CN115409446A - Flow direction completion method, device and system, model training method and electronic equipment - Google Patents
Flow direction completion method, device and system, model training method and electronic equipment Download PDFInfo
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Abstract
The embodiment of the specification provides a flow direction completion method, a flow direction completion device, a flow direction completion system, a model training method and electronic equipment, wherein the flow direction completion method is based on a historical flow direction characteristic set, cargo quantity errors of associated nodes with historical flow direction relations with target nodes in a preset time period are obtained, outflow nodes with missing cargo flow directions are determined according to the cargo quantity errors of a plurality of associated nodes, completion of the cargo flow directions is achieved, and integrity of the cargo flow directions is guaranteed. In addition, the historical flow direction characteristic set comprises a plurality of characteristics extracted from the historical flow direction information uploading behavior, the problem that the cargo quantity error is inaccurate based on a single characteristic is solved, the accuracy of the cargo quantity error is improved, and the accuracy of flow direction prediction is further improved.
Description
Technical Field
The present disclosure relates to a traceability technology in the field of computer technologies, and more particularly, to a method, an apparatus, a system, a model training method, and an electronic device for flow direction completion.
Background
The supply chain industry often involves a number of entities, including logistics, money flows, and information flows, among which there is a large amount of complex collaboration and communication.
In a traditional mode, different entities respectively store respective supply chain information, transparency is seriously lacked, so that the tracing of goods is difficult, once problems (product quality problems, product counterfeiting problems and the like) occur, accurate tracing is difficult to realize, and for enterprises with control and sale requirements, accurate goods flow direction records have great significance for preventing goods fleeing.
Disclosure of Invention
The embodiment of the specification provides a flow direction completion method, a flow direction completion device, a flow direction completion system, a model training method and electronic equipment, completion of missing cargo flow directions in a cargo flow diagram is achieved, and complete and accurate recording of the cargo flow directions is achieved.
In order to achieve the technical purpose, the embodiments of the present specification provide the following technical solutions:
in a first aspect, a flow direction completion method is provided, including:
acquiring the cargo quantity errors of a plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the associated nodes respectively; the target node comprises an inflow node in which the flow direction of the missing goods flows, the associated node comprises a node which has a historical flow direction relationship with the target node, the preset time period covers the predicted uploading time of preset flow direction information, the preset flow direction information comprises the information of the inflow node and/or the outflow node in which the flow direction of the missing goods flows, the historical flow direction characteristic set comprises a plurality of characteristics extracted from the uploading behavior of the historical flow direction information, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated node in the preset time period;
and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow node.
In a second aspect, a model training method is provided, including:
acquiring a historical flow direction characteristic set corresponding to a target node and an associated node, wherein the target node comprises an inflow node lacking the flow direction of goods, the associated node comprises a node having a historical flow direction relationship with the target node, and the historical flow direction characteristic set comprises a plurality of characteristics extracted from the uploading of historical flow direction information;
and taking the historical flow direction feature set corresponding to the target node and the associated node as a training set, and training a neural network model corresponding to the target node and the associated node to obtain a flow direction prediction model corresponding to the target node and the associated node, wherein the flow direction prediction model is used for predicting the outflow cargo amount predicted to the target node by the associated node in a preset time period.
In a third aspect, a flow direction completion system is provided, including: a server, a target node and an associated node; wherein the content of the first and second substances,
the target node and the associated node are used for uploading flow direction information to the server, and the flow direction information comprises information of an inflow node and an outflow node of goods;
the server is used for acquiring and storing the flow direction information, acquiring the cargo quantity errors of the plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to a target node and the plurality of associated nodes respectively, acquiring the outflow nodes of the missing cargo flow direction according to the cargo quantity errors of the plurality of associated nodes in the preset time period, and completing the missing cargo flow direction according to the outflow nodes;
the target node comprises an inflow node in which goods flow direction is lost, the associated node comprises a node which has historical flow direction relation with the target node, the preset time period covers the predicted uploading time of preset flow direction information, the preset flow direction information comprises the inflow node and/or outflow node information in which the goods flow direction is lost, the historical flow direction characteristic set comprises a plurality of characteristics extracted from the historical flow direction information uploading behavior, and the goods quantity error comprises the difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated node in the preset time period.
In a fourth aspect, a flow direction completion method is provided, including:
acquiring cargo quantity errors of a plurality of associated nodes according to historical flow direction characteristic sets corresponding to a target node and the associated nodes respectively; the target nodes comprise inflow nodes missing the flow direction of the goods, the associated nodes comprise nodes having historical flow direction relation with the target nodes, the historical flow direction feature set comprises a plurality of features extracted from historical flow direction information uploading behavior, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity at the predicted flow direction uploading time of the associated nodes;
and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes, and completing the missing goods flow direction according to the outflow node.
