Logistics network point signing rate analysis method, device and equipment and storage medium

Abstract

The application discloses a logistics network point signing rate analysis method and device, equipment and storage medium. The method comprises the following steps: determining a target data set; obtaining the receiving information and the signing information of the target logistics package in the target data set, wherein the receiving information is used for representing the target signing information of the target logistics package, and the signing information is used for representing the actual signing information of the target logistics package; determining the signing result of the target logistics package based on the target signing information and the actual signing information; obtaining the normal signing quantity of the target network point in the target time period based on the signing result; obtaining the target signing quantity of the target network point; and determining the normal signing rate of the target logistics package in the target network point in the target data set based on the normal signing quantity and the target signing quantity. The application can statistically analyze the normal signing rate, can more accurately reflect the operation situation of the operation network point, can improve the effective management of the operation network point, and can improve the logistics service quality.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of logistics transportation, and particularly relates to a logistics network point signing rate analysis method, device, equipment and storage medium. BACKGROUND

[0002] In the logistics industry, the signing rate of a logistics network point is one of the important indicators for measuring its operational efficiency and service quality. A higher signing rate means that goods can be timely and accurately delivered to customers, which helps to improve customer satisfaction, reduce logistics costs, and enhance the overall competitiveness of logistics enterprises. However, many logistics enterprises currently lack in monitoring and analyzing the signing rate, and mostly use manual periodic statistics or post-analysis methods. This method is not only inefficient, but also cannot timely discover problems in the signing process and take appropriate measures, resulting in some potential risks being unable to be effectively controlled, affecting the quality of logistics services and the benefits of enterprises. SUMMARY

[0003] In view of the deficiencies of the prior art, the purpose of the present application is to provide a logistics network point signing analysis method, device, equipment and storage medium to solve the problems of low signing rate analysis efficiency and reduced logistics service quality in the prior art.

[0004] According to one aspect of the present application, a logistics network point signing rate analysis method is disclosed, the method comprising:

[0005] determining a target data set, the target data set being one of a plurality of data sets in a logistics database that are sequentially counted in time order, the statistical time interval of adjacent two data sets being a target time period;

[0006] obtaining the receiving information and the signing information of a target logistics package in the target data set, wherein the receiving information is used to represent the target signing information of the target logistics package, and the signing information is used to represent the actual signing information of the target logistics package;

[0007] determining the signing result of the target logistics package based on the target signing information and the actual signing information of the target logistics package;

[0008] obtaining the normal signing quantity of a target network point in a target time period based on the signing result of the target logistics package, wherein the target network point is one of a plurality of network points matched by the target data set;

[0009] obtaining the target signing quantity of the target network point;

[0010] determining the normal signing rate of the target logistics package in the target network point in the target data set based on the normal signing quantity and the target signing quantity.

[0011] In some embodiments, determining the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package includes:

[0012] Obtaining the actual receipt point of the target logistics package, where the actual receipt point is the point corresponding to the actual receipt information;

[0013] When the actual receipt outlet is within the outlet range matched by the address information in the receipt information, determining that the receipt result of the target logistics package is normally received;

[0014] When the actual receipt outlet is outside the range of outlets matched by the address information in the receipt information, it is determined that the receipt result of the target logistics package is abnormally received.

[0015] In some embodiments, before determining the target dataset, the method further includes:

[0016] Collect order receipt information and order signature information of order logistics packages in real time;

[0017] The order receipt information and the order signature information are encrypted and transmitted in real time to the logistics database for storage.

[0018] In some embodiments, after determining the normal receipt rate of the target logistics package in the target data set at the target outlet based on the normal receipt quantity and the target receipt quantity, the method further includes:

[0019] The acceptance rate of the target outlet is sent to a target display interface, so that the target display interface displays the normal acceptance rate of the target outlet based on a target display condition.

[0020] In some embodiments, determining the normal receipt rate of the target logistics package in the target data set at the target outlet based on the normal receipt quantity and the target receipt quantity includes:

[0021] The normal acceptance rate of the target outlet is determined based on the following formula:

[0022] Normal receipt rate = normal receipt quantity ÷ target receipt quantity × 100%.

