Agricultural special product business volume statistical method and device, storage medium and program product

Abstract

The embodiment of the invention discloses an agricultural special product business volume statistical method and device, a storage medium and a program product. The method comprises the following steps: acquiring a rural special product data set, a rural branch data set, an express sheet data set and a track data set; matching the express sheet data set with the agricultural special product data set to obtain a regional product express data set; matching the track data set with the rural branch data set to obtain a rural express data set; matching the regional product express data set with the rural express data set to obtain a rural special product express data set; and carrying out statistics on the agricultural special product express delivery data set to obtain the business volume of the agricultural special product. According to the method, through fusion and matching of multiple data sets, the statistical accuracy and comprehensiveness of the express delivery business volume of the agricultural special products can be remarkably improved, so that the data support of the country in the development of the agricultural special products is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of express delivery technology, and in particular to a method, device, storage medium, and program product for statistically analyzing the business volume of agricultural products. Background Art

[0002] With the continuous improvement of my country's agricultural industrialization, large-scale agricultural operations, advantageous agriculture, and specialized agriculture are gradually becoming important drivers of rural economic development. Against this backdrop, demand for specialized logistics is growing, and urban and rural logistics development is moving towards a balance. Express parcel volume has become a key indicator of the development of rural agricultural products. However, existing express delivery volume statistics typically only provide overall regional express delivery volume, lacking detailed breakdown of the parcel contents, and therefore cannot accurately reflect the logistics demand for specific agricultural products. Summary of the Invention

[0003] In view of this, the embodiments of the present disclosure provide a method, device, storage medium, and program product for statistically analyzing the business volume of agricultural and special products, which can significantly improve the statistical accuracy and comprehensiveness of the express delivery business volume of agricultural and special products through the fusion and matching of multiple data sets, thereby enhancing the data support of rural areas in the development of agricultural and special products.

[0004] In a first aspect, the embodiments of the present disclosure provide a method for statistically analyzing the business volume of agricultural products, which adopts the following technical solutions: Obtain agricultural product datasets, rural network datasets, express delivery label datasets, and trajectory datasets; Matching the delivery note dataset with the agricultural product dataset to obtain a regional product express delivery dataset; Matching the trajectory dataset with the rural network point dataset to obtain a rural express delivery dataset; Matching the regional product express delivery dataset with the rural express delivery dataset to obtain an agricultural product express delivery dataset; Collecting statistics on the agricultural product express delivery data set to obtain the business volume of the agricultural product; Among them, the agricultural product dataset is a dataset that records the attribute information of multiple agricultural products; the rural network dataset is a dataset that records the information of logistics service outlets in rural areas; the waybill dataset is a dataset that records the waybill information of multiple express parcels; and the trajectory dataset is a dataset that records the trajectory information generated by multiple express parcels during the circulation process.

[0005] Optionally, matching the delivery note dataset with the agricultural product dataset to obtain a regional product express delivery dataset includes: Traversing the waybill information in the waybill dataset, and matching the current waybill information with the attribute information in the agricultural product dataset; If the match is successful, a set of regional product express information is extracted based on the current shipping label information and the current attribute information and stored in the first data set; If the match fails, the current bill information is stored in the second data set; After the traversal of the face order data set is completed, error type judgment is performed based on the second data set; If the error type is a general error, updating the agricultural product dataset and the first dataset; If the error type is a private error, deleting the second data set or updating the first data set based on the second data set; If the first dataset is updated, the updated first dataset is the regional product express dataset; If the first data set has not been updated, the first data set is the regional product express delivery data set.

