CN117112628A - Logistics data updating method and system - Google Patents
Logistics data updating method and system Download PDFInfo
- Publication number
- CN117112628A CN117112628A CN202311159558.XA CN202311159558A CN117112628A CN 117112628 A CN117112628 A CN 117112628A CN 202311159558 A CN202311159558 A CN 202311159558A CN 117112628 A CN117112628 A CN 117112628A
- Authority
- CN
- China
- Prior art keywords
- data
- key information
- logistics
- logistics data
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000005065 mining Methods 0.000 claims abstract description 154
- 238000004364 calculation method Methods 0.000 claims abstract description 17
- 238000012512 characterization method Methods 0.000 claims description 66
- 238000009826 distribution Methods 0.000 claims description 20
- 230000009467 reduction Effects 0.000 claims description 20
- 238000005457 optimization Methods 0.000 claims description 10
- 230000002776 aggregation Effects 0.000 claims description 3
- 238000004220 aggregation Methods 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Economics (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a system for updating logistics data, and relates to the technical field of data processing. In the invention, key information mining processing is carried out on the historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data; carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data; performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree; and based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data. Based on the method, the reliability of the update of the logistics data can be improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for updating logistics data.
Background
The application scene of the logistics data is more, the application value is higher, so the logistics data can be collected and stored, but the data volume of the logistics data is very large, so the stored data also needs to be updated, such as discarding the unnecessary logistics data, and only storing part of the logistics data. For example, in the prior art, in the process of updating and controlling the logistics data, processing is generally performed based on the formation time of the logistics data, specifically, the logistics data formed in the current time is directly discarded, so as to realize new and old updating of the logistics data, thus the problem of poor reliability of updating and controlling the logistics data is easy to occur.
Disclosure of Invention
In view of the above, the present invention is directed to providing a method and a system for updating physical distribution data, so as to improve the reliability of physical distribution data updating.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
a method of updating logistic data, comprising:
performing key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
performing key information mining processing on target logistics data to be processed to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises target logistics information of each item in the current time period, and one piece of target logistics information corresponds to one logistics cargo;
performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree;
and based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data.
In some preferred embodiments, in the method for updating logistics data, the step of performing key information mining processing on the historical logistics data to be processed to output a historical key information feature representation corresponding to the historical logistics data includes:
performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
the step of performing key information mining processing on target logistics data to be processed to output target key information characteristic representation corresponding to the target logistics data comprises the following steps:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
In some preferred embodiments, in the above method for updating logistics data, the method further includes: performing network optimization processing to form an optimized key information mining network;
the step of performing network optimization processing to form an optimized key information mining network comprises the following steps:
Extracting a typical logistics data combination, and analyzing multi-dimensional logistics characterization data corresponding to the typical logistics data combination, wherein the typical logistics data combination at least comprises first typical logistics data and second typical logistics data, a data matching relationship between the second typical logistics data and the first typical logistics data accords with a preset data matching relationship, and the multi-dimensional logistics characterization data comprises logistics characterization data with at least two dimensions;
hiding the logistics characterization data of the first dimension in the multi-dimensional logistics characterization data to output corresponding hidden multi-dimensional logistics characterization data;
performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output typical key information characteristic representation and hidden data restoration characteristic representation corresponding to the typical logistics data combination;
based on the logistics characterization data, the hidden data reduction feature representation and the typical key information feature representation of the first dimension, determining a learning cost value of the initial key information mining network so as to output a reduction learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value;
And carrying out optimization processing on the initial key information mining network according to the reduction learning cost value and the key information learning cost value so as to form an optimized key information mining network.
In some preferred embodiments, in the method for updating logistics data, the step of performing a learning cost value determining operation on the initial key information mining network based on the logistics characterization data, the hidden data restoration feature representation and the typical key information feature representation in the first dimension to output a restoration learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value includes:
based on the logistics characterization data of the first dimension and the hidden data restoration characteristic representation, carrying out first learning cost value determination operation on the initial key information mining network, and outputting restoration learning cost values corresponding to the initial key information mining network;
and according to the typical key information characteristic representation corresponding to the typical logistics data combination, carrying out the second learning cost value determining operation on the initial key information mining network, and outputting the key information learning cost value corresponding to the initial key information mining network.
