CN114077978A - Store arrival identification method and device, storage medium and electronic equipment - Google Patents

Store arrival identification method and device, storage medium and electronic equipment Download PDF

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CN114077978A
CN114077978A CN202010819800.1A CN202010819800A CN114077978A CN 114077978 A CN114077978 A CN 114077978A CN 202010819800 A CN202010819800 A CN 202010819800A CN 114077978 A CN114077978 A CN 114077978A
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wifi
merchant
arrival
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similarity
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杨也康
高久翀
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Beijing Sankuai Online Technology Co Ltd
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Beijing Sankuai Online Technology Co Ltd
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    • 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
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    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/10—Small scale networks; Flat hierarchical networks
    • H04W84/12—WLAN [Wireless Local Area Networks]

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Abstract

The present disclosure relates to an arrival store identification method, apparatus, storage medium, and electronic device, the method comprising: in response to receiving an arrival completion trigger instruction for the waybill to be identified, acquiring a first WiFi geo-fence of a target merchant corresponding to the waybill to be identified and a second WiFi geo-fence of at least one adjacent merchant of the target merchant; and identifying the arrival state of the waybill to be identified according to the first WiFi geo-fence and the second WiFi geo-fence. Therefore, the problem of low reliability caused by the fact that identification is only carried out according to the WiFi geofence of the target merchant is avoided. Through the second WiFi geo-fence adjacent to the commercial tenant, whether the distribution capacity actually reaches the target commercial tenant or not is identified in an auxiliary mode, the WiFi detection range is enlarged, the area for identifying the shop is widened, and therefore reliability and accuracy of shop identification are improved.

Description

Store arrival identification method and device, storage medium and electronic equipment
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a store-to-store identification method and apparatus, a storage medium, and an electronic device.
Background
With the continuous development of the internet, a user can place an order on line through a terminal, buy a needed article, and can go to a merchant to fetch the article by delivery capacity and deliver the article to the user.
In the distribution scene, the distribution capacity is in the process of distributing articles, in order to finish the distribution task in a shorter time, some distribution capacities often click to get to a shop before a merchant arrives, or click to get after leaving the merchant, and the irregular operation behavior is not beneficial to the accurate processing of the distribution platform on the waybill state, and meanwhile, when the phenomenon that the waybill is overtime occurs, the responsibility of the overtime waybill cannot be accurately defined between the distribution capacity and the merchant. Therefore, identifying whether or not the delivery capacity has actually arrived at the store is an important issue in the delivery field.
Disclosure of Invention
An object of the present disclosure is to provide an arrival store identification method, apparatus, storage medium, and electronic device that can improve reliability and accuracy of arrival store identification.
In order to achieve the above object, in a first aspect, the present disclosure provides an arrival store identification method including: in response to receiving an arrival completion trigger instruction for a waybill to be identified, acquiring a first WiFi geo-fence of a target merchant corresponding to the waybill to be identified and a second WiFi geo-fence of at least one nearby merchant of the target merchant; identifying an arrival state of the waybill to be identified according to the first WiFi geofence and the second WiFi geofence.
Optionally, the method further comprises: acquiring a WiFi list currently acquired by a distribution side terminal of distribution capacity corresponding to the waybill to be identified; the identifying the arrival status of the waybill to be identified according to the first WiFi geofence and the second WiFi geofence comprises: identifying the arrival status of the waybill to be identified as the store-arrived status if the similarity between the WiFi list and any one of the first WiFi geofence and the second WiFi geofence is greater than or equal to a preset WiFi similarity threshold.
Optionally, the method further comprises: identifying the arrival state of the waybill to be identified as a short-arrival store by adopting one of the following modes: identifying an arrival status of the waybill to be identified as a short-arrival in the event that a similarity between the WiFi list and each of the first and second WiFi geofences is less than the WiFi similarity threshold; if the similarity between the WiFi list and each WiFi geo-fence in the first WiFi geo-fence and the second WiFi geo-fence is smaller than the WiFi similarity threshold value, and the arrival record of the delivery capacity indicates that the delivery capacity does not arrive at the target merchant and the adjacent merchants within a preset time before the current time, identifying that the arrival state of the waybill to be identified is a short-arrival state.
Optionally, the similarity between the WiFi list and the WiFi geofence is determined by: according to a first feature vector used for characterizing the WiFi list and a second feature vector used for characterizing the WiFi geofence, determining the similarity between the first feature vector and the second feature vector, and taking the similarity as the similarity between the WiFi list and the WiFi geofence.
Optionally, the method further comprises: and generating prompt information under the condition that the arrival state of the waybill to be identified is not the arrival state, wherein the prompt information is used for prompting that the delivery capacity and the arrival completion trigger instruction are not verified.
Optionally, the neighboring merchant of the target merchant is determined by: determining the position similarity among the merchants according to the WiFi geofences of the merchants, wherein the merchants comprise the target merchant; inputting the identification information of each merchant and the position similarity between the merchants into a graph neural network model to obtain position characteristic information of each merchant output by the graph neural network model; determining the neighboring merchants of the target merchant from the plurality of merchants according to the location characteristic information of the target merchant.