[0006] Optionally, the shipping label information includes the name of the shipping area and the name of the contents; The attribute information includes a professional name of the agricultural product and several production place names, the professional name is associated with the several production place names, the several production place names each correspond to a local name set, and the local name set includes several local product names; Marking the waybill information being traversed as the current waybill information; Traverse the attribute information in the agricultural product dataset and mark the attribute information being traversed as the current attribute information; Obtaining the similarity between the item name included in the current label information and the professional name included in the current attribute information; If the similarity reaches a preset similarity threshold, the traversal of the attribute information is stopped. If the shipping area name contained in the current shipping label information successfully matches any origin name contained in the current attribute information, it indicates that the current shipping label information successfully matches the current attribute information. If the shipping area name fails to match any origin name contained in the current attribute information, it indicates that the current shipping label information fails to match the current attribute information. If the similarity does not reach the preset similarity threshold and all attribute information is traversed, the local name set corresponding to the professional name is extracted in descending order of similarity; Comparing the contents name included in the current shipping label information with the local product name in the extracted local name set; If the names are inconsistent with all local product names, it means that the current label information fails to match the current attribute information; If the name is consistent with any local product name, the local product name is recorded as the current local product name, and the shipping area name contained in the current shipping label information is matched with the origin name corresponding to the current local product name; If the match is successful, it means that the current bill information and the current attribute information are matched successfully; If the matching fails, it means that the current delivery note information fails to match the current attribute information.

[0007] Optionally, the regional product express delivery information includes the current waybill information, the professional name in the current attribute information, and the origin name that successfully matches the shipping area name contained in the current waybill information.

[0008] Optionally, the performing error type determination based on the second data set includes: Counting the total number of occurrences of each item name in the second data set; Determine whether the total number of occurrences of each internal item name reaches a preset number threshold; If the preset number of times is not reached, it is determined that the error type of the internal item name is a private error; If the preset number of times is reached, all the shipping area names corresponding to the item names are matched with all the origin names in the agricultural product dataset one by one, and the number of successful matches for each origin name is counted; Get the ratio of the number of successful matches for each place of origin name to the total number of occurrences; If any ratio is greater than the preset ratio threshold, it is determined that the error type of the internal part name is a general error; If all the ratios are not greater than the preset ratio threshold, it is determined that the error type of the internal part name is a private error.

[0009] Optionally, each set of logistics service outlet information included in the rural outlet data set includes a logistics outlet code; Traversing the trajectory information in the trajectory data set, and matching the collection point code included in the trajectory information with the logistics point code; If the match is successful, the trajectory information and the logistics service network information to which the logistics network code belongs are combined into a set of logistics network express data, and stored in a third express data set; After the trajectory dataset is traversed, the third express delivery dataset is a rural express delivery dataset.

[0010] Optionally, the delivery note information includes a first delivery note number, and the track information includes a second delivery note number. Therefore, the regional product express delivery information also includes the first delivery note number, and the logistics point express delivery data also includes the second delivery note number. Traversing the regional product express information in the regional product express data set, and matching the first side tracking number in the regional product express information with the second side tracking number in the logistics network express data; If the match is successful, the regional product express information is merged with the logistics network express data, and the merged information is stored in a fourth express data set; After the traversal of the regional product express delivery dataset is completed, the fourth express delivery dataset is the agricultural product express delivery dataset.

[0011] In a second aspect, the embodiments of the present disclosure further provide a system for statistically analyzing the business volume of agricultural products, which adopts the following technical solutions: The dataset acquisition module is used to obtain agricultural product datasets, rural network datasets, express delivery bill datasets, and trajectory datasets; A first matching module is used to match the delivery order dataset with the agricultural product dataset to obtain a regional product express delivery dataset; A second matching module is used to match the trajectory dataset with the rural network dataset to obtain a rural express delivery dataset; A third matching module is used to match the regional product express delivery dataset with the rural express delivery dataset to obtain an agricultural product express delivery dataset; A business volume statistics module is used to collect statistics on the agricultural product express delivery data set to obtain the business volume of agricultural products; Among them, the agricultural product dataset is a dataset that records the attribute information of multiple agricultural products; the rural network dataset is a dataset that records the information of logistics service outlets in rural areas; the waybill dataset is a dataset that records the waybill information of multiple express parcels; and the trajectory dataset is a dataset that records the trajectory information generated by multiple express parcels during the circulation process.

[0012] In a third aspect, the embodiments of the present disclosure further provide a computer device that adopts the following technical solution: The computer device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any of the above-mentioned agricultural product business volume statistics methods.