In some preferred embodiments, in the method for updating logistics data, the step of performing the second learning cost value determining operation on the initial key information mining network according to the representative key information feature representation corresponding to the representative logistics data combination and outputting the key information learning cost value corresponding to the initial key information mining network includes:
determining relevant typical logistics data and irrelevant typical logistics data from second typical logistics data included in the typical logistics data combination, wherein the relevant typical logistics data and the first typical logistics data have a matching relationship, and the irrelevant typical logistics data and the first typical logistics data do not have a matching relationship; according to the typical key information characteristic representation, analyzing the matching degree between the first typical logistics data and the related typical logistics data to output corresponding related dimension matching degree, and analyzing the matching degree between the first typical logistics data and the non-related typical logistics data to output corresponding non-related dimension matching degree; based on the relevant dimension matching degree and the irrelevant dimension matching degree, analyzing the key information learning cost value corresponding to the initial key information mining network;
The step of restoring the characteristic representation based on the logistics characterization data and the hidden data in the first dimension, performing a first learning cost value determining operation on the initial key information mining network, and outputting a restoring learning cost value corresponding to the initial key information mining network includes:
performing restoration processing of the characterization data based on the hidden data restoration characteristic representation to output corresponding restored logistics characterization data; and according to the logistics characterization data and the restored logistics characterization data of the first dimension, determining a first learning cost value of the initial key information mining network, and outputting a restored learning cost value corresponding to the initial key information mining network.
In some preferred embodiments, in the method for updating logistics data, the step of performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output a typical key information feature representation and a hidden data restoration feature representation corresponding to the typical logistics data combination includes:
carrying out integration processing of the distribution domain on the hidden multidimensional stream characterization data so as to output initial feature representations corresponding to each dimension in the same distribution domain;
The initial characteristic representation is subjected to aggregation processing through an initial key information mining network so as to output a typical key information characteristic representation corresponding to each typical logistics data in the typical logistics data combination;
and according to the typical key information characteristic representation, carrying out reduction processing on hidden data in the hidden multidimensional stream characterization data, and outputting hidden data reduction characteristic representation.
In some preferred embodiments, in the method for updating logistics data, the step of performing data updating control on the historical logistics data and the target logistics data based on the feature representation matching degree to save at least the target logistics data includes:
extracting a pre-configured reference matching degree;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
The embodiment of the invention also provides a system for updating the logistics data, which comprises the following steps:
the first key information mining module is used for carrying out key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
the second key information mining module is used for carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises each item of target logistics information in the current time period, and one piece of target logistics information corresponds to one piece of logistics goods;
the matching degree calculation module is used for carrying out matching degree calculation processing on the historical key information feature representation and the target key information feature representation so as to output corresponding feature representation matching degree;
and the data updating management and control module is used for carrying out data updating management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree so as to at least save the target logistics data.
In some preferred embodiments, in the above update system of logistics data, the first key information mining module is specifically configured to:
performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
the second key information mining module is specifically configured to:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
In some preferred embodiments, in the above-mentioned update system for logistics data, the data update management module is specifically configured to:
extracting a pre-configured reference matching degree;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
The method and the system for updating the logistics data can perform key information mining processing on the historical logistics data to be processed so as to output the historical key information characteristic representation corresponding to the historical logistics data; carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data; performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree; and based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data. Based on the above, since the characteristic representation matching degree is calculated on the historical logistics data and the target logistics data, the data update management and control can be performed on the historical logistics data and the target logistics data based on the obtained characteristic representation matching degree, and thus, compared with the conventional technical scheme of directly performing data update management and control based on the formation time, the reliability of the logistics data update can be improved, and the defects in the prior art are overcome.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a physical distribution data update platform according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of each step included in the method for updating logistics data according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of each module included in the system for updating logistics data according to the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention provides a platform for updating logistics data. Wherein, the update platform of the logistics data can comprise a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute an executable computer program stored in the memory, thereby implementing the method for updating the logistics data provided by the embodiment of the present invention (as described below).
Alternatively, in some embodiments, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
Alternatively, in some embodiments, the processor may be a general purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Alternatively, in some embodiments, the update platform of the logistics data may be a server with data processing capability.
With reference to fig. 2, the embodiment of the invention further provides a method for updating the logistics data, which can be applied to the platform for updating the logistics data. The method steps defined by the flow related to the method for updating the logistics data can be realized by the platform for updating the logistics data.
The specific flow shown in fig. 2 will be described in detail.
Step S110, carrying out key information mining processing on the historical logistics data to be processed so as to output historical key information characteristic representations corresponding to the historical logistics data.
