Order detection method and device

Abstract

The invention provides an order detection method and device, and the method comprises the steps: obtaining a distribution track data table and a distribution behavior data table corresponding to a target order; based on the delivery track data table and / or the delivery behavior data table, determining detection data of the target order under a preset detection index, so as to judge whether the detection index of the target order is abnormal or not based on the detection data; and in response to the abnormality of at least one detection index corresponding to the target order, marking the target order as an abnormal order. According to the embodiment, whether the target order is abnormal or not can be identified based on the delivery data of the target order without depending on specific abnormal characteristics, so that the efficiency, adaptability and timeliness of order detection are improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to an order detection method and device. Background Art

[0002] During order delivery, some couriers, in order to meet timeliness requirements, trigger delivery before the order has been delivered, reducing customer satisfaction. To avoid this, the system typically verifies the couriers' current location when they trigger delivery. If the location is far from the destination, the system prevents the couriers from triggering delivery.

[0003] However, there are currently fraudulent programs that can trigger premature delivery by changing the delivery provider's location. Existing methods typically require different system environments and collect different characteristics of fraudulent programs to detect orders. This results in high compatibility costs, low applicability and efficiency, and difficulty in quickly responding to emerging fraudulent programs. Summary of the Invention

[0004] In view of this, the embodiments of the present invention provide at least one order detection method, device, electronic device and storage medium, which can identify whether a target order is abnormal based on the delivery data of the target order, without relying on specific abnormal features, thereby improving the efficiency, adaptability and timeliness of order detection.

[0005] In a first aspect, an embodiment of the present invention provides an order detection method, comprising:

[0006] Obtain the delivery track data table and delivery behavior data table corresponding to the target order;

[0007] Based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicators, and judging whether the detection indicators of the target order are abnormal based on the detection data;

[0008] In response to at least one detection indicator corresponding to the target order being abnormal, the target order is marked as an abnormal order.

[0009] Optionally, before obtaining the delivery track data table and delivery behavior data table corresponding to the target order, the following steps are also included:

[0010] Non-target orders are eliminated from all orders to obtain target orders; non-target orders include non-instant delivery orders and smart device delivery orders.

[0011] Optionally, the detection indicator includes a delivery speed; based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicator, and judging whether the detection indicator of the target order is abnormal based on the detection data, including:

[0012] Based on the delivery trajectory data table, determine the distance and reporting time between every two adjacent positioning points in the delivery trajectory of the target order;

[0013] Based on the distance and reporting time, the delivery speed between each two adjacent positioning points is calculated;

[0014] In response to the delivery speed exceeding the predetermined speed limit, the delivery speed of the target order is marked as abnormal.

[0015] Optionally, the detection indicator includes a login device; based on the delivery trajectory data table and / or the delivery behavior data table, determining detection data of the target order under preset detection indicators, and judging whether the detection indicators of the target order are abnormal based on the detection data, including:

[0016] Based on the delivery behavior data table, determine the number of device logins corresponding to the target order;

[0017] In response to the number of device logins corresponding to the target order exceeding a preset first threshold, the login devices of the target order are marked as abnormal.

[0018] Optionally, the detection indicator includes the number of reporting points; based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicator, and judging whether the detection indicator of the target order is abnormal based on the detection data, including:

[0019] Based on the delivery trajectory data table and the delivery behavior data table, determine the total number of points corresponding to the delivery trajectory within a preset time length before the target order is delivered;

[0020] De-duplicate the total number of points based on the latitude and longitude information in the delivery trajectory to obtain the number of de-duplicated points;

[0021] In response to the total number of points exceeding a predetermined threshold range and the number of de-emphasized points being less than a preset second threshold, the reported number of points of the target order is marked as abnormal.

[0022] Optionally, after marking the target order as an abnormal order, the following steps are further included:

[0023] Determine the delivery party ID corresponding to the abnormal order and the number of abnormal orders associated with the delivery party ID;

[0024] In response to the number of abnormal orders associated with the delivery party identification exceeding a preset third threshold, the delivery party is marked as an abnormal delivery party.