Optionally, the determining the neighboring merchant of the target merchant from the multiple merchants according to the location feature information of the target merchant includes: determining the neighboring merchant of the target merchant from the plurality of merchants by one of: determining the commercial tenant of which the similarity between the position characteristic information of the plurality of commercial tenants and the position characteristic information of the target commercial tenant is greater than a preset position similarity threshold value as the adjacent commercial tenant of the target commercial tenant; determining, as the neighboring merchant of the target merchant, a merchant of which the similarity between the location feature information of the plurality of merchants and the location feature information of the target merchant is greater than the location similarity threshold and which satisfies at least one of the following conditions: the distance between the target commercial tenant and the target commercial tenant belonging to the same area block is smaller than a preset distance threshold value.
In a second aspect, the present disclosure provides an arrival identifying apparatus comprising: the system comprises a geo-fence acquisition module, a storage management module and a management module, wherein the geo-fence acquisition module is configured to respond to the receiving of an arrival completion trigger instruction aiming at a to-be-identified waybill, and acquire a first WiFi geo-fence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geo-fence of at least one adjacent merchant of the target merchant; an arrival status identification module configured to identify an arrival status of the waybill to be identified based on the first WiFi geofence and the second WiFi geofence.
Optionally, the apparatus further comprises: the WiFi list acquisition module is configured to be used for acquiring a WiFi list currently acquired by a distribution side terminal of distribution capacity corresponding to the waybill to be identified; the arrival status identification module includes: an identifying sub-module configured to identify the arrival status of the waybill to be identified as the arrived store if a similarity between the WiFi list and any of the first WiFi geofence and the second WiFi geofence is greater than or equal to a preset WiFi similarity threshold.
Optionally, the apparatus further comprises: an identification module configured to identify the arrival status of the waybill to be identified as a store-short in one of the following ways: identifying an arrival status of the waybill to be identified as a short-arrival in the event that a similarity between the WiFi list and each of the first and second WiFi geofences is less than the WiFi similarity threshold; if the similarity between the WiFi list and each WiFi geo-fence in the first WiFi geo-fence and the second WiFi geo-fence is smaller than the WiFi similarity threshold value, and the arrival record of the delivery capacity indicates that the delivery capacity does not arrive at the target merchant and the adjacent merchants within a preset time before the current time, identifying that the arrival state of the waybill to be identified is a short-arrival state.
Optionally, the apparatus further comprises: and the prompt information generation module is configured to generate prompt information under the condition that the arrival state of the waybill to be identified is not the arrival state, wherein the prompt information is used for prompting that the delivery capacity and the arrival completion trigger instruction are not verified.
In a third aspect, the present disclosure provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method provided by the first aspect of the present disclosure.
In a fourth aspect, the present disclosure provides an electronic device comprising: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to implement the steps of the method provided by the first aspect of the present disclosure.
By the technical scheme, in response to receiving the arrival-to-store completion triggering instruction aiming at the waybill to be identified, the first WiFi geofence of the target merchant corresponding to the waybill to be identified and the second WiFi geofence of at least one adjacent merchant of the target merchant can be obtained, meanwhile, the arrival-to-store state of the waybill to be identified is identified according to the first WiFi geofence and the second WiFi geofence, and the problem of low reliability caused by identification only according to the WiFi geofence of the target merchant can be avoided. If the delivery capacity is identified to be located near the nearby merchant by the second WiFi geofence of the nearby merchant, the delivery capacity may be considered to have reached the vicinity of the target merchant for fetching due to the close distance of the target merchant from the nearby merchant. Therefore, whether the distribution capacity actually reaches the target merchant is accurately identified through the second WiFi geo-fence adjacent to the merchant, the WiFi detection range is enlarged, the area for identifying the store is widened, and the reliability and accuracy of identifying the store are improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a store-to-store identification method according to an exemplary embodiment.
FIG. 2 is a schematic diagram illustrating a target merchant and neighboring merchants, according to an example embodiment.
Fig. 3 is a flow chart illustrating a method of determining neighboring merchants to a target merchant in accordance with an exemplary embodiment.
FIG. 4 is a schematic diagram illustrating a plurality of merchants, according to an example embodiment.
Fig. 5 is a block diagram illustrating an arrival recognition apparatus according to an example embodiment.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 7 is a block diagram illustrating an electronic device in accordance with another example embodiment.
Detailed Description
As described in the background, identifying whether a delivery capacity has actually arrived at a store is an important issue in the delivery field. Because there are many defects in positioning using a GPS (global positioning system), for example, GPS signals are easily blocked and easily offset, at present, a distribution platform establishes a WiFi geofence for a merchant using router information around the merchant, and the WiFi geofence is a virtual geographic boundary surrounded by a virtual fence according to WiFi signals. Through the WiFi geo-fence of the merchant and the WiFi list acquired by the distribution side terminal, whether the distribution capacity actually arrives at a store or not can be accurately identified, and compared with the identification through GPS positioning, the identification precision is remarkably improved.
