Grid warehouse charging method and device based on operation state, equipment and storage medium

Abstract

The invention discloses a grid warehouse charging method, device and equipment based on an operation state, and a storage medium. The method comprises the following steps: obtaining expert knowledge and a plurality of physical state samples with different attributes and a plurality of operation data samples with different attributes of a target grid warehouse; preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set; training a state recognition model based on expert knowledge and the target sample data set, and obtaining a current operation state of the target grid bin output by the state recognition model; and obtaining a current network point, a current operation grid bin of the current network point and a charging mode table, and when the current operation grid bin is different from the target grid bin, determining the target cost of the target grid bin based on the current operation state of the target grid bin and the charging mode table. The charging precision can be improved, the charging strategy can be automatically adjusted, the matching degree of charging and the actual operation condition is improved, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of grid warehouse billing, and specifically relates to an order settlement method, device, equipment and storage medium for logistics staggered packaging. Background Art

[0002] As a key link in the distributed warehousing system, the importance of grid warehouses is self-evident. Grid warehouses not only carry the storage function of goods, but also serve as a bridge between suppliers, e-commerce platforms and consumers. Their construction and operation status are directly related to the stability and efficiency of the entire logistics system. The construction and operation process of grid warehouses is complex and changeable, covering multiple stages from initial planning, construction, testing, to subsequent formal operation, and even possible deactivation or reactivation. Each stage has its own unique characteristics and requirements, and also poses corresponding challenges to the billing system. However, most of the billing systems on the market are relatively simple in design, and can often only charge for a specific state of the grid warehouse, but cannot accurately identify the specific stage and state of the grid warehouse. For outlets that are not correctly bound to grid warehouses, their status may be in a non-operating state, but due to the recognition error of the billing system, these outlets may be mistakenly included in the billing scope, resulting in unnecessary cost burdens for enterprises. This situation is not uncommon in the logistics industry, and often brings unnecessary economic losses and time waste to enterprises. . Summary of the invention

[0003] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a grid warehouse billing method, device, equipment and storage medium based on operating status, so as to solve the problems in the prior art of inaccurate grid warehouse billing, low matching degree between grid warehouse billing and actual operating status, and low grid warehouse operating efficiency.

[0004] According to one aspect of the present application, a grid warehouse billing method based on operating status is disclosed, the method comprising:

[0005] Acquire expert knowledge and physical state samples of multiple different attributes of the target grid warehouse and operation data samples of multiple different attributes;

[0006] Preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set;

[0007] Based on the expert knowledge and the target sample data set, a state recognition model is trained, wherein the state recognition model is used to identify the current operating state of the target grid warehouse;

[0008] Obtaining the current operation status of the target grid warehouse output by the state recognition model;

[0009] Acquire a current network point, a current operating grid warehouse of the current network point, and a billing mode table, wherein the current network point belongs to the target grid warehouse, and the billing mode table includes a correspondence between an operating state and a billing mode;

[0010] When the currently operating grid warehouse is different from the target grid warehouse, a target fee for the target grid warehouse is determined based on the current operating state of the target grid warehouse and the billing mode table.

[0011] In some embodiments, preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set includes:

[0012] After performing data cleaning, data conversion, data merging, and feature engineering on the plurality of physical state samples and the plurality of operational data samples in turn, a target sample data set is obtained.

[0013] In some embodiments, the current operating state includes under construction and out of service, and when the currently operating grid warehouse is different from the target grid warehouse, determining the target fee of the target grid warehouse based on the current operating state of the target grid warehouse and the billing mode table includes:

[0014] Acquire a target ticket item operated by the current outlet in the current grid bin, where the target ticket item is a part of all tickets operated by the current outlet in the current grid bin;

[0015] Obtaining the operating fee of the target grid warehouse and a first billing rate table between the target grid warehouse and the current network point;

[0016] Determining the ticket price of the target ticket based on the first billing rate table;

[0017] The target fee of the target grid warehouse is determined according to the operating fee and the ticket billing.