In a fifth aspect, a flow direction completion device is provided, which includes:
the flow direction prediction module is used for acquiring the cargo quantity errors of the plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the plurality of associated nodes respectively; the target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of preset flow direction information, the preset flow direction information comprises inflow node and/or outflow node information in missing goods flow direction, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period;
and the flow direction completion module is used for acquiring the outflow nodes of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow nodes.
In a sixth aspect, an electronic device is provided, comprising: a memory and a processor;
wherein the memory is connected with the processor and is used for storing programs;
the processor is configured to implement the flow direction completion method according to any one of the above descriptions or the model training method according to any one of the above descriptions by running the program stored in the storage.
In a seventh aspect, a storage medium is provided, on which a computer program is stored, which, when being executed by a processor, implements the flow direction completion method according to any one of the above items or the model training method according to any one of the above items.
In an eighth aspect, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions stored in a computer-readable storage medium, the computer instructions being read by a processor of a computer device from the computer-readable storage medium, and the processor implementing the steps of the flow direction completion method or the model training method described above when executing the computer instructions.
It can be seen from the foregoing technical solutions that embodiments of the present specification provide a flow direction completion method, apparatus, system, model training method, and electronic device, where in the flow direction completion method, based on a historical flow direction feature set, a cargo quantity error of a relevant node having a historical flow direction relationship with a target node within a preset time period is obtained, and an outflow node in a missing cargo flow direction is determined according to the cargo quantity errors of multiple relevant nodes, so that completion of a cargo flow direction is achieved, and integrity of the cargo flow direction is ensured. In addition, the historical flow direction characteristic set comprises a plurality of characteristics extracted from the historical flow direction information uploading behavior, the problem that the cargo quantity error is inaccurate based on a single characteristic is solved, the accuracy of the cargo quantity error is improved, and the accuracy of flow direction prediction is further improved. Meanwhile, the flow direction completion method considers the uncertainty of the uploading time of the node flow direction information, obtains the cargo quantity error by taking the preset time period as a unit, and reduces the flow direction prediction error caused by the delay of uploading the flow direction information of the associated node or the target node.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only the embodiments of the present specification, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart provided by an embodiment of the present disclosure;
FIG. 2 is an example scenario provided by an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an implementation environment provided by the embodiments of the present disclosure;
fig. 4 is a schematic flow chart of a flow direction completion method provided in an embodiment of the present disclosure;
fig. 5 is a fitting curve of time and item amount (i.e., cargo amount) information based on historical upload flow direction information according to an embodiment of the present specification;
FIG. 6 is a schematic flow chart of another flow direction completion method provided in the embodiments of the present disclosure;
FIG. 7 is a flow chart illustrating another flow direction completion method provided by embodiments of the present disclosure;
FIG. 8 is a schematic flow chart diagram illustrating a method for training a model provided in an embodiment of the present disclosure;
fig. 9 is a schematic diagram of flow direction completion based on a flow direction prediction model according to an embodiment of the present disclosure;
fig. 10 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification.
Detailed Description
Unless otherwise defined, technical terms or scientific terms used in the embodiments of the present specification should have the ordinary meaning as understood by those having ordinary skill in the art to which the present specification belongs. The terms "first," "second," and the like as used in the embodiments of the present specification do not denote any order, quantity, or importance, but rather are provided to avoid mixing of constituent elements.
Unless the context requires otherwise, throughout the specification, "a plurality" means "at least two" and "includes" are to be interpreted in an open, inclusive sense, i.e., as "including, but not limited to". In the description of the specification, the terms "one embodiment," "some embodiments," "an example embodiment," "an example," "a specific example" or "some examples" or the like are intended to indicate that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. The schematic representations of the above terms are not necessarily referring to the same embodiment or example.
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The following explains some terms that may be referred to in this specification.
The flow direction refers to the flow direction of people, funds, goods and the like, and in the specification, may refer to the flow direction of goods. In a cargo flow direction, the cargo flow direction comprises an inflow node and an outflow node of the cargo, wherein the inflow node refers to a transportation destination or a purchasing warehousing place of the cargo in the cargo flow direction, and the outflow node refers to a transportation starting point or a selling warehousing place of the cargo in the cargo flow direction. It will be appreciated that the ingress node in one cargo flow direction may be the egress node in other cargo flow directions, and the egress node in one cargo flow direction may also be the ingress node in other cargo flow directions.
The flow graph (flow diagram) refers to a traffic graph formed by a plurality of nodes and a plurality of flow directions, the flow directions of the goods between different nodes are indicated in the graph, and in some flow graphs, the flow rates of the goods between different nodes (i.e. the amount of the goods flowing around) can also be indicated. Referring to fig. 1, fig. 1 shows a possible flow diagram, which includes six nodes, a, B, C, D, E and F, and arrows indicate the flow of cargo between different nodes. Taking nodes a and B in the dashed box K1 as an example, the flow direction in the dashed box indicates that cargo flows from node a to node B, at this time, node a is an outgoing node of the flow direction, and node B is an incoming node of the flow direction. In the dashed box K2, the flow direction indicates that the cargo flows from node B to node C, where node B is the outgoing node of the flow direction and node C is the incoming node of the flow direction.
