Logistics data index processing method, device and equipment, and storage medium

Abstract

The present application relates to the field of logistics management, and discloses a kind of processing method, device, equipment and storage medium of logistics data index, the method comprises: obtaining logistics data, and determining the data meaning of each index in logistics data and the business type corresponding to each index;Multiple indexes in logistics data are classified and processed, at least one data subject domain is obtained, and the mapping relationship between the index in each data subject domain and data subject domain is established;Each index in mapping relationship is hierarchically divided, and an index system is obtained;According to the index system, generate index white paper.The technical scheme provided by the present application processes the index in logistics data, forms an index system, and standardizes the management of the index in logistics data, and according to the formed index system, when data analysis, mass logistics data can be accurately and effectively processed and analyzed, and the efficiency and accuracy of data analysis are improved.

Description

Technical Field

[0001] This invention relates to the field of logistics management, and in particular to a method, apparatus, equipment and storage medium for processing logistics data indicators. Background Technology

[0002] After more than a decade of rapid development, the express delivery and logistics industry has accumulated massive amounts of logistics data, tens of thousands of data reports, and hundreds of thousands of logistics data indicators. Without refined and unified management, these vast data assets lack a standardized system, leading to numerous inconveniences in practical use.

[0003] How to better utilize these accumulated historical logistics data has become a key concern in the logistics industry. In the data analysis process, analyzing the indicators within the logistics data is crucial to the accuracy and effectiveness of the analysis results. Therefore, establishing a unified indicator system using historical logistics data has become an urgent problem to be solved in the logistics industry. Summary of the Invention

[0004] The main objective of this invention is to solve the technical problem that the lack of a unified indicator system for logistics data in the prior art leads to low efficiency in processing and analyzing massive amounts of logistics data.

[0005] The first aspect of this invention provides a method for processing logistics data indicators, the method comprising:

[0006] Acquire logistics data, wherein the logistics data package contains multiple indicators;

[0007] Determine the meaning of each indicator in the logistics data and the corresponding business type for each indicator;

[0008] Based on the meaning of the data and the business type, multiple indicators in the logistics data are classified to obtain at least one data theme domain, and a mapping relationship is established between the indicators in each data theme domain and the data theme domain, wherein the mapping relationship is that one data theme domain corresponds to at least one indicator.

[0009] The indicators in the mapping relationship are hierarchically divided to obtain the indicator system;

[0010] Based on the aforementioned indicator system, an indicator white paper was generated.

[0011] Optionally, in a first implementation of the first aspect of the present invention, determining the data meaning of each indicator in the logistics data and the business type corresponding to each indicator includes:

[0012] By invoking preset semantic recognition rules, the semantics of each indicator in the logistics data are identified, and the data meaning of each indicator is obtained;

[0013] Extract business characteristic information from each of the indicators, analyze the correlation between the business characteristic information and various businesses in the logistics industry, and determine the business type of the indicator based on the correlation.

[0014] Optionally, in a second implementation of the first aspect of the present invention, the step of classifying multiple indicators in the logistics data based on the data meaning and the business type to obtain at least one data subject domain, and establishing a mapping relationship between the indicators in each data subject domain and the data subject domain, includes:

[0015] Calculate the matching value between the meaning of the same indicator in the logistics data and the business type;

[0016] Determine whether the matching value is less than a preset matching threshold;

[0017] If the matching value is determined to be less than the preset matching threshold, the corresponding indicator is removed to obtain an indicator set;

[0018] Compare whether the business types of the indicators in the indicator set are consistent;

[0019] If so, aggregate multiple indicators that are consistent with the business type to obtain at least one data subject domain;

[0020] Based on at least one of the data subject domains, establish a mapping relationship between each indicator in each data subject domain and the data subject domain.

[0021] Optionally, in a third implementation of the first aspect of the present invention, calculating the matching value between the data meaning of the same indicator in the logistics data and the business type includes:

[0022] In the semantic space, the business type of each indicator in the logistics data is analyzed from a semantic dimension to obtain the business type dimension;

[0023] Semantic dimension analysis is performed on the data meaning of each indicator to obtain the data meaning dimension;

[0024] Calculate the similarity between the business type dimension and the data meaning dimension in the same indicator to obtain the dimension similarity value;

[0025] The dimensional similarity value is used as the matching value between the data meaning of the same indicator and the business type.

[0026] Optionally, in a fourth implementation of the first aspect of the present invention, the step of hierarchically dividing the indicators in the mapping relationship to obtain the indicator system includes:

[0027] Perform hierarchical analysis of business types on each indicator in the mapping relationship, sort the business types based on the results of the hierarchical analysis, and generate the business hierarchy of all indicators;

[0028] Based on the business hierarchy, the connection relationships between the various indicators are constructed to obtain the indicator hierarchy;

[0029] An indicator system is generated based on the indicator hierarchy and the mapping relationship.

[0030] Optionally, in a fifth implementation of the first aspect of the present invention, generating an indicator white paper based on the indicator system includes:

[0031] Based on the aforementioned indicator system, the relevant information of each indicator in the indicator system is analyzed to obtain the analysis results. The relevant information includes at least indicator attributes and indicator hierarchical relationships.

[0032] Based on preset file conversion rules, the analysis results are converted into files to generate an indicator white paper.

