Logistics network signature rate analysis method, apparatus and device, and storage medium

Abstract

The invention discloses a logistics network sign-in rate analysis method, device and equipment and a storage medium. The method comprises determining a target data set; receiving information and signing information of a target logistics package in the target data set are obtained, the receiving information is used for representing target signing information of the target logistics package, and the signing information is used for representing actual signing information of the target logistics package; determining a signing result of the target logistics package based on the target signing information and the actual signing information; obtaining a normal signing number of the target website in the target time period based on the signing result; obtaining a target signing number of the target website; and determining a normal signing rate of the target logistics parcel in the target data set at the target website based on the normal signing number and the target signing number. According to the method and the system, the normal signing rate can be subjected to statistical analysis, the operation condition of the operation network can be reflected more accurately, the effective management of the operation network is improved, and the logistics service quality is improved.

Description

Technical Field

[0001] The present invention belongs to the field of logistics and transportation technology, and specifically relates to a logistics network point signing rate analysis method, device, equipment and storage medium. Background Art

[0002] In the logistics industry, the receipt rate of logistics outlets is one of the important indicators to measure its operational efficiency and service quality. A higher receipt rate means that the goods can be delivered to customers in a timely and accurate manner, which helps to improve customer satisfaction, reduce logistics costs, and enhance the overall competitiveness of logistics companies. However, many logistics companies currently have deficiencies in monitoring and analyzing the receipt rate. Most of them use manual regular statistics or post-analysis methods, which is not only inefficient, but also unable to timely discover problems in the receipt process and take corresponding measures, resulting in some potential risks that cannot be effectively controlled, affecting the quality of logistics services and the benefits of the company. Summary of the invention

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

[0004] According to one aspect of the present application, a method for analyzing the signing rate of a logistics network point is disclosed, and the method includes: Determine a target data set, where the target data set is one of a plurality of data sets that are statistically analyzed in chronological order in the logistics database, and a target time interval between two adjacent data sets; Acquire the receipt information and signing information of the target logistics package in the target data set, wherein the receipt 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; Determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package; Based on the receipt result of the target logistics package, the normal receipt quantity of the target network point within the target time period is obtained, wherein the target network point is one of the multiple network points matched by the target data set; Obtain the target number of receipts from the target outlet; Based on the normal receipt quantity and the target receipt quantity, a normal receipt rate of the target logistics package in the target data set at the target outlet is determined.

[0005] 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: Obtaining the actual receipt outlet of the target logistics package, where the actual receipt outlet is the outlet corresponding to the actual receipt information; 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; When the actual signing point is outside the range of points matched by the address information in the receipt information, it is determined that the signing result of the target logistics package is abnormally signed.

[0006] In some embodiments, before determining the target data set, the method further includes: Collect order receipt information and order signature information of order logistics packages in real time; The order receipt information and the order signature information are encrypted and transmitted in real time to the logistics database for storage.

[0007] 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: The acceptance rate of the target outlet is sent to a target display interface, so that the target display interface displays a normal acceptance rate of the target outlet based on a target display condition.

[0008] 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: The normal acceptance rate of the target outlet is determined based on the following formula: Normal receipt rate = normal receipt quantity ÷ target receipt quantity × 100%.

[0009] In some embodiments, the method further comprises: Obtaining the theoretical delivery quantity of the order logistics packages within the target time period and the actual delivery quantity of the target logistics packages; Obtain the theoretical number of signed-in orders for the logistics packages within the target time period and the actual number of signed-in orders for the target logistics packages; Determine the parcel delivery rate within the target time period based on the theoretical delivery quantity and the actual delivery quantity; Determine the parcel pickup rate within the target time period based on the theoretical number of signed-in items and the actual number of signed-in items; Based on the package delivery rate and the package pickup rate, the logistics operation status within the target time period is determined.

[0010] In some embodiments, the method further comprises: Input 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; Training the anomaly recognition model includes: Collect historical order information; Building a training set based on the historical order information; The training set is trained based on a decision tree algorithm to build an abnormal situation recognition model.

[0011] According to another aspect of the present application, a logistics network point signing analysis device is also disclosed, characterized in that the device comprises: A target data set determination module is used to determine a target data set, where the target data set is one of a plurality of data sets that are statistically analyzed in chronological order in the logistics database, and a target time interval between two adjacent data sets; A target logistics package information acquisition module is used to acquire the receipt information and the signing information of the target logistics package in the target data set, wherein the receipt 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 receipt result determination module, used to determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package; A normal receipt quantity acquisition module, used to acquire 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 network points matched by the target data set; A target receipt quantity acquisition module is used to acquire the target receipt quantity of the target outlet; 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 network point based on the normal receipt quantity and the target receipt quantity.