[0018] In some embodiments, the current operating state further includes testing and operating, and when the currently operating grid warehouse is different from the target grid warehouse, determining the target fee of the target grid warehouse based on the current operating state of the target grid warehouse and the billing mode table includes:

[0019] Obtain all tickets operated by the current outlet in the current grid warehouse;

[0020] Obtaining a second billing rate table between the target grid warehouse and the current network point;

[0021] Determining the total ticket charges for all the tickets based on the second charging rate table;

[0022] Obtaining the operating fee, arrival operation fee and arrival distribution fee of the target grid warehouse;

[0023] The target fee of the target grid warehouse is determined according to the total ticket billing, the operating fee, the arrival operation fee and the arrival distribution fee.

[0024] In some embodiments, before determining the target fee of the target grid warehouse according to the total ticket charges, the operating fee, the arrival operation fee, and the arrival distribution fee, the method further includes:

[0025] Receive a billing grid warehouse binding request from the current network point;

[0026] Based on the binding request, determining whether the billing identifier of the billing grid warehouse exists in a grid warehouse identifier library;

[0027] When the billing identifier of the billing grid warehouse exists in the grid warehouse identifier library, obtaining the current operation status of the billing grid warehouse;

[0028] When the current operating state of the billing grid warehouse is in operation, obtaining a target distance between the current grid warehouse and the billing grid warehouse;

[0029] When the target distance meets the service distance of the current network point, the target fee of the target grid warehouse is determined according to the billing of all tickets, the operating fee, the arrival operation fee and the arrival distribution fee.

[0030] In some embodiments, when the current operating state of the billing grid warehouse is non-operating, the method further includes:

[0031] The current operating status of the billing grid warehouse is marked as pending, and target information is sent to the user.

[0032] In some embodiments, the method further comprises:

[0033] Acquire historical data within a target time period, wherein the historical data includes operation status data and cost data corresponding to each operation status;

[0034] Preprocessing the historical data to obtain historical target data, wherein the historical target data includes operation target state data and cost target data corresponding to the operation target state data;

[0035] Performing sequence analysis on the historical target data using a deep learning algorithm to obtain a cost optimization model, which can output a cost optimization recommendation report based on the operational status conversion situation;

[0036] Get the current operation status and previous operation status;

[0037] The current operating status and the previous operating status are input into the cost optimization model to obtain a target optimization recommendation report output by the cost user model.

[0038] According to another aspect of the present application, a grid warehouse billing device based on operation status is also disclosed, the device comprising:

[0039] A sample acquisition module is used to acquire expert knowledge and physical state samples of multiple different attributes of the target grid warehouse and operation data samples of multiple different attributes;

[0040] A preprocessing module, used for preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set;

[0041] A model training module, used for training a state recognition model based on the expert knowledge and the target sample data set, wherein the state recognition model is used for identifying the current operating state of the target grid warehouse;

[0042] An operation status acquisition module, used to acquire the current operation status of the target grid warehouse output by the state recognition model;

[0043] A mode acquisition module, used to acquire a current network point, a current operating grid warehouse of the current network point, and a billing mode table, wherein the current network point belongs to the target grid warehouse, and the billing mode table includes a correspondence between an operating state and a billing mode;

[0044] The target fee determination module is used to determine the target fee of the target grid warehouse based on the current operating state of the target grid warehouse and the billing mode table when the current operating grid warehouse is different from the target grid warehouse.

[0045] According to another aspect of the present application, the present application also discloses an electronic device, which includes a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the various steps of the grid warehouse billing method based on operating status as described above.

[0046] According to another aspect of the present application, a computer-readable storage medium is also disclosed, on which instructions are stored, characterized in that when the instructions are executed by a processor, the various steps of the grid warehouse billing method based on operating status as described above are implemented.