[0033] Optionally, in a sixth implementation of the first aspect of the present invention, after generating the indicator white paper according to the indicator system, the method further includes:

[0034] Obtain the source information of the logistics data, and determine the output format of the indicator system based on the source information;

[0035] Based on the output format, extract indicators with the same data meaning from the same data subject domain in the indicator system and merge them to obtain a new indicator system.

[0036] Each indicator in the new indicator system is coded to generate indicator codes;

[0037] Establish the mapping relationship between the indicators in the new indicator system and the indicator codes to obtain the indicator coding system.

[0038] A second aspect of the present invention provides a processing apparatus for logistics data indicators, the processing apparatus comprising:

[0039] The acquisition module is used to acquire logistics data;

[0040] The determination module is used to determine the meaning of each indicator in the logistics data and the business type corresponding to each indicator;

[0041] The classification module is used to classify multiple indicators in the logistics data based on the meaning of the data and the business type, to obtain at least one data subject domain, and to establish a mapping relationship between the indicators in each data subject domain and the data subject domain.

[0042] The hierarchical division module is used to divide the indicators in the mapping relationship into hierarchical levels to obtain the indicator system;

[0043] The generation module is used to generate an indicator white paper based on the indicator system.

[0044] Optionally, in a first implementation of the second aspect of the present invention, the determining module is specifically used for:

[0045] By invoking preset semantic recognition rules, the semantics of each indicator in the logistics data are identified, and the data meaning of each indicator is obtained;

[0046] Extract business characteristic information from each of the indicators, analyze the correlation between the business characteristic information and various businesses in the logistics industry, and determine the business type of the indicator based on the correlation.

[0047] Optionally, in a second implementation of the second aspect of the present invention, the classification module includes:

[0048] A calculation unit is used to calculate the matching value between the data meaning of the same indicator in the logistics data and the business type;

[0049] The elimination unit is used to preset a matching threshold. If the matching value is less than the matching threshold, the corresponding indicator is eliminated to obtain an indicator set.

[0050] A judgment unit is used to determine whether the matching value is less than a preset matching threshold.

[0051] The elimination unit is used to eliminate the corresponding indicators when the matching value is less than the preset matching threshold, thereby obtaining an indicator set.

[0052] A comparison unit is used to compare whether the business types of the indicators in the indicator set are consistent.

[0053] A classification unit is used to summarize multiple indicators with the same business type when the business types of the indicators are consistent, to obtain at least one data subject area.

[0054] The mapping unit is used to establish a mapping relationship between each indicator in each data subject domain and the data subject domain based on at least one of the data subject domains.

[0055] Optionally, in a third implementation of the second aspect of the present invention, the computing unit is specifically used for:

[0056] In the semantic space, the business type of each indicator in the logistics data is analyzed from a semantic dimension to obtain the business type dimension;

[0057] Semantic dimension analysis is performed on the data meaning of each indicator to obtain the data meaning dimension;

[0058] Calculate the similarity between the business type dimension and the data meaning dimension in the same indicator to obtain the dimension similarity value;

[0059] The dimensional similarity value is used as the matching value between the data meaning of the same indicator and the business type.

[0060] Optionally, in a fourth implementation of the second aspect of the present invention, the hierarchical division module is specifically used for:

[0061] Perform hierarchical analysis of business types on each indicator in the mapping relationship, sort the business types based on the results of the hierarchical analysis, and generate the business hierarchy of all indicators;

[0062] Based on the business hierarchy, the connection relationships between the various indicators are constructed to obtain the indicator hierarchy;

[0063] An indicator system is generated based on the indicator hierarchy and the mapping relationship.

[0064] Optionally, in a fifth implementation of the second aspect of the present invention, the generation module is specifically used for:

[0065] Based on the aforementioned indicator system, the relevant information of each indicator in the indicator system is analyzed to obtain the analysis results;

[0066] Based on preset file conversion rules, the analysis results are converted into files to generate an indicator white paper.

[0067] Optionally, in a sixth implementation of the second aspect of the present invention, the processing device for the logistics data indicators further includes an update module, which is specifically used for:

[0068] Obtain the source information of the logistics data, and determine the output format of the indicator system based on the source information;

[0069] Based on the output format, extract indicators with the same data meaning from the same data subject domain in the indicator system and merge them to obtain a new indicator system.

[0070] Each indicator in the new indicator system is coded to generate indicator codes;

[0071] Establish the mapping relationship between the indicators in the new indicator system and the indicator codes to obtain the indicator coding system.

[0072] A third aspect of the present invention provides a logistics data indicator processing device, the logistics data indicator processing device comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected via a circuit; the at least one processor calling the instructions in the memory to cause the logistics data indicator processing device to perform the steps of the above-described logistics data indicator processing method.

[0073] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the steps of the above-described logistics data indicator processing method.

[0074] In the technical solution provided by this invention, logistics data is acquired, and the meaning of each indicator in the logistics data and the corresponding business type are determined. The meaning of each indicator and the corresponding business type are compared, and based on the comparison results, multiple indicators in the logistics data are classified to obtain at least one data subject domain. A mapping relationship between the indicators in each data subject domain and the data subject domain is established. The indicators in the mapping relationship are hierarchically divided to obtain an indicator system. Based on the indicator system, an indicator white paper is generated. The technical solution provided by this invention processes each indicator in the logistics data and constructs an indicator system, facilitating subsequent processing of each indicator in the logistics data using the formed indicator system. This improves the efficiency and flexibility of logistics data indicator processing. Simultaneously, the construction of the indicator system enhances the efficiency and accuracy of logistics data analysis and saves time costs. Attached Figure Description

[0075] Figure 1 This is a schematic diagram of the first embodiment of the logistics data indicator processing method in this invention.