However, in the research process, the inventor finds that, when a WiFi geofence is established for a merchant, the WiFi geofence of each merchant is constructed by an algorithm using WiFi signals near the merchant, which has many disadvantages although the accuracy is high. For example, there are the following problems: (1) for merchants with less single quantity or merchants with poorer WiFi signals, the accuracy of the WiFi geofence of the merchants built by the algorithm in the modeling process is reduced to a certain extent due to the limited data quantity reported by the WiFi signals near the merchants; (2) the method comprises the following steps that a data caching problem exists in the process of reporting a WiFi list by a distribution side terminal, the currently reported WiFi list is probably not the latest scanned WiFi list, and a plurality of scanning periods are delayed in the middle, so that distribution capacity is in a merchant corresponding to an waybill, but the distribution side terminal does not report the WiFi list collected in the merchant, and therefore the accuracy of an identification result is influenced when shop identification is carried out according to the WiFi list; (3) the distribution side terminal scans the WiFi list at a fixed period, if the time length from the time when the distribution capacity enters the merchant to the time when the distribution capacity leaves the merchant is short, namely the distribution capacity rapidly enters the store and leaves the store, the distribution capacity may not enter the merchant or leave the merchant at the moment when the terminal scans the WiFi list, and this situation also easily causes misjudgment on whether the distribution capacity arrives at the store, so that the identification result is not accurate enough.
In view of this, the present disclosure provides an arrival store identification method, apparatus, storage medium, and electronic device that can improve reliability and accuracy of arrival store identification.
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Embodiments of the present disclosure may be applied to various delivery scenarios, such as take-away delivery, express delivery, and the like. The delivery capacity in this disclosure may be a dispenser, and may also be a delivery device such as an unmanned delivery vehicle, an unmanned aerial vehicle, a delivery robot, and the like. When the delivery capacity is a delivery person, the delivery-side terminal may be a terminal device such as a mobile phone, a tablet computer, or a personal computer used for the delivery capacity. When the delivery capacity is the delivery equipment, the delivery-side terminal may be the delivery equipment itself.
Fig. 1 is a flowchart illustrating an arrival identifying method according to an exemplary embodiment, which may be applied to an electronic device with processing capability, such as a terminal or a server. As shown in fig. 1, the method may include S101 and S102.
In S101, in response to receiving an arrival completion trigger instruction for the waybill to be identified, a first WiFi geofence of a target merchant corresponding to the waybill to be identified and a second WiFi geofence of at least one nearby merchant of the target merchant are obtained.
The manifest to be identified may be a manifest whose delivery capacity has not been taken. In the distribution process, the distribution capacity needs to go to a target merchant corresponding to the waybill to be identified to take the articles purchased by the user, and then distribute the articles to the user, wherein the target merchant corresponding to the waybill to be identified refers to a merchant who provides the ordering user to purchase the articles.
The delivery capacity can be input into an arrival trigger instruction for the waybill to be identified through the delivery side terminal, for example, clicking an arrival button on a page to indicate that the delivery side terminal has arrived at the target merchant, and the delivery side terminal can receive the arrival trigger instruction output by the delivery capacity. When the arrival identifying method provided by the present disclosure is applied to the server, the distribution side terminal may transmit the arrival completion trigger instruction to the server when receiving the arrival completion trigger instruction, and thus, the server may receive the arrival completion trigger instruction. The terminal or the server may verify the arrival trigger instruction when receiving the arrival trigger instruction, to verify whether the behavior of the delivery capacity input to the arrival trigger instruction is compliant, and whether the arrival trigger instruction input when actually arriving at the store is compliant.
In response to receiving the store-to-store completion triggering instruction, the terminal or the server may obtain a first WiFi geofence of the target merchant and a second WiFi geofence of at least one neighboring merchant of the target merchant to identify whether the delivery capacity has reached the target merchant.
The neighboring merchants may refer to merchants closer to the target merchant, and which merchant or merchants are used as the neighboring merchants of the target merchant may be predetermined, for example, the neighboring merchants of the target merchant may be determined according to information such as a distance between merchants and a similarity of WiFi geofences between merchants. The number of nearby merchants is not specifically limited in this disclosure, and may be one or more. Fig. 2 is a schematic diagram illustrating a target merchant and neighboring merchants, according to an example embodiment, where, as shown in fig. 2, a neighboring merchant 202 and a neighboring merchant 203 may be the neighboring merchants of the target merchant 201. It is worth noting that fig. 2 illustrates an example in which the neighboring merchants of the target merchant 201 include two merchants, but does not constitute a limitation to the embodiments of the present disclosure.
The WiFi geofences of the merchants may be pre-constructed, and the server may store the WiFi geofences of each merchant. In an embodiment, if the distribution side terminal does not store the first WiFi geofence of the target merchant and the second WiFi geofence of at least one neighboring merchant, the distribution side terminal may send a WiFi geofence acquisition request to the server, so that the server sends the first WiFi geofence and the second WiFi geofence to the distribution side terminal, and thus, the distribution side terminal may acquire the first WiFi geofence and the second WiFi geofence.