[0025] In a second aspect, an embodiment of the present invention provides an order detection device, comprising:

[0026] The acquisition module is used to obtain the delivery trajectory data table and delivery behavior data table corresponding to the target order;

[0027] a detection module, configured to determine detection data of a target order under preset detection indicators based on the delivery trajectory data table and / or the delivery behavior data table, and to determine whether the detection indicators of the target order are abnormal based on the detection data;

[0028] The marking module is used to mark the target order as an abnormal order in response to at least one detection indicator corresponding to the target order being abnormal.

[0029] In a third aspect, an embodiment of the present invention further provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps in the above-mentioned first aspect or any optional implementation of the first aspect are performed.

[0030] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned first aspect or any optional implementation of the first aspect are executed.

[0031] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which implements the method of any of the above embodiments when executed by a processor.

[0032] Any of the above aspects or any implementation of any of the above aspects enables detection of abnormal conditions in target orders. This detection does not rely on specific abnormal characteristics generated by the cheating program during operation, but rather relies on delivery data to determine abnormalities through flexible detection indicators and data analysis. Specifically, the execution entity of the embodiment of the present invention can obtain a delivery trajectory data table and a delivery behavior data table for the target order. The delivery trajectory data table can record continuous trajectory information such as delivery path, location, and time, reflecting the spatial and temporal changes in the delivery process. The delivery behavior data table can be supplemented with key behavior nodes such as loading and unloading point stops, code scanning records, and arrival / departure events. These two types of data together constitute a comprehensive description of the order delivery process. Quantifiable detection data can then be extracted based on preset detection indicators. These detection indicators may include, but are not limited to, speed, login device, and number of reporting points during the delivery process. Rather than being fixed to a specific abnormal pattern, the system uses a unified indicator system to detect orders, making it highly versatile and adaptable. After obtaining the detection data, it can then identify whether the target order has indicator abnormalities based on indicator thresholds or judgment logic. As long as at least one detection indicator is determined to be abnormal, the order can be marked as abnormal. As a result, it still has the ability to detect newly emerging cheating programs or abnormal scenarios, while maintaining high accuracy, and improving the efficiency, applicability and timeliness of order anomaly detection.

[0033] The beneficial effects of the above-mentioned order detection device, electronic device and storage medium can be found in the description of the above-mentioned order detection method, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to illustrate the technical solutions of the present invention. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those skilled in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[0035] Figure 1 A flowchart of an order detection method provided by an embodiment of the present invention is shown;

[0036] Figure 2 A schematic diagram of a process of an order detection method provided by an embodiment of the present invention is shown;

[0037] Figure 3 A schematic diagram showing a comparison of normal and abnormal orders provided by an embodiment of the present invention is shown;

[0038] Figure 4 A schematic diagram of an order detection device provided by an embodiment of the present invention is shown;

[0039] Figure 5 An exemplary system architecture is shown in which embodiments of the present invention may be applied;

[0040] Figure 6 A schematic structural diagram of a terminal device or server computer system for implementing an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0042] It should be noted that in the technical solution of the present invention, the collection, use, storage, sharing and transfer of user personal information involved are in compliance with the provisions of relevant laws and regulations, and it is necessary to inform the user and obtain the user's consent or authorization. When applicable, the user's personal information is de-identified and / or anonymized and / or encrypted.

[0043] The raising of the above problems and the solutions are the results obtained by the inventor after practice and careful research. The process of discovering the above problems and the solutions proposed for the above problems should be the contributions made by the inventor to the present invention during the process of the invention.

[0044] The technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0045] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0046] To facilitate understanding of this embodiment, we first provide a detailed introduction to an order detection method disclosed in an embodiment of the present invention. The execution entity of the order detection method provided in this embodiment of the present invention is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing device. The terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like. In some possible implementations, the order detection method may be implemented by a processor invoking computer-readable instructions stored in a memory.

[0047] See also Figure 1 FIG. 1 is a flowchart of an order detection method provided by an embodiment of the present invention, and the method includes S101 to S103, wherein:

[0048] S101: Obtain the delivery track data table and delivery behavior data table corresponding to the target order.

[0049] In an embodiment of the present invention, before obtaining the delivery trajectory data table and delivery behavior data table corresponding to the target order, it also includes: eliminating non-target type orders from all orders to obtain the target order; non-target type orders include non-instant delivery orders and smart device delivery orders.