[0023] In some embodiments, the method further comprises:

[0024] Obtain the theoretical delivery quantity of the order logistics packages within the target time period and the actual delivery quantity of the target logistics packages;

[0025] Obtain the theoretical number of signed-in orders for logistics packages within the target time period and the actual number of signed-in orders for the target logistics packages;

[0026] Determining a package delivery rate within the target time period based on the theoretical delivery quantity and the actual delivery quantity;

[0027] Determine a parcel pickup rate within the target time period based on the theoretical number of signed-for parcels and the actual number of signed-for parcels;

[0028] The logistics operation status within the target time period is determined based on the package delivery rate and the package pickup rate.

[0029] In some embodiments, the method further comprises:

[0030] Inputting the target logistics package into an abnormal situation recognition model for recognition, so as to output an abnormal receipt result of the target logistics package, wherein the abnormal situation recognition model is pre-trained;

[0031] Training anomaly recognition models involves:

[0032] Collect historical order information;

[0033] Building a training set based on the historical order information;

[0034] The training set is trained based on a decision tree algorithm to build an abnormal situation recognition model.

[0035] According to another aspect of the present application, a logistics network point receipt analysis device is also disclosed, characterized in that the device includes:

[0036] A target data set determination module is used to determine a target data set, where the target data set is one of multiple data sets that are statistically analyzed in chronological order in the logistics database, and a target time interval between two adjacent data sets;

[0037] A target logistics package information acquisition module is used to obtain the receipt information and receipt information of the target logistics package in the target data set, wherein the receipt information is used to represent the target receipt information of the target logistics package, and the receipt information is used to represent the actual receipt information of the target logistics package;

[0038] A receipt result determination module, configured to determine a receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package;

[0039] A normal receipt quantity acquisition module is used to obtain the normal receipt quantity of a target network point within a target time period based on the receipt result of the target logistics package, wherein the target network point is one of the multiple networks matched by the target data set;

[0040] A target receipt quantity acquisition module is used to obtain the target receipt quantity of the target outlet;

[0041] The normal receipt rate determination module is used to determine the normal receipt rate of the target logistics package in the target data set at the target outlet based on the normal receipt quantity and the target receipt quantity.

[0042] According to another aspect of the present application, an electronic device is also disclosed, 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 each step of the logistics network receipt rate analysis method as described above.

[0043] According to another aspect of the present application, a computer-readable storage medium is also disclosed, on which instructions are stored. When the instructions are executed by a processor, the various steps of the logistics network receipt rate analysis method as described in any of the above items are implemented.

[0044] The present invention includes but is not limited to the following beneficial effects: (1) The present invention can perform statistical analysis on the normal receipt rate, can reflect the operating conditions of the operating network with relatively high accuracy, improve the effective management of the operating network, and improve the quality of logistics services; (2) The present invention divides the data in the database into time periods in chronological order to form multiple target data sets, which facilitates the time period analysis of the package information and can also provide timely feedback on the logistics receipt status based on the time sequence; (3) The present invention can clearly determine the receipt result of the package by comparing the target receipt information and the actual receipt information, providing a reliable basis for subsequent analysis, and by analyzing the number of normal receipts at the target network within the target time period, it can evaluate the network. (4) The present invention can accurately judge whether a package is received normally or abnormally by matching and comparing the outlets, thereby improving the accuracy of the judgment; (5) The present invention ensures the timeliness of the data by collecting the receipt information and receipt information of the order in real time, and can quickly respond to changes; (6) The present invention can send the receipt rate to the target display interface, and can intuitively show the operating performance of the outlet to the management or relevant personnel, facilitating quick decision-making; (7) The present invention can continuously improve the recognition ability of the model and enhance the intelligence level of the system by collecting historical order information and constructing a training set, and using the decision tree algorithm to train the abnormal situation recognition model. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows.