[0047] The present invention includes but is not limited to the following beneficial effects: (1) This application introduces expert knowledge and a large number of physical state samples and operation data samples to train the state recognition model, which can accurately identify the current operation state of the grid warehouse, which provides a reliable data basis for the subsequent billing process and ensures the accuracy and rationality of the billing; (2) According to the current operation state of the grid warehouse (such as under construction, decommissioned, under test and in operation) and the billing mode table, the target fee can be determined, and the billing mode can be dynamically adjusted to adapt to the cost change requirements under different states, which is helpful for refined management and cost control and improves operational efficiency; (3) This application obtains historical data and uses deep learning algorithms for sequence analysis to establish a cost optimization model. The model can output a cost optimization recommendation report based on the operation state conversion situation, helping enterprises to adjust strategies and optimize cost structures in time during operation; (4) When the current operation state of the billing grid warehouse is in operation, the target fee is determined based on the service distance, and when the current operation state of the billing grid warehouse is not in operation, it is marked as pending and the target information is sent. This mechanism improves the flexibility and reliability of billing and reduces potential billing errors and disputes; (5) In this application, by preprocessing the physical state samples and operational data samples, the data quality and consistency are improved, multi-source data are integrated, effective features are extracted, the model training efficiency and generalization ability are improved, refined management is supported, operational risks are reduced, and accurate identification and dynamic billing of grid warehouses under different operating conditions are achieved, thereby optimizing the operational efficiency and economic benefits of the enterprise; (6) By receiving the billing grid warehouse binding request of the current outlet and verifying whether the billing grid warehouse's billing identifier exists in the grid warehouse identifier library, the legitimacy and accuracy of the billing grid warehouse are improved. Furthermore, when the current operating state of the billing grid warehouse is in operation, the target distance between the current grid warehouse and the billing grid warehouse is obtained, and it is determined whether the target distance meets the service distance of the current outlet. The fee calculation can be dynamically adjusted to improve the rationality and accuracy of the billing fee. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0049] Figure 1 This is a flow chart of a grid warehouse billing method based on operating status according to an embodiment of the present application;

[0050] Figure 2 This is another flow chart of the grid warehouse billing method based on the operating status of an embodiment of the present application;

[0051] Figure 3 This is another flow chart of the grid warehouse billing method based on the operating status of an embodiment of the present application;

[0052] Figure 4 This is another flow chart of the grid warehouse billing method based on the operating status of an embodiment of the present application;

[0053] Figure 5 This is another flow chart of the grid warehouse billing method based on the operating status of an embodiment of the present application;

[0054] Figure 6 It is a structural block diagram of a grid warehouse billing device based on operating status according to an embodiment of the present application;

[0055] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The embodiment of the present invention provides a grid warehouse billing method based on operation status, including obtaining expert knowledge and multiple physical state samples of different attributes and multiple operation data samples of different attributes of a target grid warehouse; preprocessing multiple physical state samples and multiple operation data samples to obtain a target sample data set; training a state recognition model based on the expert knowledge and the target sample data set, wherein the state recognition model is used to identify the current operation status of the target grid warehouse; obtaining the current operation status of the target grid warehouse output by the state recognition model; obtaining a current network point, a current operation grid warehouse of the current network point, and a billing mode table, wherein the current network point belongs to the target grid warehouse, and the billing mode table includes a corresponding relationship between the operation status and the billing mode; when the current operation grid warehouse is different from the target grid warehouse, determining the target fee of the target grid warehouse based on the current operation status of the target grid warehouse and the billing mode table. The billing accuracy can be improved, the billing strategy can be automatically adjusted, the matching degree between the billing and the actual operation status can be improved, and the operation efficiency can be improved.

[0057] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] For ease of understanding, the specific process of the embodiment of the present invention is described below. Specifically, Figure 1 This is a flow chart of the grid warehouse billing method based on operating status of this application, and the steps include:

[0059] S100, acquiring expert knowledge and physical state samples of multiple different attributes and operation data samples of multiple different attributes of a target grid warehouse.