[0013] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the above-mentioned agricultural product business volume statistics methods.

[0014] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program / instruction, which implements the steps of any of the above methods when executed by a processor.

[0015] The agricultural product business volume statistics method provided by the embodiment of the present disclosure can classify the names of express items in detail by matching the waybill dataset with the agricultural product dataset, thereby providing more detailed express data and accurately identifying and classifying the express data of agricultural products. This subdivision can accurately reflect the logistics demand of specific agricultural products, overcoming the problem of insufficient data granularity in existing statistical methods. This precise classification can effectively support enterprises' decision-making in product demand forecasting, production planning, inventory management and logistics distribution, and improve the accuracy of data support. By matching the trajectory dataset with the rural network dataset, it is possible to accurately understand the interaction between express parcels and rural outlets during the circulation process, which helps to identify bottlenecks in the logistics process and optimize the logistics network, thereby improving the efficiency and quality of logistics services. After comprehensively matching and analyzing each data set, this method can provide more comprehensive and in-depth business volume statistical information, which provides decision-making support for enterprises and local areas.

[0016] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of a method for statistically analyzing the business volume of agricultural products provided by an embodiment of the present disclosure; Figure 2 A flowchart of a method for obtaining a regional product express delivery dataset provided by an embodiment of the present disclosure; Figure 3 A flowchart of a method for matching current delivery note information with a dataset of agricultural and specialty products provided in an embodiment of the present disclosure; Figure 4 A flowchart of a method for determining an error type according to an embodiment of the present disclosure; Figure 5 A block diagram of the principle of the agricultural product business volume statistics system provided by the embodiment of the present disclosure; Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0020] It should be clear that the following embodiments of the present disclosure are described through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0021] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0022] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0023] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0024] Reference Figure 1 The present disclosure provides a method for statistically analyzing the business volume of agricultural products, comprising the following steps: S1: Obtain agricultural product datasets, rural network datasets, express delivery label datasets, and trajectory datasets.

[0025] Among them, the agricultural product dataset is a dataset that records the attribute information of multiple agricultural products; the rural network dataset is a dataset that records the information of logistics service outlets in rural areas; the waybill dataset is a dataset that records the waybill information of multiple express parcels; and the trajectory dataset is a dataset that records the trajectory information generated by multiple express parcels during the circulation process.

[0026] S2: Match the delivery order dataset with the agricultural product dataset to obtain the regional product express delivery dataset.

[0027] S3: Match the trajectory dataset with the rural outlet dataset to obtain the rural express delivery dataset.

[0028] S4: Match the regional product express delivery dataset with the rural express delivery dataset to obtain the agricultural product express delivery dataset.

[0029] S5: Count the agricultural product express delivery data set to obtain the business volume of agricultural products.

[0030] The agricultural product business volume statistics method disclosed in the present invention can classify the names of express items in detail by matching the waybill dataset with the agricultural product dataset, thereby providing more detailed express data and accurately identifying and classifying the express data of agricultural products. This subdivision can accurately reflect the logistics demand of specific agricultural products, overcoming the problem of insufficient data granularity in existing statistical methods. This precise classification can effectively support enterprises' decision-making in product demand forecasting, production planning, inventory management and logistics distribution, and improve the accuracy of data support. By matching the trajectory dataset with the rural network dataset, it is possible to accurately understand the interaction between express parcels and rural outlets during the circulation process, which helps to identify bottlenecks in the logistics process and optimize the logistics network, thereby improving the efficiency and quality of logistics services. After comprehensively matching and analyzing each data set, this method can provide more comprehensive and in-depth business volume statistical information, which provides decision-making support for enterprises and local areas.

[0031] In summary, this method can significantly improve the statistical accuracy and comprehensiveness of the express delivery volume of agricultural products through the fusion and matching of multiple data sets, thereby enhancing the data support for rural areas in the development of agricultural products.