[0046] Figure 1 is a flow chart of a logistics network point signing rate analysis method of an embodiment of the present application;

[0047] Figure 2 is another flow chart of a logistics network point signing rate analysis method of an embodiment of the present application;

[0048] Figure 3 is another flow chart of a logistics network point signing rate analysis method of an embodiment of the present application;

[0049] Figure 4 is another flow chart of a logistics network point signing rate analysis method of an embodiment of the present application;

[0050] Figure 5 is another flow chart of a logistics network point signing rate analysis method of an embodiment of the present application;

[0051] Figure 6 is a structural block diagram of a logistics network point signing rate analysis device of an embodiment of the present application;

[0052] Figure 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] The embodiments of the present application provide a logistics network point signing rate analysis method, device, equipment and storage medium, the method comprises determining a target data set, the target data set is one of a plurality of data sets in a logistics database which are sequentially counted in time sequence, and the statistical time interval of two adjacent data sets is a target time period; obtaining the receiving information and the signing information of a target logistics package in the target data set, wherein the receiving information is used to represent the target signing information of the target logistics package, and the signing information is used to represent the actual signing information of the target logistics package; determining the signing result of the target logistics package based on the target signing information and the actual signing information of the target logistics package; obtaining the normal signing quantity of a target network point in the target time period based on the signing result of the target logistics package, wherein the target network point is one of a plurality of network points matched by the target data set; obtaining the target signing quantity of the target network point; and determining the normal signing rate of the target logistics package in the target network point in the target data set based on the normal signing quantity and the target signing quantity. The present application can statistically analyze the normal signing rate, can more accurately reflect the operation situation of the operation network point, can improve the effective management of the operation network point, and can improve the logistics service quality.

[0054] The terms "first", "second", "third", "fourth" and the like in the description and in the claims of the present application, and above-described drawings, if any, are used to distinguish between similar objects and are not necessarily used to describe a particular sequential or chronological order. It is to be understood that the use of the terms so-termed, if any, can be interchanged, where appropriate, to permit herein-described embodiments to be carried out in sequences other than those illustrated or described herein. Furthermore, the terms "comprise" or "have" and any variations thereof are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus that comprises a list of steps or units is not necessarily limited to those steps or units that are clearly recited, but can include other steps or units that are not expressly listed or inherent to such process, method, product or apparatus.

[0055] For the sake of understanding, the specific flow of the embodiments of the present application is described below, in particular, Figure 1 A flowchart of the logistics network point signing rate analysis method of the present application, the steps include:

[0056] S100, determine the target data set.

[0057] Specifically, the target data set is one of a plurality of data sets in a logistics database that are sequentially counted in time order, and the statistical time interval of the two adjacent data sets is a target time period. Each data set contains a plurality of logistics packages, and the logistics database is used to store the relevant data of each logistics package, including but not limited to delivery information (such as the name, address, and contact information of the recipient), shipping information (such as the name, address, contact information, and shipping time of the shipper), transportation track information (such as the logistics network points and time nodes passed through), and signing information (such as the signing time, the person who signed, and the signing location). The target time period can be 1h, 2h, 5h, 7h, etc. For example, taking the target time period as 2 hours, the time of starting the statistics can be taken as the starting time, the time is counted as 0, and the starting time is counted back 2 hours. The logistics packages counted in this time period form a data set. Further, the end time of the previous data set is taken as the starting time to continue the statistics, and the interval is 2 hours. All logistics packages in this time period form the next data set, and so on, to sequentially count a plurality of data sets in time order. It can be understood that the data in the database is divided into multiple target data sets in time order, which facilitates the analysis of the time period of the package information, and also can feedback the logistics signing status in time based on the time order.

[0058] S102, obtain the delivery information and signing information of the target logistics package in the target data set.

[0059] Specifically, the receipt information is used to represent the target receipt information of the target logistics package, and the receipt information is used to represent the actual receipt information of the target logistics package. It is reasonable that the target receipt information can include but is not limited to the target receipt person's name, the target receipt address, the target receipt contact information, etc., and the actual receipt information includes but is not limited to the actual receipt person's name, the actual receipt address, the actual receipt contact information, the actual receipt point, etc.