In the embodiment of the invention, the update platform of the logistics data can perform key information mining processing on the historical logistics data to be processed so as to output the historical key information characteristic representation corresponding to the historical logistics data. The history logistics data comprises each piece of history logistics information in a history time period, such as history logistics information 1, history logistics information 2, history logistics information 3, history logistics information 4, history logistics information 5, history logistics information 6, history logistics information 7, history logistics information 8, history logistics information 9 and the like, and one piece of history logistics information corresponds to one logistics cargo. Illustratively, the historical logistics information may include cargo description information, logistics transmission place information, logistics reception place information, corresponding user information, and the like of the logistics cargo.
And step S120, carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data.
In the embodiment of the invention, the update platform of the logistics data can perform key information mining processing on the target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data. The target logistics data comprises target logistics information of each item in the current time period, such as target logistics information 1, target logistics information 2, target logistics information 3, target logistics information 4, target logistics information 5, target logistics information 6, target logistics information 7, target logistics information 8, target logistics information 9 and the like, wherein one piece of target logistics information corresponds to one logistics good. For example, the target logistics information may include cargo description information, logistics transmission place information, logistics reception place information, corresponding user information, and the like of the logistics cargo.
Step S130, performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation, so as to output a corresponding feature representation matching degree.
In the embodiment of the invention, the update platform of the logistics data can perform matching degree calculation processing, such as cosine similarity calculation, on the historical key information feature representation and the target key information feature representation so as to output corresponding feature representation matching degree.
And step S140, based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data.
In the embodiment of the invention, the update platform of the logistics data can update and control the historical logistics data and the target logistics data based on the characteristic representation matching degree so as to at least save the target logistics data.
Based on the above-mentioned matters, namely, step S110, step S120, step S130 and step S140, the calculation of the characteristic representation matching degree is performed on the historical logistics data and the target logistics data, so that the data update management and control can be performed on the historical logistics data and the target logistics data based on the obtained characteristic representation matching degree, and thus, compared with the conventional technical scheme of directly performing the data update management and control based on the formation time, the reliability of the logistics data update can be improved, and the defects in the prior art are improved.
Optionally, in some embodiments, the step S110, that is, the step of performing the key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data, may further include the following contents:
and carrying out key information mining processing on the historical logistics data to be processed by utilizing an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data.
Optionally, in some embodiments, the step S120, that is, the step of performing key information mining processing on the target stream data to be processed to output the target key information feature representation corresponding to the target stream data, may further include the following contents:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
Optionally, in some embodiments, the method for updating logistics data may further include the following steps, such as performing network optimization processing, to form an optimized key information mining network. Based on this, the step of performing the network optimization process to form the optimized key information mining network may further include the following:
Extracting a typical logistics data combination, and analyzing multi-dimensional logistics characterization data corresponding to the typical logistics data combination, wherein the typical logistics data combination at least comprises first typical logistics data and second typical logistics data, a data matching relationship between the second typical logistics data and the first typical logistics data accords with a preset data matching relationship, and the multi-dimensional logistics characterization data comprises at least two-dimensional logistics characterization data, such as image dimension, text dimension and the like;
hiding the logistics characterization data of a first dimension in the multi-dimensional logistics characterization data to output corresponding hidden multi-dimensional logistics characterization data, wherein the first dimension can be any one dimension of the at least two dimensions, and is not particularly limited, if the first dimension belongs to an image dimension, part of an image corresponding to the logistics characterization data of the first dimension can be hidden, for example, a frame of full-white image is replaced;
performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output typical key information characteristic representation and hidden data restoration characteristic representation corresponding to the typical logistics data combination;
Based on the logistics characterization data, the hidden data reduction feature representation and the typical key information feature representation of the first dimension, determining a learning cost value of the initial key information mining network so as to output a reduction learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value;
and optimizing the initial key information mining network according to the reduction learning cost value and the key information learning cost value to form an optimized key information mining network, wherein the reduction learning cost value and the key information learning cost value can be weighted and summed to output a total learning cost value, and then optimizing the initial key information mining network based on the total learning cost value to form the optimized key information mining network.