In S102, an arrival status of the waybill to be identified is identified according to the first WiFi geofence and the second WiFi geofence.
And according to the second WiFi geofence and the WiFi list, whether the distribution capacity is positioned near the adjacent commercial tenants can be identified. If the shipping capacity is identified as being located near a nearby merchant, the shipping capacity may be deemed to have reached the vicinity of the target merchant for fetching, due to the closer distance of the target merchant from the nearby merchant.
Therefore, the problem of small identification range caused by identification only according to the WiFi geofence of the target merchant in the related technology is solved, even if the accuracy of the first WiFi geofence of the target merchant is insufficient, whether the delivery capacity reaches the target merchant can be identified in an auxiliary mode through the second WiFi geofence of the adjacent merchant, and the reliability of store-to-store identification is improved. Moreover, even if the distribution side terminal does not report the WiFi list acquired in the target merchant due to the fact that the distribution capacity enters the store and leaves the store fast or due to the data caching problem of the distribution side terminal and the like, whether the distribution capacity reaches the target merchant can be accurately identified through the WiFi list acquired by the distribution side terminal nearby the adjacent merchant and the second WiFi geofence of the adjacent merchant, the WiFi detection range is enlarged, the area for identifying the store is widened, and the accuracy and the reliability of identifying the store are improved.
By the technical scheme, in response to receiving the arrival-to-store completion triggering instruction aiming at the waybill to be identified, the first WiFi geofence of the target merchant corresponding to the waybill to be identified and the second WiFi geofence of at least one adjacent merchant of the target merchant can be obtained, meanwhile, the arrival-to-store state of the waybill to be identified is identified according to the first WiFi geofence and the second WiFi geofence, and the problem of low reliability caused by identification only according to the WiFi geofence of the target merchant can be avoided. If the delivery capacity is identified to be located near the nearby merchant by the second WiFi geofence of the nearby merchant, the delivery capacity may be considered to have reached the vicinity of the target merchant for fetching due to the close distance of the target merchant from the nearby merchant. Therefore, whether the distribution capacity actually reaches the target merchant is accurately identified through the second WiFi geo-fence adjacent to the merchant, the WiFi detection range is enlarged, the area for identifying the store is widened, and the reliability and accuracy of identifying the store are improved.
The store-to-store identification method provided by the present disclosure may further include: and acquiring a WiFi list currently acquired by a distribution side terminal of distribution capacity corresponding to the waybill to be identified.
When the method provided by the disclosure is applied to the terminal, the distribution side terminal can directly acquire the WiFi list; when the method provided by the disclosure is applied to the server, the distribution side terminal can send the acquired WiFi list to the server, and thus, the server can acquire the WiFi list.
Accordingly, the above S102 may include: and identifying the arrival state of the waybill to be identified as the store-arrived state if the similarity between the WiFi list and any one of the first WiFi geo-fence and the second WiFi geo-fence is greater than or equal to a preset WiFi similarity threshold value.
In an alternative embodiment, the similarity between the WiFi list and the WiFi geofence may be determined by: and determining the similarity between the first feature vector and the second feature vector according to the first feature vector for characterizing the WiFi list and the second feature vector for characterizing the WiFi geofence, and taking the similarity as the similarity between the WiFi list and the WiFi geofence.
The WiFi list and the WiFi geofence can both be represented by the form of a feature vector, and the similarity between the first feature vector and the second feature vector can be determined by calculating the distance between the two, such as cosine distance and euclidean distance. The smaller the distance between the first feature vector and the second feature vector is, the higher the similarity between the two characterizable features is, and the larger the distance is, the lower the similarity between the two characterizable features is. The similarity between the first feature vector and the second feature vector may be regarded as the similarity between the WiFi list and the WiFi geofence. If the similarity between the WiFi list and the WiFi geofence is greater than the preset WiFi similarity threshold, it may be characterized that the similarity between the WiFi list and the WiFi geofence is high, that is, that the distribution capacity is closer to the merchant corresponding to the WiFi geofence.
In a case where a similarity between the WiFi list and any one of the first WiFi geofence and the second WiFi geofence is greater than or equal to a preset WiFi similarity threshold, it may be characterized that the delivery capacity is closer to a distance between the target merchant and any one of the neighboring merchants. As shown in fig. 2, for example, the similarity between the WiFi list currently collected by the distribution-side terminal and the WiFi geofence of the neighboring merchant 202 is greater than the WiFi similarity threshold, which may indicate that the distribution capacity is closer to the neighboring merchant 202, and may consider that the distribution capacity has reached the vicinity of the target merchant 201 for fetching, and at this time, the arrival status of the waybill to be identified may be identified as the arrival store. Of course, there may be similarities between the WiFi list and the plurality of WiFi geofences that are all greater than the WiFi similarity threshold, for example, the similarities between the WiFi list and the WiFi geofences of the neighboring merchant 202 and the target merchant 201 are both greater than the WiFi similarity threshold.