[0076] Figure 2 This is a schematic diagram of a second embodiment of the method for processing logistics data indicators in this invention.

[0077] Figure 3 This is a schematic diagram of a third embodiment of the method for processing logistics data indicators in this invention.

[0078] Figure 4 This is a schematic diagram of the fourth embodiment of the logistics data indicator processing method in this invention.

[0079] Figure 5 This is a schematic diagram of one embodiment of the logistics data indicator processing device in this invention.

[0080] Figure 6 This is a schematic diagram of another embodiment of the logistics data indicator processing device in this invention;

[0081] Figure 7 This is a schematic diagram of one embodiment of the logistics data indicator processing device in this invention. Detailed Implementation

[0082] This invention provides a method, apparatus, device, and storage medium for processing logistics data indicators. The method involves acquiring logistics data and determining the meaning of each indicator and its corresponding business type. The meaning and business type of each indicator are compared, and based on the comparison results, multiple indicators in the logistics data are classified to obtain at least one data subject domain. A mapping relationship is established between the indicators in each data subject domain and the data subject domain itself. The indicators in the mapping relationship are then hierarchically divided to obtain an indicator system. Based on the indicator system, an indicator white paper is generated. The technical solution provided by this invention processes various indicators in logistics data and constructs an indicator system, facilitating subsequent processing of indicators in the logistics data using the formed indicator system. This improves the efficiency and flexibility of logistics data indicator processing. Furthermore, the construction of the indicator system enhances the efficiency and accuracy of logistics data analysis and saves time costs.

[0083] 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.

[0084] For ease of understanding, the specific details of the embodiments of the present invention are described below. Please refer to [link / reference]. Figure 1 The first embodiment of the method for processing logistics data indicators in this invention includes:

[0085] 101. Obtain logistics data;

[0086] Data reports from various management departments of merchants within the logistics industry, including operations, outlets, and centers, are collected as data sources. This involves first conducting business research to identify the relevant departments and their responsible persons, confirming the core reports and data sources, and determining the purpose and significance of the data. Then, the meaning and dimensions of each field in the data reports are confirmed. Relevant logistics data is then extracted from the collected data reports. This logistics data involves multiple business operations within the logistics industry. Because each different business corresponds to a different business scenario and has corresponding different indicators, the acquired logistics data contains multiple indicators, and these indicators correspond to different businesses and have different data meanings. In other words, the data attributes and related information corresponding to each indicator are not the same.

[0087] Specifically, logistics data can be data generated during the logistics service process, including one or more of order data, operational data, and transportation capacity data. Order data can be data related to orders submitted by users, such as order numbers, pickup addresses, and delivery addresses. Operational data can be profit or expense data related to transaction orders, such as the profit corresponding to a transaction order. Transportation capacity data can be data related to human resources, such as the number of couriers and their delivery status.

[0088] 102. Determine the meaning of each indicator in the logistics data and the corresponding business type;

[0089] Based on the relevant logistics data obtained, semantic analysis is performed on each indicator in the semantic space to obtain the data meaning of each indicator, and business characteristic information of each indicator is analyzed to determine the business type corresponding to each indicator.

[0090] Specifically, the system invokes preset semantic recognition rules to extract text from the data attributes of each indicator in the logistics data within the semantic space. This means extracting the label attributes of the indicator, which are in text form. The label attributes are then segmented into words, and semantic recognition is performed based on the results of the word segmentation to identify the data meaning of each indicator.

[0091] Logistics data involves multiple business operations within the logistics industry. Each indicator in the logistics data carries business characteristic information corresponding to that business. By extracting the business characteristic information of each indicator and analyzing the correlation between this business characteristic information and various business operations in the logistics industry, the correlation degree can be obtained based on the correlation analysis. Thus, the business type corresponding to the indicator can be determined based on the correlation degree.

[0092] 103. Based on the meaning of the data and the business type, multiple indicators in the logistics data are classified and processed to obtain at least one data subject domain, and a mapping relationship between the indicators in each data subject domain and the data subject domain is established.

[0093] Semantic analysis is performed on the data meaning and business type corresponding to the same indicator. This involves analyzing whether the semantics of the data meaning within the same indicator are consistent with the semantics of the corresponding business type. If they are consistent, it indicates that the data meaning of the indicator matches the corresponding business type, meaning the indicator's attribute information is correct. Indicators with correct attribute information are then selected and categorized according to their corresponding business types, resulting in at least one indicator set. Each business type corresponds to one indicator set, and each business type has at least one indicator. This indicator set is then used as a data subject domain. A mapping relationship is established between each indicator in each data subject domain and the data subject domain itself, ensuring that each data subject domain corresponds to at least one indicator.

[0094] 104. The indicators in the mapping relationship are divided into hierarchical levels to obtain the indicator system;

[0095] Based on the mapping relationship between data subject domains and various indicators, the indicators within each data subject domain are hierarchically divided. The logistics industry involves multiple business operations with hierarchical relationships between them. Each indicator corresponds to a different business type, and a business can correspond to different business types. Based on the hierarchical relationship between businesses, the hierarchical relationship of the business types of the corresponding indicators is analyzed, thus obtaining the hierarchical connection relationship between each indicator. Based on the analyzed hierarchical connection relationship between the indicators, each indicator is converted into an indicator system. In this process, based on the business type corresponding to the indicator, an indicator mapping relationship can be established between each business domain and each data subject domain. This allows indicators from different business data sources to be integrated into the indicator system according to this mapping relationship, and the indicator system can be continuously improved based on new data sources.