[0060] Specifically, the experience and knowledge of experts can be obtained through interviews, questionnaires, work logs, etc. Physical state samples may include but are not limited to samples determined based on sensor data and equipment data, wherein sensors may be arranged around the target grid warehouse to collect sensor data such as temperature, humidity, light, air quality, etc. as physical state samples, and equipment data such as equipment operation status, fault records, maintenance records, etc. may be collected as physical state samples. Operational data samples may include but are not limited to samples determined based on inventory records, order processing records, and employee operation records, wherein samples obtained based on inventory records may include goods in and out of the warehouse records, inventory quantity, cargo location information, etc., samples determined based on order processing records may include data samples in the process of order creation, processing, delivery, receipt, etc., and samples determined based on employee operation records may include employee operation records in the warehouse, such as inventory, picking, shelving, and other sample data.

[0061] S102: Preprocess multiple physical state samples and multiple operation data samples to obtain a target sample data set.

[0062] Specifically, preprocessing multiple physical state samples and multiple operational data samples to obtain target sample data can be performed on multiple physical state samples and multiple operational data samples in sequence, and then data cleaning, data conversion, data merging, and feature engineering are performed to obtain the target sample data set. Specifically, data cleaning is used to remove noise data and erroneous data, as well as missing values ​​and outliers; data conversion is used to ensure data quality and unify data formats and units for subsequent processing; data merging is used to align data from different data sources with timestamps and spaces, integrate data through associated keys (such as cargo ID, equipment ID, timestamp, etc.), eliminate duplicate data, and ensure that each record is unique and complete. Feature engineering is feature extraction, which is used to extract key features (such as temperature, humidity, number of cargo in and out, equipment operating time, etc.) from the integrated data. It can be understood that features can be raw data or derived features (such as average temperature, humidity change rate, etc.) after calculation and conversion. Further, constructing a sample set is to organize the extracted features into samples according to a certain time window or event window, and each sample contains multiple features, representing the comprehensive state of a certain time period or an event. Through data integration and sample set construction, machine learning models can be trained using multi-source data to improve the accuracy and reliability of predictions. The integrated sample set contains more comprehensive operational data and physical status information, which can provide rich feature support for the model and help the model better identify patterns and trends. By preprocessing physical status samples and operational data samples, data quality and consistency are improved, multi-source data is integrated, effective features are extracted, model training efficiency and generalization capabilities are improved, refined management is supported, operational risks are reduced, and accurate identification and dynamic billing of grid warehouses under different operating conditions are achieved, optimizing the company's operational efficiency and economic benefits;

[0063] S104: training a state recognition model based on expert knowledge and a target sample data set.

[0064] Specifically, the state recognition model is used to identify the current operating state of the target grid warehouse;

[0065] S106: Obtain the current operating status of the target grid warehouse output by the status recognition model.

[0066] S108, obtaining the current network point, the current operation grid bin of the current network point, and a billing mode table.

[0067] Specifically, the current outlet belongs to the target grid warehouse, and the billing mode table includes the correspondence between the operation status and the billing mode.

[0068] S110: When the current operating grid warehouse is different from the target grid warehouse, determine the target fee of the target grid warehouse based on the current operating status of the target grid warehouse and the billing mode table.

[0069] It can be understood that this application can accurately identify the current operating status of the grid warehouse by introducing expert knowledge and a large number of physical state samples and operating data samples to train the state recognition model, which provides a reliable data basis for the subsequent billing process and ensures the accuracy and rationality of the billing; further, the target fee is determined according to the current operating status of the grid warehouse and the billing model table, which can realize dynamic adjustment of the billing model to adapt to the cost changes under different states, which is conducive to refined management and cost control and improved operational efficiency.

