, The step of classifying multiple groups of objects to be classified and settled according to a preset full-area average weight calculation participation model to obtain individual average weight settlement object groups and multiple common average weight settlement object groups includes: A custom model is created based on multiple regions participating in the average weight calculation to obtain the average weight calculation participation model for the entire region; Based on the regional average weight calculation participation model, multiple groups of settlement objects with different destination information are filtered; The groups of settlement objects that meet the screening criteria are classified into common weight settlement object groups, and the groups of settlement objects that do not meet the screening criteria are classified into individual weight settlement object groups. After screening, individual weight settlement object groups and multiple common weight settlement object groups are obtained.

5. The method for quoting prices based on average weight of logistics orders according to claim 1, characterized in that, The steps include obtaining settlement weight information corresponding to the individual average weight settlement object group and the multiple common average weight settlement object groups, calculating the individual average weight settlement object group based on the settlement weight information to obtain a first calculation result, and calculating the common average weight settlement object group based on the settlement weight information to obtain a second calculation result, including: Obtain the first total weight information of the individual average weight settlement object group and the second total weight information of the multiple common average weight settlement object groups, and obtain the second category ticket quantity information of the multiple common average weight settlement object groups; Identify the proportion of each common weight settlement object group in the second category ticket quantity information, and obtain the proportion weight of each common weight settlement object group in the second total weight information based on the proportion of the quantity. Obtain a set of quotation groups, calculate the individual average weight settlement object group based on the set of quotation groups and the first total weight information to obtain a first calculation result, and calculate the common average weight settlement object group based on the set of quotation groups and the percentage weight to obtain a second calculation result.

6. The logistics order average weight pricing method according to claim 5, characterized in that, The process of obtaining the set of quotation groups involves calculating the individual average weight settlement object groups based on the set of quotation groups and the first total weight information to obtain a first calculation result, and calculating the common average weight settlement object groups based on the set of quotation groups and the percentage weight to obtain a second calculation result, including: Identify the origin information in the customer's logistics order information; A set of price groups is obtained based on the origin information, and the set of price groups is parsed to obtain multiple sub-price groups. Each sub-price group includes regional information and billing rule information corresponding to the regional information. Based on the regional information, the multiple sub-quote groups are matched with the individual average weight settlement object group and each of the common average weight settlement object groups, and each destination information corresponds to one of the regional information; The first calculation result is obtained by calculating the individual average weight settlement object group based on the first total weight information and the billing rule information in the matched sub-quote group; The second calculation result is obtained by calculating each common weight settlement object group based on the percentage weight and the billing rule information in the matched sub-quote group.

7. The logistics order average weight pricing method according to claim 1, characterized in that, The process of integrating the first calculation result and the second calculation result to obtain an integrated result, and generating a final average weight quotation table based on the integrated result, includes: The multiple second calculation results are summed to obtain the summed result; The accumulated result is added to the first calculation result to obtain the integrated result; A final average weight price list is generated based on the integration results; The final average weight price list is sent to the user terminal so that the user terminal can generate and display an average weight price page based on the final average weight price list.

8. A logistics order weight-based pricing device, characterized in that, include: The parsing and calculation module is used to obtain customer logistics order information, parse the customer logistics order information to obtain multiple ticket information, and calculate the estimated total weight based on the multiple ticket information according to the preset historical database. The identification and grouping module is used to identify the destination information in each ticket information and group multiple tickets according to the destination information to obtain multiple groups of objects to be classified and settled. The classification module is used to classify multiple groups of settlement objects to be classified according to a preset full-area average weight calculation participation model, so as to obtain individual average weight settlement object groups and multiple common average weight settlement object groups. The calculation module is used to acquire settlement weight information corresponding to the individual average weight settlement object group and the multiple common average weight settlement object groups respectively, calculate the individual average weight settlement object group according to the settlement weight information to obtain a first calculation result, and calculate the common average weight settlement object group according to the settlement weight information to obtain a second calculation result; An integration generation module is used to integrate the first calculation result and the second calculation result to obtain an integration result, and generate a final average weight quotation table based on the integration result.

9. A logistics order weight-based pricing device, characterized in that, The logistics order weight quotation device includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the logistics order average weight pricing device to perform the steps of the logistics order average weight pricing method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the steps of the logistics order weight pricing method as described in any one of claims 1-7.

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