[0050] In specific implementation, Figure 2As shown, data cleaning can be performed before compiling the delivery trajectory data table and the delivery behavior data table. The main purpose of data cleaning here is to filter out non-target orders that do not require order detection, retaining only target orders that require order detection. Specifically, the execution entity of the embodiment of the present invention can filter out target orders that meet the requirements from the entire order volume, filtering out non-immediate delivery orders, smart device delivery orders, and uncompleted orders. The reason for filtering non-immediate delivery orders is that they have lower delivery time requirements and greater delivery behavior flexibility. Smart device delivery orders are orders delivered by intelligent devices such as unmanned vehicles and drones. Since their operating logic and trajectory patterns are significantly different from manual delivery, they do not require detection. Uncompleted orders, because their delivery process has not yet been completed, the relevant behavior data is incomplete and cannot support accurate anomaly judgment. To achieve this filtering, rule filtering can be performed based on order attribute fields. For example, the order type field can be used to filter out non-immediate delivery orders, the delivery method field can be used to identify and eliminate smart device delivery orders, and the order status field can be used to exclude orders that are still in an uncompleted state such as "delivering" or "pending collection." After the above filtering, the orders that remain are the target orders that need to be tested.

[0051] In this embodiment of the present invention, the order ID of a target order can be used to query the corresponding platform's application or to retrieve corresponding data on demand by calling an interface. Delivery trajectory data is typically reported periodically by delivery personnel via mobile devices during the delivery process. The system can perform a dual correlation based on the order ID and the delivery party ID to obtain trajectory records related to the target order and organize them into a delivery trajectory data table by time. The delivery trajectory data table can include key fields such as the delivery party ID, region code, the delivery party's current latitude and longitude, positioning time, reporting time, and positioning type, thereby accurately reconstructing the delivery party's movement trajectory and real-time location changes. Positioning types can include GPS (Global Positioning System), Wi-Fi (Wireless Fidelity), or base stations, which can be used to assist in determining positioning accuracy and signal stability. Delivery behavior data can be derived from the application's behavioral event records during delivery tasks. The system can use the order ID combined with the delivery party ID to query related actions and obtain detailed information including the action identifier (e.g., pickup, code scanning, delivery), action time, system type, application version, and device model. Delivery behavior data can be used to analyze dimensions such as operational integrity, abnormal behavior sequence, and stable equipment operation during the delivery process. To improve query efficiency, the execution entity of the embodiment of the present invention can pre-filter by time interval or regional range to further narrow the data scope. On this basis, the order data table can also be linked to supplement the basic information of the order, such as order type, current status, merchant ID, consignee ID, consignee location latitude and longitude, delivery distance, delivery party's order acceptance time, delivery time and delivery location, etc.

[0052] S102: Based on the delivery trajectory data table and / or the delivery behavior data table, determine the detection data of the target order under the preset detection indicators, so as to judge whether the detection indicators of the target order are abnormal based on the detection data.

[0053] In an embodiment of the present invention, the detection indicators include delivery speed; based on the delivery trajectory data table, and / or the delivery behavior data table, the detection data of the target order under the preset detection indicators is determined, so as to judge whether the detection indicators of the target order are abnormal based on the detection data, including: based on the delivery trajectory data table, determining the distance and reporting time between each two adjacent positioning points in the delivery trajectory of the target order; based on the distance and the reporting time, calculating the delivery speed between each two adjacent positioning points; in response to the delivery speed exceeding the predetermined speed limit, marking the delivery speed of the target order as abnormal.

