Logistics Scheduling Data Processing Method, Device, Equipment and Storage Medium

Abstract

The present invention relates to the field of data processing, and discloses a method, device, equipment and storage medium for processing logistics scheduling data, which are used to improve the accuracy of analyzing logistics scheduling data based on a platform efficiency model. The method includes: collecting data from a plurality of preset logistics data terminals to obtain a heterogeneous logistics scheduling data set; performing standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set; performing homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set; and transmitting the target data set to a preset logistics data display platform.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a method, device, equipment and storage medium for processing logistics scheduling data. Background Art

[0002] In the express logistics industry, the statistical monitoring and efficiency optimization of platform efficiency are of great significance to the timeliness of express logistics. Since there are many uncertain factors during the transportation process, in order to ensure the safe and efficient transportation of goods, effective supervision measures need to be taken to effectively manage the transportation process.

[0003] The existing platform efficiency monitoring model has a slow update timeliness, high development cost, coarse data statistics granularity, and cannot be traced to the employee level. Since the data obtained through the monitoring model is heterogeneous data obtained from multiple sources, for example, the vehicle data information participating in the transportation operation, which affects the operation efficiency of the entire platform. Different vehicles need to provide different scheduling optimization strategies for loading and unloading. If multiple heterogeneous data are analyzed simultaneously, the data volume is large, resulting in certain problems such as data loss, difficulty in data matching, and difficulty in ensuring accuracy in such system solutions. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, equipment and storage medium for processing logistics scheduling data, which are used to improve the accuracy of data analysis based on the platform efficiency model.

[0005] In a first aspect of the present invention, a method for processing logistics scheduling data is provided, including: collecting data from a plurality of preset logistics data terminals to obtain a heterogeneous logistics scheduling data set; performing standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set; performing homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set; and transmitting the target data set to a preset logistics data display platform.

[0006] Optionally, in a first implementation manner of the first aspect of the present invention, the performing standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set includes: performing data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set; and performing format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set.

[0007] Optionally, in the second implementation manner of the first aspect of the present invention, the data cleaning process for the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set includes: performing data filtering on the heterogeneous logistics scheduling data set according to a preset filtering rule to obtain a filtered heterogeneous logistics scheduling data set; performing denoising processing on the filtered heterogeneous logistics scheduling data set according to a preset denoising rule to obtain a candidate heterogeneous logistics scheduling data set.

[0008] Optionally, in the third implementation manner of the first aspect of the present invention, the performing data filtering on the heterogeneous logistics scheduling data set according to a preset filtering rule to obtain a filtered heterogeneous logistics scheduling data set includes: performing missing value analysis on the heterogeneous logistics scheduling data set to obtain an analysis result; when the analysis result indicates the existence of missing values, performing data filling on the heterogeneous logistics scheduling data set to obtain a filled heterogeneous logistics scheduling data set; performing outlier removal on the filled heterogeneous logistics scheduling data set to obtain a filtered heterogeneous logistics scheduling data set.

[0009] Optionally, in the fourth implementation manner of the first aspect of the present invention, the performing format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set includes: scanning the candidate heterogeneous logistics scheduling data set to determine corresponding data identifiers; matching conversion rules based on the data identifiers to determine data conversion rules; performing format conversion processing on the candidate heterogeneous logistics scheduling data set based on the data conversion rules to obtain a standardized logistics scheduling data set.

[0010] Optionally, in the fifth implementation manner of the first aspect of the present invention, after the heterogeneous logistics scheduling data set is standardized to obtain a standardized logistics scheduling data set and before the standardized logistics scheduling data set is homogenized through a preset platform efficiency model to obtain a target data set, it further includes: performing data collection based on a preset distributed database to obtain a corresponding heterogeneous data table; performing data extraction on the heterogeneous data table to obtain a corresponding standardized heterogeneous logistics scheduling data set; training a preset deep network model based on the standardized heterogeneous logistics scheduling data set to obtain a platform efficiency model.

[0011] Optionally, in the sixth implementation manner of the first aspect of the present invention, the process of obtaining the target data set by performing isomorphic processing on the standardized logistics scheduling data set through the preset platform efficiency model includes: analyzing the configuration information through the platform efficiency model to obtain the corresponding configuration information; analyzing the data conversion threads based on the configuration information to obtain a plurality of data conversion threads; classifying the standardized logistics scheduling data set to obtain a plurality of standardized data of different types; performing thread matching on the plurality of standardized data of different types based on a preset allocation rule to obtain the target data conversion thread corresponding to each type of the standardized data; and performing isomorphic processing on the standardized logistics scheduling data set based on the target data conversion thread corresponding to each type of the standardized data to obtain the target data set.