[0096] This embodiment enables the system to automatically convert data into an indicator system through a configurable interface, generate and present visual analysis results, saving manual intervention and reducing labor costs. It also ensures that the final data analysis results using the indicator system are not erroneous due to human error, improving data processing efficiency and guaranteeing the comprehensiveness and accuracy of the data analysis results.

[0097] 105. Based on the indicator system, generate an indicator white paper.

[0098] The logistics industry has numerous sources of indicators. To better manage these indicators, a unified indicator system and calculation logic system must be established. This involves generating an indicator white paper based on the existing indicator system to standardize the management of various indicators. According to the obtained indicator system, the relevant information of each indicator is analyzed. Specifically, the attribute information and hierarchical relationships of each indicator are analyzed. Based on this analysis, a unified standard definition is established for the attribute information and hierarchical relationships of each indicator. This process summarizes and aggregates the relevant information of each indicator to form a unified standard, generating an indicator white paper. This white paper can then be used to process each indicator, standardizing indicator definitions, eliminating ambiguity, and improving the efficiency of data analysis.

[0099] Furthermore, once the indicator system is established, a complete and unified data source can be provided for business data analysis by offering a new public service interface. New business reports at each business level must be completed through the public service interface to avoid inconsistencies in logistics data across different reports caused by each business department developing and generating its own data reports independently. When the server needs to switch data sources or undergo underlying database changes, the client does not need any modifications; it only requires switching the underlying source or modifying the internal data processing logic of the public service interface, thereby achieving a loosely coupled architecture between the server and the client.

[0100] Specifically, while prioritizing the stability of business operations, the company will establish a public service hall and a new dashboard app, gradually replacing the existing independent data reports of various business systems, and ultimately pushing the system towards a brand-new, standardized, and universally accepted decision support system.

[0101] In this embodiment of the invention, when the application scenario is a logistics company, by processing various indicators in the logistics data, integrating the data assets of logistics merchants, dividing the data subject domain, generating an indicator system and indicator white paper, eliminating indicator ambiguity, and unifying indicator standards; by providing a unified data interface through the public service hall, data for the same dimension and indicator is provided through the same interface, avoiding data inconsistencies and improving data analysis efficiency.

[0102] In this embodiment of the invention, multiple indicators in logistics data are classified to obtain a data subject domain. Then, the indicators in the data subject domain are hierarchically divided to form an indicator system, thereby generating an indicator white paper. This achieves standardized management of indicators, facilitates subsequent data analysis, and improves the efficiency of data analysis.

[0103] Please see Figure 2 A second embodiment of the method for processing logistics data indicators in this invention includes:

[0104] 201. Obtain logistics data;

[0105] 202. Determine the meaning of each indicator in the logistics data and the corresponding business type;

[0106] 203. In the semantic space, the business type of each indicator in the logistics data is analyzed from a semantic dimension to obtain the business type dimension;

[0107] Within the semantic space, dimensional analysis of the business types of various indicators in logistics data involves semantic analysis of the vocabulary of each indicator's business type. This means taking the business type as a basic vocabulary, analyzing and determining the dimensions of this basic vocabulary, and thus obtaining the dimensions of the corresponding business type.

[0108] In this embodiment, a method combining basic vocabulary and hierarchical vocabulary is used to capture the semantics represented by basic vocabulary and its semantic relationships with other vocabulary. Basic vocabulary serves as the fundamental formal vocabulary of semantic hierarchical vocabulary; here, basic vocabulary refers to vocabulary with whom a basic consensus can be reached. The semantic hierarchical vocabulary composed of basic vocabulary serves as the basis for semantic interoperability. Basic vocabulary represents a basic dimension, and the associated hierarchical vocabulary represents the semantic hierarchical dimensions. The dimensional relationship is obtained by analyzing the semantic relationship between basic vocabulary and hierarchical vocabulary. For example, the business type of the first indicator is "price," which is a basic vocabulary representing a basic dimension. The dimensions of its associated hierarchical vocabulary can be "express delivery," "logistics merchant," "RMB," etc., thus establishing a semantic hierarchical dimensional relationship including information on "price," "express delivery," "logistics merchant," and "RMB." That is, when the business type corresponding to the second indicator is "logistics merchant," the business types of the first and second indicators have a hierarchical dimensional relationship in terms of semantics.

[0109] 204. Semantic dimension analysis of the data meaning of each indicator is performed to obtain the data meaning dimension;

[0110] Based on the steps of semantic dimension analysis described above, semantic dimension analysis is also performed on the data meaning of each indicator in the semantic space to obtain the data meaning dimension. Performing dimensional analysis on data meaning involves treating data meaning as a basic term, analyzing whether this basic term has semantic relationships with the basic terms of the data meanings of other indicators, and representing these semantic relationships using dimensions.