[0054] In specific implementation, Figure 3As shown, when a delivery provider uses a cheating program to change its current location, its location coordinates may vary significantly. For example, it may travel a distance that would actually take two minutes to reach in 20 seconds. If the speed is calculated based on the time difference and distance between two adjacent locations, the delivery provider's actual speed will far exceed the actual speed. This information can be used to determine whether the target order is abnormal. Specifically, the complete delivery trajectory of the target order can be extracted from the delivery trajectory data table. The delivery trajectory can be composed of location records periodically reported by the delivery provider via a mobile device during the delivery process. Each record is a location point. The execution entity of the embodiment of the present invention can sort these locations by reporting time to construct the actual delivery path of the target order. It can then process each two adjacent locations in the trajectory and calculate the geographical distance between them. The specific calculation method is not specifically limited in the embodiment of the present invention and is based on the method that can achieve the desired function. Furthermore, the time length between adjacent locations can be obtained by directly subtracting the data in the "Reporting Time" field or by using the data in the "Location Time" field. The specific method can be flexibly selected based on the actual positioning accuracy requirements. For example, if a positioning point record shows that the delivery person appears at location A at 10:00:00, and the next record appears at location B at 10:00:30, and the straight-line distance between the two places is 150 meters, then the delivery speed between adjacent positioning points is 150 meters / 30 seconds, that is, 5 meters / second. The system compares this speed with the pre-set delivery speed threshold. If it exceeds the limit, such as the urban road delivery is set to no more than 4 meters / second, it means that there are abnormal behaviors such as false positioning or illegal riding in this delivery trajectory. Alternatively, when the delivery speed of the target order is calculated to be 5 meters / second, it can be compared with the average speed of many delivery parties in this period. If it exceeds the average speed too much, the current delivery speed can be marked as abnormal. It should be noted that the embodiment of the present invention does not specifically limit the method for determining whether the delivery speed is abnormal. In actual application, it can be set according to the actual situation to achieve its function.

[0055] In an embodiment of the present invention, the detection indicators include login devices; based on the delivery trajectory data table, and / or the delivery behavior data table, the detection data of the target order under the preset detection indicators is determined to judge whether the detection indicators of the target order are abnormal based on the detection data, including: based on the delivery behavior data table, determining the number of device logins corresponding to the target order; in response to the number of device logins corresponding to the target order exceeding a pre-set first threshold, marking the login device of the target order as abnormal.

[0056] In specific implementation, the cheating program is essentially usually done by building a virtual operating environment that is different from the original device. When running the application corresponding to the delivery platform, the information of the real device is concealed at the program level, so that the delivery platform receives the error message of changing the device. Therefore, the target order can be detected by detecting the number of login devices of the delivery party when delivering the target order. Specifically, based on the delivery behavior data table of the target order, the device login records related to the order can be extracted to count the number of different login devices used by the delivery party from the time the delivery party starts to accept the order to the delivery of the order. For example, in the delivery process of the same target order, the delivery person first logs in to accept the order on mobile phone A, and then completes the positioning and delivery actions on another mobile phone B. The execution subject of the embodiment of the present invention can record the two device identifiers, and then calculate the number of login devices in the life cycle of the target order to be 2. Assuming that the pre-set first threshold is 1, the login device indicator of the target order can be marked as abnormal, indicating that there may be a risk of cheating by the delivery party. However, in actual applications, there may be situations where another device has to be replaced for login due to damage to the first device or exhaustion of power. In order to avoid accidental injury, the protection strategy can also be designed in combination with the time, frequency and abnormal trigger point of the device replacement behavior. For example, if it is found that the delivery party urgently switches the device due to equipment failure within a short period of time before the start of delivery, and the switching behavior is accompanied by the delivery party re-identifying the identity, it can be regarded as a reasonable device switching behavior and does not trigger an abnormal judgment. In addition, the device that logs in for the first time within a certain period of time can also be recorded as the "main device", and a short-term switch is allowed in non-high-risk behaviors to improve the robustness of the strategy. It should be noted that the above-mentioned abnormality judgment method and protection strategy for the login device are only used as examples of feasible implementation methods in the embodiments of the present invention, and do not constitute an improper limitation of the present invention. In actual applications, they can be set according to actual conditions and needs. The embodiments of the present invention do not make specific limitations on this, and the function can be realized.

[0057] In an embodiment of the present invention, the detection index includes the number of reported points; based on the delivery trajectory data table, and / or the delivery behavior data table, the detection data of the target order under the preset detection index is determined, so as to judge whether the detection index of the target order is abnormal based on the detection data, including: based on the delivery trajectory data table and the delivery behavior data table, determining the total number of points corresponding to the delivery trajectory within a preset time length before the target order is delivered; deduplicating the total number of points based on the latitude and longitude information in the delivery trajectory to obtain the number of deduplicated points; in response to the total number of points exceeding a predetermined threshold range and the number of deduplicated points being less than a predetermined second threshold, marking the number of reported points for the target order as abnormal.