[0012] The second aspect of the present invention provides a logistics scheduling data processing device, including: a collection module, configured to collect data from a plurality of preset logistics data terminals to obtain a heterogeneous logistics scheduling data set; a processing module, configured to perform standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set; an isomorphic module, configured to perform isomorphic processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set; and a transmission module, configured to transmit the target data set to a preset logistics data display platform.

[0013] Optionally, in the first implementation manner of the second aspect of the present invention, the processing module specifically includes: a cleaning unit, configured to perform data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set; and a conversion unit, configured to perform format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set.

[0014] Optionally, in the second implementation manner of the second aspect of the present invention, the cleaning unit specifically includes: a filtering subunit, configured to perform data filtering processing on the heterogeneous logistics scheduling data set according to a preset filtering rule to obtain a filtered heterogeneous logistics scheduling data set; and a denoising subunit, configured to perform denoising processing on the filtered heterogeneous logistics scheduling data set according to a preset denoising processing rule to obtain a candidate heterogeneous logistics scheduling data set.

[0015] Optionally, in the third implementation manner of the second aspect of the present invention, the filtering subunit is specifically configured to: analyze the missing values in the heterogeneous logistics scheduling data set to obtain an analysis result; when the analysis result indicates the existence of missing values, perform data filling processing on the heterogeneous logistics scheduling data set to obtain a filled heterogeneous logistics scheduling data set; and perform outlier removal processing on the filled heterogeneous logistics scheduling data set to obtain a filtered heterogeneous logistics scheduling data set.

[0016] Optionally, in the fourth implementation manner of the second aspect of the present invention, the conversion unit is specifically configured to: scan the candidate heterogeneous logistics scheduling data set to determine the corresponding data identifier; match the conversion rule based on the data identifier to determine the data conversion rule; and perform format conversion processing on the candidate heterogeneous logistics scheduling data set based on the data conversion rule to obtain a standardized logistics scheduling data set.

[0017] Optionally, in the fifth implementation manner of the second aspect of the present invention, the logistics scheduling data processing device further includes: an acquisition module, configured to perform data acquisition based on a preset distributed database to obtain a corresponding heterogeneous data table; an extraction module, configured to perform data extraction processing on the heterogeneous data table to obtain a corresponding standardized heterogeneous logistics scheduling data set; and a training module, configured to perform model training on a preset deep network model based on the standardized heterogeneous logistics scheduling data set to obtain a platform efficiency model.

[0018] Optionally, in the sixth implementation manner of the second aspect of the present invention, the homogeneous module is specifically configured to: analyze configuration information through the platform efficiency model to obtain corresponding configuration information; analyze data conversion threads based on the configuration information to obtain a plurality of data conversion threads; classify the standardized logistics scheduling data set to obtain a plurality of standardized data of different types; perform thread matching on the plurality of standardized data of different types based on a preset allocation rule to obtain a target data conversion thread corresponding to each type of the standardized data; and perform homogenization processing on the standardized logistics scheduling data set based on the target data conversion thread corresponding to each type of the standardized data to obtain a target data set.

[0019] The third aspect of the present invention provides a computer device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to enable the computer device to execute the above-mentioned logistics scheduling data processing method.

[0020] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the instructions run on a computer, the computer is enabled to execute the above-mentioned logistics scheduling data processing method.

[0021] In the technical solution provided by the present invention, the server homogenizes the data from different data sources and establishes the association relationship between each data source. Specifically, by establishing a mapping relationship for the fields with identity recognition significance in each data source, the association relationship between the data of each data source is established, so that the data of multiple data sources is homogenized. The relevant data tables extracted from different data sources through data homogenization processing can obtain a standardized logistics scheduling data set of heterogeneous data after the multi-source heterogeneous data is processed according to a preset unified data standard, which can improve the accuracy and adaptability of the data, thereby improving the accuracy of logistics scheduling data analysis based on the platform efficiency model. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of an embodiment of the logistics scheduling data processing method in an embodiment of the present invention;

[0023] Figure 2 It is a schematic diagram of another embodiment of the logistics scheduling data processing method in an embodiment of the present invention;

[0024] Figure 3 It is a schematic diagram of an embodiment of the logistics scheduling data processing device in an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of another embodiment of the logistics scheduling data processing device in an embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of an embodiment of the computer device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The embodiment of the present invention provides a logistics scheduling data processing method, device, equipment and storage medium for improving the accuracy of logistics scheduling data analysis based on the platform efficiency model.