[0012] 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 outlet receipt rate analysis method as described in any one of the above.

[0013] 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 point receipt rate analysis method as described in any of the above items are implemented.

[0014] 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, which can reflect the operating conditions of the operating outlets with relatively high accuracy, improve the effective management of the operating outlets, and improve the quality of logistics services; (2) The present invention divides the data in the database into time periods in time sequence 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 with the actual receipt information, providing a reliable basis for subsequent analysis, and by analyzing the number of normal receipts at the target outlets within the target time period, the network can be evaluated. (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 judgment; (5) The present invention ensures the timeliness of data by collecting the receipt information and receipt information of the order in real time, and can respond quickly 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, so as to facilitate 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

[0015] 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.

[0016] Figure 1 This is a flow chart of the logistics network point receipt rate analysis method of the embodiment of the present application; Figure 2 It is another flow chart of the logistics network point receipt rate analysis method of the embodiment of the present application; Figure 3 It is another flow chart of the logistics network point receipt rate analysis method of the embodiment of the present application; Figure 4 It is another flow chart of the logistics network point receipt rate analysis method of the embodiment of the present application; Figure 5 It is another flow chart of the logistics network point receipt rate analysis method of the embodiment of the present application; Figure 6It is a structural block diagram of a logistics network point receipt rate analysis device according to an embodiment of the present application; Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The embodiment of the present invention provides a method, device, equipment and storage medium for analyzing the receipt rate of logistics. The method includes determining a target data set, which is one of multiple data sets that are sequentially counted in a time sequence in a logistics database, and a target time period for the statistical time interval between two adjacent data sets; obtaining the receipt information and receipt information of the target logistics package in the target data set, wherein the receipt information is used to characterize the target receipt information of the target logistics package, and the receipt information is used to characterize the actual receipt information of the target logistics package; based on the target receipt information and the actual receipt information of the target logistics package, determining the receipt result of the target logistics package; based on the receipt result of the target logistics package, obtaining the normal receipt number of the target network point within the target time period, wherein the target network point is one of the multiple network points matched by the target data set; obtaining the target receipt number of the target network point; based on the normal receipt number and the target receipt number, determining the normal receipt rate of the target logistics package in the target data set at the target network point. The present invention can perform statistical analysis on the normal receipt rate, can more accurately reflect the operation status of the operation network point, improve the effective management of the operation network point, and improve the quality of logistics services.

[0018] 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.

[0019] 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 logistics network point acceptance rate analysis method of this application, and the steps include: S100: Determine a target data set.

[0020] Specifically, the target data set is one of the multiple data sets that are counted in time sequence in the logistics database, and the statistical time interval between two adjacent data sets is the target time period. In which, each data set contains multiple logistics packages, and the logistics database is used to store relevant data of each logistics package, including but not limited to receiving information (such as recipient name, address, contact information), shipping information (such as shipper name, address, contact information, shipping time), transportation track information (such as logistics outlets and time nodes passed through), receipt information (such as receipt time, recipient, receipt location), etc. The target time period can be 1h, 2h, 5h, 7h, etc. For example, taking the target time period of 2 hours as an example, the time when the statistics start can be used as the starting time, the count is 0, and the start time is counted back 2 hours. The logistics packages counted in this time period constitute a data set. Further, continue to count again with the end time of the previous data set as the starting time, with an interval of 2 hours, and obtain all logistics packages in this time period to form the next data set, so as to count multiple data sets in time sequence. It is understandable that the data in the database is divided into time periods in chronological order to form multiple target data sets, which facilitates the time period analysis of package information and can also provide timely feedback on the logistics receipt status based on chronological order.

[0021] S102: Obtain the receipt information and signing information of the target logistics package in the target data set.

[0022] 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 rational. The target receipt information may include but is not limited to the name of the target recipient, the target receipt address, the target receipt contact information, etc. The actual receipt information includes but is not limited to the name of the actual recipient, the actual receipt address, the actual receipt contact information, the actual receipt outlet, etc.

[0023] S104: Determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package.