[0058] In specific implementation, Figure 3As shown, a normal order reported a total of 17 locations during delivery, while an abnormal order only reported 11 locations due to location jumps. Therefore, the number of reported locations can be used to determine whether a target order is abnormal. Specifically, based on the target order's delivery trajectory data table and delivery behavior data table, the trajectory point information reported by the delivery party within a preset period of time before the order was delivered can be extracted. The total number of trajectory points within the corresponding time period can be counted, referred to as the "total number of locations," representing the raw number of location records reported by the delivery party through the client for the target order. These locations can then be further deduplicated based on their latitude and longitude information to obtain the "deduplicated number of locations," i.e., the number of trajectory points that have actually changed geographically. This is used to measure the effective movement trajectory of the delivery route. For example, if a delivery party reported 200 locations during the delivery of a target order (i.e., the total number of locations is 200), but points with the same longitude and latitude appear repeatedly, leaving only 10 locations after deduplication, this indicates that the delivery trajectory is highly overlapping, has extremely low mobility, and is likely a forged trajectory. Since cheating programs may conceal the real delivery status by frequently simulating and quickly reporting positioning points, but their longitude and latitude are often static or preset, resulting in a large number of points piling up without a real motion trajectory. Therefore, two-dimensional judgment criteria can be set: the total number of points and the number of removed points. If the total number of points is much higher than the first threshold, such as normal delivery reports one positioning per minute, 30 minutes should be about 30 points, and the delivery party reported 60 points when delivering the target order, it means that there is frequent reporting behavior. Secondly, if the corresponding number of removed points is much lower than the second threshold, it means that the vast majority of the points in these reporting behaviors are repeated and cannot reflect the real movement. It should be noted that the above-mentioned method of judging whether the corresponding detection indicator is abnormal based on the number of points is only used as an example of a feasible implementation method in the embodiment of the present invention, and does not constitute an improper limitation of the present invention. In actual application, it can be set according to actual conditions and needs. The embodiment of the present invention does not make specific limitations on this, and its function can be realized.

[0059] In an embodiment of the present invention, in addition to detection indicators such as delivery speed, login device, and number of reporting points, multiple dimensions of preset detection indicators can also be introduced. For example, the delivery path deviation can be introduced as a detection indicator. By comparing the deviation distance or deviation time between the actual trajectory of the delivery party and the system-recommended path, it can be determined whether there are abnormal behaviors such as detours and invalid movements in the delivery. Alternatively, the delivery time can be introduced as a detection indicator. Specifically, the actual delivery time can be calculated based on the order acceptance time and delivery time fields of the target order, and compared with the average delivery time of the area and type of order. If it is significantly extended or shortened, there may be risks such as false reporting of delivery in advance and the use of cheating programs. For another example, the integrity of action behavior can also be used for order detection. The delivery behavior data usually records action nodes such as "arrive at the store", "pick up the goods", "start delivery", and "deliver". The system can analyze whether these actions are complete, the order is reasonable, and the time interval is abnormal based on the order life cycle. If the "arrive at the store" action is missing but "deliver" appears, it means that there is a problem of skipped delivery or the use of cheating programs. It should be noted that the above examples of detection indicators are only used as examples of feasible implementation methods in the embodiments of the present invention, and do not constitute improper limitations on the present invention. In actual applications, they can be selected and set according to actual conditions. The embodiments of the present invention do not make specific limitations on this, and the ability to achieve its functions shall prevail.

[0060] S103: In response to at least one detection indicator corresponding to the target order being abnormal, marking the target order as an abnormal order.

[0061] In an embodiment of the present invention, a plurality of detection indicators can be used to jointly determine whether a target order is an abnormal order. For example, when one abnormal indicator appears among multiple detection indicators, the target order can be marked as an abnormal order; or when an abnormal indicator exceeding a preset threshold appears among multiple detection indicators, the target order can be marked as an abnormal order. Alternatively, corresponding weight values ​​can be set for different detection indicators. Based on these weight values, the abnormal value corresponding to the target order can be calculated. When the abnormal value exceeds a certain threshold, the target order can be marked as an abnormal order. It should be noted that the above method for determining whether a target order is abnormal is only used as an example of a feasible implementation method in an embodiment of the present invention, and does not constitute an improper limitation on the present invention. In actual applications, it can be selected and set according to actual conditions. The embodiment of the present invention does not make specific limitations on this, and its function shall be realized.

[0062] In an embodiment of the present invention, after marking the target order as an abnormal order, it also includes: determining the delivery party identifier corresponding to the abnormal order, and determining the number of abnormal orders associated with the delivery party identifier; in response to the number of abnormal orders associated with the delivery party identifier exceeding a pre-set third threshold, marking the delivery party as an abnormal delivery party.