Optionally, in some embodiments, the step of performing, through an initial key information mining network, key information mining processing on the hidden multidimensional logistics characterization data to output a typical key information feature representation and a hidden data restoration feature representation corresponding to the typical logistics data combination may further include the following contents:
Carrying out integration processing on the hidden multi-dimensional logistics characterization data in a distribution domain to output initial feature representations corresponding to each dimension in the same distribution domain, wherein the hidden multi-dimensional logistics characterization data can be respectively mapped into the same distribution domain, so that initial feature representations corresponding to each dimension in the same distribution domain can be obtained, and the distribution domain can be also understood as feature space or data set;
the initial characteristic representation is subjected to aggregation processing through an initial key information mining network so as to output a typical key information characteristic representation corresponding to each typical logistics data in the typical logistics data combination;
and according to the typical key information characteristic representation, carrying out reduction processing on hidden data in the hidden multidimensional stream characterization data to output hidden data reduction characteristic representation, namely carrying out association analysis prediction on the characteristic representation corresponding to the first dimension based on the typical key information characteristic representation to output corresponding hidden data reduction characteristic representation.
Optionally, in some embodiments, the step of aggregating the initial feature representations through the initial critical information mining network to output a typical critical information feature representation corresponding to each typical logistics data in the typical logistics data combination may further include the following contents:
Constructing an initial key information mining network, wherein the initial key information mining network comprises a first key information mining unit and a second key information mining unit;
the first key information mining unit is used for carrying out cascade combination on the initial feature representations to output corresponding cascade initial feature representations (such as initial feature representation 1-initial feature representation 2-initial feature representation 3-initial feature representation 4), then carrying out key information mining processing on the cascade initial feature representations, such as convolution operation or filtering processing, and outputting first typical key information feature representations;
through the second key information mining unit, each initial feature representation is subjected to key information mining processing, such as convolution operation or filtering processing, local key information feature representations are output, and then cascade combination (such as local key information feature representation 1-local key information feature representation 2-local key information feature representation 3-local key information feature representation 4) is performed on each local key information feature representation, so that a second typical key information feature representation is output;
and performing cascading combination on the first typical key information feature representation and the second typical key information feature representation (such as first typical key information feature representation-second typical key information feature representation) to output a typical key information feature representation corresponding to the typical logistics data combination.
Optionally, in some embodiments, the step of performing the learning cost value determining operation on the initial key information mining network based on the logistics characterization data of the first dimension, the hidden data restoration feature representation and the typical key information feature representation to output a restoration learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value may further include the following contents:
based on the logistics characterization data of the first dimension and the hidden data restoration characteristic representation, carrying out first learning cost value determination operation on the initial key information mining network, and outputting restoration learning cost values corresponding to the initial key information mining network;
and according to the typical key information characteristic representation corresponding to the typical logistics data combination, carrying out the second learning cost value determining operation on the initial key information mining network, and outputting the key information learning cost value corresponding to the initial key information mining network.
Optionally, in some embodiments, the step of performing the second learning cost value determining operation on the initial key information mining network according to the representative key information feature representation corresponding to the representative logistics data combination and outputting the key information learning cost value corresponding to the initial key information mining network may further include the following contents:
Determining relevant typical logistics data and irrelevant typical logistics data from second typical logistics data included in the typical logistics data combination, wherein the relevant typical logistics data and the first typical logistics data have a matching relationship (the matching degree between data is larger than a first preset matching degree, the first preset matching degree can be 0.6, 0.8, 0.9 and the like), and the irrelevant typical logistics data and the first typical logistics data do not have a matching relationship (the matching degree between data is smaller than a second preset matching degree, and the second preset matching degree can be 0.2, 0.3, 0.4 and the like);
according to the typical key information characteristic representation, analyzing the matching degree between the first typical logistics data and the related typical logistics data to output corresponding related dimension matching degree, and analyzing the matching degree between the first typical logistics data and the non-related typical logistics data to output corresponding non-related dimension matching degree;
based on the related dimension matching degree and the non-related dimension matching degree, analyzing the key information learning cost value corresponding to the initial key information mining network (illustratively, error calculation can be performed on the related dimension matching degree and the corresponding actual matching degree, error calculation can be performed on the non-related dimension matching degree and the corresponding actual matching degree, then errors of the two dimensions are fused, so that the key information learning cost value is obtained, or in the case of no actual matching degree, the positive correlation value of the absolute difference between the related dimension matching degree and 1 can be used as the corresponding error, the positive correlation value of the absolute difference between the non-related dimension matching degree and 0 can be used as the corresponding error, and then the errors of the two dimensions are fused, so that the key information learning cost value is obtained).