[0024] Specifically, the target receipt information and the actual receipt information may be compared to determine the receipt result of the target logistics package.

[0025] S106. Based on the receipt result of the target logistics package, obtain the normal receipt quantity of the target outlet within the target time period.

[0026] Wherein, the target outlet is one of the multiple outlets 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, wherein, step S106 is based on the receipt result of the target logistics package, and obtaining the normal receipt quantity of the target outlet within the target time period can be to determine whether the target receipt address and the actual receipt address are 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. It can also be to determine whether the current signatory name is the same as the actual signatory name. When the target signatory name is the same as the actual signatory name, it can be considered that the target logistics package is correctly received. It can also be that the target receipt address is the same as the actual receipt address, and the target signatory name is the same as the actual signatory name. The target logistics package is considered to be correctly received only when it is the same. It can be understood that the above normal receipt condition is only an exemplary description. In other feasible schemes, other conditions can also be set to judge whether the normal receipt is received or not, which is not specifically limited here.

[0027] S108. Obtain the target number of receipts from the target outlets.

[0028] In one example, the target receipt quantity refers to the quantity that the target outlet should sign for.

[0029] S110. Determine the normal receipt rate of the target logistics parcels in the target data set at the target outlet based on the normal receipt quantity and the target receipt quantity.

[0030] Specifically, the normal sign-in rate can be determined based on the following formula: Normal receipt rate = normal receipt quantity ÷ target receipt quantity × 100%.

[0031] It can be understood that this technical solution can conduct statistical analysis on the normal signature rate, which can reflect the operating conditions of the operating outlets with relatively high accuracy, improve the effective management of the operating outlets, and improve the quality of logistics services; further, by comparing the target signature information and the actual signature information, the signature result of the package can be clearly determined, providing a reliable basis for subsequent analysis, and by analyzing the number of normal signatures at the target outlet within the target time period, the operating performance of the outlet can be evaluated, which helps to identify the operating efficiency and potential problems of the outlet.

[0032] Further, such as Figure 2 , which is another flow chart of the logistics network point receipt rate analysis method of the present application. Specifically, the flow chart is an exemplary description of 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 in step S104. Figure 2 The process includes the following steps: S200: Obtain the actual receipt point of the target logistics package.

[0033] Specifically, the actual signing point is the point corresponding to the actual signing information.

[0034] S202: When the actual signing point is within the range of points matched by the address information in the receipt information, it is determined that the signing result of the target logistics package is normally signed.

[0035] S204: When the actual receipt outlet is outside the outlet range matched by the address information in the receipt information, determining that the receipt result of the target logistics package is abnormally received.

[0036] It can be understood that the present invention can more accurately determine whether a package is normally received or abnormally received through matching and comparison of outlets, thereby improving the accuracy of the judgment.

[0037] Further, such as Figure 3 FIG. 1 is another flow chart of the logistics network point acceptance rate analysis method of the present application. The method is applied before step S100 determines the target data set. Figure 3 The process includes the following steps: S300, collecting order receipt information and order receipt information of order logistics packages in real time.

[0038] In one example, the order receipt information includes, but is not limited to, the name of the order recipient, the order receipt address, the order recipient contact information, and the order receipt information includes, but is not limited to, the name of the order recipient, the order receipt address, the contact information before and after the order, and the like. Exemplarily, a mobile device (such as a handheld terminal, a tablet computer) or an automated system (such as a scanner) can be used to collect and input the order receipt information and the order receipt information in real time. For example, when a courier delivers a package, he uses the device to scan the barcode or QR code on the package, the system automatically extracts the order number, and prompts to enter the order receipt information. When the signatory signs for the package, the signatory uses the device to scan the barcode or QR code on the package, the system automatically extracts the order number, and prompts to enter the order receipt information.

[0039] S302 encrypts and transmits the order receipt information and order signature information in real time to the logistics database for storage.

[0040] Furthermore, the collected order receipt information and order signature information are transmitted to the logistics database in an encrypted manner. For example, a secure encryption algorithm (such as AES or RSA) can be used to ensure data security.

[0041] It can be understood that the present invention ensures the timeliness of data and can respond quickly to changes by collecting order receipt information and signature information in real time.

[0042] Further, such as Figure 4 As shown, it is another flow chart of the logistics network point acceptance rate analysis method of this application, refer to Figure 4 , including the following steps: S400: Determine a target data set.