In the above technical solution, it is not limited to only identifying whether the delivery capacity has arrived at the store through the WiFi list and the WiFi geofence of the target merchant, and in a case that the similarity between the WiFi list collected by the delivery side terminal and any WiFi geofence of the first WiFi geofence and the second WiFi geofence is greater than the WiFi similarity threshold, it may be identified that the arrival state of the waybill to be identified is the arrival at the store. Through the WiFi geo-fences of the adjacent merchants, whether the distribution capacity reaches the target merchant is identified in an auxiliary mode, the WiFi detection range and the identification range are enlarged, and the reliability of the identification result is improved.
For example, one of the following two embodiments may be employed to identify the arrival status of the waybill to be identified as a short-arrival store.
In one embodiment, the method further comprises identifying an arrival status of the waybill to be identified as a short-arrival store if a similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is less than a WiFi similarity threshold.
If the similarity between the WiFi list currently collected by the distribution side terminal and each WiFi geo-fence is smaller than the WiFi similarity threshold, it can be characterized that the distribution capacity is far away from the target merchant and the neighboring merchants, and the distribution capacity is not located near the target merchant, so that the arrival state of the waybill to be identified is identified as a short-arrival shop.
In another embodiment, if the similarity between the WiFi list and each WiFi geofence in the first WiFi geofence and the second WiFi geofence is less than the WiFi similarity threshold, and the store-to-store record of the delivery capacity indicates that the delivery capacity has not reached the target merchant and the neighboring merchants within a preset time period before the current time, the store-to-store status of the waybill to be identified is identified as a store-to-store status.
In one scenario, the shop completion trigger instruction may not be input when the delivery capacity reaches the target merchant, but the shop completion trigger instruction is input after the delivery capacity leaves the shop, and since the delivery capacity leaves the shop and is not near the target merchant, the similarity between the WiFi list collected by the delivery side terminal and each WiFi geofence may be less than the WiFi similarity threshold, at this time, it may be determined, through the record of the arrival of the delivery capacity, whether the delivery capacity reaches the target merchant and neighboring merchants within a preset time period (e.g., 5min) before the current time, if not, the arrival state of the waybill to be identified may be the not-arrived shop, and if so, the arrival state of the waybill to be identified may be the already-arrived shop. The store-to-store record of the delivery capacity can be recorded according to the positioning information of the delivery capacity, and the record is used for representing which merchant the delivery capacity reaches.
Therefore, under the condition that the similarity between the WiFi list acquired by the distribution side terminal and each WiFi geo-fence is smaller than the WiFi similarity threshold value, judgment can be further carried out through the store arrival record of distribution transport capacity, and the reliability and accuracy of the identification result are improved.
The store-to-store identification method provided by the present disclosure may further include: and generating prompt information under the condition that the arrival state of the waybill to be identified is not the arrival state, wherein the prompt information can be used for prompting that the delivery capacity arrival trigger instruction is not verified.
If the arrival state of the waybill to be identified is the non-arrival state, the delivery capacity is not near the target merchant currently, and at this time, prompt information can be generated, and the prompt information can be used for prompting that the delivery capacity is not actually at the target merchant, and the behavior of the trigger instruction input to the store is not in accordance with the specification, namely the trigger instruction input to the store is not verified. When the method provided by the disclosure is applied to the terminal, the distribution side terminal can generate the prompt information; when the method provided by the disclosure is applied to a server, the server can send the prompt information to the distribution side terminal after generating the prompt information. The prompt information can be prompted by the distribution side terminal through a pop-up window, a prompt box, voice and the like, and the prompt mode is not particularly limited by the disclosure. In this way, the behavior that the delivery capacity is out of specification can be timely reminded, so that the behavior that the delivery capacity is input to the store to complete the trigger instruction under the condition that the delivery capacity is not in the store is prevented from appearing again.
Exemplary embodiments of determining neighboring merchants to a target merchant in the present disclosure are described below. Fig. 3 is a flowchart illustrating a method of determining neighboring merchants to a target merchant according to an example embodiment, which may include S301-S303, as shown in fig. 3.
In S301, the location similarity between merchants is determined according to their respective WiFi geofences.
Wherein the target merchant may be included in the plurality of merchants. Fig. 4 is a schematic diagram illustrating a plurality of merchants according to an exemplary embodiment, as shown in fig. 4, the plurality of merchants includes a merchant a, a merchant B, a merchant C, a merchant D, and a merchant E, for example, the merchant a may be a target merchant corresponding to the waybill to be identified, such as the target merchant 201 shown in fig. 2. The disclosure is not particularly limited with respect to the number of the multiple merchants, and fig. 4 is only an exemplary illustration and does not constitute a limitation to the embodiments of the disclosure, and in practical applications, the number of the multiple merchants is not limited thereto.