[0032] In S1, each express delivery company transmits data related to agricultural products frequently mailed in various regions to a pre-set agricultural products platform through a pre-set interface. The agricultural products platform cleans this data (e.g., removing spaces and special characters), corrects errors (e.g., correcting obvious typos), standardizes this data (e.g., standardizing capitalization), and compiles statistics, ultimately compiling it into an agricultural products dataset. Similarly, the rural outlet dataset is constructed by compiling information on logistics service outlets in rural areas that express delivery companies frequently collaborate with through the agricultural products platform. The waybill and trajectory datasets are constructed by compiling information on each express delivery company's delivery information over a pre-set period (typically one year) through the agricultural products platform.

[0033] Furthermore, each set of express bill information in the express bill dataset includes at least one of the following: express bill number, shipping region name, content name, and mailing company identifier. The express bill number refers to the identification code of the express package, the mailing company identifier refers to the brand name of the company responsible for shipping the express package, and the content name refers to the name of the item within the express package. To distinguish it from the data included in the subsequent trajectory information, the express bill number included in the express bill information is referred to as the first express bill number, and the mailing company identifier included in the express bill information is referred to as the first mailing company identifier.

[0034] When sending express delivery, some users will fill in the name of the items in the express package in the column of the inner item name, as well as the weight or number of items in the express package. Therefore, it is necessary to pre-process the inner item name to make the use of the inner item name more standardized, which will help improve the accuracy of subsequent matching. Specifically, determine whether the inner item name contains the unit of measurement. If it does, delete the unit of measurement and several numbers before and adjacent to the unit of measurement; if it does not, it means that the inner item name meets the standard and does not need to be deleted. Among them, the measurement units include but are not limited to kg, jin, piece, piece, ml, and liter. For example, if the inner item name is 5 jin of peaches in District D, delete "5 jin". However, please note that the unit of measurement does not include "number" to avoid affecting the names of agricultural products that contain model numbers, such as Zhongpan No. 13.

[0035] In S2, refer to Figure 2 The flowchart of the method for obtaining a regional product express delivery dataset is shown in the figure. "Matching the delivery note dataset with the agricultural product dataset to obtain the regional product express delivery dataset" includes the following steps: S21: traverse the waybill information in the waybill dataset and match the current waybill information with the attribute information in the agricultural product dataset; if the match is successful, execute S22; if the match fails, execute S23; S22: Based on the current shipping label information and the current attribute information, extract a set of regional product express information and store it in the first data set; S23: storing the current delivery note information into the second data set; S24: After the traversal of the face order dataset is completed, the error type is determined based on the second dataset; if the error type is a general error, S25 is executed; if the error type is a private error, S26 is executed; S25: Update the agricultural product dataset and the first dataset; S26: Delete the second data set or update the first data set based on the second data set; S27: Determine whether the first data set is updated; if so, execute S28; if not, execute S29; S28: The updated first dataset is the regional product express delivery dataset; S29: The first dataset without update is the regional product express delivery dataset.

[0036] In S21, the attribute information of each agricultural product includes the professional name of the agricultural product and several names of origin. For one attribute information, the professional name contained therein is associated with several names of origin, and several names of origin respectively correspond to a local name set. This means that each agricultural product will have a unified professional name, and there may be one or more regions producing the agricultural product. Therefore, the professional name of an agricultural product may correspond to several names of origin. In different places of origin, there may be other local names for agricultural products (i.e., local product names or local aliases). For example, honeysuckle may be called Erhua in a certain area. Therefore, these local product names constitute a local name set, and for one attribute information, each place of origin name contained therein will be associated with an independent local name set.

[0037] Traverse all the waybill information, record the waybill information being traversed as the current waybill information, and match the current waybill information with the attribute information in the agricultural product dataset. Specifically, mark the item name contained in the current waybill information as the current item name, and mark the shipping area name contained in the current waybill information as the current shipping area name, refer to Figure 3 The flowchart of the method for matching the current delivery note information with the agricultural product dataset is shown. S21 includes the following steps: S211: Traverse the attribute information in the agricultural product dataset.