[0070] Figure 2 This is another flow chart of the grid warehouse billing method based on the operating status of this application. The method shown in the flow chart is applicable when the current operating status includes under construction and out of service. Specifically, the steps are as follows:

[0071] S200: Obtain the target ticket that the current outlet operates in the current grid warehouse.

[0072] Among them, the current outlet refers to the current logistics operation point, which can be a warehouse, distribution center or any node responsible for handling logistics tickets. Specifically, the current grid warehouse refers to the specific grid warehouse used by the current outlet to store, process or distribute tickets. The target ticket is a part of all the tickets operated by the current outlet in the current grid warehouse. Specifically, when obtaining the target ticket operated by the current outlet in the current grid warehouse, all the tickets operated by the current outlet in the current grid warehouse can be obtained first, and further, the conditions for filtering some tickets can be determined, such as time window, ticket type or attribute, order priority, random sampling and other filtering conditions to achieve the filtering of some tickets, and the filtered tickets are used as target tickets.

[0073] S202: Obtain the operating fee of the target grid warehouse and a first billing rate table between the target grid warehouse and the current network point.

[0074] The first billing rate table is used to characterize the corresponding relationship between tickets and rates.

[0075] S204: Determine the ticket billing of the target ticket based on the first billing rate table.

[0076] In one example, you can filter by ticket type, for example, filter out tickets of type clothing. The relationship between clothing and rates is that the first weight is 10 yuan, and the additional weight over 1 kg is 2 yuan. Therefore, the ticket billing of the target ticket can be determined based on the first billing rate table (the corresponding relationship between tickets and rates).

[0077] S206. Determine the target cost of the target grid warehouse based on the operating expenses and ticket charges.

[0078] Specifically, the target cost of the target grid warehouse includes the sum of operating costs and ticket billing. Specifically, operating costs refer to the various costs incurred by the grid warehouse during operation, including labor costs, equipment maintenance costs, water and electricity costs, etc. These costs are usually calculated by time period (such as day, month, year) and allocated to the processed tickets.

[0079] It is understandable that by determining the target fee based on the current operating status of the grid warehouse (under construction, out of service) and the billing model table, the billing model can be adjusted dynamically to adapt to the changing needs of fees under different states, which is conducive to refined management and cost control and improves operational efficiency;

[0080] Figure 3 This is another flowchart of the grid warehouse billing method based on the operating status of this application. The method shown in the flowchart is applicable to the current operating status including testing and operation. Specifically, the steps are as follows:

[0081] S300: Obtain all tickets operated by the current outlet in the current grid warehouse.

[0082] S302: Obtain a second billing rate table between the target grid warehouse and the current network point.

[0083] The second charging rate table is used to represent the corresponding relationship between tickets and charging rates under the current operating status.

[0084] S304: Determine the billing of all tickets for all tickets based on the second billing rate table.

[0085] Specifically, this step may refer to step S204, which will not be described in detail here.

[0086] S306: Obtain the operating fee, arrival operation fee and arrival distribution fee of the target grid warehouse.

[0087] Specifically, the operating fee refers to the various costs incurred by the grid warehouse during operation, including labor costs, equipment maintenance costs, water and electricity costs, etc. These costs are usually calculated by time period (such as day, month, year) and allocated to the processed tickets. When the operating fee is fixed, it can be obtained from the predefined rate table. When the operating fee needs to be calculated dynamically, it can be dynamically calculated based on specific operating data, such as the number of tickets processed and the operating time. The arrival operation fee refers to the operating fee required after the ticket arrives at the target grid warehouse, including the cost of unloading, inspection, classification and other operations. The fee can be calculated based on the type of ticket, the complexity of the operation and the time spent. The arrival distribution fee refers to the cost of distributing the ticket to the next-level outlet or the final destination after the operation is completed in the target grid warehouse, which can include packaging, loading, transportation and other costs. The fee can be calculated based on the distance to the distribution destination, or the weight or volume of the ticket. Based on the above introduction, the operating fee, arrival operation fee and arrival distribution fee of the target grid warehouse are obtained.