In an embodiment, the location similarity between each two merchants may be determined according to the WiFi geofences of each of the multiple merchants. Preferably, in another embodiment, a merchant relatively close to the merchant location may be determined as a potential neighboring merchant of the merchant according to the location information of the merchant, and the location similarity between each merchant and its potential neighboring merchant may be determined separately. In this embodiment, for two merchants relatively far away from each other, the possibility of merchants adjacent to each other is low, and the position similarity between the two merchants does not need to be calculated, so that the data processing amount can be reduced, and the calculation efficiency can be improved.
As shown in fig. 4, for example, for merchant a, the location similarities with merchant B, merchant C and merchant D may be determined respectively, and since the distance between merchant a and merchant E is relatively long, the probability that merchant E is a neighboring merchant of merchant a is relatively low, the location similarity between merchant a and merchant E may no longer be determined. For example, for merchant B, the location similarity with merchant a and merchant D may be determined separately. Similar for other merchants. Fig. 4 may represent a position relationship diagram of multiple merchants, and the position similarity between merchants may be used as the weight of the connecting edge between merchants.
In an alternative embodiment, the WiFi geofences may be represented in the form of a feature vector, and the similarity of locations between merchants may be determined by determining the similarity by the distance (e.g., cosine distance, euclidean distance, etc.) between the merchant's WiFi geofences. The greater the distance between the WiFi geofences is, the lower the position similarity between the characterizable merchants is, and the smaller the distance between the WiFi geofences is, the higher the position similarity between the characterizable merchants is.
In another alternative embodiment, the location similarity between merchants may be determined by the size of the overlapping area of WiFi geofences between merchants. The larger the overlapping area is, the higher the position similarity between the characterizable merchants is, and the smaller the overlapping area is, the lower the position similarity between the characterizable merchants is.
In S302, the identification information of each merchant and the location similarity between merchants are input into the graph neural network model to obtain location characteristic information of each merchant output by the graph neural network model.
The position similarity between the merchants determined in S301 may be used as a preliminary proximity between the merchants. The graph neural network model can mine hidden probability correlation among the merchants through algorithms such as sampling and wandering, and the more accurate proximity degree and proximity relation among the merchants can be further determined through results output by the graph neural network model. Any of the Graph neural network models, such as the Graph Embedding model, may be employed in the present disclosure.
The identification information (such as merchant ID) of each merchant and the location similarity between merchants are input into the graph neural network model, and the graph neural network model can output location feature information of each merchant, and the location feature information can be represented in a form of multidimensional vector.
In S303, a neighboring merchant of the target merchant is determined from the plurality of merchants according to the location feature information of the target merchant.
For example, one of two embodiments may be employed to determine the neighboring merchants of the target merchant from among the plurality of merchants.
In one embodiment, merchants with the similarity between the location characteristic information of the multiple merchants and the location characteristic information of the target merchant being greater than a preset location similarity threshold may be determined as neighboring merchants of the target merchant.
For example, the position feature information may be represented in the form of vectors, and the similarity between the position feature information may be determined by the distance between the vectors. As shown in fig. 4, according to the location characteristic information of the target merchant a, for example, the similarity between the location characteristic information of the merchant B and the location characteristic information of the target merchant a is greater than a preset location similarity threshold, the merchant B may be a neighboring merchant of the target merchant a, and the merchant B may be, for example, the neighboring merchant 202 shown in fig. 2. The similarity between the location characteristic information of the merchant C and the location characteristic information of the target merchant a is greater than the location similarity threshold, and then the merchant C may be a neighboring merchant of the target merchant a, and the merchant C may be, for example, the neighboring merchant 203 shown in fig. 2.
In addition, in an embodiment, there may be a plurality of merchants with the similarity to the location feature information of the target merchant being greater than the location similarity threshold, and the similarity may be determined according to a fluctuation situation of the similarity. For example, as shown in fig. 4, for example, the similarity between the location characteristic information of the merchant B, the merchant C, and the merchant D and the location characteristic information of the target merchant a is greater than a location similarity threshold, and the corresponding similarities are, from large to small, merchant B, merchant C, and merchant D in sequence, but the fluctuation between the similarity corresponding to the merchant D and the similarity corresponding to the merchant C is large, for example, the absolute value of the difference between the two is greater than the difference threshold, which may represent that the proximity between the merchant D and the target merchant a is significantly lower than that between the merchant C and the merchant B, and may not regard the merchant D as a neighboring merchant of the target merchant a, but only regard the merchant B and the merchant C as neighboring merchants.
In another embodiment, the merchants with the similarity between the location feature information of the target merchant and the location feature information of the plurality of merchants being greater than the location similarity threshold and satisfying at least one of the following conditions may be determined as neighboring merchants of the target merchant: the distance between the target commercial tenant and the target commercial tenant belonging to the same area block is smaller than a preset distance threshold value.