[0063] In a specific implementation, after marking a target order as an abnormal order, the target order can be associated with the delivery provider via the order identifier, and the number of abnormal orders associated with the delivery provider can be updated. The number of abnormal orders associated with each delivery provider on the current delivery platform can then be counted over a fixed period or over a period of time. When the number of abnormal orders associated with a delivery provider exceeds a pre-set third threshold, the delivery provider can be marked as abnormal, and a corresponding penalty mechanism can be implemented for the delivery provider during subsequent order allocation or salary settlement. Furthermore, corresponding incentive measures can be implemented for delivery providers whose number of associated abnormal orders is less than a certain threshold.

[0064] According to the second aspect of the embodiment of the present invention, Figure 4 As shown, an order detection device 400 is provided, comprising:

[0065] Acquisition module 401, used to obtain the delivery track data table and delivery behavior data table corresponding to the target order;

[0066] A detection module 402 is configured to determine detection data of a target order under preset detection indicators based on the delivery trajectory data table and / or the delivery behavior data table, so as to determine whether the detection indicators of the target order are abnormal based on the detection data;

[0067] The marking module 403 is configured to mark the target order as an abnormal order in response to at least one detection indicator corresponding to the target order being abnormal.

[0068] Optionally, the acquisition module 401 is further configured to:

[0069] Non-target orders are eliminated from all orders to obtain target orders; non-target orders include non-instant delivery orders and smart device delivery orders.

[0070] Optionally, the detection indicator includes delivery speed; the detection module 402 is specifically configured to:

[0071] Based on the delivery trajectory data table, determine the distance and reporting time between every two adjacent positioning points in the delivery trajectory of the target order;

[0072] Based on the distance and reporting time, the delivery speed between each two adjacent positioning points is calculated;

[0073] In response to the delivery speed exceeding the predetermined speed limit, the delivery speed of the target order is marked as abnormal.

[0074] Optionally, the detection indicator includes a login device; the detection module 402 is specifically configured to:

[0075] Based on the delivery behavior data table, determine the number of device logins corresponding to the target order;

[0076] In response to the number of device logins corresponding to the target order exceeding a preset first threshold, the login devices of the target order are marked as abnormal.

[0077] Optionally, the detection indicator includes the number of reported points; the detection module 402 is specifically configured to:

[0078] Based on the delivery trajectory data table and the delivery behavior data table, determine the total number of points corresponding to the delivery trajectory within a preset time length before the target order is delivered;

[0079] De-duplicate the total number of points based on the latitude and longitude information in the delivery trajectory to obtain the number of de-duplicated points;

[0080] In response to the total number of points exceeding a predetermined threshold range and the number of de-emphasized points being less than a preset second threshold, the reported number of points of the target order is marked as abnormal.

[0081] Optionally, the marking module 403 is further configured to:

[0082] Determine the delivery party ID corresponding to the abnormal order and the number of abnormal orders associated with the delivery party ID;

[0083] In response to the number of abnormal orders associated with the delivery party identification exceeding a preset third threshold, the delivery party is marked as an abnormal delivery party.

[0084] According to the third aspect of an embodiment of the present invention, an electronic device for order detection is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the method provided by the first aspect of the embodiment of the present invention.

[0085] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0086] According to a fifth aspect of an embodiment of the present invention, a computer program product is provided, comprising a computer program, which implements the method of any of the above embodiments when executed by a processor.

[0087] Figure 5 An exemplary system architecture 500 is shown to which the order detection method or order detection apparatus implemented by the present invention can be applied.

[0088] like Figure 5As shown, system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0089] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, and 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0090] The terminal devices 501 , 502 , and 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0091] Server 505 may be a server that provides various services, such as a backend management server (for example only) that supports shopping websites browsed by users using terminal devices 501, 502, and 503. The backend management server may process received order verification requests and provide feedback (for example only) on the processing results to the terminal devices.

[0092] It should be noted that the order detection method provided in the embodiment of the present invention is generally executed by the server 505, and accordingly, the order detection device is generally set in the server 505. The order detection method provided in the embodiment of the present invention can also be executed by the terminal devices 501, 502, and 503, and accordingly, the order detection device can be set in the terminal devices 501, 502, and 503.