[0028] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] For ease of understanding, the specific process of the embodiment of the present invention is described below. Please refer toFigure 1 , one embodiment of the logistics scheduling data processing method in the embodiments of the present invention includes the following steps 101-104:

[0030] 101. Collect data from multiple preset logistics data terminals to obtain a heterogeneous logistics scheduling data set;

[0031] It can be understood that the execution subject of the present invention can be a logistics scheduling data processing device or a server, and specifically, it is not limited here. The embodiments of the present invention will be described by taking the server as the execution subject as an example.

[0032] It should be noted that the heterogeneous logistics scheduling data set refers to a standardized logistics scheduling data set obtained from multiple channels, including data collected by sensors and Internet data obtained from each information platform. For example, image data collected by an image sensor, global spatio-temporal data of remote sensing images obtained by GPS, etc., and specific spatio-temporal data at specific locations collected by drones and autonomous driving. In the standardized logistics scheduling data set, there are various data sources, and the data structures of each data source are different, so it is called a heterogeneous logistics scheduling data set. Specifically, it can be a logistics transportation business unit such as a logistics enterprise. Specifically, the server collects data from multiple preset logistics data terminals to obtain a heterogeneous logistics scheduling data set.

[0033] 102. Perform standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set;

[0034] It should be noted that since the information sources of the heterogeneous logistics scheduling data set are diverse and there is a lot of information redundancy, it is necessary to adopt a unified data standard to give a standardized and consistent description of the original data. After processing the multi-source heterogeneous data according to the preset unified data standard, a standardized logistics scheduling data set of the heterogeneous data can be obtained. Specifically, the server performs standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set.

[0035] 103. Perform homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set;

[0036] It should be noted that based on this platform efficiency model, data extraction rules and related data sources are set in advance according to business applications, and the corresponding relationship among the three is established. When it is necessary to fuse the heterogeneous logistics scheduling data set of this business scenario, the relevant data sources are processed according to the data processing rules. By querying the index of the pre-set distributed database, the field content required by the data processing rules is extracted, and the data of multiple data sources are fused to establish a fusion database for business applications. Furthermore, the server performs homogenization processing on the standardized logistics scheduling data set through the pre-set platform efficiency model to obtain the target data set.

[0037] 104. Transmit the target data set to the pre-set logistics data display platform.

[0038] It should be noted that the server can combine with an electronic map to comprehensively display the distribution of logistics information on the map, including the display of logistics information description and the visualization display of the electronic map. The displayed logistics information can include cargo information, transport vehicle information, administrative license information, electronic fence information, transport route information, etc. Specifically, the server transmits the target data set to the pre-set logistics data display platform for data display.

[0039] In the embodiment of the present invention, the server performs homogenization processing on the data of different data sources and establishes the association relationship among the data sources. Specifically, by establishing a mapping relationship for the fields with identity recognition significance in each data source, the association relationship among the data of each data source is established, so that the data of multiple data sources are homogenized. The relevant data tables extracted from different data sources through data homogenization processing can be processed according to the pre-set unified data standard for multi-source heterogeneous data, and then the standardized logistics scheduling data set of heterogeneous data can be obtained, which can improve the accuracy and adaptability of the data, thereby enhancing the accuracy of data analysis based on the platform efficiency model.

[0040] Please refer to Figure 2 , another embodiment of the logistics scheduling data processing method in the embodiment of the present invention includes the following steps 201-205:

[0041] 201. Collect data from multiple pre-set logistics data terminals to obtain a heterogeneous logistics scheduling data set;

[0042] Specifically, in this embodiment, the specific implementation of step 201 is similar to step 101 above and will not be elaborated here.

[0043] 202. Perform data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set;

[0044] Specifically, the server performs data filtering processing on the heterogeneous logistics scheduling data set according to a preset filtering rule to obtain a filtered heterogeneous logistics scheduling data set; the server performs denoising processing on the filtered heterogeneous logistics scheduling data set according to a preset denoising rule to obtain a candidate heterogeneous logistics scheduling data set.

