CN114077978B - Store identification method and device, storage medium and electronic equipment - Google Patents
Store identification method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure relates to a method, an apparatus, a storage medium, and an electronic device for identifying a store, the method comprising: in response to receiving an arrival completion trigger instruction for a to-be-identified waybill, acquiring a first WiFi geofence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geofence of at least one neighboring merchant of the target merchant; and identifying the arrival state of the to-be-identified waybill according to the first WiFi geofence and the second WiFi geofence. In this way, the problem of low reliability caused by identification based on the WiFi geofence of the target merchant alone is avoided. Through the second WiFi geofence adjacent to the merchant, whether the delivery capacity actually reaches the target merchant is assisted to be identified, the WiFi detection range is enlarged, the area for identifying the arrival store is widened, and therefore reliability and accuracy of identifying the arrival store are improved.
Description
Technical Field
The disclosure relates to the technical field of internet, in particular to a store identification method, a store identification device, a storage medium and electronic equipment.
Background
With the continuous development of the internet, users can make online orders through terminals, purchase needed articles, and the delivery capacity can go to merchants to take the articles and deliver the articles to the users.
In the process of delivering articles, in order to complete the delivering task in a shorter time, some delivering capacity often clicks the already-arrived store without reaching the merchant or clicks the already-fetched article after leaving the merchant, which is unfavorable for the accurate processing of the bill state by the delivering platform, and meanwhile, the responsibility of the bill overtime cannot be accurately defined between the delivering capacity and the merchant when the phenomenon of the bill overtime occurs. Therefore, identifying whether the shipping capacity has actually arrived at the store is an important issue in the distribution field.
Disclosure of Invention
The object of the present disclosure is to provide a store identification method, apparatus, storage medium, and electronic device, which can improve reliability and accuracy of store identification.
To achieve the above object, in a first aspect, the present disclosure provides a method of identifying a store, including: in response to receiving an arrival completion trigger instruction for a to-be-identified waybill, acquiring a first WiFi geofence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geofence of at least one neighboring merchant of the target merchant; and identifying the arrival state of the to-be-identified waybill according to the first WiFi geofence and the second WiFi geofence.
Optionally, the method further comprises: acquiring a WiFi list currently acquired by a delivery side terminal of delivery capacity corresponding to the to-be-identified waybill; the identifying the arrival status of the to-be-identified waybill according to the first WiFi geofence and the second WiFi geofence comprises: and identifying that the arrival state of the to-be-identified waybill is an arrived store under the condition that 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 status of the to-be-identified waybill as an unreached store by one of the following means: identifying that the arrival status of the to-be-identified waybill is an unreachable store if a similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is less than the WiFi similarity threshold; and if the similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is smaller than the WiFi similarity threshold, and the arrival record of the delivery capacity indicates that the delivery capacity does not reach the target merchant and the adjacent merchant within a preset time before the current time, identifying that the arrival state of the to-be-identified waybill is an unreachable store.
Optionally, the similarity between the WiFi list and the WiFi geofence is determined by: and determining the similarity between the first feature vector and the second feature vector according to the first feature vector used for representing the WiFi list and the second feature vector used for representing the WiFi geofence, 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 to-be-identified waybill is not an arrival store, wherein the prompt information is used for prompting that the arrival triggering instruction of the delivery capacity is not verified.
Optionally, the neighboring merchants of the target merchant are determined by: determining the position similarity among the merchants according to the WiFi geofences of the merchants, wherein the target merchants are included in the merchants; inputting the identification information of each merchant and the position similarity between the merchants into a graphic neural network model to obtain the position characteristic information of each merchant output by the graphic neural network model; and determining the adjacent merchant of the target merchant from the multiple merchants according to the position characteristic information of the target merchant.
Optionally, the determining the neighboring merchants of the target merchant from the plurality of 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 merchants with similarity between the position characteristic information in the plurality of merchants and the position characteristic information of the target merchant being greater than a preset position similarity threshold as the adjacent merchants of the target merchant; determining, as the neighboring merchant of the target merchant, a merchant of the plurality of merchants having a similarity between location feature information and location feature information of the target merchant that is greater than the location similarity threshold and that satisfies at least one of: the distance between the target merchant and the target merchant, which belongs to the same area block, is smaller than a preset distance threshold.