The missing goods flow direction refers to a flow direction missing caused by the fact that the node does not upload the sales delivery documents or purchase the warehousing documents, still referring to fig. 1, in fig. 1, a dotted arrow between the node C and the node D indicates that the flow direction missing caused by the fact that the node C and the node D do not upload the goods documents, which may cause the incomplete flow diagram shown in fig. 1, and complete tracing of the goods cannot be realized.
In this specification, a node refers to a participant of production and sales connected by a flow in a flow graph, and may be a manufacturer, a distributor, or the like.
The target node is an inflow node of the missing cargo flow direction, that is, in fig. 1, a dashed arrow indicates the missing cargo flow direction, and then the node D is the target node.
Time window (Time Windows) refers to a window in which data is counted or calculated over a period of Time of a certain length. The Time windows may include a rolling Time Window (rolling Time Window) and a Sliding Time Window (Sliding Time Window). The time window of the rolling time window is fixed, and if the length of the time window is set to be 1 minute, the time window only calculates the data in the current 1 minute. The time window of the sliding time window is sliding, and not only the length of the sliding time window but also the sliding size in the window need to be defined.
The Internet of Things (IoT) is to collect any object or process needing monitoring, connection and interaction in real time and collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology and location through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors and laser scanners, and to realize ubiquitous connection of objects and people through various possible network accesses, and to realize intelligent sensing, identification and management of objects and processes.
In one example scenario provided in this specification, referring to fig. 2, goods produced by manufacturer a sequentially pass through dealer B, dealer C, and dealer D, respectively reach respective outlets of dealer C and dealer D (for example, outlets 1, 2, 3 of dealer C and outlets 1, 2 of dealer D), and are finally sold to users. Specifically, when goods are circulated among different manufacturers or distributors, workers of the manufacturers or distributors can realize warehousing and ex-warehouse operation of the goods by scanning the two-dimensional codes attached to the goods or the transportation equipment, and the server can realize traceability of the goods in the circulation process through uploaded related information of warehousing and ex-warehouse operation. For example, in fig. 1, after manufacturer a finishes producing goods, a two-dimensional code is attached to the goods or the transportation equipment of the goods, manufacturer a uploads relevant information of manufacturer a and relevant information such as lot, production date, specification of the goods by scanning the two-dimensional code, and after receiving the relevant information uploaded by manufacturer a, the server records relevant information of the lot of goods at manufacturer a. And then the goods arrive at a distributor B through transportation, the distributor B uploads the related information of the distributor B and the related information of the goods by scanning the same two-dimensional code, and the server records the related information of the batch of goods in the distributor B after receiving the related information uploaded by the distributor B. Similar processes are carried out between the dealers B and C, so that goods are circulated between the dealers B and C, and finally, the goods are circulated to the hands of the users through the dealers C. When the user is using this batch of goods, if the accident appears, can be through the goods information of tracing to the source of server record, the accident of tracing to the source finally blays the person, so can urge the participant in processes such as goods circulation, production to try hard to ensure the safety of goods in processes such as circulation, avoid leading to the goods to appear the security problem and finally being blamed for because of the error of oneself. The tracing technology has great significance for the safety management of food and medicine. In addition, the complete traceability of the goods can also ensure that the regional sales strategy of the manufacturer A can be implemented, the control and sales requirements of the manufacturer A can be realized, and the problem of goods fleeing is avoided. For example, goods that manufacturer a wants to ship to distributor B, are actually shipped to distributor E, which is called "fleeing. In an actual application scenario, it may happen that a certain flow direction or flow directions in the flow diagram are lost due to the fact that a document is not uploaded by a dealer or a manufacturer, in fig. 2, the flow direction between the dealer B and the dealer D and the flow direction between the dealer C and the network node 2 of the dealer C are in a missing state, when the flow direction of missing goods exists in the flow diagram, adverse effects are caused on the accuracy and the complete traceability of the goods, when the flow direction of missing goods is too much, the goods cannot be traced, and the control and marketing strategy of the manufacturer a cannot be realized.
In order to implement completion of a flow direction of missing goods in a flow diagram, an embodiment of the present specification provides a flow direction completion method, where a prediction of a goods quantity error is implemented according to a historical flow direction feature set of a target node and a plurality of associated nodes having a historical flow direction relationship with the target node, and then a determination of an outflow node of the flow direction of the missing goods is implemented according to the predicted goods quantity error, so as to finally implement completion of the flow direction of the missing goods.
The flow direction completion method provided by the embodiments of the present specification will be described below with reference to the accompanying drawings.