[0060] S104, determining the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package.

[0061] Specifically, the target receipt information and the actual receipt information can be compared to determine the receipt result of the target logistics package.

[0062] S106, obtaining the normal receipt quantity of the target point in the target time period based on the receipt result of the target logistics package.

[0063] The target point is one of the multiple points matched by the target data set. It can be understood that the receipt result of the target logistics package includes normal receipt and abnormal receipt. In step S106, the normal receipt quantity of the target point in the target time period is obtained based on the receipt result of the target logistics package. The target receipt address and the actual receipt address can be determined to be the same. When the target receipt address and the actual receipt address are the same, it can be considered that the target logistics package is correctly received. The target receipt person's name and the actual receipt person's name can also be determined to be the same. When the target receipt person's name and the actual receipt person's name are the same, it can be considered that the target logistics package is correctly received. In addition, when the target receipt address and the actual receipt address are the same, and the target receipt person's name and the actual receipt person's name are also the same, it can be considered that the target logistics package is correctly received. It can be understood that the above conditions for normal receipt are only an example of description, and other conditions for normal receipt can be set in other implementable schemes, which are not limited here.

[0064] S108, obtaining the target receipt quantity of the target point.

[0065] In an example, the target receipt quantity refers to the quantity that should be received by the target point.

[0066] S110, determining the normal receipt rate of the target logistics package in the target point in the target data set based on the normal receipt quantity and the target receipt quantity.

[0067] Specifically, the normal receipt rate can be determined based on the following formula:

[0068] Normal receipt rate = normal receipt quantity ÷ target receipt quantity × 100%.

[0069] It can be understood that the technical scheme can statistically analyze the normal signing rate, can more accurately reflect the operation of the operation site, improve the effective management of the operation site, and improve the logistics service quality; further, by comparing the target signing information and the actual signing information, the signing result of the package can be clearly determined, and a reliable basis is provided for subsequent analysis, and by analyzing the normal signing quantity of the target site in the target time period, the operation performance of the site can be evaluated, which is helpful to identify the operation efficiency and potential problems of the site.

[0070] Further, as shown in Figure 2 , another flowchart of the logistics site signing rate analysis method of the present application, specifically, the flow is an exemplary description of determining the signing result of the target logistics package based on the target signing information and the actual signing information of the target logistics package in step S104, refer to Figure 2 , the flow includes the following steps:

[0071] S200, acquiring the actual signing site of the target logistics package.

[0072] Specifically, the actual signing site is the site corresponding to the actual signing information.

[0073] S202, when the actual signing site is within the site range matched by the address information in the receiving information, determining that the signing result of the target logistics package is normally signed.

[0074] S204, when the actual signing site is outside the site range matched by the address information in the receiving information, determining that the signing result of the target logistics package is abnormally signed.

[0075] It can be understood that the present application can more accurately determine whether the package is normally signed or abnormally signed through the matching comparison of the site, which improves the accuracy of the determination.

[0076] Further, as shown in Figure 3 , another flowchart of the logistics site signing rate analysis method of the present application, which is applied before step S100 determines the target data set, refer to Figure 3 , the flow includes the following steps:

[0077] S300, real-time collecting order receiving information and order signing information of order logistics packages.

[0078] In an example, the order receiving information includes, but is not limited to, the order receiving person's name, the order receiving address, the order receiving contact information, etc., and the order signing information includes, but is not limited to, the order signing person's name, the order signing address, the order contact information before and after signing, etc. Exemplarily, a mobile device (such as a handheld terminal, a tablet computer) or an automatic system (such as a scanning gun) can be used to collect and input the order receiving information and the order signing information in real time. For example, when the courier delivers the package, the courier uses the device to scan the bar code or the two-dimensional code on the package, the system automatically extracts the order number, and prompts to input the order receiving information. When the signing person signs the package, the signing person uses the device to scan the bar code or the two-dimensional code on the package, the system automatically extracts the order number, and prompts to input the order signing information.