In a second aspect, the present disclosure provides a store-to-store identification device comprising: the system comprises a geofence acquisition module, a target business identification module and a target business identification module, wherein the geofence acquisition module is used for acquiring a first WiFi geofence of the target business corresponding to a to-be-identified waybill and a second WiFi geofence of at least one adjacent business of the target business in response to receiving an arrival completion trigger instruction for the to-be-identified waybill; an arrival status identification module configured to identify an arrival status of the to-be-identified waybill based on the first WiFi geofence and the second WiFi geofence.
Optionally, the apparatus further comprises: the WiFi list acquisition module is configured to acquire a WiFi list currently acquired by a delivery side terminal of the delivery capacity corresponding to the to-be-identified waybill; the store-arrival status identification module comprises: and the identification sub-module is configured to identify that the arrival state of the to-be-identified waybill is an arrived store under the condition that 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 apparatus further comprises: an identification module configured to identify that the arrival status of the to-be-identified waybill is not an arrival store in one of: identifying that the arrival status of the to-be-identified waybill is an unreachable store if a similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is less than the WiFi similarity threshold; and if the similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is smaller than the WiFi similarity threshold, and the arrival record of the delivery capacity indicates that the delivery capacity does not reach the target merchant and the adjacent merchant within a preset time before the current time, identifying that the arrival state of the to-be-identified waybill is an unreachable store.
Optionally, the apparatus further comprises: and the prompt information generation module is configured to generate prompt information for prompting that the arrival triggering instruction of the delivery capacity is not verified under the condition that the arrival state of the to-be-identified waybill is not arrived.
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 implements 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.
According to the technical scheme, the first WiFi geofence of the target merchant corresponding to the to-be-identified waybill and the second WiFi geofence of at least one adjacent merchant of the target merchant can be obtained in response to receiving the to-be-identified waybill arrival completion triggering instruction, and meanwhile, the arrival state of the to-be-identified waybill is identified according to the first WiFi geofence and the second WiFi geofence, so that the problem of low reliability caused by identification according to the WiFi geofence of the target merchant can be avoided. If the shipping capacity is identified by the second WiFi geofence of the neighboring merchant as being in the vicinity of the neighboring merchant, the target merchant may be considered to have arrived in the vicinity of the target merchant for pickup due to the closer distance of the target merchant from the neighboring merchant. In this way, through the second WiFi geofence adjacent to the merchant, whether the delivery capacity actually reaches the target merchant is accurately identified, the WiFi detection range is enlarged, the area for identifying the arrival stores is widened, and therefore reliability and accuracy of identifying the arrival stores are improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a method of identifying a store to a user according to an exemplary embodiment.
FIG. 2 is a schematic diagram of a target merchant and a neighboring merchant, according to an example embodiment.
FIG. 3 is a flowchart illustrating a method of determining a target merchant's proximity to a merchant, according to an example embodiment.
FIG. 4 is a schematic diagram of a plurality of merchants, shown according to one exemplary embodiment.
Fig. 5 is a block diagram illustrating a store-to-store identification device according to an exemplary embodiment.
Fig. 6 is a block diagram of an electronic device, according to an example embodiment.
Fig. 7 is a block diagram of an electronic device, shown in accordance with another exemplary embodiment.
Detailed Description
As described in the background, identifying whether a shipping capacity has actually arrived at a store is an important issue in the distribution field. Because of many disadvantages of using GPS (GlobalPositioningSystem ) for positioning, for example, GPS signals are easily blocked and easily offset, currently, the distribution platform utilizes router information around the merchant to establish a WiFi geofence for the merchant, which is a virtual geoboundary surrounded by a virtual fence according to the WiFi signals. Through the WiFi geofence of the merchant and the WiFi list acquired by the delivery side terminal, whether the delivery capacity actually arrives at the store or not can be accurately identified, and compared with the identification through GPS positioning, the identification precision is remarkably improved.