Referring to fig. 3, fig. 3 illustrates an implementation environment that may be involved in the method for stream completion provided in the embodiment of the present disclosure, where fig. 3 is an internet of things system, the internet of things system includes a server 10 and a plurality of nodes 20, the server 10 may establish a communication connection with the node 20, the node 20 may be a manufacturer, a dealer, a cargo depository, a dealer site, or the like, and the establishing of the communication connection between the server 10 and the node 20 may specifically refer to establishing a communication connection between the server 10 and an intelligent device (e.g., a computer) in the node 20, so that the node 20 may upload a document to the server 10, and the server 10 may also send a request for the document to the node 20. A communication connection may be established between the nodes 20, but the communication connection between the nodes 20 is not a necessary condition for implementing the flow-to-completion method.
For the server 10, it may be any server 10 device having certain computing and communication capabilities and capable of responding to the request of the smart device and providing corresponding service or data support for the smart device, for example, it may be a conventional server 10, a cloud host, a virtual center or a server 10 array, etc. The server 10 mainly includes a processor, a hard disk, a memory, a system bus, and the like.
Alternatively, the server 10 may be deployed in the cloud, and the smart device may access the internet (e.g., a wide Area Network or a Metropolitan Area Network) through a mobile Network such as WiFi, ethernet, fiber, 2/3/4/5G, and establish a communication connection with the server 10 through the internet. Of course, in addition to being deployed in the cloud, the server 10 may also be deployed with a smart device. The present description does not limit the deployment location of the server 10.
Referring to fig. 4, taking a server applied in the internet of things system shown in fig. 3 as an example, an embodiment of the present specification provides a method for stream completion, including:
s401: acquiring the cargo quantity errors of a plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the associated nodes respectively; the target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of the missing goods flow direction information, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period.
As mentioned above, the destination node refers to an inflow node (e.g., node D in fig. 1) to which missing cargo flows. The associated node is a node having a historical flow relationship with a target node, for example, assuming that all of the nodes a, B and C in fig. 1 have transported goods to the node D, all of the nodes a, B and C are associated nodes of the node D. The flow direction completion method provided in the embodiment of the present specification is mainly used to solve the flow direction completion problem when there are multiple associated nodes, and when a target node has one associated node, the associated node may be directly used as an outflow node of the missing cargo flow direction. For example, if the historical forward node of the node D is only the node C, when the flow direction indicated by the dashed arrow shown in fig. 1 is missing, the node C may be directly used as the forward node of the missing cargo flow direction.
The historical flow direction information uploading behavior refers to the uploading behavior of the flow direction information of the goods which has occurred between the nodes by the node pair, for example, in fig. 1, the goods flow direction has occurred between the node a and the node D, the node a uploads a sale delivery receipt to the node D, which indicates that a certain amount of goods flow occurs from the node a to the node D, the node D also uploads a purchase storage receipt of the goods from the node a, which also indicates that a certain amount of goods flow occurs from the node a to the node D, and the server can merge the two receipts into the flow direction information of the goods between the node a and the node D and record the flow direction information. The behavior that the node A and the node D upload the receipt containing the goods flow conversion information is called flow direction information uploading behavior, and the historical receipt uploading behavior is called historical flow direction information uploading behavior.
From the historical flow to the information uploading behavior, a series of characteristics related to the uploading behavior can be extracted, and information such as time and/or cargo quantity of the document information which is possibly uploaded to the server by the node can be deduced from the characteristics. In the history flow direction information uploading behavior, the flow direction information history uploading time of the node may also be obtained, a curve of the node uploading time may be fitted from the history uploading time, referring to fig. 5, fig. 5 shows the time and the goods amount (i.e., the goods amount) information of the history flow direction information uploaded by a certain node, a fitted curve of the node uploading flow direction information may be fitted from the information, and information such as the time (the expected uploading time) of the node to which the flow direction information is expected to be uploaded and the size of the goods amount of the outflow node included in the uploaded flow direction information may be obtained from the fitted curve. For example, in fig. 5, it can be seen that the flow information upload behavior of the primary node is missing at the time (X = 7) when the node expects to upload the flow information. The load of the node at the time point (X = 7) can be roughly predicted according to the fitted curve of fig. 5, and after the load of the outflow cargo predicted by the node at the time point of X =7 is obtained, the difference between the predicted load of the outflow cargo and the actually uploaded load of the outflow cargo is taken as the error of the load, that is, the possibility that the node may have the non-uploaded flow direction information is represented.
It will be appreciated that when the target node has multiple associated nodes, the historical flow direction feature set is different between the target node and the different associated nodes. Still taking fig. 1 as an example, assuming that both nodes a and B are associated nodes of the target node D, that is, both nodes a and B have a historical over-flow relationship with the target node D, historical flow direction information between the node a and the target node D is generally different from historical flow direction information between the node B and the target node D, and accordingly, historical flow direction information uploading behavior between the node a and the target node D is generally different from historical flow direction information uploading behavior between the node B and the target node D. It is understood that the historical flow direction feature sets corresponding to the target node and the plurality of associated nodes respectively means that the target node and each associated node respectively have a historical flow direction feature set.