[0038] Specifically, the attribute information being traversed is marked as the current attribute information, the professional name included in the current attribute information is recorded as the current professional name, and the multiple origin names included in the current attribute information are recorded as multiple current origin names.

[0039] S212: Obtain the similarity between the current internal part name and the current professional name.

[0040] Specifically, a fuzzy matching algorithm is used to obtain the similarity between the item name contained in the current label information and the current professional name. The fuzzy matching algorithm can use Levenshtein distance, Jaccard similarity or cosine similarity, etc.

[0041] S213: Determine whether the similarity reaches a preset similarity threshold; if so, execute S214; if not, execute S217.

[0042] Specifically, the preset similarity threshold may be 1, indicating a complete match.

[0043] S214: Stop traversing the attribute information and match the current shipping area name with several current origin names; if the match with any of the several current origin names is successful, execute S215; if the match with all the several current origin names fails, execute S216.

[0044] Specifically, a geographic location matching algorithm (such as hierarchical matching based on administrative divisions or proximity matching based on geographic coordinates) is used to match the current shipping area name with several current origin names.

[0045] S215: Indicates that the current delivery note information and the current attribute information are matched successfully.

[0046] S216: Indicates that the current delivery note information fails to match the current attribute information.

[0047] S217: Determine whether all attribute information has been traversed; if so, execute S218; if not, return to S211 and continue to traverse subsequent attribute information.

[0048] S218: Extract all local name sets corresponding to the professional name in descending order of similarity, and compare the current internal product name with the local product names in all the extracted local name sets; if the comparison is consistent with any local product name, execute S219; if the comparison is inconsistent with all local product names, execute S216.

[0049] Specifically, all professional names will be sorted in descending order of similarity, and several local name sets corresponding to the professional names will be extracted in the sorted order, and then corresponding comparisons will be performed. If the current internal product name is consistent with any local product name in the extracted local name set, the extraction of the local name set will be stopped and S219 will be executed. If the local name sets of all professional names have been extracted and there is no local product name that is consistent with the current internal product name, S216 will be executed.

[0050] S219: The local product name that is consistent with the current package name is recorded as the current local product name, and the current shipping area name is matched with the origin name corresponding to the current local product name; if the match is successful, execute S215; if the match fails, execute S216.

[0051] Based on the above double traversal of the face order dataset and the agricultural product dataset, accurate data matching can be completed in a short time, and a batch of regional product express delivery information can be preliminarily screened out and stored in the first dataset.

[0052] In S22, the regional product express information includes the current waybill information, the professional name in the current attribute information, and the origin name that successfully matches the shipping area name contained in the current waybill information. The first data set storing the regional product express information is initially empty.

[0053] In S23 , the second data set is initially empty.

[0054] In S24, refer to Figure 4 The flowchart of the error type determination method shown, "Determining the error type based on the second data set," includes the following steps: S241: Count the total number of occurrences of each internal item name in the second data set.

[0055] For example, the second data set includes 100 sets of face label information. Statistics show that the 100 sets of face label information contain A and B internal item names. Among them, the total number of occurrences of internal item name A in the second data set is 20, and the total number of occurrences of internal item name B in the second data set is 80.

[0056] S242: Determine whether the total number of occurrences of each internal item name reaches a preset threshold; if not, execute S243; if so, execute S244.

[0057] S243: Determine that the error type of the internal item name is a private error.

[0058] S244: Match all the shipping area names corresponding to the item names with all the origin names in the agricultural and specialty product dataset one by one, and count the number of times each origin name is successfully matched.

[0059] S245: Obtain the ratio of the number of times each place of origin name is successfully matched to the total number of occurrences. If any ratio is greater than a preset ratio threshold, execute S246; if all ratios are not greater than the preset ratio threshold, execute S243.

[0060] Specifically, the number of times the origin name is successfully matched is equivalent to the number of times the product represented by the inner item name is mailed from the region represented by the origin name, and the ratio of the number of times the origin name is successfully matched to the total number of occurrences is equivalent to the ratio of the number of times the product represented by the inner item name is mailed from the region represented by the origin name to the total number of times the product represented by the inner item name is mailed.