The area blocks may be pre-divided according to geographic locations, for example, a geo-hash algorithm may be used to divide a city into a plurality of area blocks, and merchants belonging to the same area block are relatively close to each other. The distance between the target merchant and the target merchant is smaller than a preset distance threshold value, which can represent that the distance between the two merchants is short, wherein the preset distance threshold value can be calibrated in advance. In this embodiment, if the similarity between the location characteristic information of the merchant and the location characteristic information of the target merchant is greater than the threshold of the location similarity, it may be further determined whether the merchant satisfies at least one of the following two conditions: the distance between the target commercial tenant and the target commercial tenant belonging to the same area block is smaller than a preset distance threshold value. The merchant may be determined as a neighboring merchant of the target merchant when any one of the two conditions is satisfied, or may be determined as a neighboring merchant of the target merchant when the two conditions are satisfied at the same time. Therefore, the accuracy of the proximity relation between the merchants can be further ensured.
According to the technical scheme, the position similarity between the merchants is determined according to the WiFi geofences of the merchants respectively, the position similarity can be used as the preliminary proximity degree between the merchants, and then the more accurate proximity degree between the merchants can be obtained through the graph neural network model, so that the determined nearby merchants of the target merchants can better reflect the real proximity relation between the merchants.
Based on the same inventive concept, the present disclosure also provides an arriving store identification apparatus, and fig. 5 is a block diagram illustrating an arriving store identification apparatus according to an exemplary embodiment, as shown in fig. 5, the apparatus 500 may include:
a geo-fence acquisition module 501 configured to, in response to receiving an arrival completion trigger instruction for a to-be-identified waybill, acquire a first WiFi geo-fence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geo-fence of at least one neighboring merchant of the target merchant;
an arrival status identification module 502 configured to identify an arrival status of the waybill to be identified based on the first WiFi geofence and the second WiFi geofence.
By the technical scheme, in response to receiving the arrival-to-store completion triggering instruction aiming at the waybill to be identified, the first WiFi geofence of the target merchant corresponding to the waybill to be identified and the second WiFi geofence of at least one adjacent merchant of the target merchant can be obtained, meanwhile, the arrival-to-store state of the waybill to be identified is identified according to the first WiFi geofence and the second WiFi geofence, and the problem of low reliability caused by identification only according to the WiFi geofence of the target merchant can be avoided. If the delivery capacity is identified to be located near the nearby merchant by the second WiFi geofence of the nearby merchant, the delivery capacity may be considered to have reached the vicinity of the target merchant for fetching due to the close distance of the target merchant from the nearby merchant. Therefore, whether the distribution capacity actually reaches the target merchant is accurately identified through the second WiFi geo-fence adjacent to the merchant, the WiFi detection range is enlarged, the area for identifying the store is widened, and the reliability and accuracy of identifying the store are improved.
Optionally, the apparatus 500 may further include: the WiFi list acquisition module is configured to be used for acquiring a WiFi list currently acquired by a distribution side terminal of distribution capacity corresponding to the waybill to be identified; the store arrival status identification module 502 includes: an identifying sub-module configured to identify the arrival status of the waybill to be identified as the arrived store if a similarity between the WiFi list and any of the first WiFi geofence and the second WiFi geofence is greater than or equal to a preset WiFi similarity threshold.
Optionally, the apparatus 500 may further include: an identification module configured to identify the arrival status of the waybill to be identified as a store-short in one of the following ways: identifying an arrival status of the waybill to be identified as a short-arrival in the event that a similarity between the WiFi list and each of the first and second WiFi geofences is less than the WiFi similarity threshold; if the similarity between the WiFi list and each WiFi geo-fence in the first WiFi geo-fence and the second WiFi geo-fence is smaller than the WiFi similarity threshold value, and the arrival record of the delivery capacity indicates that the delivery capacity does not arrive at the target merchant and the adjacent merchants within a preset time before the current time, identifying that the arrival state of the waybill to be identified is a short-arrival state.
Optionally, the apparatus 500 may further include: and the prompt information generation module is configured to generate prompt information under the condition that the arrival state of the waybill to be identified is not the arrival state, wherein the prompt information is used for prompting that the delivery capacity and the arrival completion trigger instruction are not verified.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 6 is a block diagram illustrating an electronic device 600 according to an example embodiment. As shown in fig. 6, the electronic device 600 may include: a processor 601 and a memory 602. The electronic device 600 may also include one or more of a multimedia component 603, an input/output (I/O) interface 604, and a communications component 605.
The processor 601 is configured to control the overall operation of the electronic device 600 to complete all or part of the steps of the above-mentioned store-to-store identification method. The memory 602 is used to store various types of data to support operation at the electronic device 600, such as instructions for any application or method operating on the electronic device 600 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and so forth. The Memory 602 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 603 may include a screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 602 or transmitted through the communication component 605. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 604 provides an interface between the processor 601 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 605 may therefore include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described store-to-store identification method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described to-store identification method is also provided. For example, the computer readable storage medium may be the memory 602 described above including program instructions that are executable by the processor 601 of the electronic device 600 to perform the store-to-store identification method described above.