[0079] S302 encrypts the order receiving information and the order signing information in real time and transmits them to the logistics database for storage.

[0080] Further, the collected order receiving information and order signing information are transmitted to the logistics database in an encrypted manner. Exemplarily, a secure encryption algorithm (such as AES or RSA) can be used to ensure the security of the data.

[0081] It can be understood that the present application can quickly respond to changes by collecting the order receiving information and the signing information in real time to ensure the timeliness of the data.

[0082] Further, as shown in FIG. 4, another flowchart of the logistics network point signing rate analysis method of the present application is provided, and as shown in FIG. 5, the method includes the following steps: Figure 4 Figure 4 S400, determining a target data set.

[0083] S402, obtaining the order receiving information and the signing information of a target logistics package in the target data set.

[0084] S404, determining the signing result of the target logistics package based on the target signing information and the actual signing information of the target logistics package.

[0085] S406, obtaining the normal signing quantity of the target network point in the target time period based on the signing result of the target logistics package.

[0086] S408, obtaining the target signing quantity of the target network point.

[0087] S410, determining the normal signing rate of the target logistics package in the target network point based on the normal signing quantity and the target signing quantity.

[0088] S410, determining the normal signing rate of the target logistics package in the target network point based on the normal signing quantity and the target signing quantity.

[0089] ​Specifically, the introduction of steps S00-S410 can refer to the introduction of steps S1400-S110 described above, and will not be repeated here.

[0090] S412, sending the target net point's signing rate to the target display interface, so that the target display interface displays the target net point's normal signing rate based on the target display condition.

[0091] In an example, the target display condition can display the normal signing rate of the target net point in the form of percentage, normal signing rate icon, and score. The percentage represents the normal signing rate of the target net point, the score represents the ratio of the normal signing quantity to the target signing quantity, and the normal signing rate icon is in the form of a ring, and the color part represents the normal signing rate. It can be understood that the present application can send the signing rate to the target display interface, and can intuitively display the operation performance of the net point to the management layer or relevant personnel, facilitating quick decision-making

[0092] Further, as shown in Figure 5 , another flowchart of the logistics net point signing rate analysis method of the present application, please refer to Figure 5 , comprising the following steps:

[0093] S500, obtaining the theoretical delivery quantity of order logistics packages in a target time period and the actual delivery quantity of target logistics packages.

[0094] Specifically, the theoretical delivery quantity is the quantity that should be delivered in the target time period.

[0095] S502, obtaining the theoretical signing quantity of order logistics packages in a target time period and the actual signing quantity of target logistics packages.

[0096] Specifically, the theoretical signing quantity is the quantity that should be delivered in the target time period.

[0097] S504, determining the package delivery rate in the target time period based on the theoretical delivery quantity and the actual delivery quantity.

[0098] Specifically, the package delivery rate is the ratio of the actual delivery quantity to the theoretical delivery quantity.

[0099] S506, determining the package pickup rate in the target time period based on the theoretical signing quantity and the actual signing quantity.

[0100] Specifically, the package pickup rate is the ratio of the actual signing quantity to the theoretical signing quantity.

[0101] S508, determining the logistics operation state in the target time period based on the package delivery rate and the package pickup rate.

[0102] In an example, the logistics operation state includes a good operation state, a poor operation state, etc. For example, when the absolute value of the difference between the package delivery rate and the package pickup rate is within the target absolute value range, the logistics operation state is considered good, otherwise the logistics operation state is considered poor.

[0103] It can be understood that by analyzing the logistics operation state through the package pickup rate and the package delivery rate, the operation state of the network point can be clearly mastered, and early decision-making is facilitated.

[0104] Further, the target logistics package can be input into the abnormal situation recognition model for recognition to output an abnormal delivery result of the target logistics package.