However, the inventor finds that when establishing the WiFi geofence for the merchant, the WiFi geofence of each merchant is established by adopting a WiFi signal nearby the merchant through an algorithm, and the precision is high, but the method has a plurality of defects. For example, the following problems exist: (1) For merchants with fewer single merchants or merchants with poorer WiFi signals, the accuracy of the WiFi geofence of the merchant constructed in the modeling process by the algorithm is reduced to a certain extent due to the limited data volume reported by the WiFi signals near the merchant; (2) The problem of data caching exists in the process of reporting the WiFi list by the delivery side terminal, the currently reported WiFi list is possibly not the latest scanned WiFi list, and a plurality of scanning periods are delayed in the middle, so that the delivery capacity is the merchant corresponding to the waybill, but the delivery side terminal does not report the WiFi list acquired in the merchant, and therefore the accuracy of the identification result can be influenced when the delivery side terminal performs store identification according to the WiFi list; (3) The delivery side terminal scans the WiFi list in a fixed period, if the duration of the delivery capacity from entering the merchant to leaving the merchant is short, namely the delivery capacity enters the store and leaves the store rapidly, the delivery capacity may not enter the merchant or leave the merchant at the moment of the terminal scanning the WiFi list, and the situation also easily causes misjudgment on whether the delivery capacity arrives at the store, so that the identification result is inaccurate.
In view of this, the present disclosure provides a store-to-store identification method, apparatus, storage medium, and electronic device, which can improve reliability and accuracy of store identification.
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
The embodiment of the disclosure can be applied to various delivery scenes, such as take-away delivery, express delivery and the like. The delivery capacity in the present disclosure may be a delivery person, or may be a delivery device such as an unmanned delivery vehicle, an unmanned plane, or a delivery robot. 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 a delivery apparatus, the delivery-side terminal may be the delivery apparatus itself.
Fig. 1 is a flow chart illustrating a method of identifying a store, which may be applied to an electronic device having processing capabilities, such as a terminal or server, according to an exemplary embodiment. As shown in fig. 1, the method may include S101 and S102.
In S101, in response to receiving the arrival completion trigger instruction for the to-be-identified waybill, a first WiFi geofence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geofence of at least one neighboring merchant of the target merchant are acquired.
The manifest to be identified may be a manifest for which the delivery capacity has not been completed. In the delivery process, the delivery capacity needs to take the articles purchased by the user from the target merchants corresponding to the to-be-identified waybills before delivering the articles to the user, wherein the target merchants corresponding to the to-be-identified waybills refer to merchants providing the ordered user to purchase the articles.
The delivery capacity can input a store arrival completion trigger instruction for the to-be-identified waybill through the delivery side terminal, for example, clicking a store arrival button on a page to indicate that the delivery capacity reaches a target merchant, and the delivery side terminal can receive the store arrival completion trigger instruction output by the delivery capacity. When the store arrival identification method provided by the disclosure is applied to the server, the distribution side terminal can send the store arrival completion trigger instruction to the server under the condition that the store arrival completion trigger instruction is received, and therefore the server can receive the store arrival completion trigger instruction. The terminal or server, upon receiving the arrival completion trigger instruction, may verify the arrival completion trigger instruction to verify whether the behavior of the delivery capacity input to the arrival completion trigger instruction is compliant, or whether the arrival completion trigger instruction is input in the case that the arrival has actually arrived.
In the present disclosure, the terminal or 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 in response to receiving the arrival completion trigger instruction to identify whether the shipping capacity has arrived at the target merchant.
The proximity merchant may refer to a merchant closer to the target merchant, and which merchant or merchants are used as the proximity merchant of the target merchant may be determined in advance, for example, the proximity merchant of the target merchant is determined according to the distance between the merchants, the similarity of WiFi geofences between the merchants, and the like. The number of the neighboring merchants is not particularly limited, and may be one or more. FIG. 2 is a schematic diagram of a target merchant and a neighboring merchant, as shown in FIG. 2, where neighboring merchant 202 and neighboring merchant 203 may be neighboring merchants of target merchant 201, according to an example embodiment. It should be noted 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 of the embodiments of the present disclosure.
The WiFi geofences of the merchants can be pre-constructed, and the server can store the WiFi geofences of the merchants. In one embodiment, if the first WiFi geofence of the target merchant and the second WiFi geofence of at least one neighboring merchant are not stored in the delivery-side terminal, the delivery-side terminal may send a WiFi geofence acquisition request to the server to cause the server to send the first WiFi geofence and the second WiFi geofence to the delivery-side terminal, such that the first WiFi geofence and the second WiFi geofence are acquired by the delivery-side terminal.
In S102, an arrival status of a waybill to be identified is identified based on the first WiFi geofence and the second WiFi geofence.
Whether the delivery capacity is located at the target merchant can be identified according to the first WiFi geofence and the WiFi list acquired by the delivery side terminal, and whether the delivery capacity is located near the neighboring merchant can be identified according to the second WiFi geofence and the WiFi list. If the shipping capacity is identified as being located near a neighboring merchant, then the shipping capacity may be deemed to have arrived near the target merchant for pickup due to the closer distance of the target merchant from the neighboring merchant.