[0088] S308. Determine the target cost of the target grid warehouse based on the total ticket charges, operation fees, arrival handling fees, and arrival distribution fees.

[0089] Specifically, the target cost of the target grid warehouse is the total cost of all ticket billing, operating expenses, arrival handling fees, and arrival distribution fees.

[0090] It can be understood that by determining the target fee based on the current operating status of the grid warehouse (under testing and in operation) and the billing model table, the billing model can be dynamically adjusted to adapt to the changing cost requirements under different states, which is helpful for refined management and cost control and improved operational efficiency.

[0091] Figure 4 This is another flow chart of the grid warehouse billing method based on the operating status of this application. The flow chart is applied to the steps: before determining the target fee of the target grid warehouse according to the billing of all tickets, the operating fee, the arrival operation fee, and the arrival distribution fee, the specific steps are as follows:

[0092] S400: Receive a billing grid warehouse binding request from the current network point.

[0093] S402: Based on the binding request, determine whether the billing identity of the billing grid warehouse exists in the grid warehouse identity library.

[0094] It is understandable that the association and binding operation between outlets and grid warehouses in the logistics or distribution system is to ensure that the tickets of a specific outlet can be correctly calculated and processed and distributed by the appropriate grid warehouse. Specifically, the binding request contains the identifier of the current outlet (such as the outlet ID) and the identifier of the target billing grid warehouse (such as the grid warehouse ID). When the system receives a binding request, it verifies the validity of the request, including checking whether the outlet and grid warehouse exist.

[0095] S404: When the billing identification of the billing grid warehouse exists in the grid warehouse identification library, obtain the current operation status of the billing grid warehouse.

[0096] S406: When the current operating state of the billing grid warehouse is in operation, obtain the target distance between the current grid warehouse and the billing grid warehouse.

[0097] Specifically, the target distance refers to the distance between the current grid warehouse and the billing grid warehouse, which is usually used to calculate transportation costs or plan logistics routes. The geographical locations (such as longitude and latitude) of the current grid warehouse and the billing grid warehouse can be obtained, and the target distance between the two grid warehouses can be calculated using the geographical locations.

[0098] S408. When the target distance meets the service distance of the current outlet, the target fee of the target grid warehouse is determined based on the total ticket billing, operation fee, arrival handling fee and arrival distribution fee.

[0099] Specifically, the service distance is the service range that the current outlet can serve.

[0100] It can be understood that the embodiment of the application improves the legitimacy and accuracy of the billing grid warehouse by receiving the billing grid warehouse binding request of the current outlet and verifying whether the billing identifier of the billing grid warehouse exists in the grid warehouse identifier library. Furthermore, when the current operating status of the billing grid warehouse is in operation, the target distance between the current grid warehouse and the billing grid warehouse is obtained, and it is determined whether the target distance meets the service distance of the current outlet. The fee calculation can be dynamically adjusted to improve the rationality and accuracy of the billing fees.

[0101] Furthermore, when the current operating state of the billing grid warehouse is non-operating, the current operating state of the billing grid warehouse may be marked as pending, and target information may be sent to the user.

[0102] It is understandable that when the current operating status of the billing grid warehouse is non-operating, it is marked as pending and the target information is sent. This mechanism improves the flexibility and reliability of billing and reduces potential billing errors and disputes.

[0103] Figure 5 This is another flow chart of the grid warehouse billing method based on the operating status of this application, and the steps are as follows:

[0104] S500: Obtain historical data within a target time period.

[0105] Specifically, the target time period may be pre-set, and the historical data includes operation status data and cost data corresponding to each operation status. When acquiring the historical data within the target time period, the start time and the end time may be determined first, and the operation status and its time range within the target time period may be queried from the database, and the corresponding cost data may be queried according to the time range of the operation status.