[0043] S402: Obtain the receipt information and signing information of the target logistics package in the target data set.

[0044] S404: Determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package.

[0045] S406. Based on the receipt result of the target logistics package, obtain the normal receipt quantity of the target outlet within the target time period.

[0046] S408: Obtain the target receipt quantity of the target outlet.

[0047] S410: Determine the normal receipt rate of the target logistics packages in the target data set at the target outlet based on the normal receipt quantity and the target receipt quantity.

[0048] Specifically, the introduction of steps S00-S410 can refer to the above introduction of steps S1400-S110, which will not be repeated here.

[0049] S412: Send the acceptance rate of the target outlet to the target display interface, so that the target display interface displays the normal acceptance rate of the target outlet based on the target display condition.

[0050] In one example, the target display condition can display the normal acceptance rate of the target outlet in the form of a percentage, a normal acceptance rate icon, or a score. The percentage represents the normal acceptance rate of the target outlet, the score represents the ratio of the normal acceptance number to the target acceptance number, the normal acceptance rate icon is in the form of a ring, and the color part represents the normal acceptance rate. It is understandable that the present invention can send the acceptance rate to the target display interface, which can intuitively display the operating performance of the outlet to the management or relevant personnel, facilitating quick decision-making. Further, such as Figure 5 As shown, it is another flow chart of the logistics network point acceptance rate analysis method of this application, refer to Figure 5 , including the following steps: S500: 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.

[0051] Specifically, the theoretical delivery quantity is the quantity that should be delivered within the target time period.

[0052] S502: Obtain the theoretical number of signed-in orders for logistics packages within a target time period and the actual number of signed-in orders for target logistics packages.

[0053] Specifically, the theoretical signed-for quantity is the quantity that should be delivered within the target time period.

[0054] S504: Determine the package delivery rate within the target time period based on the theoretical delivery quantity and the actual delivery quantity.

[0055] Specifically, the package delivery rate is the ratio of the actual delivery quantity to the theoretical delivery quantity.

[0056] S506: Determine the package pickup rate within the target time period based on the theoretical number of signed-in items and the actual number of signed-in items.

[0057] Specifically, the parcel pickup rate is the ratio of the actual number of signed-for parcels to the theoretical number of signed-for parcels.

[0058] S508: Determine the logistics operation status within the target time period based on the package delivery rate and the package pickup rate.

[0059] In one example, the logistics operation status includes good operation status, poor operation status, 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 status is considered to be good, otherwise the logistics operation status is poor.

[0060] It is understandable that by analyzing the logistics operation status through the parcel pickup rate and parcel receipt rate, we can have a clearer grasp of the operation status of the outlets and facilitate early decision-making.

[0061] Furthermore, in this solution, the target logistics package can also be input into the abnormal situation recognition model for identification to output the abnormal receipt result of the target logistics package.

[0062] Among them, the abnormal situation recognition model is pre-trained to include: Model training can be achieved based on the following steps: First, collect historical order information.

[0063] Among them, historical order information may include but is not limited to: the success rate of signature in the past period, personnel information (such as the experience and working hours of the delivery personnel), equipment information (such as the status of the delivery equipment, fault history, etc.), and external environment information (such as weather conditions, traffic conditions, and other factors that may affect delivery).

[0064] Furthermore, a training set is constructed based on historical order information.

[0065] Specifically, the collected historical data is organized into a training set, which contains labeled data for normal and abnormal situations (such as package loss, damage, delivery delays, etc.).

[0066] Furthermore, the training set is trained based on the decision tree algorithm to build an abnormal situation recognition model.

[0067] Specifically, the decision tree algorithm is used to train the training set and build a model that can identify different abnormal situations. The decision tree forms a series of rules to determine the category of the input data through feature selection and splitting nodes.

[0068] Furthermore, after the abnormal situation identification model is obtained, a step is performed to input the target logistics package into the abnormal situation identification model for identification, so as to output the abnormal receipt result of the target logistics package.

[0069] Further, according to one aspect of the present application, a logistics network point signing analysis device is also disclosed, such as Figure 6 As shown, the device comprises: A target data set determination module is used to determine a target data set, where the target data set is one of a plurality of data sets that are statistically analyzed in chronological order in the logistics database, and the statistical time interval between two adjacent data sets is a target time period; The target logistics package information acquisition module is used to obtain the receipt information and the signing information of the target logistics package in the target data set, wherein the receipt 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 receipt result determination module is used to determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package; A normal receipt quantity acquisition module is used to acquire the normal receipt quantity of a target outlet within a target time period based on the receipt result of a target logistics package, wherein the target outlet is one of the multiple outlets matched by the target data set; A target receipt quantity acquisition module is used to obtain the target receipt quantity of a target outlet; 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.