S402: and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow node.
After the cargo quantity errors of the plurality of associated nodes in the preset time period are obtained through S301, the associated node most likely to be the inflow node of the missing cargo flow direction may be determined through the cargo quantity errors of the plurality of associated nodes in the preset time period.
In this embodiment, the flow direction completion method obtains the cargo volume error of the associated node having a historical flow direction relationship with the target node in a preset time period based on the historical flow direction feature set, and determines the outflow node missing the cargo flow direction according to the cargo volume errors of the associated nodes, thereby completing the cargo flow direction and ensuring the integrity of the cargo flow direction. In addition, the historical flow direction characteristic set comprises a plurality of characteristics extracted from the historical flow direction information uploading behavior, the problem that the cargo quantity error based on a single characteristic is inaccurate is solved, the accuracy of the cargo quantity error is improved, and the accuracy of flow direction prediction is further improved.
Meanwhile, the flow direction completion method considers the uncertainty of the uploading time of the node flow direction information, obtains the cargo quantity error by taking the preset time period as a unit, and reduces the flow direction prediction error caused by the delay of uploading the flow direction information of the associated node or the target node. For example, a certain node should upload a bill representing flow direction information of goods sent on the same day when the goods are sent, but the bill is not uploaded on the same day due to reasons such as network abnormality of the node, and the bill is not uploaded until the third day when the goods are sent, so that the error of the quantity of the goods is obtained by taking a preset time period as a unit, and the error caused by the fact that the node delays uploading the flow direction information can be avoided.
The length of the preset time period may be determined according to actual requirements, and may be 3 days, 7 days, 10 days, 15 days, and the like, which are not limited in this specification, and in order to be more suitable for the document uploading behavior of the node, the preset time period may be a periodic average value of historical uploading flow direction information of the target node and the associated node. For example, the periods of historical uploading flow direction information of the target node are respectively 6 days, 6 days and 5 days, and the periods of historical uploading flow direction information of the associated node are respectively 7 days, 9 days and 9 days, so that the average value of the periods of historical uploading flow direction information of the target node and the associated node may be (6 +5+7+ 9)/6 =7 days. Of course, in some embodiments, the time length of the preset time may also be greater than a periodic average value of historical uploading flow-to-information of the target node and the associated node.
Optionally, in an embodiment of the present specification, the historical flow direction feature set includes an uploading time feature of the historical flow direction information and a cargo volume feature of the historical flow direction information.
The uploading time characteristic of the historical flow direction information refers to the correlation characteristic of the time point of uploading the historical flow direction information by the representation node, and the cargo quantity characteristic of the historical flow direction information refers to the correlation characteristic of the cargo quantity of the historical flow direction information uploaded by the representation node. When the historical flow direction characteristic set comprises the uploading time characteristic and the cargo quantity characteristic of the historical flow direction information, cargo quantity and cargo quantity errors of the associated nodes in a preset time period can be more accurately predicted based on the historical flow direction characteristic set, and accuracy of the flow direction completion method is guaranteed.
Optionally, the upload time characteristic includes an index of upload time within a preset period, the preset period including at least one of week, month and year. That is, the upload time characteristics may include at least one of an index of the upload time within a week, an index within a month, and an index in a year. The index of the upload time in one week means that the upload time is the day four in one week, for example, the upload time is 2022, 8, month, and 17 days, and wednesday, and the index of the upload time in one week represents that the upload time is the day four in the week (day of week is the first day of week). Also for example, if the upload time is 2022, 6, month 11, then the index of the upload time in a month indicates that the upload time is day 11 in the month. For another example, if the upload time is 2022 years, 1 month and 2 days, the index of the upload time in one year indicates that the upload time is the 2 nd day in the year.
The index of the uploading time characteristic in the preset period can be obtained through Dayofweek functions, dayofmonth functions and Dayoffyear functions, and the method has the characteristic of being simple and convenient to obtain.
Alternatively, in a possible implementation of the present specification, with reference to fig. 6, the flow direction completion method includes:
s601: and generating a flow direction prediction model corresponding to the target node and each associated node according to the historical flow direction feature set corresponding to the target node and each associated node. The flow direction prediction model may be a fitted curve as shown in fig. 5, or may be a neural network model trained by training samples generated based on historical flow direction feature sets corresponding to the target node and the plurality of associated nodes, respectively.
S602: and predicting the amount of the outflow goods to the target node by the associated nodes in a preset time period by utilizing a sliding time window according to the flow direction prediction model corresponding to the target node and each associated node.