[0105] The abnormal situation recognition model is pre-trained to include:

[0106] The model training can be implemented based on the following steps:

[0107] First, historical order information is collected.

[0108] The historical order information can include, but is not limited to, the delivery success rate in the past period, personnel information (such as the experience of delivery personnel, working hours, etc.), equipment information (such as the status of delivery equipment, fault history, etc.), external environment information (such as weather conditions, traffic conditions, etc. Factors that may affect delivery).

[0109] Further, a training set is constructed based on the historical order information.

[0110] Specifically, the collected historical data is sorted into a training set, including labeled data of normal and abnormal situations (such as package loss, damage, delivery delay, etc.).

[0111] Further, the training set is trained based on a decision tree algorithm to construct an abnormal situation recognition model.

[0112] Specifically, the training set is trained using a decision tree algorithm to construct a model that can identify different abnormal situations. The decision tree forms a series of rules to determine the category of input data through feature selection and node splitting.

[0113] Further, after obtaining the abnormal situation recognition model, the step of inputting the target logistics package into the abnormal situation recognition model for recognition to output an abnormal delivery result of the target logistics package is performed.

[0114] Further, according to an aspect of the present application, a logistics network point delivery analysis device is disclosed, as shown in Figure 6 The device includes:

[0115] A target data set determination module is configured to determine a target data set, the target data set being one of a plurality of data sets in a logistics database, the plurality of data sets being sequentially counted in time order, and a statistical time interval of two adjacent data sets being a target time period;

[0116] A target logistics package information acquisition module is configured to acquire receiving information and signed information of a target logistics package in the target data set, wherein the receiving information is used to represent target signed information of the target logistics package, and the signed information is used to represent actual signed information of the target logistics package;

[0117] A signed result determination module is configured to determine a signed result of the target logistics package based on the target signed information and the actual signed information of the target logistics package;

[0118] A normal signed quantity acquisition module is configured to acquire a normal signed quantity of a target network point in the target time period based on the signed result of the target logistics package, wherein the target network point is one of a plurality of network points matched by the target data set;

[0119] A target signed quantity acquisition module is configured to acquire a target signed quantity of the target network point;

[0120] A normal signed rate determination module is configured to determine a normal signed rate of the target logistics package in the target data set at the target network point based on the normal signed quantity and the target signed quantity.

[0121] The application introduction of the related modules of the device in this example can refer to the related introduction of the above method principles, and will not be repeated here.

[0122] According to another aspect of the present application, the present application further discloses an electronic device, the electronic device comprising a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to enable the electronic device to perform each step of the above logistics network point signed rate analysis method.

[0123] The above Figure 6 The logistics network point signed rate analysis device in the embodiment of the present application is described in detail from the perspective of a modular functional entity, and the electronic device in the embodiment of the present application is described in detail from the perspective of hardware processing.

[0124] Figure 7is a structural schematic diagram of an electronic device provided by an embodiment of the present application. The electronic device 700 can have great differences due to different configurations or performances, and can include one or more processors (central processing units, CPUs) 710 (for example, one or more processors) and a memory 720, one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. The memory 720 and the storage media 730 can be temporary storage or persistent storage. The programs stored in the storage media 730 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 can be configured to communicate with the storage media 730 and execute the series of instruction operations in the storage media 730 on the electronic device 700.

[0125] The electronic device 700 can also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input / output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that the electronic device structure shown does not constitute a limitation based on the electronic device, and can include more or fewer components than shown, or combine certain components, or different component arrangements. Figure 7 The electronic device structure shown does not constitute a limitation based on the electronic device, and can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0126] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium. The computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the logistics network point signing rate analysis method.