[0106] S502: Preprocess the historical data to obtain historical target data, where the historical target data includes operation target status data and cost target data corresponding to the operation target status data.

[0107] Specifically, the preprocessing steps can refer to the processing steps for the sample data introduced above, which will not be repeated here.

[0108] S504. Use deep learning algorithms to perform sequence analysis on historical target data to obtain a cost optimization model. The cost optimization model can output a cost optimization recommendation report based on the operation status conversion.

[0109] Among them, the deep learning algorithm may include but is not limited to a recurrent neural network (RNN) or a long short-term memory network (LSTM) algorithm. Specifically, the deep learning algorithm is used to perform sequence analysis on historical target data, that is, by modeling the time series data, identifying patterns and trends in the data, so as to build a cost optimization model that predicts future costs and provides optimization suggestions.

[0110] S506: Obtain the current operation status and the previous operation status.

[0111] Specifically, the previous operating state is the previous operating state of the current operating state.

[0112] S508: Input the current operation status and the previous operation status into the cost optimization model to obtain a target optimization recommendation report output by the cost user model.

[0113] Further, according to another aspect of the present application, a grid warehouse billing device based on operation status is also disclosed, such as Figure 6 As shown, the device comprises:

[0114] A sample acquisition module is used to acquire expert knowledge and physical state samples of multiple different attributes of the target grid warehouse and operation data samples of multiple different attributes;

[0115] A preprocessing module, used for preprocessing a plurality of physical state samples and a plurality of operation data samples to obtain a target sample data set;

[0116] A model training module is used to train a state recognition model based on expert knowledge and a target sample data set, wherein the state recognition model is used to identify the current operating state of the target grid warehouse;

[0117] An operation status acquisition module is used to obtain the current operation status of the target grid warehouse output by the state recognition model;

[0118] A mode acquisition module is used to acquire the current network point, the current operation grid warehouse of the current network point, and the billing mode table, wherein the current network point belongs to the target grid warehouse, and the billing mode table includes the corresponding relationship between the operation status and the billing mode;

[0119] The target fee determination module is used to determine the target fee of the target grid warehouse based on the current operating status of the target grid warehouse and the billing mode table when the current operating grid warehouse is different from the target grid warehouse.

[0120] The application introduction of the relevant modules of the device in this example can refer to the relevant introduction of the principle of the above method, which will not be repeated here.

[0121] above Figure 6 The grid warehouse billing device based on operating status in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0122] Figure 7 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 833 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 can be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0123] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 7 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0124] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of a method for settling an order for logistics mismatched packages.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0127] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A grid warehouse billing method based on operation status, characterized in that: The method comprises: Acquire expert knowledge and physical state samples of multiple different attributes of the target grid warehouse and operation data samples of multiple different attributes; Preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set; Based on the expert knowledge and the target sample data set, a state recognition model is trained, wherein the state recognition model is used to identify the current operating state of the target grid warehouse; Obtaining the current operation status of the target grid warehouse output by the state recognition model; Acquire a current network point, a current operating grid warehouse of the current network point, and a billing mode table, wherein the current network point belongs to the target grid warehouse, and the billing mode table includes a correspondence between an operating state and a billing mode; When the currently operating grid warehouse is different from the target grid warehouse, a target fee for the target grid warehouse is determined based on the current operating state of the target grid warehouse and the billing mode table.

2. The grid warehouse billing method based on operating status according to claim 1 is characterized in that: Preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set includes: After performing data cleaning, data conversion, data merging, and feature engineering on the plurality of physical state samples and the plurality of operational data samples in turn, a target sample data set is obtained.

3. The grid warehouse billing method based on operating status according to claim 1 is characterized in that: The current operation state includes under construction and out of service, and when the currently operating grid warehouse is different from the target grid warehouse, determining the target fee of the target grid warehouse based on the current operation state of the target grid warehouse and the billing mode table includes: Acquire a target ticket item operated by the current outlet in the current grid bin, where the target ticket item is a part of all tickets operated by the current outlet in the current grid bin; Obtaining the operating fee of the target grid warehouse and a first billing rate table between the target grid warehouse and the current network point; Determining the ticket price of the target ticket based on the first billing rate table; The target fee of the target grid warehouse is determined according to the operating fee and the ticket billing.