[0111] 205. Calculate the similarity between the business type dimension and the data meaning dimension of the same indicator to obtain the dimension similarity value;

[0112] 206. Use dimensional similarity values ​​as matching values ​​between the data meaning of the same indicator and the business type;

[0113] Calculating the similarity between the business type dimension and the data meaning dimension of the same metric involves calculating the semantic similarity of the business type and data meaning of the same metric within the semantic space, at the semantic dimension level. Specifically, calculating the similarity of dimensions involves analyzing whether the business type and data meaning corresponding to the same metric belong to the same semantic dimension, that is, analyzing whether the data meaning corresponding to the metric is consistent with the semantics of its business type. A predefined rule for calculating dimension similarity is defined, and this rule is called to calculate the dimensional similarity between the business type dimension and the data meaning dimension of the same metric, obtaining the dimension similarity value. This dimension similarity value is the matching value between the data meaning and the business type of the metric.

[0114] 207. Determine if the matching value is less than the preset matching threshold;

[0115] 208. When the matching value is less than the preset matching threshold, the corresponding indicator is removed to obtain the indicator set.

[0116] A matching threshold is pre-set for comparison with the matching value. When the matching value is less than the matching threshold, it means that the data meaning and business type of the indicator do not belong to the same dimension in the semantic space. In other words, the data meaning and business type do not match, indicating that the attribute information of the indicator is incorrect. Therefore, the indicator needs to be removed. After all the indicators are processed in the above way, they are summarized to form an indicator set.

[0117] 209. Compare whether the business types of the indicators in the indicator set are consistent;

[0118] 210. When the business types of the indicators are consistent, the multiple indicators with consistent business types are aggregated to obtain at least one data subject area.

[0119] The business types of each indicator in the indicator set are compared to see if the business types corresponding to each indicator are consistent. If the business types are consistent, the indicators corresponding to that business type are summarized to form a new indicator set. The new indicator set contains at least one indicator. Since each indicator corresponds to more than one business type, there will be more than one new indicator set. The new indicator set is used as the data subject domain, thus obtaining at least one data subject domain.

[0120] 211. Based on at least one data subject domain, establish the mapping relationship between each indicator in each data subject domain and the data subject domain;

[0121] Based on at least one data subject domain, establish a mapping relationship between each indicator in the data subject domain and the data subject domain. That is, one data subject domain corresponds to at least one indicator, and there is a one-to-many mapping relationship between the data subject domain and the indicator.

[0122] 212. The indicators in the mapping relationship are divided into hierarchical levels to obtain the indicator system;

[0123] 213. Generate an indicator white paper based on the indicator system.

[0124] In this embodiment of the invention, steps 201-202 and 212-213 are the same as steps 101-102 and 104-105 in the first embodiment of the above-described method for processing logistics data indicators, and will not be repeated here.

[0125] In this embodiment of the invention, by calculating the matching value between the data meaning and business type of the same indicator, indicators with incorrect attribute information are filtered out. Then, indicators with consistent business types are summarized to form a data subject domain, thereby eliminating indicator ambiguity and ensuring the accuracy of indicator attribute information.

[0126] Please see Figure 3 A third embodiment of the method for processing logistics data indicators in this invention includes:

[0127] 301, obtain logistics data;

[0128] 302. Determine the meaning of each indicator in the logistics data and the corresponding business type;

[0129] 303. Based on the meaning of the data and the business type, multiple indicators in the logistics data are classified and processed to obtain at least one data subject domain, and a mapping relationship between the indicators in each data subject domain and the data subject domain is established.

[0130] 304. Perform hierarchical analysis of business types for each indicator in the mapping relationship, sort the business types based on the results of the hierarchical analysis, and generate the business hierarchy of all indicators.

[0131] Based on the mapping relationship between data subject domains and indicators, hierarchical analysis of business types is performed on each indicator corresponding to the data subject domain. In the previous step, the business characteristic information of each indicator was extracted, and the correlation between the business characteristic information and each business in the logistics industry was analyzed, thereby determining the business type of the corresponding indicator. Through the business characteristic information, there is a correspondence between each indicator and each business, and the correspondence is that one business corresponds to at least one indicator.

[0132] Because there are interrelationships among various business operations in the logistics industry—that is, hierarchical relationships exist between them—we can analyze the hierarchical relationships of each indicator based on the correspondence between business operations and indicators, and the hierarchical relationships among business operations. Specifically, we perform hierarchical analysis on the business types corresponding to each indicator, sort the business types according to their corresponding hierarchical relationships, and then sort the indicators corresponding to the business types in that sorted business type, forming the hierarchical ranking of all indicators, thus generating the business hierarchy of all indicators.

[0133] 305. Based on the business hierarchy, construct the connection relationship between various indicators to obtain the indicator hierarchy;

[0134] Based on the business hierarchy of all the obtained indicators, establish the connection relationship between the indicators. That is, establish the connection relationship between the indicators that are related by business hierarchy, including establishing the connection relationship of the same level and establishing the connection relationship between superiors and subordinates. After establishing the connection relationship between the levels of all indicators, the hierarchy of all indicators is obtained.

[0135] 306. Generate an indicator system based on indicator hierarchy and mapping relationships;

[0136] Based on the mapping relationship between data subject domains and various indicators, as well as the hierarchical connection relationship between indicators, all indicators are hierarchically structured. That is, the data subject domain is used as the root node, the corresponding indicators under the data subject domain are used as child nodes, and the indicator hierarchy is used as the hierarchical connection relationship between indicators. Connecting the corresponding child nodes, that is, connecting the corresponding indicators, forms an indicator system.