[0127] 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 or device, unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0128] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the various embodiment methods of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0129] The above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A logistics network point signing rate analysis method, characterized in that, The method comprises: Real-time collection of order receiving information and order signing information of an order logistics package; Real-time encryption transmission of the order receiving information and the order signing information to storage in the logistics database; Determining a target data set, the target data set being one of a plurality of data sets in the logistics database that are sequentially counted in time order, the statistical time interval of adjacent two data sets being a target time period; Obtaining receiving information and signing information of a target logistics package in the target data set, wherein the receiving information is used to represent target signing information of the target logistics package, and the signing information is used to represent actual signing information of the target logistics package; Based on the target signing information and the actual signing information of the target logistics package, determining the signing result of the target logistics package; Based on the signing result of the target logistics package, obtaining the normal signing quantity of a target network point in a target time period, wherein the target network point is one of a plurality of network points matched by the target data set; Obtaining a target signing quantity of the target network point; Based on the normal signing quantity and the target signing quantity, determining the normal signing rate of the target logistics package in the target network point in the target data set; Sending the signing rate of the target network point to a target display interface, so that the target display interface displays the normal signing rate of the target network point based on a target display condition.

2. The method of claim 1, wherein, The method further comprises: Obtaining the actual signing network point of the target logistics package, the actual signing network point being a network point corresponding to the actual signing information; When the actual signing network point is within the network point range matched by the address information in the receiving information, determining that the signing result of the target logistics package is normally signed; When the actual signing network point is outside the network point range matched by the address information in the receiving information, determining that the signing result of the target logistics package is abnormally signed.

3. The method of claim 1, wherein, The method further comprises: Based on the following formula, the normal signing rate of the target network point is determined: Normal signing rate = normal signing quantity ÷ target signing quantity × 100%.

4. The method of claim 1, wherein, The method further comprises: Obtaining a theoretical delivery quantity of order logistics packages in the target time period and an actual delivery quantity of the target logistics package; Obtaining a theoretical signing quantity of order logistics packages in the target time period and an actual signing quantity of the target logistics package; Based on the theoretical delivery quantity and the actual delivery quantity, determining a package delivery rate in the target time period; Based on the theoretical signing quantity and the actual signing quantity, determining a package pickup rate in the target time period; Based on the package delivery rate and the package pickup rate, determining a logistics operation state in the target time period.

5. The method of claim 1, wherein, The method further comprises: The target logistics package is input into an abnormal situation identification model for identification, to output an abnormal signing result of the target logistics package, wherein the abnormal situation identification model is obtained by pre-training; The training of the abnormal situation identification model comprises: Collecting historical order information; Based on the historical order information, a training set is constructed; Based on the decision tree algorithm, the training set is trained to construct an abnormal situation identification model.

6. A logistics network point receipt rate analysis device characterized by comprising: The device comprises: An information collection module for collecting order receiving information and order signing information of an order logistics package in real time; An encryption transmission module for encrypting and transmitting the order receiving information and the order signing information to the logistics database in real time; A target data set determination module for determining a target data set, which is one of a plurality of data sets in the logistics database that are sequentially counted in time order, and the statistical time interval of adjacent two data sets is a target time period; A target logistics package information acquisition module for acquiring receiving information and signing information of a target logistics package in the target data set, wherein the receiving information is used to represent the target signing information of the target logistics package, and the signing information is used to represent the actual signing information of the target logistics package; A signing result determination module for determining the signing result of the target logistics package based on the target signing information and the actual signing information of the target logistics package; A normal signing quantity acquisition module for acquiring the normal signing quantity of a target time period of a target network point based on the signing result of the target logistics package, wherein the target network point is one of a plurality of network points matched by the target data set; A target signing quantity acquisition module for acquiring a target signing quantity of the target network point; A normal signing rate determination module for determining the normal signing rate of the target logistics package in the target data set at the target network point based on the normal signing quantity and the target signing quantity; A signing rate sending module for sending the signing rate of the target network point to a target display interface, so that the target display interface displays the normal signing rate of the target network point based on a target display condition.

7. An electronic device, comprising: The electronic device comprises a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the logistics network point signing rate analysis method of any one of claims 1-5.

8. A computer-readable storage medium having stored thereon instructions, the computer-readable storage medium comprising: The instructions are executed by the processor to implement the steps of the logistics network point signing rate analysis method of any one of claims 1-5.

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