[0045] Among them, for each name data in the data set, it is judged whether the name data is a null value or a garbled code according to a preset filtering rule. If a name data is a null value or a garbled code, the name data is deleted from the data set. If a name data is not a null value and not a garbled code, the name data is retained in the data set. Name data is usually obtained by using a crawler program to collect from a logistics platform. Since the logistics platform may not enter the corresponding name data, a null value name data will be collected at this time. In addition, the logistics platform may have an anti-crawler function, and the name data collected from the logistics platform is a garbled code at this time. The name data that is a null value or a garbled code belongs to invalid name data, which will not play a positive role in data analysis, but will affect the accuracy of the data analysis result. Therefore, deleting the name data that is a null value or a garbled code from the data set can not only reduce the workload of name data standardization processing, but also improve the accuracy of the analysis result obtained when performing data analysis based on the standard name data subsequently. Furthermore, the server performs denoising processing on the filtered heterogeneous logistics scheduling data set according to a preset denoising rule to obtain a candidate heterogeneous logistics scheduling data set. It should be noted that for each name data in the data set, according to a preset noise removal rule, the noise information included in the name data is removed, where the noise information includes characters such as spaces and parentheses.

[0046] Optionally, performing data filtering processing on the heterogeneous logistics scheduling data set according to a preset filtering rule to obtain a filtered heterogeneous logistics scheduling data set may include: the server performs missing value analysis on the heterogeneous logistics scheduling data set to obtain an analysis result; when the analysis result is that there are missing values, the server performs data filling processing on the heterogeneous logistics scheduling data set to obtain a filled heterogeneous logistics scheduling data set; the server performs outlier removal processing on the filled heterogeneous logistics scheduling data set to obtain a filtered heterogeneous logistics scheduling data set.

[0047] Among them, it is determined whether there are missing values in the heterogeneous logistics scheduling data set. When there are missing values in the heterogeneous logistics scheduling data set, data filling is performed on the heterogeneous logistics scheduling data set, or it is determined whether there are outliers in the heterogeneous logistics scheduling data set. When there are outliers in the heterogeneous logistics scheduling data set, the outliers included in the heterogeneous logistics scheduling data set are deleted. Specifically, the missing value is the data missing in the heterogeneous logistics scheduling data set, and the outlier is the data that exists in the heterogeneous logistics scheduling data set but has an abnormal value. It should be noted that in the embodiments of the present invention, java statements with missing value detection functions are used to determine whether there are missing values in the heterogeneous logistics scheduling data set.

[0048] 203. Perform format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set;

[0049] Specifically, the server scans the candidate heterogeneous logistics scheduling data set to determine the corresponding data identifier; the server matches the conversion rules based on the data identifier to determine the data conversion rules; the server performs format conversion processing on the candidate heterogeneous logistics scheduling data set based on the data conversion rules to obtain a standardized logistics scheduling data set.

[0050] Among them, since there is a large amount of pre-set data identifier data, and the data of the data identifier is not necessarily standard data, in order to make full use of the information provided by the data identifier, the data processing method provided in the embodiments of the present invention performs standardized data processing on the data identifier. Standardized data processing is used to make the data after performing standardized data processing conform to the conversion rules corresponding to the data to be processed. For example, the unit price of an item is in "yuan" of RMB, the quantity of an item is in "kilograms" as the unit, and the item names of the same type are unified into the same text. After performing standardized data processing on the data identifier, the obtained standardized data conforms to the conversion rules corresponding to the data identifier. The conversion rules corresponding to the data identifier can be determined first, and then the data identifier can be converted based on the conversion rules to obtain the standardized data. Specifically, the conversion rules corresponding to the data identifier can be determined according to the data represented by the data identifier, and the chicken server performs format conversion processing on the candidate heterogeneous logistics scheduling data set based on the data conversion rules to obtain a standardized logistics scheduling data set.

[0051] Optionally, after step 203 and before step 204, it may further include: the server performs data collection based on a pre-set distributed database to obtain the corresponding heterogeneous data table; the server performs data extraction processing on the heterogeneous data table to obtain the corresponding standard heterogeneous logistics scheduling data set; the server performs model training on the pre-set deep network model based on the standard heterogeneous logistics scheduling data set to obtain a platform efficiency model.