Therefore, the problem that the recognition range is smaller due to the fact that the recognition is carried out only according to the WiFi geofence of the target merchant in the related technology is avoided, 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 assisted to be recognized through the second WiFi geofence of the adjacent merchant, and reliability of the recognition to the store is improved. In addition, even if the delivery side terminal does not report the WiFi list collected in the target merchant due to the fact that the delivery capacity enters the store and leaves the store at a high speed or due to the fact that the data of the delivery side terminal is cached, whether the delivery capacity reaches the target merchant or not can be accurately identified through the WiFi list collected near the adjacent merchant by the delivery side terminal and the second WiFi geofence of the adjacent merchant, wiFi detection range is enlarged, the area for identifying the store is widened, and accuracy and reliability of identifying the store are improved.
According to the technical scheme, the first WiFi geofence of the target merchant corresponding to the to-be-identified waybill and the second WiFi geofence of at least one adjacent merchant of the target merchant can be obtained in response to receiving the to-be-identified waybill arrival completion triggering instruction, and meanwhile, the arrival state of the to-be-identified waybill is identified according to the first WiFi geofence and the second WiFi geofence, so that the problem of low reliability caused by identification according to the WiFi geofence of the target merchant can be avoided. If the shipping capacity is identified by the second WiFi geofence of the neighboring merchant as being in the vicinity of the neighboring merchant, the target merchant may be considered to have arrived in the vicinity of the target merchant for pickup due to the closer distance of the target merchant from the neighboring merchant. In this way, through the second WiFi geofence adjacent to the merchant, whether the delivery capacity actually reaches the target merchant is accurately identified, the WiFi detection range is enlarged, the area for identifying the arrival stores is widened, and therefore reliability and accuracy of identifying the arrival stores are improved.
The method for identifying the store provided by the present disclosure may further include: and acquiring a WiFi list which is currently acquired by a delivery side terminal of the delivery capacity corresponding to the to-be-identified waybill.
When the method provided by the disclosure is applied to a terminal, the terminal at the delivery side 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, so that the server can acquire the WiFi list.
Accordingly, the step S102 may include: and identifying that the arrival state of the to-be-identified waybill is an arrived store under the condition that 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.
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 representing the WiFi list and the second feature vector for representing the WiFi geofence, and taking the similarity as the similarity between the WiFi list and the WiFi geofence.
Both the WiFi list and the WiFi geofence may be represented in the form of feature vectors, and the similarity between the two may be determined by calculating a distance between the first feature vector and the second feature vector, e.g., a cosine distance, a euclidean distance. Wherein, the smaller the distance between the first feature vector and the second feature vector, the higher the similarity between the first feature vector and the second feature vector, and the larger the distance, the lower the similarity between the first feature vector and the second feature vector. The similarity between the first feature vector and the second feature vector may be used as a similarity between the WiFi list and the WiFi geofence. If the similarity between the WiFi list and the WiFi geofence is greater than a preset WiFi similarity threshold, the similarity between the WiFi list and the WiFi geofence can be characterized as high, namely the distance between the distribution capacity and the merchant corresponding to the WiFi geofence is characterized as short.
In the event that the 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, the distance between the shipping capacity and any of the target merchant and the neighboring merchant may be characterized as being closer. As shown in fig. 2, for example, the similarity between the WiFi list currently collected by the delivery side terminal and the WiFi geofence of the neighboring merchant 202 is greater than the WiFi similarity threshold, which may indicate that the distance between the delivery capacity and the neighboring merchant 202 is relatively short, and the delivery capacity may be considered to have arrived near the target merchant 201 for taking, where the arrival state of the to-be-identified waybill may be identified as the arrival state. Of course, there may be a similarity between the WiFi list and the plurality of WiFi geofences that is greater than the WiFi similarity threshold, e.g., the similarity 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, the method is not limited to identifying whether the delivery capacity has arrived at the store only through the WiFi list and the WiFi geofence of the target merchant, and the arrival state of the to-be-identified waybill can be identified as the arrived store when the similarity between the WiFi list acquired by the delivery side terminal and any one of the first WiFi geofence and the second WiFi geofence is greater than the WiFi similarity threshold. Through the WiFi geofences of adjacent merchants, whether the delivery capacity reaches a target merchant is identified in an auxiliary mode, the WiFi detection range and the identification range are increased, and the reliability of the identification result is improved.
For example, one of two implementations may be employed to identify the arrival status of the to-be-identified waybill as not being an arrival.
In one embodiment, the arrival status of the to-be-identified waybill is identified as an unreachable store if the 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 acquired by the delivery side terminal and each WiFi geofence is smaller than the WiFi similarity threshold, the distance between the delivery capacity and the target merchant can be characterized as being far, and the distance between the delivery capacity and the adjacent merchant is also far, and the delivery capacity is not located near the target merchant, so that the arrival state of the to-be-identified waybill can be identified as not arriving at the store.
In another embodiment, if the similarity between the WiFi list and each of the first and second WiFi geofences is less than a WiFi similarity threshold, and the arrival record of the shipping capacity characterizes the arrival of the shipping capacity at the target merchant and the neighboring merchant within a preset time period prior to the current time, identifying that the arrival status of the to-be-identified waybill is an unreachable store.
In one scenario, the delivery capacity may not be input to the store completion trigger command when reaching the target merchant, but may be input to the store completion trigger command after leaving the store, because the delivery capacity is not in the vicinity of the target merchant, the similarity between the WiFi list collected by the delivery side terminal and each WiFi geofence may be smaller than the WiFi similarity threshold, at this time, whether the delivery capacity reaches the target merchant and the adjacent merchant within a preset time period (such as 5 min) before the current time may be determined by the record of the arrival of the delivery capacity, if the arrival of the delivery capacity does not reach the target merchant, the arrival state of the to-be-identified bill may be identified as an unread store, and if the arrival state is identified as an arrived store. The record of arrival of the delivery capacity can be recorded according to the positioning information of the delivery capacity and used for representing which merchant the delivery capacity arrives at.
Therefore, under the condition that the similarity between the WiFi list acquired by the distribution side terminal and each WiFi geofence is smaller than the WiFi similarity threshold, the judgment can be further carried out through the arrival record of the distribution capacity, and the reliability and the accuracy of the identification result are improved.
The method for identifying the store provided by the present disclosure may further include: and generating prompt information which can be used for prompting that the delivery capacity to the store is not verified by the completion triggering instruction of the store under the condition that the arrival state of the to-be-identified waybill is not arrived.
If the arrival state of the to-be-identified waybill is identified as not arriving at the store, the delivery capacity is not currently near the target merchant, and prompt information can be generated at the moment, wherein the prompt information can be used for prompting that the delivery capacity is not actually at the target merchant, and the behavior of the to-be-identified waybill input to the store completion triggering instruction is not in accordance with the specification, namely, the to-be-identified store completion triggering instruction is not verified. When the method provided by the disclosure is applied to a terminal, the terminal at the delivery side can generate the prompt information; when the method provided by the disclosure is applied to the 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 delivery side terminal through popup windows, prompt boxes, voices and the like, and the prompt mode is not particularly limited by the present disclosure. In this way, the out-of-specification delivery capacity behavior can be timely reminded, so that the behavior that the delivery capacity is input to the store to complete the trigger instruction is prevented from appearing again under the condition that the delivery capacity is not arrived at the store.
Exemplary embodiments of determining a proximity merchant to a target merchant in the present disclosure are described below. FIG. 3 is a flowchart illustrating a method of determining a vicinity merchant of a target merchant, as shown in FIG. 3, which may include S301-S303, according to an exemplary embodiment.
In S301, a location similarity between merchants is determined according to respective WiFi geofences of the plurality of merchants.
Wherein the plurality of merchants may include target merchants. Fig. 4 is a schematic diagram of a plurality of merchants including a merchant a, a merchant B, a merchant C, a merchant D, and a merchant E as shown in fig. 4, for example, the merchant a may be a target merchant corresponding to a to-be-identified manifest, such as target merchant 201 shown in fig. 2, according to an exemplary embodiment. The present disclosure is not limited in particular to the number of the plurality of merchants, and fig. 4 is only an exemplary illustration, and does not constitute a limitation of the embodiments of the present disclosure, but the number of the plurality of merchants is not limited thereto in practical applications.
In one embodiment, the location similarity between each two merchants may be determined based on the respective WiFi geofences of the plurality of merchants. Preferably, in another embodiment, a merchant relatively close to the merchant 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, the probability of being adjacent to each other is small, so that the position similarity between the two merchants does not need to be calculated, the data processing amount can be reduced, and the calculation efficiency is improved.
As shown in fig. 4, for example, for the merchant a, the position similarity between the merchant a and the merchants B, C, and D may be determined, respectively, and the probability that the merchant E is a neighboring merchant of the merchant a is low because the distance between the merchant a and the merchant E is long, so that the position similarity between the merchant a and the merchant E may not be determined. For example, for merchant B, its location similarity to merchant a and merchant D, respectively, may be determined. Similar for other merchants. FIG. 4 may represent a graph of the positional relationship among a plurality of merchants, and the positional similarity among the merchants may be used as a weight for connecting edges among the merchants.
In an alternative embodiment, the WiFi geofences may be represented in the form of feature vectors, and the location similarity between merchants may be determined by the distance (e.g., cosine distance, euclidean distance, etc.) between the merchant's WiFi geofences. The larger the distance between the WiFi geofences, the lower the position similarity between the merchants can be represented, and the smaller the distance between the WiFi geofences, the higher the position similarity between the merchants can be represented.
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 merchants can be represented, the smaller the overlapping area is, and the lower the position similarity between the merchants can be represented.
In S302, the identification information of each merchant and the position similarity between merchants are input into the neural network model to obtain the position feature information of each merchant output by the neural network model.
The similarity of the positions between the merchants determined in S301 may be used as the preliminary proximity between the merchants. The neural network model can mine hidden probability association between merchants through sampling, wandering and other algorithms, and more accurate proximity degree and proximity relation between merchants can be further determined through the result output by the neural network model. Any of a number of graph neural network models, such as Graph Embedding models, may be employed in the present disclosure.
The identification information (such as merchant ID) of each merchant and the position similarity between merchants are input into the neural network model, and the neural network model can output the position characteristic information of each merchant, wherein the position characteristic information can be represented in a multi-dimensional vector form.
In S303, a neighboring merchant of the target merchant is determined from the plurality of merchants according to the location characteristic information of the target merchant.
For example, one of two implementations may be employed to determine a target merchant's neighboring merchants from a plurality of merchants.
In one embodiment, the merchants of the plurality of merchants, in which the similarity between the position characteristic information and the position characteristic information of the target merchant is greater than a preset position similarity threshold value, may be determined as neighboring merchants of the target merchant.
For example, the location feature information may be represented in the form of vectors, and the similarity between the location feature information may be determined by the distance between the vectors. As shown in fig. 4, according to the location feature information of the target merchant a, for example, the similarity between the location feature information of the merchant B and the location feature information of the target merchant a is greater than the 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. If the similarity between the location feature information of the merchant C and the location feature information of the target merchant a is greater than the location similarity threshold, 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 similarity with the location feature information of the target merchant greater than the location similarity threshold, and the determination may be performed according to the fluctuation condition of the similarity. For example, as shown in fig. 4, for example, the similarity between the position feature information of the merchant B, the merchant C and the merchant D and the position feature information of the target merchant a is greater than the position similarity threshold, and the corresponding similarity is the merchant B, the merchant C and the merchant D in sequence from large to small, but the similarity corresponding to the merchant D and the similarity corresponding to the merchant C fluctuate greatly, for example, the absolute value of the difference between the two is greater than the difference threshold, which can characterize that the proximity degree between the merchant D and the target merchant a is obviously reduced compared with the proximity degree between the merchant C and the merchant B, and the merchant D may not be taken as the proximity merchant of the target merchant a, and the merchant B and the merchant C may be taken as the proximity merchants.
In another embodiment, a merchant of the plurality of merchants having a similarity between the location feature information and the location feature information of the target merchant greater than a location similarity threshold and satisfying at least one of the following conditions may be determined to be a neighboring merchant of the target merchant: the distance between the target merchant and the target merchant, which belongs to the same area block, is smaller than a preset distance threshold.
The regional blocks may be pre-divided according to geographic locations, for example, a Geohash algorithm may be used to divide a city into a plurality of regional blocks, where merchants belonging to the same regional block have relatively close distances. The distance between the target merchant and the target merchant is smaller than a preset distance threshold, which can be used for representing that the distance between the target merchant and the target merchant is relatively short, wherein the preset distance threshold can be pre-calibrated. In this embodiment, if the similarity between the location feature information of the merchant and the location feature information of the target merchant is greater than the location similarity threshold, it may be further determined whether the merchant satisfies at least one of the following two conditions: the distance between the target merchant and the target merchant, which belongs to the same area block, is smaller than a preset distance threshold. Wherein, the merchant may be determined as a neighboring merchant of the target merchant if either of the two conditions is satisfied, or may be determined as a neighboring merchant of the target merchant if both conditions are satisfied. Thus, the accuracy of the proximity relation between merchants can be further ensured.
According to the technical scheme, the position similarity among the merchants is determined according to the WiFi geofences of the merchants, the position similarity can be used as the preliminary proximity degree among the merchants, and then the more accurate proximity degree among the merchants can be obtained through the neural network model, so that the determined proximity merchants of the target merchants can reflect the real proximity relation among the merchants more.
Based on the same inventive concept, the present disclosure also provides an arrival identification apparatus, and fig. 5 is a block diagram of an arrival identification apparatus according to an exemplary embodiment, as shown in fig. 5, the apparatus 500 may include:
a geofence acquisition module 501 configured to acquire a first WiFi geofence of a target merchant corresponding to a to-be-identified waybill and a second WiFi geofence of at least one neighboring merchant of the target merchant in response to receiving an arrival completion trigger instruction for the to-be-identified waybill;
an arrival status identification module 502 configured to identify an arrival status of the to-be-identified waybill based on the first WiFi geofence and the second WiFi geofence.
According to the technical scheme, the first WiFi geofence of the target merchant corresponding to the to-be-identified waybill and the second WiFi geofence of at least one adjacent merchant of the target merchant can be obtained in response to receiving the to-be-identified waybill arrival completion triggering instruction, and meanwhile, the arrival state of the to-be-identified waybill is identified according to the first WiFi geofence and the second WiFi geofence, so that the problem of low reliability caused by identification according to the WiFi geofence of the target merchant can be avoided. If the shipping capacity is identified by the second WiFi geofence of the neighboring merchant as being in the vicinity of the neighboring merchant, the target merchant may be considered to have arrived in the vicinity of the target merchant for pickup due to the closer distance of the target merchant from the neighboring merchant. In this way, through the second WiFi geofence adjacent to the merchant, whether the delivery capacity actually reaches the target merchant is accurately identified, the WiFi detection range is enlarged, the area for identifying the arrival stores is widened, and therefore reliability and accuracy of identifying the arrival stores are improved.
Optionally, the apparatus 500 may further include: the WiFi list acquisition module is configured to acquire a WiFi list currently acquired by a delivery side terminal of the delivery capacity corresponding to the to-be-identified waybill; the store-arrival status identification module 502 includes: and the identification sub-module is configured to identify that the arrival state of the to-be-identified waybill is an arrived store under the condition that 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 apparatus 500 may further include: an identification module configured to identify that the arrival status of the to-be-identified waybill is not an arrival store in one of: identifying that the arrival status of the to-be-identified waybill is an unreachable store if a similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is less than the WiFi similarity threshold; and if the similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is smaller than the WiFi similarity threshold, and the arrival record of the delivery capacity indicates that the delivery capacity does not reach the target merchant and the adjacent merchant within a preset time before the current time, identifying that the arrival state of the to-be-identified waybill is an unreachable store.
Optionally, the apparatus 500 may further include: and the prompt information generation module is configured to generate prompt information for prompting that the arrival triggering instruction of the delivery capacity is not verified under the condition that the arrival state of the to-be-identified waybill is not arrived.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 6 is a block diagram of an electronic device 600, according to an example embodiment. As shown in fig. 6, the electronic device 600 may include: a processor 601, 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 communication component 605.
Wherein the processor 601 is configured to control the overall operation of the electronic device 600 to perform all or part of the steps of the store identification method described above. The memory 602 is used to store various types of data to support operations at the electronic device 600, which may include, for example, instructions for any application or method operating on the electronic device 600, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and the like. 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 (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 603 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 602 or transmitted through the communication component 605. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 604 provides an interface between the processor 601 and other interface modules, which may be 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) for short, 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 605 may thus comprise: 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 (DIGITAL SIGNAL processors, DSPs), digital signal processing devices (DIGITAL SIGNAL Processing Device, DSPDs), programmable logic devices (Programmable Logic Device, PLDs), field programmable gate arrays (Field Programmable GATE ARRAY, FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the store-to-store identification method described above.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the store-to-store identification method described above. For example, the computer readable storage medium may be the memory 602 described above including program instructions executable by the processor 601 of the electronic device 600 to perform the store identification method described above.
Fig. 7 is a block diagram of an electronic device 700, shown in accordance with another exemplary embodiment. For example, the electronic device 700 may be provided as a server. Referring to fig. 7, the electronic device 700 includes a processor 722, which may be one or more in number, and a memory 732 for storing computer programs executable by the processor 722. The computer program stored in memory 732 may include one or more modules each corresponding to a set of instructions. Further, the processor 722 may be configured to execute the computer program to perform the store-arrival identification method described above.
In addition, the electronic device 700 can further include a power component 726 and a communication component 750, the power component 726 can be configured to perform power management of the electronic device 700, and the communication component 750 can be configured to enable communication of the electronic device 700, e.g., wired or wireless communication. In addition, the electronic device 700 may also include an input/output (I/O) interface 758. The electronic device 700 may operate based on an operating system stored in memory 732, such as Windows Server TM,Mac OS XTM,UnixTM,LinuxTM or the like.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the store-to-store identification method described above. For example, the computer readable storage medium may be the memory 732 described above that includes program instructions executable by the processor 722 of the electronic device 700 to perform the store identification method described above.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of identifying a store when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the embodiments described above, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (6)
1. A method of identifying a store, comprising:
In response to receiving an arrival completion trigger instruction for a to-be-identified waybill, acquiring a first WiFi geofence of a target merchant corresponding to the to-be-identified waybill and a second WiFi geofence of at least one neighboring merchant of the target merchant;
Identifying the arrival status of the to-be-identified waybill according to the first and second WiFi geofences;
The method further comprises the steps of:
acquiring a WiFi list currently acquired by a delivery side terminal of delivery capacity corresponding to the to-be-identified waybill;
the identifying the arrival status of the to-be-identified waybill according to the first WiFi geofence and the second WiFi geofence comprises:
Identifying that the arrival state of the to-be-identified waybill is an arrived store under the condition that 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;
Wherein the neighboring merchants of the target merchant are determined by:
determining the position similarity among the merchants according to the WiFi geofences of the merchants, wherein the target merchants are included in the merchants;
Inputting the identification information of each merchant and the position similarity between the merchants into a graphic neural network model to obtain the position characteristic information of each merchant output by the graphic neural network model;
Determining the adjacent merchants of the target merchant from the plurality of merchants according to the position characteristic information of the target merchant;
The determining the neighboring merchants of the target merchant from the plurality of merchants according to the position characteristic information of the target merchant comprises the following steps:
determining the neighboring merchant of the target merchant from the plurality of merchants by one of:
determining the merchants with similarity between the position characteristic information in the plurality of merchants and the position characteristic information of the target merchant being greater than a preset position similarity threshold as the adjacent merchants of the target merchant;
Determining, as the neighboring merchant of the target merchant, a merchant of the plurality of merchants having a similarity between location feature information and location feature information of the target merchant that is greater than the location similarity threshold and that satisfies at least one of: the distance between the target merchant and the target merchant, which belongs to the same area block, is smaller than a preset distance threshold.
2. The method according to claim 1, wherein the method further comprises:
identifying the arrival status of the to-be-identified waybill as an unreached store by one of the following means:
Identifying that the arrival status of the to-be-identified waybill is an unreachable store if a similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is less than the WiFi similarity threshold;
And if the similarity between the WiFi list and each of the first WiFi geofence and the second WiFi geofence is smaller than the WiFi similarity threshold, and the arrival record of the delivery capacity indicates that the delivery capacity does not reach the target merchant and the adjacent merchant within a preset time before the current time, identifying that the arrival state of the to-be-identified waybill is an unreachable store.
3. The method of claim 1 or 2, wherein the similarity between the WiFi list and the WiFi geofence is determined by:
And determining the similarity between the first feature vector and the second feature vector according to the first feature vector used for representing the WiFi list and the second feature vector used for representing the WiFi geofence, and taking the similarity as the similarity between the WiFi list and the WiFi geofence.
4. The method according to claim 1, wherein the method further comprises:
And generating prompt information under the condition that the arrival state of the to-be-identified waybill is not an arrival store, wherein the prompt information is used for prompting that the arrival triggering instruction of the delivery capacity is not verified.
5. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-4.
6. 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 of any one of claims 1-4.
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