[0061] S246: Determine that the error type of the internal part name is a general error.

[0062] In S25, when the error type of the internal product name is a general error, that is, when the ratio of the number of times a place of origin name is successfully matched to the total number of occurrences of the internal product name is greater than a preset ratio threshold, it indicates that the internal product name is an alias used for the region represented by the place of origin name. The internal product name is added as a new local product name to the local name set corresponding to the place of origin name, thereby achieving the purpose of updating the agricultural and special products data set. The updated agricultural and special products data set is used to update the first data set, thereby completing a good cycle of the system and making data screening and statistics more accurate.

[0063] In S26 , when the error type of the internal part name is a private error, the private error is ignored, the second data set is deleted, or the first data set is updated manually.

[0064] The dataset extracted in step S2 may contain data from non-rural areas, even though the shipping area is initially identified as a rural area. This data set is therefore referred to as the Regional Product Express Dataset. Next, steps S3 and S4 further remove data from non-rural areas.

[0065] Based on the above-mentioned method of constructing the second data set and judging the error type, it is possible to continuously analyze and gradually verify the waybill information that has not been successfully matched with the attribute information. If it is a common error, it can also achieve the purpose of automatically improving the agricultural product data set.

[0066] In S3, the trajectory dataset contains trajectory information for multiple express parcels. Each set of trajectory information includes at least one of the following: a shipping label number, a mailing company ID, a collection point code, and a collection time. The collection point code refers to the code of the location where the express parcel was collected, and the collection time refers to the time the express parcel was collected. To distinguish it from the data included in the shipping label information, the shipping label number included in the trajectory information is referred to as the second shipping label number, and the mailing company ID included in the trajectory information is referred to as the second mailing company ID.

[0067] The rural network point dataset contains multiple groups of logistics network point information, and each group of logistics network point information includes at least one of the following data: logistics network point code and logistics network point name.

[0068] Traverse the trajectory information in the trajectory dataset and match the collection point codes included in the trajectory information with the logistics point codes in the logistics service point information. If a match is successful, merge the trajectory information and the logistics service point information to which the logistics point code belongs into a set of logistics point express data, which is then stored in an initially empty third express data set. If a match fails, the product represented by the trajectory information is deemed not to be a specialty agricultural product, and the trajectory information is ignored or deleted, and traversal of subsequent trajectory information is continued. After traversal of the trajectory dataset is complete, the resulting third express data set is the rural express data set.

[0069] In S4, the regional product express delivery information in the regional product express delivery dataset is traversed, and the first side tracking number in the regional product express delivery information is matched with the second side tracking number in the logistics network express delivery data. If the match is successful, the regional product express delivery information and the logistics network express delivery data are merged. Duplicate items in the merged information may also be cleaned and stored in the fourth express delivery dataset. For example, if the first mailing company identifier and the second mailing company identifier are repeated in the merged information, only one of them is retained and used as the third mailing company identifier. If the match fails, the regional product express delivery information is ignored or deleted. After the traversal of the regional product express delivery dataset is completed, the fourth express delivery dataset becomes the agricultural product express delivery dataset.

[0070] In S5, statistics are collected from the agricultural product express delivery dataset based on several preset dimensions to obtain the business volume of agricultural products. For example, based on the origin name, third-party mail company identifier, collection time, and professional name in the agricultural product express delivery dataset, the business volume of agricultural products in rural areas of several specified regions can be calculated. Specifically, the following can be used: District D, City C, Express Delivery Company X, August 8, 2024, District D peaches, and a business volume of 10,000.

[0071] Optionally, the attribute information may also include the maturity period of agricultural products, etc., so as to facilitate statistics on the final agricultural products express delivery data set obtained from different dimensions. The obtained agricultural products business volume can be used to guide rural economic development.

[0072] Optionally, the various data sets mentioned above may exist in the form of a list or a database.