Optionally, in some embodiments, the step of performing the first learning cost value determining operation on the initial critical information mining network based on the logistics characterization data of the first dimension and the hidden data restoration feature representation and outputting the restored learning cost value corresponding to the initial critical information mining network may further include some contents as follows:
performing restoration processing of the characterization data based on the hidden data restoration feature representation, for example, the process of key information mining may be reversed to output corresponding restored stream characterization data;
according to the logistics characterization data and the restored logistics characterization data of the first dimension, the initial key information mining network is subjected to first learning cost value determining operation, and the restored learning cost value corresponding to the initial key information mining network is output; that is, the difference between the logistics characterization data of the first dimension and the restored logistics characterization data can be calculated, so as to obtain the restored learning cost value corresponding to the initial key information mining network.
Optionally, in some embodiments, the step S140, that is, the step of performing data update control on the historical logistics data and the target logistics data based on the feature representation matching degree to save at least the target logistics data may further include the following steps:
Extracting a pre-configured reference matching degree, wherein the specific numerical value of the reference matching degree is not limited, and the reference matching degree can be selectively configured according to actual requirements, such as 0.3, 0.5, 0.7 and the like;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
Optionally, in other embodiments, the step S140 of updating and controlling the historical logistics data and the target logistics data based on the feature representation matching degree to save at least the target logistics data may further include the following steps:
extracting a pre-configured reference matching degree, wherein the specific numerical value of the reference matching degree is not limited, and the reference matching degree can be selectively configured according to actual requirements, such as 0.3, 0.5, 0.7 and the like;
Comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding part of the historical logistics data, saving the part of the historical logistics data which is not discarded, and saving the target logistics data under the condition that the characteristic representing matching degree is smaller than the reference matching degree.
With reference to fig. 3, the embodiment of the invention further provides a system for updating the logistics data, which can be applied to the platform for updating the logistics data. Wherein, the update system of the logistics data can comprise:
the first key information mining module is used for carrying out key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
the second key information mining module is used for carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises each item of target logistics information in the current time period, and one piece of target logistics information corresponds to one piece of logistics goods;
The matching degree calculation module is used for carrying out matching degree calculation processing on the historical key information feature representation and the target key information feature representation so as to output corresponding feature representation matching degree;
and the data updating management and control module is used for carrying out data updating management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree so as to at least save the target logistics data.
Optionally, in some embodiments, the first critical information mining module is specifically configured to: performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
optionally, in some embodiments, the second key information mining module is specifically configured to: and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
Alternatively, in some embodiments, the update system of the logistics data may further include other software functional modules, which may be used to: and performing network optimization processing to form an optimized key information mining network.
Alternatively, in some embodiments, the other software functional modules may be specifically configured to: extracting a typical logistics data combination, and analyzing multi-dimensional logistics characterization data corresponding to the typical logistics data combination, wherein the typical logistics data combination at least comprises first typical logistics data and second typical logistics data, a data matching relationship between the second typical logistics data and the first typical logistics data accords with a preset data matching relationship, and the multi-dimensional logistics characterization data comprises logistics characterization data with at least two dimensions; hiding the logistics characterization data of the first dimension in the multi-dimensional logistics characterization data to output corresponding hidden multi-dimensional logistics characterization data; performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output typical key information characteristic representation and hidden data restoration characteristic representation corresponding to the typical logistics data combination; based on the logistics characterization data, the hidden data reduction feature representation and the typical key information feature representation of the first dimension, determining a learning cost value of the initial key information mining network so as to output a reduction learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value; and carrying out optimization processing on the initial key information mining network according to the reduction learning cost value and the key information learning cost value so as to form an optimized key information mining network.
Optionally, in some embodiments, the data update management module is specifically configured to:
extracting a pre-configured reference matching degree; comparing the reference matching degree with the characteristic representation matching degree; storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree; and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
In summary, the method and the system for updating the logistics data provided by the invention can perform key information mining processing on the historical logistics data to be processed so as to output the historical key information characteristic representation corresponding to the historical logistics data; carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data; performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree; and based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data. Based on the above, since the characteristic representation matching degree is calculated on the historical logistics data and the target logistics data, the data update management and control can be performed on the historical logistics data and the target logistics data based on the obtained characteristic representation matching degree, and thus, compared with the conventional technical scheme of directly performing data update management and control based on the formation time, the reliability of the logistics data update can be improved, and the defects in the prior art are overcome.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus and method embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for updating logistic data, comprising:
performing key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
performing key information mining processing on target logistics data to be processed to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises target logistics information of each item in the current time period, and one piece of target logistics information corresponds to one logistics cargo;
performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree;
And based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data.