[0137] 307. Based on the indicator system, the relevant information of each indicator in the indicator system is analyzed to obtain the analysis results;

[0138] Based on the generated indicator system, the relevant information of each indicator in the indicator system is analyzed and defined. The relevant information of each indicator includes at least the indicator attributes and the hierarchical relationship of the indicators. That is, in this step, the relevant information of each indicator in the obtained indicator system is analyzed, and then the indicators in the logistics industry are defined in a standard way to form a unified indicator definition, that is, to eliminate indicator ambiguity and unify indicator standards.

[0139] Specifically, based on the different business types and data meanings corresponding to each indicator in the indicator system, the relationship between the meaning of indicator data and business types can be analyzed. This allows for the determination of the data meaning of each indicator under different business types, including the indicator attributes, dimensions, types, and conditions for creating indicators based on business types. Furthermore, by analyzing the connection relationships between indicators at different levels within the indicator system, the conditions for establishing connections between indicators can be determined, defining the hierarchical relationships between indicators. The calculation logic of each indicator in the indicator system can be summarized and defined, defining the calculation logic corresponding to different indicators. Finally, the relevant information of each indicator in the indicator system can be analyzed to achieve a unified and standardized definition of the relevant information of each indicator in the logistics industry, yielding analytical results.

[0140] 308. Based on preset file conversion rules, the analysis results are converted into files to generate an indicator white paper.

[0141] In this embodiment of the invention, a preset file conversion rule is used to convert the file format and generate a file. Specifically, the analysis results of the indicator-related information obtained in the previous step are converted into a file format and output as a file. The output text is then used as an indicator white paper to uniformly manage the relevant information of each indicator.

[0142] In this embodiment of the invention, steps 301-303 are the same as steps 101-103 in the first embodiment of the above-described method for processing logistics data indicators, and will not be described again here.

[0143] In this embodiment of the invention, the connection relationship between indicators is constructed, the indicators are transformed into an indicator system, and the indicator system is converted into an indicator white paper. This enables the standardization and management of the relevant information of each indicator, unifies the indicator caliber, and facilitates the subsequent use of the indicator white paper to perform data analysis on logistics data, thereby improving the efficiency of data analysis.

[0144] Please see Figure 4 The fourth embodiment of the logistics data indicator processing method in this invention includes:

[0145] 401, obtain logistics data;

[0146] 402. Determine the meaning of each indicator in the logistics data and the corresponding business type;

[0147] 403. Based on the meaning of the data and the business type, multiple indicators in the logistics data are classified and processed to obtain at least one data subject domain, and a mapping relationship between the indicators in each data subject domain and the data subject domain is established.

[0148] 404. The indicators in the mapping relationship are divided into hierarchical levels to obtain the indicator system;

[0149] 405. Based on the indicator system, generate an indicator white paper;

[0150] 406. Obtain the source information of logistics data and determine the output format of the indicator system based on the source information;

[0151] Based on the indicator hierarchy of each indicator in the logistics data as specified in the indicator white paper, we analyze and investigate the source of each logistics data to obtain the source information of each logistics data, and determine the output form of the indicator system corresponding to the logistics data based on the source information of the logistics data.

[0152] Specifically, the indicator white paper can be defined as dividing different indicator levels among different logistics companies. Based on these different indicator levels, each indicator is analyzed and defined. Then, based on the corresponding hierarchical structure type of each indicator in the logistics data in the indicator white paper, we can analyze and investigate which logistics company the logistics data comes from. Based on the logistics company's definition of the hierarchical structure of the indicators, we can obtain the output form of the corresponding indicator system.

[0153] 407. Based on the output format, extract indicators with the same data meaning from the same data subject area in the indicator system and merge them to obtain a new indicator system.

[0154] Based on the output format of the obtained indicator system, the generated indicator system is converted into the corresponding output format for output. Then, the data meanings of each indicator within the same data subject domain in the output indicator system are examined to detect whether there are indicators with identical data meanings. Indicators with identical data meanings are then extracted; that is, based on the data meaning of each indicator, semantic analysis of the attributes is performed to filter indicators with the same attributes. Indicators with the same attributes refer to those that are of the same type and have the same meaning, meaning that these indicators are actually parameters measuring the same objective. When indicators with identical data meanings are detected, these indicators are merged within the existing indicator system to obtain a new indicator system.

[0155] 408. Encode each indicator in the new indicator system to generate indicator codes;

[0156] The indicators in the newly formed indicator system are coded. Specifically, each indicator in the new indicator system is numbered to distinguish it from the others. The numbering of indicators can be set according to the different levels of each indicator, or it can be set according to the different business corresponding to the indicator. The specific numbering format is not limited here. After each indicator in the new indicator system is numbered, the number set for each indicator will be used as the indicator code.

[0157] 409. Establish the mapping relationship between indicators and indicator codes in the new indicator system to obtain the indicator coding system.

[0158] A mapping relationship is established between each indicator and its code in the new indicator system, forming a correspondence between indicators and their codes. That is, one indicator code corresponds to one indicator. Based on the mapping relationship between indicators and their codes, the conversion from the indicator system to the indicator code system can be realized.

[0159] In this embodiment of the invention, steps 401-405 are the same as steps 101-105 in the first embodiment of the above-described logistics data indicator processing method, and will not be described again here.

[0160] In this embodiment of the invention, by determining the output format of the generated indicator system, outputting the indicator system in its corresponding output format, and encoding each indicator in the indicator system, a mapping relationship between the indicator code and the indicator is established, thereby automatically completing the conversion from the indicator system to the indicator code system, generating visual analysis results for each indicator, saving manpower costs, and ensuring the accuracy of the presented visual analysis results.