[0052] Among them, data extraction rules and relevant data sources are set in advance according to business applications, and the corresponding relationships among the three are established. The server processes the relevant data sources according to the data processing rules. By querying the index of the distributed database of the data source, the field content required by the data processing rules is extracted, and the data of multiple data sources are fused to collect a heterogeneous logistics scheduling data set for model training. Furthermore, the server trains a preset deep network model based on the standardized heterogeneous logistics scheduling data set to obtain a platform efficiency model.

[0053] 204. Perform homogenization processing on the standardized logistics scheduling data set through the preset platform efficiency model to obtain a target data set;

[0054] Specifically, the server analyzes the configuration information through the platform efficiency model to obtain the corresponding configuration information; the server analyzes the data conversion threads based on the configuration information to obtain multiple data conversion threads; the server classifies the standardized logistics scheduling data set to obtain multiple standardized data of different types; the server performs thread matching on the multiple standardized data of different types based on a preset allocation rule to obtain the target data conversion thread corresponding to each type of standardized data; the server performs homogenization processing on the standardized logistics scheduling data set based on the target data conversion thread corresponding to each type of standardized data to obtain a target data set.

[0055] Among them, the server distributes all the standardized logistics scheduling data sets into each of the data conversion threads, and sends the data conversion rules to each of the data conversion threads. Then, through each of the data conversion threads, based on the data conversion rules, homogenization conversion processing is simultaneously performed on the standardized logistics scheduling data sets contained therein to obtain the processed standardized logistics scheduling data set. Specifically, the server classifies the standardized logistics scheduling data set to obtain multiple standardized data of different types; the server performs thread matching on the multiple standardized data of different types based on a preset allocation rule to obtain the target data conversion thread corresponding to each type of standardized data; the server performs homogenization processing on the standardized logistics scheduling data set based on the target data conversion thread corresponding to each type of standardized data to obtain a target data set.

[0056] 205. Transmit the target data set to a preset logistics data display platform.

[0057] Specifically, in this embodiment, the specific implementation manner of step 205 is similar to that of the above step 104 and will not be elaborated here.

[0058] In the embodiments of the present invention, deleting name data that is null or contains garbled characters from the data set can not only reduce the workload of standardizing name data, but also improve the accuracy of the analysis results obtained when performing data analysis based on the standard name data subsequently. Moreover, for each name data in the data set, the server removes characters such as spaces and parentheses included in the name data according to the preset noise removal rules. After performing standardized data processing on the data identifier, the obtained standardized data conforms to the conversion rules corresponding to the data identifier. First, the conversion rules corresponding to the data identifier can be determined, and then the data identifier can be converted based on the conversion rules to obtain the standardized data. The server analyzes the configuration information through the platform efficiency model to obtain the corresponding configuration information; the server analyzes the data conversion threads based on the configuration information to obtain multiple data conversion threads; the server classifies the standardized logistics scheduling data set to obtain multiple different types of standardized data; the server performs thread matching on the multiple different types of standardized data based on the preset allocation rules to obtain the target data conversion thread corresponding to each type of standardized data. By matching the conversion threads, the efficiency of converting different types of heterogeneous data can be improved, and the situation of data analysis errors caused by excessive data volume can be avoided, thereby improving the accuracy of data analysis based on the platform efficiency model.

[0059] Please refer to Figure 3 , an embodiment of the logistics scheduling data processing device in the embodiments of the present invention includes:

[0060] The acquisition module 301 is used to collect data from a plurality of preset logistics data terminals to obtain a heterogeneous logistics scheduling data set;

[0061] The processing module 302 is used to perform standardized processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set;

[0062] The isomorphism module 303 is used to perform isomorphism processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set;

[0063] The transmission module 304 is used to transmit the target data set to a preset logistics data display platform.

[0064] Please refer to Figure 4 , another embodiment of the logistics scheduling data processing device in the embodiments of the present invention includes:

[0065] The acquisition module 301 is used to collect data from a plurality of preset logistics data terminals to obtain a heterogeneous logistics scheduling data set;

[0066] The processing module 302 is configured to perform standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set;

[0067] The homogeneous module 303 is configured to perform homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set;

[0068] The transmission module 304 is configured to transmit the target data set to a preset logistics data display platform.