2. The method for updating logistics data according to claim 1, wherein the step of performing key information mining processing on the historical logistics data to be processed to output a historical key information feature representation corresponding to the historical logistics data comprises the steps of:
performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
the step of performing key information mining processing on target logistics data to be processed to output target key information characteristic representation corresponding to the target logistics data comprises the following steps:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
3. The method for updating physical distribution data according to claim 2, wherein the method for updating further comprises: performing network optimization processing to form an optimized key information mining network;
The step of performing network optimization processing to form an optimized key information mining network comprises the following steps:
extracting a typical logistics data combination, and analyzing multi-dimensional logistics characterization data corresponding to the typical logistics data combination, wherein the typical logistics data combination at least comprises first typical logistics data and second typical logistics data, a data matching relationship between the second typical logistics data and the first typical logistics data accords with a preset data matching relationship, and the multi-dimensional logistics characterization data comprises logistics characterization data with at least two dimensions;
hiding the logistics characterization data of the first dimension in the multi-dimensional logistics characterization data to output corresponding hidden multi-dimensional logistics characterization data;
performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output typical key information characteristic representation and hidden data restoration characteristic representation corresponding to the typical logistics data combination;
based on the logistics characterization data, the hidden data reduction feature representation and the typical key information feature representation of the first dimension, determining a learning cost value of the initial key information mining network so as to output a reduction learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value;
And carrying out optimization processing on the initial key information mining network according to the reduction learning cost value and the key information learning cost value so as to form an optimized key information mining network.
4. The method for updating logistics data according to claim 3, wherein the step of performing a learning cost value determining operation on the initial key information mining network based on the logistics characterization data, the hidden data restoration feature representation and the typical key information feature representation in the first dimension to output a restoration learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value comprises:
based on the logistics characterization data of the first dimension and the hidden data restoration characteristic representation, carrying out first learning cost value determination operation on the initial key information mining network, and outputting restoration learning cost values corresponding to the initial key information mining network;
and according to the typical key information characteristic representation corresponding to the typical logistics data combination, carrying out the second learning cost value determining operation on the initial key information mining network, and outputting the key information learning cost value corresponding to the initial key information mining network.
5. The method for updating physical distribution data according to claim 4, wherein the step of performing a second learning cost value determination operation on the initial critical information mining network according to the representative critical information feature representation corresponding to the representative physical distribution data combination, and outputting the critical information learning cost value corresponding to the initial critical information mining network includes:
determining relevant typical logistics data and irrelevant typical logistics data from second typical logistics data included in the typical logistics data combination, wherein the relevant typical logistics data and the first typical logistics data have a matching relationship, and the irrelevant typical logistics data and the first typical logistics data do not have a matching relationship; according to the typical key information characteristic representation, analyzing the matching degree between the first typical logistics data and the related typical logistics data to output corresponding related dimension matching degree, and analyzing the matching degree between the first typical logistics data and the non-related typical logistics data to output corresponding non-related dimension matching degree; based on the relevant dimension matching degree and the irrelevant dimension matching degree, analyzing the key information learning cost value corresponding to the initial key information mining network;
The step of restoring the characteristic representation based on the logistics characterization data and the hidden data in the first dimension, performing a first learning cost value determining operation on the initial key information mining network, and outputting a restoring learning cost value corresponding to the initial key information mining network includes:
performing restoration processing of the characterization data based on the hidden data restoration characteristic representation to output corresponding restored logistics characterization data; and according to the logistics characterization data and the restored logistics characterization data of the first dimension, determining a first learning cost value of the initial key information mining network, and outputting a restored learning cost value corresponding to the initial key information mining network.
6. The method for updating logistics data according to claim 3, wherein the step of performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output a typical key information feature representation and a hidden data restoration feature representation corresponding to the typical logistics data combination comprises the steps of:
carrying out integration processing of the distribution domain on the hidden multidimensional stream characterization data so as to output initial feature representations corresponding to each dimension in the same distribution domain;
The initial characteristic representation is subjected to aggregation processing through an initial key information mining network so as to output a typical key information characteristic representation corresponding to each typical logistics data in the typical logistics data combination;
and according to the typical key information characteristic representation, carrying out reduction processing on hidden data in the hidden multidimensional stream characterization data, and outputting hidden data reduction characteristic representation.