[0093] It should be understood that Figure 5 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0094] Reference below Figure 6 , which shows a schematic structural diagram of a computer system 600 of a terminal device suitable for implementing an embodiment of the present invention. Figure 6 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0095] like Figure 6As shown, computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of system 600 are also stored in RAM 603. CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0096] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, mouse, and the like; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 608 including devices such as a hard disk; and a communication section 609 including a network interface card such as a LAN card or a modem. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read from the removable media can be installed in the storage section 608 as needed.

[0097] In particular, according to embodiments disclosed herein, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program containing program code for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609 and / or installed from removable media 611. When executed by central processing unit (CPU) 601, the computer program performs the aforementioned functions defined in the system of the present invention.

[0098] It should be noted that the computer-readable medium described in the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0100] The modules described in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be provided in a processor. For example, a processor may include an acquisition module, a detection module, and a marking module. The names of these modules do not, in some cases, limit the modules themselves. For example, the acquisition module may also be described as a "module for acquiring the delivery trajectory data table and delivery behavior data table corresponding to the target order."

[0101] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device implements the following method: obtaining a delivery trajectory data table and a delivery behavior data table corresponding to a target order; based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under preset detection indicators, so as to determine whether the detection indicators of the target order are abnormal based on the detection data; in response to at least one detection indicator corresponding to the target order being abnormal, marking the target order as an abnormal order.

[0102] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An order detection method, characterized in that: include: Obtain the delivery trajectory data table and delivery behavior data table corresponding to the target order; Based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicators, so as to judge whether the detection indicators of the target order are abnormal based on the detection data; In response to at least one detection indicator corresponding to the target order being abnormal, the target order is marked as an abnormal order.

2. The method according to claim 1, characterized in that Before obtaining the delivery track data table and delivery behavior data table corresponding to the target order, it also includes: Non-target orders are eliminated from all orders to obtain target orders; the non-target orders include non-instant delivery orders and smart device delivery orders.

3. The method according to claim 1, characterized in that The detection indicator includes a delivery speed; based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicator, and judging whether the detection indicator of the target order is abnormal based on the detection data, including: Based on the delivery trajectory data table, determine the distance and reporting time between every two adjacent positioning points in the delivery trajectory of the target order; Based on the distance and the reporting time, the delivery speed between each two adjacent positioning points is calculated; In response to the delivery speed exceeding a predetermined speed limit, marking the delivery speed of the target order as abnormal.

4. The method according to claim 1, wherein The detection indicator includes a login device; based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicator, and judging whether the detection indicator of the target order is abnormal based on the detection data, including: Determine the number of device logins corresponding to the target order based on the delivery behavior data table; In response to the number of device logins corresponding to the target order exceeding a preset first threshold, the login devices of the target order are marked as abnormal.

5. The method according to claim 1, wherein The detection indicator includes the number of reporting points; based on the delivery trajectory data table and / or the delivery behavior data table, determining the detection data of the target order under the preset detection indicator, and judging whether the detection indicator of the target order is abnormal based on the detection data, including: Based on the delivery trajectory data table and the delivery behavior data table, determining the total number of points corresponding to the delivery trajectory within a preset time length before the target order is delivered; Deduplicating the total number of points based on the latitude and longitude information in the delivery trajectory to obtain the number of deduplicated points; In response to the total number of points exceeding a predetermined threshold range and the number of de-emphasized points being less than a preset second threshold, the reported number of points of the target order is marked as abnormal.

6. The method according to claim 1, characterized in that After marking the target order as an abnormal order, the method further includes: Determining the delivery party identifier corresponding to the abnormal order, and determining the number of abnormal orders associated with the delivery party identifier; In response to the number of abnormal orders associated with the delivery party identification exceeding a preset third threshold, the delivery party is marked as an abnormal delivery party.

7. An order detection device, characterized in that: include: The acquisition module is used to obtain the delivery trajectory data table and delivery behavior data table corresponding to the target order; a detection module, configured to determine detection data of the target order under preset detection indicators based on the delivery trajectory data table and / or the delivery behavior data table, so as to determine whether the detection indicators of the target order are abnormal based on the detection data; The marking module is configured to mark the target order as an abnormal order in response to at least one detection indicator corresponding to the target order being abnormal.

8. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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