Based on the sliding time window, the method can predict the outflow cargo volume of the target node, on one hand, the lock-free design can be realized, the throughput of the method within a certain time is improved, on the other hand, errors caused by the fact that the node uploads the documents at a plurality of time points in a centralized mode can be eliminated, and the accuracy of the method is improved.
S603: and calculating the difference value between the predicted outflow cargo quantity to the target node and the outflow cargo quantity actually uploaded to the target node in a preset time period of each associated node to obtain the cargo quantity error of each associated node in the preset time period.
S604: and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow node.
Ideally, if the flow direction information is not uploaded by the associated node, the outflow cargo amount actually uploaded by the associated node to the target node in the preset time period is zero, and the predicted outflow cargo amount to the target node is also zero, but due to the prediction accuracy problem of the flow direction prediction model generated in S601, the predicted outflow cargo amount to the target node may be not zero in the preset time period by the associated node, so that the cargo amount error calculated in S603 is not zero, and in order to predict the outflow node more accurately, in a possible implementation, the obtaining the outflow node in the flow direction of the missing cargo according to the cargo amount errors of a plurality of associated nodes in the preset time period includes:
and determining the associated node with the largest cargo quantity error as an outflow node of the flow direction of the missing cargo.
Therefore, when the cargo quantity errors of the plurality of associated nodes and the target node are all larger than zero, the outflow node can be acquired more accurately, and the prediction accuracy of the method is improved.
Based on the same concept, an embodiment of the present specification further provides a flow direction completion method, as shown in fig. 7, including:
s701: acquiring cargo quantity errors of a plurality of associated nodes according to historical flow direction characteristic sets corresponding to a target node and the associated nodes respectively; the target nodes comprise inflow nodes missing the flow direction of the goods, the associated nodes comprise nodes having historical flow direction relation with the target nodes, the historical flow direction feature set comprises a plurality of features extracted from historical flow direction information uploading behavior, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity at the expected flow direction uploading time of the associated nodes.
S702: and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes, and completing the missing goods flow direction according to the outflow node.
The main differences between the flow direction completion method provided in this embodiment and the flow direction completion method shown in fig. 3 are: the load error of the plurality of associated nodes obtained by the flow direction completion method provided by this embodiment is a difference between the predicted flow direction load and the actually uploaded flow-out load of the associated nodes at the predicted flow direction uploading time, so that the data amount required to be processed in S701 is reduced, and the processing speed of the method is increased on the basis of achieving the flow direction completion purpose.
Correspondingly, based on the same concept, the embodiment of the present specification further provides a model training method, as shown in fig. 8, including:
s801: the method comprises the steps of obtaining a historical flow direction feature set corresponding to a target node and an associated node, wherein the target node comprises an inflow node missing the flow direction of goods, the associated node comprises a node having a historical flow direction relation with the target node, and the historical flow direction feature set comprises a plurality of features extracted from historical flow direction information uploading behaviors.
S802: and training a neural network model corresponding to the target node and the associated node by taking the historical flow direction feature set corresponding to the target node and the associated node as a training set to obtain a flow direction prediction model corresponding to the target node and the associated node, wherein the flow direction prediction model is used for predicting the amount of outflow goods predicted to the target node by the associated node in a preset time period.
The method for completing the flow direction of the missing goods comprises the steps of obtaining the goods quantity errors of a target node and a plurality of associated nodes in a preset time period by using a flow direction prediction model obtained through training, further obtaining an outflow node of the flow direction of the missing goods according to the goods quantity errors of the associated nodes in the preset time period, and completing the flow direction of the missing goods according to the outflow node, so that the flow direction completing method of any embodiment is realized.
Referring to fig. 9, fig. 9 illustrates a possible usage of the flow direction prediction model. And respectively acquiring a corresponding flow direction prediction model among the nodes A, B, C and the node D, inputting a corresponding historical flow direction characteristic set into the corresponding flow direction prediction model, namely respectively acquiring the cargo quantity errors from the nodes A, B, C to the node D, and determining an outflow node by comparing the maximum values of the cargo quantity errors so as to complement the missing cargo flow direction.
Optionally, the historical flow direction feature set includes an uploading time feature of the historical flow direction information and a cargo volume feature of the historical flow direction information.
The upload time characteristics include an index of upload time over a preset period, the preset period including at least one of a week, a month, and a year.
The cargo amount characteristics include a total cargo amount, a mean cargo amount, and a daily cargo amount over a last of the time periods covering the upload time.
Exemplary devices and systems
In an exemplary embodiment of the present specification, there is also provided a flow direction completion system including: a server, a target node and an associated node; wherein the content of the first and second substances,
the target node and the associated node are both used for uploading flow direction information to the server, and the flow direction information comprises information of an inflow node and an outflow node of goods.
The server is used for acquiring and storing the flow direction information, acquiring the cargo quantity errors of the plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the plurality of associated nodes respectively, acquiring the outflow nodes of the missing cargo flow direction according to the cargo quantity errors of the plurality of associated nodes in the preset time period, and completing the missing cargo flow direction according to the outflow nodes.