[0069] Optionally, the processing module 302 specifically includes: a cleaning unit 3021 configured to perform data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set; a conversion unit 3022 configured to perform format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set.

[0070] Optionally, the cleaning unit 3021 specifically includes: a filtering subunit 30211 configured to perform data filtering processing on the heterogeneous logistics scheduling data set according to a preset filtering rule to obtain a filtered heterogeneous logistics scheduling data set; a denoising subunit 30212 configured to perform denoising processing on the filtered heterogeneous logistics scheduling data set according to a preset denoising processing rule to obtain a candidate heterogeneous logistics scheduling data set.

[0071] Optionally, the filtering subunit 30211 is specifically configured to: perform missing value analysis on the heterogeneous logistics scheduling data set to obtain an analysis result; when the analysis result indicates the existence of missing values, perform data filling processing on the heterogeneous logistics scheduling data set to obtain a filled heterogeneous logistics scheduling data set; perform outlier elimination processing on the filled heterogeneous logistics scheduling data set to obtain a filtered heterogeneous logistics scheduling data set.

[0072] Optionally, the conversion unit 3022 is specifically configured to: scan the candidate heterogeneous logistics scheduling data set to determine corresponding data identifiers; match conversion rules based on the data identifiers to determine data conversion rules; perform format conversion processing on the candidate heterogeneous logistics scheduling data set based on the data conversion rules to obtain a standardized logistics scheduling data set.

[0073] Optionally, the logistics scheduling data processing device further includes: an acquisition module 305 configured to perform data acquisition based on a preset distributed database to obtain a corresponding heterogeneous data table; an extraction module 306 configured to perform data extraction processing on the heterogeneous data table to obtain a corresponding standardized heterogeneous logistics scheduling data set; a training module 307 configured to perform model training on a preset deep network model based on the standardized heterogeneous logistics scheduling data set to obtain a platform efficiency model.

[0074] Optionally, the isomorphism module 303 is specifically configured to: analyze configuration information through the platform efficiency model to obtain corresponding configuration information; analyze data conversion threads based on the configuration information to obtain multiple data conversion threads; classify the standardized logistics scheduling data set to obtain multiple different types of standardized data; perform thread matching on the multiple different types of standardized data based on a preset allocation rule to obtain a target data conversion thread corresponding to each type of the standardized data; and perform isomorphism processing on the standardized logistics scheduling data set based on the target data conversion thread corresponding to each type of the standardized data to obtain a target data set.

[0075] Figure 5 FIG. 6 is a schematic structural diagram of a computer device provided by an embodiment of the present invention. The computer device 500 may vary greatly due to different configurations or performances, and may include one or more processors (Central Processing Units, CPUs) 510 (for example, one or more processors) and a memory 520, and one or more storage media 530 (for example, one or more mass storage devices) for storing application programs 533 or data 532. Among them, the memory 520 and the storage media 530 may be transient storage or persistent storage. The program stored in the storage media 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the computer device 500. Further, the processor 510 may be configured to communicate with the storage media 530 and execute a series of instruction operations in the storage media 530 on the computer device 500.

[0076] The computer device 500 may further include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or one or more operating systems 531, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 5 the shown computer device structure does not constitute a limitation on the computer device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0077] The present invention further provides a computer device, which includes a memory and a processor. When a computer-readable instruction stored in the memory is executed by the processor, the processor is caused to execute the steps of the logistics scheduling data processing method in the above embodiments.

[0078] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the logistics scheduling data processing method.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disc that can store program codes.

Claims

1. A method for processing logistics scheduling data, characterized in that, The method includes: Collect data from multiple pre - set logistics data terminals to obtain a heterogeneous logistics scheduling data set; Perform standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set; Collect data based on a pre - set distributed database to obtain a corresponding heterogeneous data table; Perform data extraction processing on the heterogeneous data table to obtain a corresponding standardized heterogeneous logistics scheduling data set; Based on the standardized heterogeneous logistics scheduling data set, train a pre - set deep network model to obtain a platform efficiency model; Perform homogenization processing on the standardized logistics scheduling data set through the pre - set platform efficiency model to obtain a target data set; Among them, the step of performing homogenization processing on the standardized logistics scheduling data set through the pre - set platform efficiency model to obtain a target data set includes: extracting the field content required by the data processing rules by querying the index of the pre - set distributed database, fusing the data of multiple data sources, establishing a fusion database for business applications, and performing homogenization processing on the standardized logistics scheduling data set through the pre - set platform efficiency model to obtain a target data set. Among them, based on this platform efficiency model, data extraction rules and related data sources are pre - set according to business applications, and the corresponding relationship among the three is established; Transmit the target data set to a pre - set logistics data display platform.