[0161] The above describes the method for processing logistics data indicators in the embodiments of the present invention. The following describes the apparatus for processing logistics data indicators in the embodiments of the present invention. Please refer to... Figure 5 One embodiment of the logistics data indicator processing device in this invention includes:

[0162] Module 501 is used to acquire logistics data;

[0163] The determination module 502 is used to determine the data meaning of each indicator in the logistics data and the business type corresponding to each indicator;

[0164] The classification module 503 is used to classify multiple indicators in the logistics data based on the meaning of the data and the business type, to obtain at least one data subject domain, and to establish a mapping relationship between the indicators in each data subject domain and the data subject domain.

[0165] The hierarchical division module 504 is used to perform hierarchical division of each indicator in the mapping relationship to obtain an indicator system.

[0166] The generation module 505 is used to generate an indicator white paper based on the indicator system.

[0167] In this embodiment of the invention, the logistics data indicator processing device executes the steps of the above-mentioned logistics data indicator processing method to process each indicator in the logistics data, automatically construct an indicator system, form an indicator white paper, realize unified management of indicators, and improve the efficiency of logistics data indicator processing.

[0168] Please see Figure 6 Another embodiment of the logistics data indicator processing device in this invention includes:

[0169] Module 501 is used to acquire logistics data;

[0170] The determination module 502 is used to determine the data meaning of each indicator in the logistics data and the business type corresponding to each indicator;

[0171] The classification module 503 is used to classify multiple indicators in the logistics data based on the meaning of the data and the business type, to obtain at least one data subject domain, and to establish a mapping relationship between the indicators in each data subject domain and the data subject domain.

[0172] The hierarchical division module 504 is used to perform hierarchical division of each indicator in the mapping relationship to obtain an indicator system.

[0173] The generation module 505 is used to generate an indicator white paper based on the indicator system.

[0174] Optionally, the determining module 502 is specifically used for:

[0175] By invoking preset semantic recognition rules, the semantics of each indicator in the logistics data are identified, and the data meaning of each indicator is obtained;

[0176] Extract business characteristic information from each of the indicators, analyze the correlation between the business characteristic information and various businesses in the logistics industry, and determine the business type of the indicator based on the correlation.

[0177] Optionally, the classification module 503 includes:

[0178] Calculation unit 5031 is used to calculate the matching value between the data meaning of the same indicator in the logistics data and the business type;

[0179] The judgment unit 5032 is used to determine whether the matching value is less than a preset matching threshold;

[0180] The elimination unit 5033 is used to eliminate the corresponding indicators when the matching value is less than the preset matching threshold, thereby obtaining an indicator set.

[0181] Comparison unit 5034 is used to compare whether the business types of each indicator in the indicator set are consistent;

[0182] Classification unit 5035 is used to summarize multiple indicators with the same business type when the business types of the indicators are consistent, to obtain at least one data subject area.

[0183] The mapping unit 5036 is used to establish a mapping relationship between each indicator in each data topic domain and the data topic domain based on at least one of the data topic domains.

[0184] Optionally, the computing unit 5031 is specifically used for:

[0185] In the semantic space, the business type of each indicator in the logistics data is analyzed from a semantic dimension to obtain the business type dimension;

[0186] Semantic dimension analysis is performed on the data meaning of each indicator to obtain the data meaning dimension;

[0187] Calculate the similarity between the business type dimension and the data meaning dimension in the same indicator to obtain the dimension similarity value;

[0188] The dimensional similarity value is used as the matching value between the data meaning of the same indicator and the business type.

[0189] Optionally, the hierarchy division module 504 is specifically used for:

[0190] Perform hierarchical analysis of business types on each indicator in the mapping relationship, sort the business types based on the results of the hierarchical analysis, and generate the business hierarchy of all indicators;

[0191] Based on the business hierarchy, the connection relationships between the various indicators are constructed to obtain the indicator hierarchy;

[0192] An indicator system is generated based on the indicator hierarchy and the mapping relationship.

[0193] Optionally, the generation module 505 is specifically used for:

[0194] Based on the aforementioned indicator system, the relevant information of each indicator in the indicator system is analyzed to obtain the analysis results;

[0195] Based on preset file conversion rules, the analysis results are converted into files to generate an indicator white paper.

[0196] Optionally, the processing device for the logistics data indicators further includes an update module 506, which is specifically used for:

[0197] Obtain the source information of the logistics data, and determine the output format of the indicator system based on the source information;

[0198] Based on the output format, extract indicators with the same data meaning from the same data subject domain in the indicator system and merge them to obtain a new indicator system.

[0199] Each indicator in the new indicator system is coded to generate indicator codes;

[0200] Establish the mapping relationship between the indicators in the new indicator system and the indicator codes to obtain the indicator coding system.

[0201] In this embodiment of the invention, the logistics data indicator processing device can output a new indicator system according to the different output methods of the indicator system, and encode each indicator in the indicator system to obtain an indicator coding system, thereby realizing the conversion from indicator system to indicator coding system.

[0202] Please see Figure 7 The following is a detailed description of an embodiment of the logistics data indicator processing device in this invention from the perspective of hardware processing.