[0073] Reference Figure 5 The present disclosure provides a business volume statistics system for agricultural products, including: The data set acquisition module 101 is used to acquire agricultural product data sets, rural network data sets, express delivery bill data sets, and trajectory data sets; The first matching module 102 is used to match the delivery note dataset with the agricultural product dataset to obtain a regional product express delivery dataset; The second matching module 103 is used to match the trajectory dataset with the rural network dataset to obtain the rural express delivery dataset; The third matching module 104 is used to match the regional product express delivery dataset with the rural express delivery dataset to obtain the agricultural product express delivery dataset; The business volume statistics module 105 is used to collect statistics on the agricultural product express delivery data set to obtain the business volume of agricultural products; Among them, the agricultural product dataset is a dataset that records the attribute information of multiple agricultural products; the rural network dataset is a dataset that records the information of logistics service outlets in rural areas; the waybill dataset is a dataset that records the waybill information of multiple express parcels; and the trajectory dataset is a dataset that records the trajectory information generated by multiple express parcels during the circulation process.

[0074] The various variations and specific examples of the agricultural and special products business volume statistics method provided above are also applicable to the agricultural and special products business volume statistics system provided in the present disclosure. Through the above detailed description of the agricultural and special products business volume statistics method, those skilled in the art can clearly understand the implementation method of the agricultural and special products business volume statistics system. For the sake of brevity of the specification, it will not be described in detail here.

[0075] A computer device according to an embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0076] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer-readable instructions stored in the memory, causing the computer device to execute all or part of the steps of the agricultural product business volume statistics method described in each embodiment of the present disclosure.

[0077] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0078] like Figure 6 The present invention provides a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Figure 6 The computer device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0079] like Figure 6 As shown, a computer device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) or programs loaded from a storage device into a random access memory (RAM). The RAM also stores various programs and data required for the operation of the computer device. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0080] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes and hard disks; and communication devices. The communication device can allow the computer device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Figure 6 A computer device having various devices is shown, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0081] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the agricultural product business volume statistics method of the embodiment of the present disclosure are executed.

[0082] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0083] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the agricultural product business volume statistics method described in each embodiment of the present disclosure are executed.

[0084] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0085] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0086] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0087] In the present disclosure, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0088] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0089] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0090] Various changes, substitutions, and modifications may be made to the technology described herein without departing from the teachings defined by the appended claims. Moreover, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of things, means, methods, and actions described above. Currently existing or later developed processes, machines, manufactures, compositions of things, means, methods, or actions that perform substantially the same function or achieve substantially the same results as the corresponding aspects described herein may be utilized. Accordingly, the appended claims include within their scope such processes, machines, manufactures, compositions of things, means, methods, or actions.

[0091] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0092] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A statistical method for agricultural product business volume, characterized in that: include: Obtain agricultural product datasets, rural network datasets, express delivery label datasets, and trajectory datasets; Matching the delivery note dataset with the agricultural product dataset to obtain a regional product express delivery dataset; Matching the trajectory dataset with the rural network point dataset to obtain a rural express delivery dataset; Matching the regional product express delivery dataset with the rural express delivery dataset to obtain an agricultural product express delivery dataset; Collecting statistics on the agricultural product express delivery data set to obtain the business volume of agricultural products; Among them, the agricultural product dataset is a dataset that records the attribute information of multiple agricultural products; the rural network dataset is a dataset that records the information of logistics service outlets in rural areas; the waybill dataset is a dataset that records the waybill information of multiple express parcels; and the trajectory dataset is a dataset that records the trajectory information generated by multiple express parcels during the circulation process.

2. The method for statistically analyzing the business volume of agricultural products according to claim 1, wherein: The matching of the delivery note dataset with the agricultural product dataset to obtain a regional product express delivery dataset includes: Traversing the waybill information in the waybill dataset, and matching the current waybill information with the attribute information in the agricultural product dataset; If the match is successful, a set of regional product express information is extracted based on the current shipping label information and the current attribute information and stored in the first data set; If the match fails, the current bill information is stored in the second data set; After the traversal of the face order data set is completed, error type judgment is performed based on the second data set; If the error type is a general error, updating the agricultural product dataset and the first dataset; If the error type is a private error, deleting the second data set or updating the first data set based on the second data set; If the first dataset is updated, the updated first dataset is the regional product express dataset; If the first data set has not been updated, the first data set is the regional product express delivery data set.