Fig. 7 is a block diagram illustrating an electronic device 700 in accordance with another example embodiment. For example, the electronic device 700 may be provided as a server. Referring to fig. 7, an electronic device 700 includes a processor 722, which may be one or more in number, and a memory 732 for storing computer programs that are executable by the processor 722. The computer programs stored in memory 732 may include one or more modules that each correspond to a set of instructions. Further, the processor 722 may be configured to execute the computer program to perform the above-described store-to-store identification method.
Additionally, the electronic device 700 may also include a power component 726 that may be configured to perform power management of the electronic device 700 and a communication component 750 that may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 700. The electronic device 700 may also include input/output (I/O) interfaces 758. The electronic device 700 may operate based on an operating system, such as Windows Server, stored in the memory 732TM，Mac OS XTM，UnixTM，LinuxTMAnd so on.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described to-store identification method is also provided. For example, the computer readable storage medium may be the memory 732 described above including program instructions that are executable by the processor 722 of the electronic device 700 to perform the store-to-store identification method described above.
In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned store-to-store identification method when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.

Claims (10)

1. An arrival store identification method, comprising:
in response to receiving an arrival completion trigger instruction for a waybill to be identified, acquiring a first WiFi geo-fence of a target merchant corresponding to the waybill to be identified and a second WiFi geo-fence of at least one nearby merchant of the target merchant;
identifying an arrival state of the waybill to be identified according to the first WiFi geofence and the second WiFi geofence.
2. The method of claim 1, further comprising:
acquiring a WiFi list currently acquired by a distribution side terminal of distribution capacity corresponding to the waybill to be identified;
the identifying the arrival status of the waybill to be identified according to the first WiFi geofence and the second WiFi geofence comprises:
identifying the arrival status of the waybill to be identified as the store-arrived status if the similarity between the WiFi list and any one of the first WiFi geofence and the second WiFi geofence is greater than or equal to a preset WiFi similarity threshold.
3. The method of claim 2, further comprising:
identifying the arrival state of the waybill to be identified as a short-arrival store by adopting one of the following modes:
identifying an arrival status of the waybill to be identified as a short-arrival in the event that a similarity between the WiFi list and each of the first and second WiFi geofences is less than the WiFi similarity threshold;
if the similarity between the WiFi list and each WiFi geo-fence in the first WiFi geo-fence and the second WiFi geo-fence is smaller than the WiFi similarity threshold value, and the arrival record of the delivery capacity indicates that the delivery capacity does not arrive at the target merchant and the adjacent merchants within a preset time before the current time, identifying that the arrival state of the waybill to be identified is a short-arrival state.
4. The method of claim 2 or 3, wherein the similarity between the WiFi list and the WiFi geofence is determined by:
according to a first feature vector used for characterizing the WiFi list and a second feature vector used for characterizing the WiFi geofence, determining the similarity between the first feature vector and the second feature vector, and taking the similarity as the similarity between the WiFi list and the WiFi geofence.
5. The method of claim 1, further comprising:
and generating prompt information under the condition that the arrival state of the waybill to be identified is not the arrival state, wherein the prompt information is used for prompting that the delivery capacity and the arrival completion trigger instruction are not verified.
6. The method of claim 1, wherein the neighboring merchant of the target merchant is determined by:
determining the position similarity among the merchants according to the WiFi geofences of the merchants, wherein the merchants comprise the target merchant;
inputting the identification information of each merchant and the position similarity between the merchants into a graph neural network model to obtain position characteristic information of each merchant output by the graph neural network model;
determining the neighboring merchants of the target merchant from the plurality of merchants according to the location characteristic information of the target merchant.
7. The method according to claim 6, wherein the determining the neighboring merchants of the target merchant from the plurality of merchants according to the location characteristic information of the target merchant comprises:
determining the neighboring merchant of the target merchant from the plurality of merchants by one of:
determining the commercial tenant of which the similarity between the position characteristic information of the plurality of commercial tenants and the position characteristic information of the target commercial tenant is greater than a preset position similarity threshold value as the adjacent commercial tenant of the target commercial tenant;
determining, as the neighboring merchant of the target merchant, a merchant of which the similarity between the location feature information of the plurality of merchants and the location feature information of the target merchant is greater than the location similarity threshold and which satisfies at least one of the following conditions: the distance between the target commercial tenant and the target commercial tenant belonging to the same area block is smaller than a preset distance threshold value.
8. An arrival identifying apparatus, comprising:
the system comprises a geo-fence acquisition module, a storage management module and a management module, wherein the geo-fence acquisition module is configured to respond to the receiving of an arrival completion trigger instruction aiming at a to-be-identified waybill, and acquire a first WiFi geo-fence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geo-fence of at least one adjacent merchant of the target merchant;
an arrival status identification module configured to identify an arrival status of the waybill to be identified based on the first WiFi geofence and the second WiFi geofence.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
CN202010819800.1A 2020-08-14 2020-08-14 Store arrival identification method and device, storage medium and electronic equipment Pending CN114077978A (en)

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