The target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of the missing goods flow direction, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period.
In an exemplary embodiment of the present specification, there is also provided a flow direction completion apparatus including:
the flow direction prediction module is used for acquiring the cargo quantity errors of the plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the plurality of associated nodes respectively; the target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of the missing goods flow direction, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading behavior, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period;
and the flow direction completion module is used for acquiring the outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow node.
The modules in the flow direction complementing device can be fully or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The flow direction completion device and the flow direction completion system provided by the embodiment belong to the same application concept as the flow direction completion method provided by the above embodiments in the present specification, and can execute the flow direction completion method provided by any of the above embodiments in the present application, and have the corresponding beneficial effects of executing the flow direction completion method. For details of the technique not described in detail in this embodiment, reference may be made to specific processing contents of the flow completion method provided in the foregoing embodiments of this specification, and details thereof are not repeated herein.
Exemplary electronic device
Another embodiment of the present application further provides an electronic device, and referring to fig. 10, an exemplary embodiment of the present specification further provides an electronic device, including: a memory storing a computer program and a processor executing the computer program to perform the steps of the flow direction completion method or the model training method according to various embodiments of the present specification described in the above embodiments of the present specification.
The internal structure of the electronic apparatus may be as shown in fig. 10, and the electronic apparatus includes a processor, a memory, a network interface, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the central control device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to perform the steps of the flow direction completion method or the model training method according to various embodiments of the present specification described in the above embodiments of the present specification.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is a block diagram of only a portion of the structure associated with the aspects of the present description, and does not constitute a limitation on the electronic devices to which the aspects of the present description apply, as particular electronic devices may include more or fewer components than shown in the figures, or may combine certain components, or have a different arrangement of components.
Exemplary computer program product and storage Medium
In addition to the methods and apparatus described above, the flow direction completion method or model training method provided by embodiments of the present specification may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the flow direction completion method or model training method according to various embodiments of the present specification described in the "exemplary methods" section above of the present specification.
The computer program product may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages, for performing the operations of embodiments of the present specification. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, the embodiments of the present specification further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the flow direction completion method or the model training method according to various embodiments of the present specification described in the above section "exemplary method" of the present specification.
It should be understood that the specific examples are included merely for purposes of illustrating the embodiments of the disclosure and are not intended to limit the scope of the disclosure.
It should be understood that, in the various embodiments of the present specification, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic, and should not limit the implementation process of the embodiments of the present specification.
It is to be understood that the various embodiments described in the present specification may be implemented individually or in combination, and the embodiments in the present specification are not limited thereto.
Unless otherwise defined, all technical and scientific terms used in the embodiments of the present specification have the same meaning as commonly understood by one of ordinary skill in the art to which this specification belongs. The terminology used in the description is for the purpose of describing particular embodiments only and is not intended to limit the scope of the description. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. As used in the specification embodiments and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It is to be understood that the processor of the embodiments of the present description may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off the shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method.
It will be appreciated that the memory in the implementations of the specification can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), or a flash memory. The volatile memory may be Random Access Memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present specification.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present specification may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present specification may be substantially or partially embodied in the form of a software product stored in a storage medium, and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope disclosed in the present disclosure, and all the changes or substitutions should be covered by the scope of the present disclosure. Therefore, the protection scope of the present specification shall be subject to the protection scope of the claims.
Claims (14)
1. A method of flow direction completion, comprising:
acquiring the cargo quantity errors of a plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the associated nodes respectively; the target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of the missing goods flow direction information, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period;
and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow node.
2. The method of claim 1, wherein the set of historical flow direction characteristics includes an upload time characteristic of the historical flow direction information and a cargo volume characteristic of the historical flow direction information.
3. The method of claim 2, wherein the upload time characteristic comprises an index of upload times over a preset period, the preset period comprising at least one of a week, month, and year.
4. The method of claim 2, wherein the cargo amount characteristics include a total cargo amount, a mean cargo amount, and a daily cargo amount for a most recent of the time periods covering the upload time.
5. The method of claim 1, wherein the obtaining the egress node of the missing cargo flow direction according to the cargo volume error of the plurality of associated nodes in the preset time period comprises:
and determining the associated node with the largest cargo quantity error as an outflow node of the missing cargo flow direction.
6. The method according to any one of claims 1 to 5, wherein the time length of the preset time period is greater than or equal to a periodic average value of historical upload flow information of the target node and the associated node.