3. The method for statistically analyzing the business volume of agricultural products according to claim 2, wherein: The shipping label information includes the name of the shipping area and the name of the contents; The attribute information includes a professional name of the agricultural product and several production place names, the professional name is associated with the several production place names, the several production place names each correspond to a local name set, and the local name set includes several local product names; Marking the waybill information being traversed as the current waybill information; Traverse the attribute information in the agricultural product dataset and mark the attribute information being traversed as the current attribute information; Obtaining the similarity between the item name included in the current label information and the professional name included in the current attribute information; If the similarity reaches a preset similarity threshold, the traversal of the attribute information is stopped. If the shipping area name contained in the current shipping label information successfully matches any origin name contained in the current attribute information, it indicates that the current shipping label information successfully matches the current attribute information. If the shipping area name fails to match any origin name contained in the current attribute information, it indicates that the current shipping label information fails to match the current attribute information. If the similarity does not reach the preset similarity threshold and all attribute information is traversed, the local name set corresponding to the professional name is extracted in descending order of similarity; Comparing the contents name included in the current shipping label information with the local product name in the extracted local name set; If the names are inconsistent with all local product names, it means that the current label information fails to match the current attribute information; If the name is consistent with any local product name, the local product name is recorded as the current local product name, and the shipping area name contained in the current shipping label information is matched with the origin name corresponding to the current local product name; If the match is successful, it means that the current bill information and the current attribute information are matched successfully; If the matching fails, it means that the current delivery note information fails to match the current attribute information.

4. The method for statistically analyzing the business volume of agricultural products according to claim 3, wherein: The regional product express delivery information includes the current waybill information, the professional name in the current attribute information, and the origin name that successfully matches the shipping area name contained in the current waybill information.

5. The method for statistically analyzing the business volume of agricultural products according to claim 3, wherein: The performing error type determination based on the second data set includes: Counting the total number of occurrences of each item name in the second data set; Determine whether the total number of occurrences of each internal item name reaches a preset number threshold; If the preset number of times is not reached, it is determined that the error type of the internal item name is a private error; If the preset number of times is reached, all the shipping area names corresponding to the item names are matched with all the origin names in the agricultural product dataset one by one, and the number of successful matches for each origin name is counted; Get the ratio of the number of successful matches for each place of origin name to the total number of occurrences; If any of the ratios is greater than a preset ratio threshold, the error type of the internal part name is determined to be a general error; If all the ratios are not greater than the preset ratio threshold, it is determined that the error type of the internal part name is a private error.

6. The method for statistically analyzing the business volume of agricultural products according to claim 2, wherein: Each set of logistics service outlet information included in the rural outlet data set includes a logistics outlet code; Traversing the trajectory information in the trajectory data set, and matching the collection point code included in the trajectory information with the logistics point code; If the match is successful, the trajectory information and the logistics service network information to which the logistics network code belongs are combined into a set of logistics network express data, and stored in a third express data set; After the trajectory dataset is traversed, the third express delivery dataset is a rural express delivery dataset.

7. The method for statistically analyzing the business volume of agricultural products according to claim 6, characterized in that: The shipping label information includes a first shipping label number, and the track information includes a second shipping label number. Therefore, the regional product express delivery information also includes the first shipping label number, and the logistics network express delivery data also includes the second shipping label number. Traversing the regional product express information in the regional product express data set, and matching the first side tracking number in the regional product express information with the second side tracking number in the logistics network express data; If the match is successful, the regional product express information is merged with the logistics network express data, and the merged information is stored in a fourth express data set; After the traversal of the regional product express delivery dataset is completed, the fourth express delivery dataset is the agricultural product express delivery dataset.

8. A computer device, characterized in that: The computer device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the agricultural product business volume statistics method described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the agricultural product business volume statistics method described in any one of claims 1-7.

10. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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