2. The logistics scheduling data processing method according to claim 1, wherein The step of performing standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set includes: Perform data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set; Perform format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set.

3. The logistics scheduling data processing method according to claim 2, wherein The step of performing data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set includes: Perform data filtering processing on the heterogeneous logistics scheduling data set according to a pre - set filtering rule to obtain a filtered heterogeneous logistics scheduling data set; Perform denoising processing on the filtered heterogeneous logistics scheduling data set according to a pre - set denoising rule to obtain a candidate heterogeneous logistics scheduling data set.

4. The logistics scheduling data processing method according to claim 3, wherein The step of performing data filtering processing on the heterogeneous logistics scheduling data set according to a pre - set filtering rule to obtain a filtered heterogeneous logistics scheduling data set includes: Perform missing value analysis on the heterogeneous logistics scheduling data set to obtain an analysis result; When the analysis result indicates the existence of missing values, perform data filling processing on the heterogeneous logistics scheduling data set to obtain a filled heterogeneous logistics scheduling data set; Perform outlier elimination processing on the filled heterogeneous logistics scheduling data set to obtain a filtered heterogeneous logistics scheduling data set.

5. The logistics scheduling data processing method according to claim 2, wherein The step of performing format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set includes: Scan the candidate heterogeneous logistics scheduling data set to determine the corresponding data identifier; Match the conversion rule based on the data identifier to determine the data conversion rule; Perform format conversion processing on the candidate heterogeneous logistics scheduling data set based on the data conversion rules to obtain a standardized logistics scheduling data set.

6. The logistics scheduling data processing method according to any one of claims 1-5, characterized in that The process of performing homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set includes: Analyze the configuration information through the platform efficiency model to obtain the corresponding configuration information; Based on the configuration information, perform data conversion thread analysis to obtain multiple data conversion threads; Classify the standardized logistics scheduling data set to obtain multiple different types of standardized data; Based on a preset allocation rule, perform thread matching on the multiple different types of standardized data to obtain the target data conversion thread corresponding to each type of the standardized data; Based on the target data conversion thread corresponding to each type of the standardized data, perform homogenization processing on the standardized logistics scheduling data set to obtain a target data set.

7. A logistics scheduling data processing device, characterized in that, The logistics scheduling data processing device includes: A collection module for collecting data from a plurality of preset logistics data terminals to obtain a heterogeneous logistics scheduling data set; A processing module for performing standardization processing on the heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set; An acquisition module for collecting data based on a preset distributed database to obtain a corresponding heterogeneous data table; An extraction module for performing data extraction processing on the heterogeneous data table to obtain a corresponding standardized heterogeneous logistics scheduling data set; A training module for training a preset deep network model based on the standardized heterogeneous logistics scheduling data set to obtain a platform efficiency model; A homogenization module for performing homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set; wherein, the process of performing homogenization processing on the standardized logistics scheduling data set through a preset platform efficiency model to obtain a target data set includes: extracting the field content required for data processing rules by querying the index of the preset distributed database, and fusing the data of multiple data sources to establish a fusion database for business applications, so as to perform homogenization processing on the standardized logistics scheduling data set through the preset platform efficiency model to obtain a target data set, wherein, based on this platform efficiency model, data extraction rules and related data sources are set in advance according to business applications, and the corresponding relationship among the three is established; A transmission module for transmitting the target data set to a preset logistics data display platform.

8. The logistics scheduling data processing device according to claim 7, wherein The processing module specifically includes: A cleaning unit for performing data cleaning processing on the heterogeneous logistics scheduling data set to obtain a candidate heterogeneous logistics scheduling data set; A conversion unit for performing format conversion on the candidate heterogeneous logistics scheduling data set to obtain a standardized logistics scheduling data set.

9. A computer device, characterized in that, The computer device includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor invokes the instructions in the memory to cause the computer device to execute the logistics scheduling data processing method according to any one of claims 1-6.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, the logistics scheduling data processing method according to any one of claims 1-6 is implemented.

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