7. The method for updating physical distribution data according to any one of claims 1 to 6, wherein the step of performing data updating management on the historical physical distribution data and the target physical distribution data based on the feature representation matching degree to save at least the target physical distribution data comprises the steps of:
extracting a pre-configured reference matching degree;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
8. A system for updating logistic data, comprising:
the first key information mining module is used for carrying out key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
the second key information mining module is used for carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises each item of target logistics information in the current time period, and one piece of target logistics information corresponds to one piece of logistics goods;
the matching degree calculation module is used for carrying out matching degree calculation processing on the historical key information feature representation and the target key information feature representation so as to output corresponding feature representation matching degree;
and the data updating management and control module is used for carrying out data updating management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree so as to at least save the target logistics data.
9. The system for updating logistics data according to claim 8, wherein the first key information mining module is specifically configured to:
performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
the second key information mining module is specifically configured to:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
10. The system for updating physical distribution data according to claim 8, wherein the data updating management module is specifically configured to:
extracting a pre-configured reference matching degree;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311159558.XA CN117112628B (en) | 2023-09-08 | 2023-09-08 | Logistics data updating method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311159558.XA CN117112628B (en) | 2023-09-08 | 2023-09-08 | Logistics data updating method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117112628A true CN117112628A (en) | 2023-11-24 |
| CN117112628B CN117112628B (en) | 2024-09-27 |
Family
ID=88802075
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311159558.XA Active CN117112628B (en) | 2023-09-08 | 2023-09-08 | Logistics data updating method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117112628B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117807377A (en) * | 2024-03-01 | 2024-04-02 | 深圳市快金数据技术服务有限公司 | Multidimensional logistics data mining and predicting method and system |
| CN117910906A (en) * | 2024-02-19 | 2024-04-19 | 广东康利达物联科技有限公司 | Data visualization method and system applied to intelligent logistics |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106547740A (en) * | 2016-11-24 | 2017-03-29 | 四川无声信息技术有限公司 | Text message processing method and device |
| CN109597881A (en) * | 2018-12-17 | 2019-04-09 | 北京百度网讯科技有限公司 | Matching degree determines method, apparatus, equipment and medium |
| CN110609958A (en) * | 2019-09-19 | 2019-12-24 | Oppo广东移动通信有限公司 | Data pushing method and device, electronic equipment and storage medium |
| CN112182295A (en) * | 2019-07-05 | 2021-01-05 | 浙江宇视科技有限公司 | Business processing method and device based on behavior prediction and electronic equipment |
| WO2021139146A1 (en) * | 2020-01-09 | 2021-07-15 | 平安国际智慧城市科技股份有限公司 | Information recommendation method, device, computer-readable storage medium, and apparatus |
| CN114693409A (en) * | 2022-04-24 | 2022-07-01 | 中国工商银行股份有限公司 | Product matching method, device, computer equipment, storage medium and program product |
| CN115049446A (en) * | 2021-03-09 | 2022-09-13 | 腾讯科技（深圳）有限公司 | Merchant identification method and device, electronic equipment and computer readable medium |
| CN115544214A (en) * | 2022-12-02 | 2022-12-30 | 广州数说故事信息科技有限公司 | Event processing method and device and computer readable storage medium |
| CN116361440A (en) * | 2023-04-06 | 2023-06-30 | 江苏擎虎智能科技有限公司 | Digital financial product session interaction method and system based on artificial intelligence |
| CN116467606A (en) * | 2023-03-03 | 2023-07-21 | 苏州凌云光工业智能技术有限公司 | Determination method, device, equipment and medium of decision suggestion information |
-
2023
- 2023-09-08 CN CN202311159558.XA patent/CN117112628B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106547740A (en) * | 2016-11-24 | 2017-03-29 | 四川无声信息技术有限公司 | Text message processing method and device |
| CN109597881A (en) * | 2018-12-17 | 2019-04-09 | 北京百度网讯科技有限公司 | Matching degree determines method, apparatus, equipment and medium |