7. The method according to any one of claims 1 to 5, wherein the obtaining of the cargo volume error of the plurality of associated nodes within the preset time period according to the historical flow direction feature sets corresponding to the target node and the plurality of associated nodes respectively comprises:
generating a flow direction prediction model corresponding to the target node and each associated node according to the historical flow direction feature set corresponding to the target node and each associated node;
predicting the amount of the outflow cargo to the target node within a preset time period of each associated node by using a sliding time window according to the flow direction prediction model corresponding to the target node and each associated node;
and calculating the difference value between the predicted outflow cargo quantity to the target node and the outflow cargo quantity actually uploaded to the target node in a preset time period of each associated node to obtain the cargo quantity error of each associated node in the preset time period.
8. A method of flow direction completion, comprising:
acquiring cargo quantity errors of a plurality of associated nodes according to historical flow direction characteristic sets corresponding to a target node and the associated nodes respectively; the target nodes comprise inflow nodes missing the flow direction of the goods, the associated nodes comprise nodes having historical flow direction relation with the target nodes, the historical flow direction feature set comprises a plurality of features extracted from historical flow direction information uploading behavior, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity at the predicted flow direction uploading time of the associated nodes;
and acquiring an outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes, and completing the missing goods flow direction according to the outflow node.
9. A method of model training, comprising:
acquiring a historical flow direction feature set corresponding to a target node and an associated node, wherein the target node comprises an inflow node missing the flow direction of goods, the associated node comprises a node having a historical flow direction relationship with the target node, and the historical flow direction feature set comprises a plurality of features extracted from historical flow direction information uploading behavior;
and training a neural network model corresponding to the target node and the associated node by taking the historical flow direction feature set corresponding to the target node and the associated node as a training set to obtain a flow direction prediction model corresponding to the target node and the associated node, wherein the flow direction prediction model is used for predicting the amount of outflow goods predicted to the target node by the associated node in a preset time period.
10. The model training method of claim 9, wherein the historical flow direction feature set comprises an upload time feature of the historical flow direction information and a cargo volume feature of the historical flow direction information;
the upload time characteristics comprise an index of the upload time within a preset period, the preset period comprising at least one of week, month and year;
the cargo amount characteristics include a total cargo amount, a mean cargo amount, and a daily cargo amount during a most recent one of the time periods covering the upload time.
11. A flow direction completion system, comprising: a server, a target node and an associated node; wherein the content of the first and second substances,
the target node and the associated node are used for uploading flow direction information to the server, and the flow direction information comprises information of an inflow node and an outflow node of goods;
the server is used for acquiring and storing the flow direction information, acquiring the cargo quantity errors of the plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to a target node and the plurality of associated nodes respectively, acquiring the outflow nodes of the missing cargo flow direction according to the cargo quantity errors of the plurality of associated nodes in the preset time period, and completing the missing cargo flow direction according to the outflow nodes;
the target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of the missing goods flow direction, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period.
12. A flow direction completion apparatus, comprising:
the flow direction prediction module is used for acquiring the cargo quantity errors of the plurality of associated nodes in a preset time period according to the historical flow direction characteristic sets corresponding to the target node and the plurality of associated nodes respectively; the target nodes comprise inflow nodes in missing goods flow direction, the associated nodes comprise nodes in historical flow direction relation with the target nodes, the preset time period covers the predicted uploading time of the missing goods flow direction, the historical flow direction characteristic set comprises a plurality of characteristics extracted from historical flow direction information uploading behavior, and the goods quantity error comprises a difference value between the predicted outflow goods quantity and the actually uploaded outflow goods quantity of the associated nodes in the preset time period;
and the flow direction completion module is used for acquiring the outflow node of the missing goods flow direction according to the goods quantity errors of the plurality of associated nodes in the preset time period, and completing the missing goods flow direction according to the outflow node.
13. An electronic device, comprising: a memory and a processor;
wherein the memory is connected with the processor and is used for storing programs;
the processor is configured to implement the flow direction completion method according to any one of claims 1 to 8 or the model training method according to any one of claims 9 to 10 by executing a program stored in the storage.
14. A storage medium having stored thereon a computer program which, when executed by a processor, implements the flow-direction completion method according to any one of claims 1 to 8 or the model training method according to any one of claims 9 to 10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116136989A (en) * | 2023-04-14 | 2023-05-19 | 江苏物润船联网络股份有限公司 | Port cargo flow direction statistical method and system based on BP neural network algorithm |
| CN116629464A (en) * | 2023-07-25 | 2023-08-22 | 阿里健康科技(中国)有限公司 | Method, device, equipment and medium for generating flow chart data of goods |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116136989A (en) * | 2023-04-14 | 2023-05-19 | 江苏物润船联网络股份有限公司 | Port cargo flow direction statistical method and system based on BP neural network algorithm |
| CN116629464A (en) * | 2023-07-25 | 2023-08-22 | 阿里健康科技(中国)有限公司 | Method, device, equipment and medium for generating flow chart data of goods |
| CN116629464B (en) * | 2023-07-25 | 2023-09-29 | 阿里健康科技(中国)有限公司 | Method, device, equipment and medium for generating flow chart data of goods |
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