[0070] 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.

[0071] 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 instructions are stored in the memory; at least one processor calls the instructions in the memory so that the electronic device executes each step of the above-mentioned logistics outlet receipt rate analysis method.

[0072] above Figure 6 The logistics network point receipt rate analysis device 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.

[0073] 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 733 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 may 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.

[0074] 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.

[0075] 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 the logistics network receipt rate analysis method.

[0076] 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.

[0077] 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 is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments 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.

[0078] 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 logistics network point signing rate analysis method, characterized in that: The method comprises: Determine a target data set, where the target data set is one of a plurality of data sets that are statistically analyzed in chronological order in the logistics database, and a target time interval between two adjacent data sets; Acquire the receipt information and signing information of the target logistics package in the target data set, wherein the receipt 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; Determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package; Based on the receipt result of the target logistics package, the normal receipt quantity of the target network point within the target time period is obtained, wherein the target network point is one of the multiple network points matched by the target data set; Obtain the target number of receipts from the target outlet; Based on the normal receipt quantity and the target receipt quantity, a normal receipt rate of the target logistics package in the target data set at the target outlet is determined.

2. The logistics network point receipt analysis method according to claim 1, characterized in that: The 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: Obtaining the actual receipt outlet of the target logistics package, where the actual receipt outlet is the outlet corresponding to the actual receipt information; 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; 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.

3. The logistics network point receipt analysis method according to claim 1, characterized in that: Before determining the target data set, the method further includes: Collect order receipt information and order signature information of order logistics packages in real time; The order receipt information and the order signature information are encrypted and transmitted in real time to the logistics database for storage.

4. The logistics network point receipt analysis method according to claim 1, characterized in that: 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: The acceptance rate of the target outlet is sent to a target display interface, so that the target display interface displays a normal acceptance rate of the target outlet based on a target display condition.

5. The logistics network point receipt analysis method according to claim 1, characterized in that: The determining, based on the normal receipt quantity and the target receipt quantity, of the normal receipt rate of the target logistics package in the target data set at the target outlet comprises: The normal acceptance rate of the target outlet is determined based on the following formula: Normal receipt rate = normal receipt quantity ÷ target receipt quantity × 100%.

6. The logistics network point receipt analysis method according to claim 1, characterized in that: The method further comprises: Obtaining the theoretical delivery quantity of the order logistics packages within the target time period and the actual delivery quantity of the target logistics packages; Obtain the theoretical number of signed-in orders for the logistics packages within the target time period and the actual number of signed-in orders for the target logistics packages; Determine the parcel delivery rate within the target time period based on the theoretical delivery quantity and the actual delivery quantity; Determine the parcel pickup rate within the target time period based on the theoretical number of signed-in items and the actual number of signed-in items; Based on the package delivery rate and the package pickup rate, the logistics operation status within the target time period is determined.

7. The logistics network point receipt analysis method according to claim 1, characterized in that: The method further comprises: Input 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; Training the anomaly recognition model includes: Collect historical order information; Building a training set based on the historical order information; The training set is trained based on a decision tree algorithm to build an abnormal situation recognition model.

8. A logistics network point receipt analysis device, characterized in that: The device comprises: A target data set determination module is used to determine a target data set, where the target data set is one of a plurality of data sets that are statistically analyzed in chronological order in the logistics database, and a target time interval between two adjacent data sets; A target logistics package information acquisition module is used to acquire the receipt information and the signing information of the target logistics package in the target data set, wherein the receipt 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 receipt result determination module, used to determine the receipt result of the target logistics package based on the target receipt information and the actual receipt information of the target logistics package; A normal receipt quantity acquisition module, used to acquire 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 network points matched by the target data set; A target receipt quantity acquisition module is used to acquire the target receipt quantity of the target outlet; 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 network point based on the normal receipt quantity and the target receipt quantity.

9. An electronic device, characterized in that: The electronic device includes a memory and at least one processor, and instructions are stored in the memory; 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 in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, each step of the logistics network point receipt rate analysis method as described in any one of claims 1 to 7 is implemented.

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