| CN112182295A (en) * | 2019-07-05 | 2021-01-05 | 浙江宇视科技有限公司 | Business processing method and device based on behavior prediction and electronic equipment |
| CN110609958A (en) * | 2019-09-19 | 2019-12-24 | Oppo广东移动通信有限公司 | Data pushing method and device, electronic equipment and storage medium |
| WO2021139146A1 (en) * | 2020-01-09 | 2021-07-15 | 平安国际智慧城市科技股份有限公司 | Information recommendation method, device, computer-readable storage medium, and apparatus |
| CN115049446A (en) * | 2021-03-09 | 2022-09-13 | 腾讯科技（深圳）有限公司 | Merchant identification method and device, electronic equipment and computer readable medium |
| CN114693409A (en) * | 2022-04-24 | 2022-07-01 | 中国工商银行股份有限公司 | Product matching method, device, computer equipment, storage medium and program product |
| CN115544214A (en) * | 2022-12-02 | 2022-12-30 | 广州数说故事信息科技有限公司 | Event processing method and device and computer readable storage medium |
| CN116467606A (en) * | 2023-03-03 | 2023-07-21 | 苏州凌云光工业智能技术有限公司 | Determination method, device, equipment and medium of decision suggestion information |
| CN116361440A (en) * | 2023-04-06 | 2023-06-30 | 江苏擎虎智能科技有限公司 | Digital financial product session interaction method and system based on artificial intelligence |
Non-Patent Citations (2)
| Title |
|---|
| HUI GAO, YU-YU WU: "Research on the Development of Personalized Recommendation", 2021 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND ARTIFICIAL INTELLIGENCE (CISAI), 28 February 2022 (2022-02-28) * |
| 董庆兴;李赛;张大斌;李延晖;: "基于匹配属性相似度的应急决策方案推荐方法", 控制与决策, no. 07, 27 April 2016 (2016-04-27) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117910906A (en) * | 2024-02-19 | 2024-04-19 | 广东康利达物联科技有限公司 | Data visualization method and system applied to intelligent logistics |
| CN117807377A (en) * | 2024-03-01 | 2024-04-02 | 深圳市快金数据技术服务有限公司 | Multidimensional logistics data mining and predicting method and system |
| CN117807377B (en) * | 2024-03-01 | 2024-05-14 | 深圳市快金数据技术服务有限公司 | Multidimensional logistics data mining and predicting method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117112628B (en) | 2024-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117112628B (en) | Logistics data updating method and system | |
| CN114896454B (en) | Short video data recommendation method and system based on label analysis | |
| CN117271522B (en) | Logistics data processing method and system | |
| CN111639607A (en) | Model training method, image recognition method, model training device, image recognition device, electronic equipment and storage medium | |
| CN114511037A (en) | Automatic feature screening method and device, electronic equipment and storage medium | |
| CN113569965A (en) | User behavior analysis method and system based on Internet of things | |
| Bianchi et al. | A randomised approach for NARX model identification based on a multivariate Bernoulli distribution | |
| CN108595685B (en) | Data processing method and device | |
| CN116738067B (en) | Vendor recommendation method and system based on big data | |
| US20140294316A1 (en) | Variable blocking artifact size and offset detection | |
| CN116701411A (en) | Multi-field data archiving method, device, medium and equipment | |
| JP6946542B2 (en) | Learning system, estimation system and trained model | |
| CN116702220A (en) | Data comparison method and system based on encryption characteristic analysis | |
| CN110807466A (en) | Method and device for processing order data | |
| CN116127083A (en) | Content recommendation method, device, equipment and storage medium | |
| CN113537087A (en) | Intelligent traffic information processing method and device and server | |
| CN113255806A (en) | Sample feature determination method, sample feature determination device and electronic equipment | |
| CN113239381A (en) | Data security encryption method | |
| CN113626647A (en) | Data storage method and system for intelligent cell | |
| CN113626419A (en) | Data screening method and system for intelligent cell | |
| CN113239031A (en) | Big data denoising processing method | |
| CN113361703A (en) | Data processing method and device | |
| CN114867046B (en) | Wireless network equipment firmware updating method and wireless network equipment | |
| CN117150124B (en) | User characteristic analysis method and system based on smart home | |
| CN117236617B (en) | Enterprise business management method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240905 Address after: No. 2369 Chanba Avenue, Weiyang District, Xi'an City, Shaanxi Province 710016 Applicant after: Han Chengcheng Country or region after: China Address before: 065907 Zhang Du Village, Beiwei Town, Dacheng County, Langfang City, Hebei Province Applicant before: Langfang Jungle Technology Co.,Ltd. Country or region before: China |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |