Pre-bin address information generation method, apparatus and device, and computer readable medium

Abstract

The embodiment of the invention discloses a prepositive bin address information generation method, device and equipment and a computer readable medium. One specific embodiment of the method comprises the following steps: acquiring a historical order information set and a candidate preposed warehouse information set; generating a historical order cluster information set, a target candidate preposed warehouse information set and a prediction order cluster information set based on the historical order information set and the candidate preposed warehouse information set; based on the prediction order cluster information set, feature extraction processing is carried out on each piece of historical order cluster information in the historical order cluster information set to generate feature order cluster information, and a feature order cluster information set is obtained; and generating preposed warehouse address information based on the target candidate preposed warehouse information set, the prediction order cluster information set and the feature order cluster information set. The implementation mode is related to artificial intelligence, and the accuracy of generating the front bin address information can be improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and more particularly to a method, device, apparatus, and computer-readable medium for generating forward warehouse address information. Background Art

[0002] How to select the location of the forward warehouse is a major problem for the forward warehouse service. The method usually adopted to generate the forward warehouse address information is: using a neural network model to predict the item flow value corresponding to each candidate forward warehouse address information, so as to select the optimal forward warehouse address information from each candidate forward warehouse address information.

[0003] However, the inventors have found that when the above method is used to generate the forward warehouse address information, the following technical problems often occur:

[0004] The item turnover value of the candidate forward warehouse address information is predicted without considering the historical item turnover value corresponding to the candidate forward warehouse address information and future changes in user demand, resulting in reduced accuracy in generating the forward warehouse address information.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose forward warehouse address information generation methods, devices, equipment and computer-readable media to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for generating forward warehouse address information, the method comprising: obtaining a historical order information set and a candidate forward warehouse information set; based on the above historical order information set and the above candidate forward warehouse information set, generating a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set; based on the above predicted order cluster information set, performing feature extraction processing on each historical order cluster information in the above historical order cluster information set to generate feature order cluster information, and obtaining a feature order cluster information set; based on the above target candidate forward warehouse information set, the above predicted order cluster information set and the above feature order cluster information set, generating forward warehouse address information.

[0009] Optionally, the above-mentioned forward warehouse address information generation method also includes: sending the above-mentioned forward warehouse address information to a display terminal for staff to select the forward warehouse address.

[0010] Optionally, the above-mentioned acquisition of historical order information sets and candidate forward warehouse information sets includes: acquiring a full order information set; filtering the above-mentioned full order information set to obtain a filtered order information set; integrating each filtered order information in the above-mentioned filtered order information set to generate historical order information and candidate forward warehouse information, and obtaining the above-mentioned historical order information set and the above-mentioned candidate forward warehouse information set.

[0011] Optionally, the above-mentioned historical order cluster information set, target candidate forward warehouse information set and predicted order cluster information set are generated based on the above-mentioned historical order information set and the above-mentioned candidate forward warehouse information set, including: clustering the above-mentioned historical order information set to obtain the above-mentioned historical order cluster information set; based on the above-mentioned historical order cluster information set, updating each candidate forward warehouse information in the above-mentioned candidate forward warehouse information set to obtain the above-mentioned target candidate forward warehouse information set; generating predicted order cluster information corresponding to each historical order cluster information in the above-mentioned historical order cluster information set to obtain the above-mentioned predicted order cluster information set.

[0012] Optionally, the historical order information in the above-mentioned historical order information set includes: order address information; and the above-mentioned clustering processing of the above-mentioned historical order information set to obtain the historical order cluster information set includes: matching processing of the order address information included in every two historical order information in the above-mentioned historical order information set to generate an address matching result, to obtain an address matching result set; based on the above-mentioned address matching result set, integrating processing of the above-mentioned historical order information set to obtain a matching historical order information set; clustering processing of the above-mentioned matching historical order information set to obtain the above-mentioned historical order cluster information set.

[0013] Optionally, the above-mentioned generation of predicted order cluster information corresponding to each historical order cluster information in the above-mentioned historical order cluster information set includes: splitting the above-mentioned historical order cluster information to obtain historical order cluster trend information and historical order cluster remainder information; generating a predicted order cluster trend value sequence based on the above-mentioned historical order cluster trend information; generating a predicted order cluster remainder value sequence based on the above-mentioned historical order cluster trend information; for each predicted order cluster trend value in the above-mentioned predicted order cluster trend value sequence, determining the sum of the predicted order cluster remainder value corresponding to the above-mentioned predicted order cluster trend value in the above-mentioned predicted order cluster remainder value sequence and the above-mentioned predicted order cluster trend value as the predicted order cluster item circulation value to obtain the predicted order cluster item circulation value sequence; generating the above-mentioned predicted order cluster information based on the above-mentioned predicted order cluster item circulation value sequence.

[0014] Optionally, based on the above-mentioned predicted order cluster information set, feature extraction processing is performed on each historical order cluster information in the above-mentioned historical order cluster information set to generate feature order cluster information, including: determining the predicted order cluster information corresponding to the above-mentioned historical order cluster information in the above-mentioned predicted order cluster information set as the target predicted order cluster information; performing feature extraction processing on the above-mentioned historical order cluster information and the above-mentioned target predicted order cluster information respectively to obtain historical order cluster feature information and predicted order cluster feature information; based on the above-mentioned historical order cluster feature information, determining the historical fluctuation value, the historical trend value and the historical predictable value; based on the above-mentioned predicted order cluster feature information, determining the predicted fluctuation value, the predicted trend value and the predicted predictable value; based on the above-mentioned historical fluctuation value, the above-mentioned historical trend value, the above-mentioned historical predictable value, the above-mentioned predicted fluctuation value, the above-mentioned predicted trend value and the above-mentioned predicted predictable value, determining the feature order cluster fluctuation value, the feature order cluster predicted trend value and the feature order cluster predicted predictable value; fusing the above-mentioned feature order cluster fluctuation value, the above-mentioned feature order cluster predicted trend value and the above-mentioned feature order cluster predicted predictable value to obtain the above-mentioned feature order cluster information.

[0015] Optionally, the above-mentioned generation of forward warehouse address information based on the above-mentioned target candidate forward warehouse information set, the above-mentioned predicted order cluster information set and the above-mentioned characteristic order cluster information set includes: generating forward warehouse coverage range constraint information based on the above-mentioned target candidate forward warehouse information set, the preset maximum forward warehouse construction quantity and the preset order fulfillment rate; generating forward warehouse capacity constraint information based on the above-mentioned target candidate forward warehouse information set and the above-mentioned predicted order cluster information set; generating forward warehouse delivery range constraint information based on the above-mentioned target candidate forward warehouse information set and the preset distance adjustment coefficient; determining the above-mentioned forward warehouse address information based on the above-mentioned characteristic order cluster information set, the above-mentioned forward warehouse coverage range constraint information, the above-mentioned forward warehouse capacity constraint information, the above-mentioned forward warehouse delivery range constraint information and the preset forward warehouse address constraint information.

[0016] In a second aspect, some embodiments of the present disclosure provide a forward warehouse address information generating device, the device comprising: an acquisition unit, configured to acquire a historical order information set and a candidate forward warehouse information set; a first generation unit, configured to generate a target candidate forward warehouse information set and a predicted order cluster information set based on the above-mentioned historical order information set and the above-mentioned candidate forward warehouse information set; a feature extraction unit, configured to perform feature extraction processing on each historical order cluster information in the above-mentioned historical order cluster information set based on the above-mentioned predicted order cluster information set to generate feature order cluster information, and obtain the feature order cluster information set; a second generation unit, configured to generate the forward warehouse address information based on the above-mentioned target candidate forward warehouse information set, the above-mentioned predicted order cluster information set and the above-mentioned feature order cluster information set.

[0017] Optionally, the forward warehouse address information generating device further includes: a sending unit configured to: send the forward warehouse address information to a display terminal for the staff to select the forward warehouse address.

[0018] Optionally, the acquisition unit is further configured to: acquire a full order information set; filter the full order information set to obtain a filtered order information set; integrate each filtered order information in the filtered order information set to generate historical order information and candidate forward warehouse information, and obtain the historical order information set and the candidate forward warehouse information set.

[0019] Optionally, the above-mentioned first generating unit is further configured to: perform clustering processing on the above-mentioned historical order information set to obtain the above-mentioned historical order cluster information set; based on the above-mentioned historical order cluster information set, update processing on each candidate forward warehouse information in the above-mentioned candidate forward warehouse information set to obtain the above-mentioned target candidate forward warehouse information set; generate predicted order cluster information corresponding to each historical order cluster information in the above-mentioned historical order cluster information set to obtain the above-mentioned predicted order cluster information set.

[0020] Optionally, the historical order information in the above-mentioned historical order information set includes: order address information; and the above-mentioned first generating unit is further configured to: match the order address information included in every two historical order information in the above-mentioned historical order information set to generate an address matching result, and obtain an address matching result set; based on the above-mentioned address matching result set, integrate the above-mentioned historical order information set to obtain a matching historical order information set; cluster the above-mentioned matching historical order information set to obtain the above-mentioned historical order cluster information set.

[0021] Optionally, the first generating unit is further configured to: split the historical order cluster information to obtain historical order cluster trend information and historical order cluster remainder information; generate a predicted order cluster trend value sequence based on the historical order cluster trend information; generate a predicted order cluster remainder value sequence based on the historical order cluster trend information; for each predicted order cluster trend value in the predicted order cluster trend value sequence, determine the sum of the predicted order cluster remainder value corresponding to the predicted order cluster trend value in the predicted order cluster remainder value sequence and the predicted order cluster trend value as the predicted order cluster item circulation value to obtain a predicted order cluster item circulation value sequence; generate the predicted order cluster information based on the predicted order cluster item circulation value sequence.

[0022] Optionally, the feature extraction unit is further configured to: determine the forecast order cluster information corresponding to the historical order cluster information in the forecast order cluster information set as the target forecast order cluster information; perform feature extraction processing on the historical order cluster information and the target forecast order cluster information respectively to obtain the historical order cluster feature information and the forecast order cluster feature information; determine the historical fluctuation value, the historical trend value and the historical predictable value based on the historical order cluster feature information; determine the predicted fluctuation value, the predicted trend value and the predicted predictable value based on the forecast order cluster feature information; determine the characteristic order cluster fluctuation value, the characteristic order cluster predicted trend value and the characteristic order cluster predicted predictable value based on the historical fluctuation value, the historical trend value, the historical predictable value, the predicted fluctuation value, the predicted trend value and the predicted predictable value; perform fusion processing on the characteristic order cluster fluctuation value, the characteristic order cluster predicted trend value and the characteristic order cluster predicted predictable value to obtain the characteristic order cluster information.

[0023] Optionally, the second generation unit is further configured to: generate forward warehouse coverage range constraint information based on the target candidate forward warehouse information set, the preset maximum forward warehouse construction quantity and the preset order fulfillment rate; generate forward warehouse capacity constraint information based on the target candidate forward warehouse information set and the predicted order cluster information set; generate forward warehouse delivery range constraint information based on the target candidate forward warehouse information set and the preset distance adjustment coefficient; determine the forward warehouse address information based on the characteristic order cluster information set, the forward warehouse coverage range constraint information, the forward warehouse capacity constraint information, the forward warehouse delivery range constraint information and the preset forward warehouse address constraint information.

[0024] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation method in the above-mentioned first aspect.

[0025] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any one of the implementations in the first aspect above is implemented.

[0026] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which implements the method described in any implementation manner in the above-mentioned first aspect when executed by a processor.

[0027] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the forward warehouse address information generation method of some embodiments of the present disclosure, the accuracy of generating the forward warehouse address information can be improved. Specifically, the reason for the reduced accuracy of generating the forward warehouse address information is that only the item turnover value of the candidate forward warehouse address information is predicted, and the historical item turnover value corresponding to the candidate forward warehouse address information and the future user demand changes are not considered. Based on this, the forward warehouse address information generation method of some embodiments of the present disclosure, first, obtains the historical order information set and the candidate forward warehouse information set. Secondly, based on the above-mentioned historical order information set and the above-mentioned candidate forward warehouse information set, a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set are generated. Thus, the obtained historical order cluster information set can characterize the historical item turnover value corresponding to the candidate forward warehouse address information, the predicted order cluster information set can characterize the future user demand changes corresponding to the candidate forward warehouse address information, and the target candidate forward warehouse information set can characterize the forward warehouse address information of each candidate. Then, based on the above-mentioned predicted order cluster information set, feature extraction processing is performed on each historical order cluster information in the above-mentioned historical order cluster information set to generate characteristic order cluster information and obtain a characteristic order cluster information set. Thus, the historical item turnover value corresponding to the candidate forward warehouse address information and future user demand changes can be combined to obtain a characteristic order cluster information set that can characterize the order cluster characteristics. Finally, based on the above-mentioned target candidate forward warehouse information set, the above-mentioned predicted order cluster information set and the above-mentioned characteristic order cluster information set, the forward warehouse address information is generated. Thus, the forward warehouse address information can be selected from the target candidate forward warehouse information set. Therefore, the forward warehouse address information generation method disclosed in the present invention, by combining the predicted order cluster information and the historical order cluster information to obtain the characteristic order cluster information, takes into account the historical item turnover value and future user demand changes, and can improve the accuracy of generating the forward warehouse address information. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0029] Figure 1 is a schematic diagram of an application scenario of a method for generating forward warehouse address information according to some embodiments of the present disclosure;

[0030] Figure 2 is a flow chart of some embodiments of the method for generating forward warehouse address information according to the present disclosure;

[0031] Figure 3 is a flow chart of other embodiments of the method for generating forward warehouse address information according to the present disclosure;

[0032] Figure 4 It is a schematic diagram of the forward warehouse coverage area according to the forward warehouse address information generation method disclosed in the present invention;

[0033] Figure 5 It is a schematic diagram of the structure of some embodiments of the forward warehouse address information generating device according to the present disclosure;

[0034] Figure 6 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0035] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0036] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0037] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0038] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0039] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0040] With regard to the collection, storage, and use of user personal information (such as user portraits and historical user behaviors) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the authorization and consent of the personal information subject in advance.

[0041] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0042] Figure 1 It is a schematic diagram of an application scenario of the method for generating recommendation information according to some embodiments of the present disclosure.

[0043] exist Figure 1 and Figure 2 In the application scenario, first, the electronic device 101 can obtain the historical order information set 102 and the candidate forward warehouse information set 103. The above-mentioned historical order information set 102 may include historical order information 1021, historical order information 1022, historical order information 1023 and historical order information 1024. For example, the above-mentioned historical order information 1021 may be "order code: 0049, item code: 020, item weight: 2 kg". The above-mentioned candidate forward warehouse information set 103 may include candidate forward warehouse information 1031, candidate forward warehouse information 1032, candidate forward warehouse information 1033 and candidate forward warehouse information 1034. For example, the above-mentioned candidate forward warehouse information 1031 may be "forward warehouse code: 001, average capacity per employee in the warehouse: 50, average capacity per rider: 50, upper limit of the number of employees in the warehouse: 100, upper limit of the number of riders: 100". Secondly, the electronic device 101 may generate a historical order cluster information set 104, a target candidate forward warehouse information set 105, and a predicted order cluster information set 106 based on the historical order information set 102 and the candidate forward warehouse information set 103. In this application scenario, the historical order cluster information set 104 may include historical order cluster information 1041 and historical order cluster information 1042. The target candidate forward warehouse information set 105 may include target candidate forward warehouse information 1051 corresponding to the candidate forward warehouse information 1031, target candidate forward warehouse information 1052 corresponding to the candidate forward warehouse information 1032, target candidate forward warehouse information 1053 corresponding to the candidate forward warehouse information 1033, and target candidate forward warehouse information 1054 corresponding to the candidate forward warehouse information 1034. The predicted order cluster information set 106 may include predicted order cluster information 1061 corresponding to the historical order cluster information 1041 and predicted order cluster information 1062 corresponding to the historical order cluster information 1042. Then, the electronic device 101 may perform feature extraction processing on each historical order cluster information in the historical order cluster information set 104 based on the predicted order cluster information set 106 to generate feature order cluster information and obtain feature order cluster information set 107. In this application scenario, the feature order cluster information set 107 may include feature order cluster information 1071 corresponding to the historical order cluster information 1041 and feature order cluster information 1072 corresponding to the historical order cluster information 1042. Finally, the electronic device 101 may generate the forward warehouse address information 108 based on the target candidate forward warehouse information set 105, the predicted order cluster information set 106 and the feature order cluster information set 107.

[0044] It should be noted that the electronic device 101 can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0045] It should be understood that Figure 1 The number of electronic devices in the embodiment is only for illustration. Any number of electronic devices may be provided according to implementation requirements.

[0046] Continue to refer Figure 2 , Figure 2 The process 200 of some embodiments of the method for generating forward warehouse address information according to the present disclosure is shown. The method for generating forward warehouse address information includes the following steps:

[0047] Step 201, obtaining a historical order information set and a candidate forward warehouse information set.

[0048] In some embodiments, the execution entity of the forward warehouse address information generation method (for example Figure 1 The electronic device 101 shown can obtain the historical order information set and the candidate forward warehouse information set. In practice, the above-mentioned execution subject can obtain the above-mentioned historical order information set and the candidate forward warehouse information set from the terminal device through a wired connection or a wireless connection.

[0049] In some optional implementations of some embodiments, the execution subject obtains the historical order information set and the candidate forward warehouse information set, which may include the following steps:

[0050] The first step is to obtain a full order information set. The full order information set can be obtained from a terminal device via a wired connection or a wireless connection. The full order information in the full order information set may include but is not limited to at least one of the following: historical order information, historical waybill information, order item information, candidate forward warehouse address information, and order cost information.

[0051] As an example, the above historical order information may include but is not limited to at least one of the following: user order date, order code, item code, item circulation value (item sales), the province of the delivery place, the city of the delivery place and order address information. Here, the above user order date can represent the time when the user places an order. The above order code can uniquely identify an order. The above item code can uniquely represent an item. For example, the above item can be but is not limited to at least one of the following: apple, spinach or pork. The above historical waybill information may include but is not limited to at least one of the following: waybill code, order code, geographical longitude of the delivery place and geographical latitude of the delivery place. The above order item information may include but is not limited to at least one of the following: item code, item name, first-level category to which the item belongs (e.g., clothing category), second-level category to which the item belongs (e.g., windbreaker), third-level category to which the item belongs (e.g., short thin windbreaker), item weight, item volume and selection mark. Here, the above selection mark can represent whether the above item is to be placed in the forward warehouse. When the above-mentioned selection mark is 1, it can represent that the above-mentioned items should be placed in the forward warehouse, and when the above-mentioned selection mark is 0, it can represent that the above-mentioned items should not be placed in the forward warehouse. The above-mentioned candidate forward warehouse address information may include but is not limited to at least one of the following: forward warehouse code, forward warehouse coverage, geographical longitude of the forward warehouse address, geographical dimension of the forward warehouse address, upper limit of forward warehouse capacity, average capacity per employee in the warehouse, average capacity per rider, upper limit of the number of employees in the warehouse and upper limit of the number of riders. Here, the above-mentioned forward warehouse code can uniquely represent a forward warehouse. The above-mentioned forward warehouse coverage can represent the maximum delivery range of the forward warehouse corresponding to the above-mentioned forward warehouse code. The above-mentioned upper limit of the forward warehouse capacity can represent the maximum capacity that can be placed in the forward warehouse corresponding to the above-mentioned forward warehouse code. The above-mentioned average capacity per employee in the warehouse can represent the number of orders produced per person per day by the employees of the forward warehouse corresponding to the above-mentioned forward warehouse code. The above-mentioned average capacity per rider can represent the average daily number of orders completed by the riders of the forward warehouse corresponding to the above-mentioned forward warehouse code. The above-mentioned order cost information may include but is not limited to at least one of the following: forward warehouse code, average daily production order quantity, total average order cost, average production order cost of large warehouse, average replenishment order cost, average cost of in-warehouse operation order of forward warehouse and average cost of rider delivery order. Here, the above-mentioned total average order cost may be the sum of the above-mentioned average production order cost of large warehouse, average replenishment order cost, average cost of in-warehouse operation order of forward warehouse and average cost of rider delivery order. The above-mentioned average daily production order quantity may characterize the order quantity generated every day by the forward warehouse corresponding to the above-mentioned forward warehouse code. The above-mentioned average production order cost of large warehouse may characterize the average production order cost of the large warehouse that provides replenishment for the forward warehouse corresponding to the above-mentioned forward warehouse code.

[0052] The second step is to filter the above-mentioned full order information set to obtain a filtered order information set. The above-mentioned filtering of the above-mentioned full order information set may be to remove empty spaces, remove duplicates and perform field type verification on the above-mentioned full order information set. Specifically, removing empty spaces from the above-mentioned full order information set may be to delete the full order information that is empty in the above-mentioned full order information set from the above-mentioned full order information set. The above-mentioned full order information set may be de-duplicated by a preset de-duplication algorithm. The above-mentioned full order information set may be subjected to field type verification by a preset field verification algorithm.

[0053] As an example, the preset deduplication algorithm may be, but is not limited to, at least one of the following: SimHash (similarity hash) algorithm or MinHash (minimum hash) algorithm. The preset field check algorithm may be a CRC (Cyclic Redundancy Check) algorithm.

[0054] The third step is to integrate each of the filtered order information in the filtered order information set to generate historical order information and candidate forward warehouse information, and obtain the historical order information set and the candidate forward warehouse information set. Among them, the order code, item code, item weight, item volume and item turnover value corresponding to the item code, and order address information corresponding to the order code included in the filtered order information can be determined as the order code, item code, item weight, item volume, item turnover value, and order address information included in the historical order information. Then, the forward warehouse code, the geographical longitude of the forward warehouse address, the geographical dimension of the forward warehouse address, the upper limit of the forward warehouse capacity, the average production capacity per capita of warehouse employees, the average production capacity per rider, the upper limit of the number of employees in the warehouse, the upper limit of the number of riders and the average daily production order quantity and the total order cost corresponding to the above-mentioned forward warehouse code included in the above-mentioned filtered order information are determined as the forward warehouse code, the geographical longitude of the forward warehouse address, the geographical dimension of the forward warehouse address, the upper limit of the forward warehouse capacity, the average production capacity per capita of warehouse employees, the average production capacity per rider, the upper limit of the number of employees in the warehouse, the upper limit of the number of riders, the average daily production order quantity and the total order cost included in the above-mentioned candidate forward warehouse information.

[0055] Step 202: Based on the historical order information set and the candidate forward warehouse information set, generate a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set.

[0056] In some embodiments, the execution entity may generate a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set based on the historical order information set and the candidate forward warehouse information set. The historical order cluster information in the historical order cluster information set may include but is not limited to at least one of the following: order cluster code, order cluster date, order quantity and forward warehouse code set, where the order cluster code may correspond to multiple forward warehouse codes. The target candidate forward warehouse information in the target candidate forward warehouse information set may include but is not limited to at least one of the following: forward warehouse code, geographical longitude of the forward warehouse address, geographical dimension of the forward warehouse address, upper limit of forward warehouse capacity, per capita capacity of warehouse employees, per capita capacity of riders, upper limit of the number of warehouse employees, upper limit of the number of riders, average daily production order quantity, total average cost per order, order cluster code set and number of order clusters covered by the forward warehouse, where the forward warehouse code may correspond to multiple order cluster codes, and the number of order clusters covered by the forward warehouse may be the number of order cluster codes in the order cluster code set. The predicted order cluster information in the predicted order cluster information set may include: average daily item turnover value.

[0057] In practice, the above historical order information set can be clustered to obtain the above historical order cluster information set. Then, based on the above historical order cluster information set, the above candidate forward warehouse information set is integrated to obtain the above target candidate forward warehouse information set. Finally, each historical order cluster information in the above historical order cluster information set is predicted to generate the above predicted order cluster information to obtain the above predicted order cluster information set. Here, the above historical order information set can be clustered by a preset clustering algorithm. Each historical order cluster information in the above historical order cluster information set can be predicted by a preset prediction algorithm.

[0058] As an example, the above-mentioned preset clustering algorithm may be, but is not limited to: DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering points to identify the clustering structure), and DENCLUE (Density Clustering). The above-mentioned preset prediction algorithm may be, but is not limited to: a gray prediction algorithm or a Markov prediction algorithm.

[0059] Step 203: Based on the predicted order cluster information set, feature extraction processing is performed on each historical order cluster information in the historical order cluster information set to generate feature order cluster information, thereby obtaining the feature order cluster information set.

[0060] In some embodiments, the execution subject may perform feature extraction processing on each historical order cluster information in the historical order cluster information set based on the predicted order cluster information set to generate feature order cluster information and obtain the feature order cluster information set. Based on the predicted order cluster information set, feature extraction processing may be performed on each historical order cluster information in the historical order cluster information set through a preset feature extraction model to generate feature order cluster information.

[0061] As an example, the above-mentioned preset feature extraction model can be but is not limited to at least one of the following: an ARCH (Autoregressive Conditional Heteroskedasticity) model or an SVAR (Structural Vector Autoregression) model.

[0062] In some optional implementations of some embodiments, the execution subject performs feature extraction processing on each historical order cluster information in the historical order cluster information set based on the predicted order cluster information set to generate feature order cluster information, which may include the following steps:

[0063] In the first step, the predicted order cluster information corresponding to the historical order cluster information in the predicted order cluster information set is determined as the target predicted order cluster information.

[0064] In the second step, feature extraction is performed on the above historical order cluster information and the above target predicted order cluster information respectively to obtain historical order cluster feature information and predicted order cluster feature information. Among them, feature extraction can be performed on the above historical order cluster information and the above target predicted order cluster information respectively through a preset feature extraction algorithm. The above historical order cluster feature information may include but is not limited to at least one of the following: historical coefficient of variation value, historical discontinuity value, historical trend strength value, historical seasonal strength value, historical stationarity test value and historical white noise test value. The above predicted order cluster feature information may include but is not limited to at least one of the following: predicted coefficient of variation value, predicted discontinuity value, predicted trend strength value, predicted seasonal strength value, predicted stationarity test value and predicted white noise test value.

[0065] As an example, the above-mentioned preset feature extraction algorithm may be a TsFresh (time series data feature mining) algorithm.

[0066] The third step is to determine the historical fluctuation value, historical trend value and historical predictable value based on the above historical order cluster characteristic information. The historical fluctuation value, historical trend value and historical predictable value can be determined through the following sub-steps:

[0067] In the first sub-step, the sum of the historical coefficient of variation value and the historical seasonal intensity value included in the historical order cluster characteristic information is determined as the historical volatility value.

[0068] In a second sub-step, the historical trend strength value included in the historical order cluster characteristic information is determined as the historical trend value.

[0069] In the third sub-step, the difference between the historical white noise test value and the historical discontinuity value included in the above historical order cluster characteristic information is determined as the historical difference value.

[0070] The fourth sub-step is to determine the difference between the above historical difference value and the historical stability test value included in the above historical order cluster characteristic information as the above historical observable value.

[0071] The fourth step is to determine the predicted fluctuation value, predicted trend value and predicted predictable value based on the above predicted order cluster characteristic information.

[0072] In practice, the above-mentioned determination of the predicted fluctuation value, the predicted trend value and the predicted predictable value based on the above-mentioned predicted order cluster characteristic information may include the following sub-steps:

[0073] In the first sub-step, the sum of the predicted coefficient of variation value and the predicted seasonal intensity value included in the predicted order cluster characteristic information is determined as the predicted volatility value.

[0074] The second sub-step is to determine the predicted trend strength value included in the predicted order cluster characteristic information as the predicted trend value.

[0075] In the third sub-step, the difference between the predicted white noise test value and the predicted discontinuity value included in the above-mentioned predicted order cluster feature information is determined as the predicted difference value.

[0076] The fourth sub-step is to determine the difference between the above-mentioned prediction difference value and the prediction stationarity test value included in the above-mentioned prediction order cluster characteristic information as the above-mentioned prediction observable value.

[0077] The fifth step is to determine the characteristic order cluster fluctuation value, the characteristic order cluster prediction trend value and the characteristic order cluster prediction predictable value based on the above historical fluctuation value, the above historical trend value, the above historical predictable value, the above predicted fluctuation value, the above predicted trend value and the above predicted predictable value.

[0078] Wherein, based on the above historical fluctuation value, the above historical trend value, the above historical predictable value, the above predicted fluctuation value, the above predicted trend value and the above predicted predictable value, the characteristic order cluster fluctuation value, the characteristic order cluster predicted trend value and the characteristic order cluster predicted predictable value may be determined by the following sub-steps:

[0079] The first sub-step is to determine the average of the above historical fluctuation value and the above predicted fluctuation value as the above characteristic order cluster fluctuation value.

[0080] The second sub-step is to determine the average of the above historical trend value and the above predicted trend value as the above characteristic order cluster trend value.

[0081] The third sub-step is to determine the average of the above historical predictable value and the above predicted predictable value as the above characteristic order cluster predictable value.

[0082] Step 6: The characteristic order cluster fluctuation value, the characteristic order cluster prediction trend value and the characteristic order cluster prediction predictable value are merged to obtain the characteristic order cluster information. The characteristic order cluster fluctuation value, the characteristic order cluster prediction trend value and the characteristic order cluster prediction predictable value can be determined as the characteristic order cluster fluctuation value, the characteristic order cluster prediction trend value and the characteristic order cluster prediction predictable value included in the characteristic order cluster information.

[0083] Step 204, based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set, generate the forward warehouse address information.

[0084] In some embodiments, the execution entity may generate the forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set, and the characteristic order cluster information set. The forward warehouse address information includes the forward warehouse code set, the order cluster code set, the number of forward warehouse personnel, and the number of forward warehouse riders. The forward warehouse address information may be generated by a preset site selection model.

[0085] As an example, the above-mentioned preset site selection model can be but is not limited to at least one of the following: coverage model, maximum coverage model, p-cernter Problem model, p-dispersion Problem model or p-median Problem model.

[0086] In some optional implementations of some embodiments, the generating of the forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set may include the following steps:

[0087] The first step is to generate forward warehouse coverage constraint information based on the above target candidate forward warehouse information set, the preset maximum forward warehouse construction quantity and the preset order fulfillment rate. Among them, the above preset order fulfillment rate can represent the ratio of orders that can be delivered by a forward warehouse to the total number of orders. The above preset maximum forward warehouse construction quantity can represent the maximum number of forward warehouses that can be built. The following formula can be determined as the above forward warehouse coverage constraint information:

[0088]

[0089] Wherein, I represents a forward warehouse code set consisting of forward warehouse codes included in each target candidate forward warehouse information in the above target candidate forward warehouse information set. i represents the forward warehouse sequence number. K represents an order cluster code set consisting of order cluster codes included in each target candidate forward warehouse information in the above target candidate forward warehouse information set. j represents the order cluster sequence number. x represents the forward warehouse delivery value, which can indicate whether the forward warehouse needs to deliver the order cluster. x i，j represents the forward warehouse delivery value corresponding to the jth order cluster included in the i-th forward warehouse. When x i，j When x is 1, it means that the first forward warehouse needs the jth order cluster. i，j When y is 0, it means that the i-th forward warehouse does not need to deliver the j-th order cluster. y represents the selected value of the forward warehouse. When y is 1, it means that the forward warehouse is selected. When y is 0, it means that the forward warehouse is not selected. i Indicates the selected value of the forward warehouse corresponding to the i-th forward warehouse. When y i When it is 1, it means that the jth forward warehouse is selected. i When it is 0, it means that the i-th forward warehouse is not selected. N represents the maximum number of forward warehouses built as preset above. 1 represents the preset order fulfillment rate. m represents the number of order clusters covered by the target candidate forward warehouse information in the target candidate forward warehouse information set. i Indicates the number of order clusters covered by the forward warehouse corresponding to the i-th forward warehouse. When a is 1, it means that the forward warehouse corresponds to the order cluster, and when a is 0, it means that the forward warehouse does not correspond to the order cluster. i，j When it is 1, it means that the i-th forward warehouse corresponds to the j-th order cluster. i，j When it is 0, it means that the i-th forward warehouse does not correspond to the j-th order cluster.

[0090] As an example, the preset maximum number of forward warehouses to be built may be 3. The preset order fulfillment rate may be 0.9.

[0091] The second step is to generate forward warehouse capacity constraint information based on the target candidate forward warehouse information set and the predicted order cluster information set. The forward warehouse capacity constraint information can be determined as follows:

[0092]

[0093] Wherein, c represents the average daily item turnover value included in the predicted order cluster information in the above-mentioned predicted order cluster information set. j represents the daily average item turnover value corresponding to the jth order cluster. cmax represents the upper limit of the forward warehouse capacity included in the target candidate forward warehouse information set. cmaxi represents the upper limit of the forward warehouse capacity of the i-th forward warehouse. s represents the number of employees in the forward warehouse. i represents the number of forward warehouse employees corresponding to the i-th forward warehouse. sefa represents the average productivity of the warehouse employees included in the target candidate forward warehouse information set. seff i represents the average productivity of the warehouse staff corresponding to the i-th forward warehouse. smax represents the upper limit of the number of warehouse staff included in the target candidate forward warehouse information set. smax i represents the upper limit of the number of employees in the warehouse corresponding to the i-th forward warehouse. q represents the number of riders in the forward warehouse. i Indicates the number of forward warehouse riders corresponding to the i-th forward warehouse. qef indicates the average rider capacity included in the target candidate forward warehouse information set. qef i represents the average capacity per rider corresponding to the i-th forward warehouse. qmax represents the upper limit of the number of riders included in the target candidate forward warehouse information set. qmax i Indicates the upper limit of the number of riders corresponding to the i-th forward warehouse.

[0094] The third step is to generate the forward warehouse delivery range constraint information based on the above target candidate forward warehouse information set and the preset distance adjustment coefficient. The following formula can be used to determine the forward warehouse delivery range constraint information:

[0095]

[0096] Wherein, d represents the forward warehouse order cluster distance value in the forward warehouse order cluster distance value set included in the target candidate forward warehouse information in the target candidate forward warehouse information set. i，j Indicates the distance between the i-th forward warehouse and the j-th order cluster. md indicates the minimum delivery radius. i Indicates the minimum delivery radius value corresponding to the i-th forward warehouse. 2 Indicates the above preset distance adjustment coefficient.

[0097] As an example, the preset distance adjustment coefficient may be 0.3.

[0098] The fourth step is to determine the forward warehouse address information based on the characteristic order cluster information set, the forward warehouse coverage range constraint information, the forward warehouse capacity constraint information, the forward warehouse delivery range constraint information, the preset weight coefficient set and the preset forward warehouse address constraint information. The preset forward warehouse address constraint information can be the following formula:

[0099]

[0100] First, the target optimization function and the target forward warehouse delivery value included in the forward warehouse address information can be determined by the following formula:

[0101]

[0102] Wherein, A represents the first objective optimization subfunction. cost represents the total average cost per unit of the target candidate forward warehouse information included in the target candidate forward warehouse information set. cost i represents the total average cost per order corresponding to the i-th forward warehouse, BB represents the second objective optimization subfunction, and w represents the weight coefficient in the above-mentioned preset weight coefficient set. 1 represents the first weight coefficient in the above preset weight coefficient set. 2 represents the second weight coefficient in the above preset weight coefficient set. 3 represents the third weight coefficient in the above preset weight coefficient set. 4 represents the fourth weight coefficient in the above preset weight coefficient set. 5 represents the fifth weight coefficient in the above-mentioned preset weight coefficient set. C represents the third objective optimization subfunction. st represents the characteristic order cluster fluctuation value included in the characteristic order cluster information in the above-mentioned characteristic order cluster information set. st j represents the characteristic order cluster fluctuation value corresponding to the jth order cluster. tr represents the characteristic order cluster trend value included in the characteristic order cluster information in the above characteristic order cluster information set. tr j represents the characteristic order cluster trend value corresponding to the jth order cluster. fr represents the characteristic order cluster predictable value included in the characteristic order cluster information in the above characteristic order cluster information set. fr j represents the predictable value of the characteristic order cluster corresponding to the jth order cluster. z represents the above objective optimization function. Indicates the target forward warehouse delivery value included in the forward warehouse address information. argmin represents a minimum value function, which can be used to determine the value of the independent variable when the target optimization function takes the minimum value. Here, there is no limitation on the value of each weight coefficient in the weight coefficient set.

[0103] Then, the target forward warehouse selected value, target forward warehouse employee quantity set and target forward warehouse rider quantity set included in the above-mentioned forward warehouse address information can be determined through various formulas included in the above-mentioned forward warehouse coverage range constraint information, the above-mentioned forward warehouse production capacity constraint information, the above-mentioned forward warehouse delivery range constraint information and the preset forward warehouse address constraint information.

[0104] Optionally, the execution entity may also send the forward warehouse address information to a display terminal for the staff to select the forward warehouse address.

[0105] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the forward warehouse address information generation method of some embodiments of the present disclosure, the accuracy of generating the forward warehouse address information can be improved. Specifically, the reason for the reduced accuracy of generating the forward warehouse address information is that only the item turnover value of the candidate forward warehouse address information is predicted, and the historical item turnover value corresponding to the candidate forward warehouse address information and the future user demand changes are not considered. Based on this, the forward warehouse address information generation method of some embodiments of the present disclosure, first, obtains the historical order information set and the candidate forward warehouse information set. Secondly, based on the above-mentioned historical order information set and the above-mentioned candidate forward warehouse information set, a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set are generated. Thus, the obtained historical order cluster information set can characterize the historical item turnover value corresponding to the candidate forward warehouse address information, the predicted order cluster information set can characterize the future user demand changes corresponding to the candidate forward warehouse address information, and the target candidate forward warehouse information set can characterize the forward warehouse address information of each candidate. Then, based on the above-mentioned predicted order cluster information set, feature extraction processing is performed on each historical order cluster information in the above-mentioned historical order cluster information set to generate characteristic order cluster information and obtain a characteristic order cluster information set. Thus, the historical item turnover value corresponding to the candidate forward warehouse address information and future user demand changes can be combined to obtain a characteristic order cluster information set that can characterize the order cluster characteristics. Finally, based on the above-mentioned target candidate forward warehouse information set, the above-mentioned predicted order cluster information set and the above-mentioned characteristic order cluster information set, the forward warehouse address information is generated. Thus, the forward warehouse address information can be selected from the target candidate forward warehouse information set. Therefore, the forward warehouse address information generation method disclosed in the present invention, by combining the predicted order cluster information and the historical order cluster information to obtain the characteristic order cluster information, takes into account the historical item turnover value and future user demand changes, and can improve the accuracy of generating the forward warehouse address information.

[0106] Further references Figure 3 , Figure 3 The process 300 of another embodiment of the method for generating the forward warehouse address information according to the present disclosure is shown. The method for generating the forward warehouse address information comprises the following steps:

[0107] Step 301, obtaining a historical order information set and a candidate forward warehouse information set.

[0108] In some embodiments, the specific implementation of step 301 and the technical effects thereof can be referred to in Figure 2 The corresponding step 201 in the embodiment will not be described in detail here.

[0109] Step 302: cluster the historical order information set to obtain a historical order cluster information set.

[0110] In some embodiments, the execution entity of the forward warehouse address information generation method (for example Figure 1 The electronic device 101 shown can perform clustering processing on the above historical order information set to obtain a historical order cluster information set.

[0111] In some optional implementations of some embodiments, clustering the historical order information set to obtain the historical order cluster information set may include the following steps:

[0112] In the first step, the order address information included in each two pieces of historical order information in the above historical order information set is matched to generate an address matching result, thereby obtaining an address matching result set. The order address information included in each two pieces of historical order information in the above historical order information set can be matched to generate an address matching result by using a preset address matching algorithm. The above address matching result can be "address match" or "address mismatch".

[0113] As an example, the above-mentioned preset address matching algorithm may be a regular matching algorithm.

[0114] The second step is to integrate the above historical order information set based on the above address matching result set to obtain a matching historical order information set. For each historical order information in the above historical order information set, the above historical order information and each historical order information in the above historical order information set whose address matching result corresponding to the above historical order information is "address matching" can be merged into initial matching historical order information to obtain an initial matching historical order information set. Then, the above initial matching historical order information set is deduplicated to obtain the above matching historical order information set.

[0115] The third step is to cluster the matching historical order information set to obtain the historical order cluster information set. The matching historical order information set can be clustered using a preset order clustering algorithm. The historical order cluster information in the historical order cluster information set may include: order cluster code.

[0116] As an example, the preset order clustering algorithm may be a k-means clustering algorithm. Each historical order cluster information in the historical order cluster information set may be cluster center information of each cluster cluster obtained by the k-means clustering algorithm.

[0117] Step 303: Based on the historical order cluster information set, update the information of each candidate forward warehouse in the candidate forward warehouse information set to obtain the target candidate forward warehouse information set.

[0118] In some embodiments, the execution subject may update each candidate forward warehouse information in the candidate forward warehouse information set based on the historical order cluster information set to obtain the target candidate forward warehouse information set. The target candidate forward warehouse information set may be obtained by determining the correspondence between the forward warehouse coverage included in the candidate forward warehouse information and the order cluster address information included in each historical order cluster information in the historical order cluster information set, determining the correspondence between the forward warehouse code included in the candidate forward warehouse information and the order cluster code included in each historical order cluster information in the historical order cluster information set, and then determining each order cluster code corresponding to the forward warehouse code as an order cluster code set and adding it to the candidate forward warehouse information in the candidate forward warehouse information set, and determining the number of order cluster codes in the order cluster code set as the number of order clusters covered by the forward warehouse and adding it to the candidate forward warehouse information in the candidate forward warehouse information set.

[0119] As an example, the above forward warehouse coverage can be referred to Figure 4 A schematic diagram of a forward warehouse coverage range 400 according to the forward warehouse address information generation method disclosed in the present invention is shown. The forward warehouse corresponding to the candidate forward warehouse information in the candidate forward warehouse information set can refer to Figure 4 The forward warehouse 401 or the forward warehouse 402. Figure 4 The circle corresponding to the forward warehouse 401 in the middle can represent the forward warehouse coverage range corresponding to the forward warehouse 401 mentioned above. Figure 4 The circle corresponding to the forward warehouse 402 in the figure can represent the forward warehouse coverage area corresponding to the forward warehouse 402. The order clusters corresponding to the order cluster codes in the order cluster code set included in the historical order cluster information set can be referred to Figure 4 The order clusters 4011 and 4012 included in the forward warehouse 401 or the order clusters 4021 or 4022 included in the forward warehouse 402. For example, the value "321" in the order cluster 4011 can represent that the number of order clusters included in the order cluster 4011 is 321.

[0120] Step 304: Generate predicted order cluster information corresponding to each piece of historical order cluster information in the historical order cluster information set to obtain the predicted order cluster information set.

[0121] In some embodiments, the execution entity may generate predicted order cluster information corresponding to each piece of historical order cluster information in the historical order cluster information set to obtain the predicted order cluster information set.

[0122] In some optional implementations of some embodiments, the generating of predicted order cluster information corresponding to each piece of historical order cluster information in the historical order cluster information set to obtain the predicted order cluster information set may include the following steps:

[0123] The first step is to split the historical order cluster information to obtain historical order cluster trend information and historical order cluster residual information. The historical order cluster information can be split using a preset splitting algorithm.

[0124] As an example, the preset splitting algorithm may be an STL (Seasonal and Trend decomposition using Loess, time series decomposition using LOSS as a smoothing method) algorithm. The historical order cluster trend information may include but is not limited to at least one of the following: order cluster address information, item turnover value of the historical order cluster in the past month, and the average item turnover value of the historical order cluster in the past three months. The historical order cluster residual information may include but is not limited to at least one of the following: the month of the historical order cluster, the quarter of the historical order cluster, and the item turnover value of the historical order cluster in the past month.

[0125] The second step is to generate a predicted order cluster trend value sequence based on the above historical order cluster trend information. The predicted order cluster trend value sequence can be generated by a preset trend value generation model.

[0126] As an example, the above-mentioned preset trend value generation model may be a linear regression model.

[0127] The third step is to generate a predicted order cluster residual value sequence based on the above historical order cluster trend information. The predicted order cluster residual value sequence can be generated by a preset residual value generation model.

[0128] As an example, the above-mentioned preset residual value generation model can be an XGB (eXtreme Gradient Boosting, extreme gradient boosting) model.

[0129] The fourth step, for each predicted order cluster trend value in the above-mentioned predicted order cluster trend value sequence, the sum of the predicted order cluster residual item value corresponding to the above-mentioned predicted order cluster trend value in the above-mentioned predicted order cluster residual item value sequence and the above-mentioned predicted order cluster trend value is determined as the predicted order cluster item circulation value, and the predicted order cluster item circulation value sequence is obtained.

[0130] Step 5: Based on the above-mentioned predicted order cluster item turnover value sequence, the above-mentioned predicted order cluster information is generated. The order cluster code, order cluster date and the above-mentioned predicted order cluster item turnover value sequence included in the above-mentioned historical order cluster information can be determined as the order cluster number, order cluster date and the predicted order cluster item turnover value sequence included in the predicted order cluster information.

[0131] Step 305 , based on the predicted order cluster information set, feature extraction processing is performed on each historical order cluster information in the historical order cluster information set to generate feature order cluster information, thereby obtaining the feature order cluster information set.

[0132] In some embodiments, the execution entity may perform feature extraction processing on each historical order cluster information in the historical order cluster information set based on the predicted order cluster information set to generate feature order cluster information and obtain the feature order cluster information set.

[0133] Step 306, generating forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set.

[0134] In some embodiments, the execution entity may generate the forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set.

[0135] from Figure 3 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 3 The process 300 of the forward warehouse address information generation method in some corresponding embodiments further highlights the specific steps of generating the historical order cluster information set, the target candidate forward warehouse information set and the predicted order cluster information set. Therefore, the schemes described in these embodiments can concentrate the information that can characterize the order characteristics through clustering and prediction methods, so as to determine the orders that can be delivered by the forward warehouse, thereby improving the traffic diversion capacity of the constructed forward warehouse.

[0136] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a device for generating forward warehouse address information. These device embodiments are similar to Figure 3 Corresponding to the method embodiments shown, the forward warehouse address information generating device can be specifically applied to various electronic devices.

[0137] like Figure 5As shown, the forward warehouse address information generating device 500 of some embodiments includes: an acquisition unit 501, a first generation unit 502, a feature extraction unit 503 and a second generation unit 504. The acquisition unit 501 is configured to acquire a historical order information set and a candidate forward warehouse information set; the first generation unit 502 is configured to generate a target candidate forward warehouse information set and a predicted order cluster information set based on the historical order information set and the candidate forward warehouse information set; the feature extraction unit 503 is configured to perform feature extraction processing on each historical order cluster information in the historical order cluster information set based on the predicted order cluster information set to generate feature order cluster information and obtain the feature order cluster information set; the second generation unit 504 is configured to generate the forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set and the feature order cluster information set.

[0138] Optionally, the forward warehouse address information generating device further includes: a sending unit configured to: send the forward warehouse address information to a display terminal for the staff to select the forward warehouse address.

[0139] Optionally, the acquisition unit 501 is further configured to: acquire a full order information set; filter the full order information set to obtain a filtered order information set; integrate each filtered order information in the filtered order information set to generate historical order information and candidate forward warehouse information, and obtain the historical order information set and the candidate forward warehouse information set.

[0140] Optionally, the first generation unit 502 is further configured to: perform clustering processing on the historical order information set to obtain the historical order cluster information set; based on the historical order cluster information set, update each candidate forward warehouse information in the candidate forward warehouse information set to obtain the target candidate forward warehouse information set; generate predicted order cluster information corresponding to each historical order cluster information in the historical order cluster information set to obtain the predicted order cluster information set.

[0141] Optionally, the historical order information in the above-mentioned historical order information set includes: order address information; and the above-mentioned first generating unit 502 is further configured to: match the order address information included in every two historical order information in the above-mentioned historical order information set to generate an address matching result, and obtain an address matching result set; based on the above-mentioned address matching result set, integrate the above-mentioned historical order information set to obtain a matching historical order information set; cluster the above-mentioned matching historical order information set to obtain the above-mentioned historical order cluster information set.

[0142] Optionally, the first generating unit 502 is further configured to: split the historical order cluster information to obtain historical order cluster trend information and historical order cluster remainder information; generate a predicted order cluster trend value sequence based on the historical order cluster trend information; generate a predicted order cluster remainder value sequence based on the historical order cluster trend information; for each predicted order cluster trend value in the predicted order cluster trend value sequence, determine the sum of the predicted order cluster remainder value corresponding to the predicted order cluster trend value in the predicted order cluster remainder value sequence and the predicted order cluster trend value as the predicted order cluster item circulation value to obtain a predicted order cluster item circulation value sequence; generate the predicted order cluster information based on the predicted order cluster item circulation value sequence.

[0143] Optionally, the feature extraction unit 503 is further configured to: determine the forecast order cluster information corresponding to the historical order cluster information in the forecast order cluster information set as the target forecast order cluster information; perform feature extraction processing on the historical order cluster information and the target forecast order cluster information respectively to obtain the historical order cluster feature information and the forecast order cluster feature information; determine the historical fluctuation value, the historical trend value and the historical predictable value based on the historical order cluster feature information; determine the predicted fluctuation value, the predicted trend value and the predicted predictable value based on the forecast order cluster feature information; determine the characteristic order cluster fluctuation value, the characteristic order cluster predicted trend value and the characteristic order cluster predicted predictable value based on the historical fluctuation value, the historical trend value, the historical predictable value, the predicted fluctuation value, the predicted trend value and the predicted predictable value; perform fusion processing on the characteristic order cluster fluctuation value, the characteristic order cluster predicted trend value and the characteristic order cluster predicted predictable value to obtain the characteristic order cluster information.

[0144] Optionally, the second generation unit 504 is further configured to: generate forward warehouse coverage constraint information based on the target candidate forward warehouse information set, the preset maximum forward warehouse construction quantity and the preset order fulfillment rate; generate forward warehouse capacity constraint information based on the target candidate forward warehouse information set and the predicted order cluster information set; generate forward warehouse delivery range constraint information based on the target candidate forward warehouse information set and the preset distance adjustment coefficient; determine the forward warehouse address information based on the characteristic order cluster information set, the forward warehouse coverage constraint information, the forward warehouse capacity constraint information, the forward warehouse delivery range constraint information and the preset forward warehouse address constraint information.

[0145] It can be understood that the units recorded in the forward warehouse address information generating device 500 are similar to the reference Figure 3Therefore, the operations, features and beneficial effects described above for the forward warehouse address information generation method are also applicable to the forward warehouse address information generation device 500 and the units contained therein, and will not be repeated here.

[0146] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The terminal device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0147] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0148] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0149] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0150] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0151] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0152] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains a historical order information set and a candidate forward warehouse information set; based on the historical order information set and the candidate forward warehouse information set, generates a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set; based on the predicted order cluster information set, performs feature extraction processing on each historical order cluster information in the historical order cluster information set to generate feature order cluster information and obtain a feature order cluster information set; based on the target candidate forward warehouse information set, the predicted order cluster information set and the feature order cluster information set, generates forward warehouse address information.

[0153] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0154] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0155] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor includes an acquisition unit, a first generation unit, a feature extraction unit, and a second generation unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the acquisition unit may also be described as a "unit for acquiring a historical order information set and a candidate forward warehouse information set".

[0156] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0157] Some embodiments of the present disclosure also provide a computer program product, including a computer program, which implements any of the above-mentioned methods for generating forward warehouse address information when executed by a processor.

[0158] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for generating forward warehouse address information, comprising: Obtain historical order information sets and candidate forward warehouse information sets; Based on the historical order information set and the candidate forward warehouse information set, generate a historical order cluster information set, a target candidate forward warehouse information set and a predicted order cluster information set; Based on the predicted order cluster information set, performing feature extraction processing on each historical order cluster information in the historical order cluster information set to generate feature order cluster information, thereby obtaining a feature order cluster information set; Based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set, the forward warehouse address information is generated.

2. The method according to claim 1, wherein: The method further comprises: The forward warehouse address information is sent to the display terminal for the staff to select the forward warehouse address.

3. The method according to claim 1, wherein: The obtaining of the historical order information set and the candidate forward warehouse information set includes: Get the full order information set; Filtering the full order information set to obtain a filtered order information set; Each filtered order information in the filtered order information set is integrated to generate historical order information and candidate forward warehouse information, thereby obtaining the historical order information set and the candidate forward warehouse information set.

4. The method according to claim 1, wherein: The generating of the historical order cluster information set, the target candidate forward warehouse information set and the predicted order cluster information set based on the historical order information set and the candidate forward warehouse information set includes: Performing clustering processing on the historical order information set to obtain the historical order cluster information set; Based on the historical order cluster information set, updating each candidate forward warehouse information in the candidate forward warehouse information set to obtain the target candidate forward warehouse information set; Generate predicted order cluster information corresponding to each piece of historical order cluster information in the historical order cluster information set to obtain the predicted order cluster information set.

5. The method according to claim 4, wherein: The historical order information in the historical order information set includes: order address information; and The clustering process of the historical order information set to obtain the historical order cluster information set includes: Performing matching processing on the order address information included in every two pieces of historical order information in the historical order information set to generate an address matching result, thereby obtaining an address matching result set; Based on the address matching result set, the historical order information set is integrated to obtain a matching historical order information set; The matching historical order information set is clustered to obtain the historical order cluster information set.

6. The method according to claim 4, wherein: The generating of predicted order cluster information corresponding to each piece of historical order cluster information in the historical order cluster information set includes: Splitting the historical order cluster information to obtain historical order cluster trend information and historical order cluster remaining item information; Based on the historical order cluster trend information, generating a predicted order cluster trend value sequence; Based on the historical order cluster trend information, generating a predicted order cluster remainder value sequence; For each predicted order cluster trend value in the predicted order cluster trend value sequence, the sum of the predicted order cluster residual value corresponding to the predicted order cluster trend value in the predicted order cluster residual value sequence and the predicted order cluster trend value is determined as the predicted order cluster item circulation value, to obtain a predicted order cluster item circulation value sequence; The predicted order cluster information is generated based on the predicted order cluster item flow value sequence.

7. The method according to claim 1, wherein: The step of performing feature extraction processing on each historical order cluster information in the historical order cluster information set based on the predicted order cluster information set to generate feature order cluster information includes: Determining the predicted order cluster information corresponding to the historical order cluster information in the predicted order cluster information set as the target predicted order cluster information; Performing feature extraction processing on the historical order cluster information and the target predicted order cluster information respectively to obtain historical order cluster feature information and predicted order cluster feature information; Based on the historical order cluster characteristic information, determine the historical fluctuation value, the historical trend value and the historical predictable value; Based on the predicted order cluster characteristic information, determining a predicted fluctuation value, a predicted trend value, and a predicted predictable value; Determine a characteristic order cluster fluctuation value, a characteristic order cluster prediction trend value, and a characteristic order cluster prediction predictable value based on the historical fluctuation value, the historical trend value, the historical predictable value, the predicted fluctuation value, the predicted trend value, and the predicted predictable value; The characteristic order cluster fluctuation value, the characteristic order cluster prediction trend value and the characteristic order cluster prediction predictable value are fused to obtain the characteristic order cluster information.

8. The method according to claim 1, wherein: The generating the forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set includes: Generate forward warehouse coverage constraint information based on the target candidate forward warehouse information set, a preset maximum forward warehouse construction quantity, and a preset order fulfillment rate; Generate forward warehouse capacity constraint information based on the target candidate forward warehouse information set and the predicted order cluster information set; Generate forward warehouse delivery range constraint information based on the target candidate forward warehouse information set and a preset distance adjustment coefficient; The forward warehouse address information is determined based on the characteristic order cluster information set, the forward warehouse coverage range constraint information, the forward warehouse capacity constraint information, the forward warehouse delivery range constraint information, a preset weight coefficient set and a preset forward warehouse address constraint information.

9. A device for generating forward warehouse address information, comprising: An acquisition unit, configured to acquire a historical order information set and a candidate forward warehouse information set; A first generating unit is configured to generate a target candidate forward warehouse information set and a predicted order cluster information set based on the historical order information set and the candidate forward warehouse information set; a feature extraction unit configured to perform feature extraction processing on each historical order cluster information in the historical order cluster information set based on the predicted order cluster information set to generate feature order cluster information, thereby obtaining the feature order cluster information set; The second generating unit is configured to generate the forward warehouse address information based on the target candidate forward warehouse information set, the predicted order cluster information set and the characteristic order cluster information set.

10. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

Images