[0203] Figure 7 This is a schematic diagram of the structure of a logistics data indicator processing device 700 provided in an embodiment of the present invention. The logistics data indicator processing device 700 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 710 (e.g., one or more processors) and a memory 720, and one or more storage media 730 (e.g., one or more mass storage devices) for storing application programs 733 or data 732. The memory 720 and storage media 730 can be temporary or persistent storage. The program stored in the storage media 730 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the logistics data indicator processing device 700. Furthermore, the processor 710 may be configured to communicate with the storage media 730 and execute the series of instruction operations in the storage media 730 on the logistics data indicator processing device 700.

[0204] The logistics data processing device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input / output interfaces 760, and / or one or more operating systems 731, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 7The illustrated processing equipment structure for logistics data indicators does not constitute a limitation on the processing equipment for logistics data indicators. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0205] 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 the instructions are executed on a computer, cause the computer to perform the steps of the processing method for the logistics data indicators.

[0206] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0207] 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.

[0208] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing logistics data indicators, characterized in that, The processing methods for the logistics data indicators include: Acquire logistics data, wherein the logistics data package contains multiple indicators; Determine the meaning of each indicator in the logistics data and the corresponding business type for each indicator; Calculate the matching value between the meaning of the same indicator in the logistics data and the business type; Determine whether the matching value is less than a preset matching threshold; If the matching value is determined to be less than the preset matching threshold, the corresponding indicator is removed to obtain an indicator set; Compare whether the business types of the indicators in the indicator set are consistent; If so, aggregate multiple indicators that are consistent with the business type to obtain at least one data subject domain; Based on at least one of the data subject domains, establish a mapping relationship between each indicator in each data subject domain and the data subject domain, wherein the mapping relationship is that one data subject domain corresponds to at least one indicator; The indicators in the mapping relationship are hierarchically divided to obtain the indicator system; Based on the aforementioned indicator system, an indicator white paper was generated.

2. The method for processing logistics data indicators according to claim 1, characterized in that, Determining the meaning of each indicator in the logistics data and the corresponding business type includes: By invoking preset semantic recognition rules, the semantics of each indicator in the logistics data are identified, and the data meaning of each indicator is obtained; Extract business characteristic information from each of the indicators, analyze the correlation between the business characteristic information and various businesses in the logistics industry, and determine the business type of the indicator based on the correlation.

3. The method for processing logistics data indicators according to claim 1, characterized in that, The calculation of the matching value between the meaning of the same indicator in the logistics data and the business type includes: In the semantic space, the business type of each indicator in the logistics data is analyzed from a semantic dimension to obtain the business type dimension; Semantic dimension analysis is performed on the data meaning of each indicator to obtain the data meaning dimension; Calculate the similarity between the business type dimension and the data meaning dimension in the same indicator to obtain the dimension similarity value; The dimensional similarity value is used as the matching value between the data meaning of the same indicator and the business type.

4. The method for processing logistics data indicators according to any one of claims 1-3, characterized in that, The hierarchical division of the indicators in the mapping relationship yields an indicator system including: Perform hierarchical analysis of business types on each indicator in the mapping relationship, sort the business types based on the results of the hierarchical analysis, and generate the business hierarchy of all indicators; Based on the business hierarchy, the connection relationships between the various indicators are constructed to obtain the indicator hierarchy; An indicator system is generated based on the indicator hierarchy and the mapping relationship.

5. The method for processing logistics data indicators according to claim 4, characterized in that, The process of generating the indicator white paper based on the indicator system includes: Based on the aforementioned indicator system, the relevant information of each indicator in the indicator system is analyzed to obtain the analysis results. The relevant information includes at least indicator attributes and indicator hierarchical relationships. Based on preset file conversion rules, the analysis results are converted into files to generate an indicator white paper.

6. The method for processing logistics data indicators according to any one of claims 1-3, characterized in that, After generating the indicator white paper based on the indicator system, the following is also included: Obtain the source information of the logistics data, and determine the output format of the indicator system based on the source information; Based on the output format, extract indicators with the same data meaning from the same data subject domain in the indicator system and merge them to obtain a new indicator system. Each indicator in the new indicator system is coded to generate indicator codes; Establish the mapping relationship between the indicators in the new indicator system and the indicator codes to obtain the indicator coding system.

7. A processing device for logistics data indicators, characterized in that, The processing device for the logistics data indicators includes: The acquisition module is used to acquire logistics data; The determination module is used to determine the meaning of each indicator in the logistics data and the business type corresponding to each indicator; A classification module is used to calculate the matching value between the data meaning of the same indicator in the logistics data and the business type; determine whether the matching value is less than a preset matching threshold; if the matching value is less than the preset matching threshold, remove the corresponding indicator to obtain an indicator set; compare whether the business types of each indicator in the indicator set are consistent; if so, summarize multiple indicators with the same business type to obtain at least one data theme domain; based on at least one data theme domain, establish a mapping relationship between each indicator in each data theme domain and the data theme domain, wherein the mapping relationship is that one data theme domain corresponds to at least one indicator; The hierarchical division module is used to divide the indicators in the mapping relationship into hierarchical levels to obtain the indicator system; The generation module is used to generate an indicator white paper based on the indicator system.

8. A processing device for logistics data indicators, characterized in that, The equipment for processing the logistics data indicators includes: A memory and at least one processor, wherein the memory stores instructions and the memory and the at least one processor are interconnected via a circuit; The at least one processor invokes the instructions in the memory to cause the logistics data indicator processing device to perform the steps of the logistics data indicator processing method as described in any one of claims 1-6.

9. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the steps of the processing method for logistics data indicators as described in any one of claims 1-6.

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