4. The grid warehouse billing method based on operating status according to claim 1 is characterized in that: The current operation state also includes testing and operation. When the current operation grid warehouse is different from the target grid warehouse, determining the target fee of the target grid warehouse based on the current operation state of the target grid warehouse and the billing mode table includes: Obtain all tickets operated by the current outlet in the current grid warehouse; Obtaining a second billing rate table between the target grid warehouse and the current network point; Determining the total ticket charges for all the tickets based on the second charging rate table; Obtaining the operating fee, arrival operation fee and arrival distribution fee of the target grid warehouse; The target fee of the target grid warehouse is determined according to the total ticket billing, the operating fee, the arrival operation fee and the arrival distribution fee.

5. The grid warehouse billing method based on operating status according to claim 4 is characterized in that: Before determining the target fee of the target grid warehouse according to the total ticket charges, the operating fee, the arrival operation fee, and the arrival distribution fee, the method further includes: Receive a billing grid warehouse binding request from the current network point; Based on the binding request, determining whether the billing identifier of the billing grid warehouse exists in a grid warehouse identifier library; When the billing identifier of the billing grid warehouse exists in the grid warehouse identifier library, obtaining the current operation status of the billing grid warehouse; When the current operating state of the billing grid warehouse is in operation, obtaining a target distance between the current grid warehouse and the billing grid warehouse; When the target distance meets the service distance of the current network point, the target fee of the target grid warehouse is determined according to the billing of all tickets, the operating fee, the arrival operation fee and the arrival distribution fee.

6. The grid warehouse billing method based on operating status according to claim 5 is characterized in that: When the current operating state of the billing grid warehouse is non-operating, the method further includes: The current operating status of the billing grid warehouse is marked as pending, and target information is sent to the user.

7. The grid warehouse billing method based on operating status according to claim 1 is characterized in that: The method further comprises: Acquire historical data within a target time period, wherein the historical data includes operation status data and cost data corresponding to each operation status; Preprocessing the historical data to obtain historical target data, wherein the historical target data includes operation target state data and cost target data corresponding to the operation target state data; Performing sequence analysis on the historical target data using a deep learning algorithm to obtain a cost optimization model, which can output a cost optimization recommendation report based on the operational status conversion situation; Get the current operation status and previous operation status; The current operating status and the previous operating status are input into the cost optimization model to obtain a target optimization recommendation report output by the cost user model.

8. A grid warehouse billing device based on operating status, characterized in that: The device comprises: A sample acquisition module is used to acquire expert knowledge and physical state samples of multiple different attributes of the target grid warehouse and operation data samples of multiple different attributes; A preprocessing module, used for preprocessing the plurality of physical state samples and the plurality of operation data samples to obtain a target sample data set; A model training module, used for training a state recognition model based on the expert knowledge and the target sample data set, wherein the state recognition model is used for identifying the current operating state of the target grid warehouse; An operation status acquisition module, used to acquire the current operation status of the target grid warehouse output by the state recognition model; A mode acquisition module, used to acquire a current network point, a current operating grid warehouse of the current network point, and a billing mode table, wherein the current network point belongs to the target grid warehouse, and the billing mode table includes a correspondence between an operating state and a billing mode; The target fee determination module is used to determine the target fee of the target grid warehouse based on the current operating state of the target grid warehouse and the billing mode table when the current operating grid warehouse is different from the target grid warehouse.

9. An electronic device, comprising a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes each step of the grid warehouse billing method based on operating status as described in claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the grid warehouse billing method based on operating status as described in any one of claims 1 to 7 are implemented.

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