Logistics interaction data processing method and system based on intelligent Internet of Things

Abstract

The invention provides a logistics interaction data processing method and system based on intelligent Internet of Things, and the method comprises the steps: firstly obtaining a logistics interaction request which is initiated by a target user through a logistics service terminal and comprises to-be-processed logistics data and an operation instruction set, and then analyzing the operation instruction set to determine a distribution time period reservation and other target operation types; the method comprises the following steps: receiving logistics data, performing dynamic authority verification on the logistics data, generating a logistics interaction instruction set, sending the logistics interaction instruction set to an Internet of Things lock control and wide-angle monitoring module, triggering access control unlocking, synchronously collecting logistics operation video streams, receiving monitoring data, generating logistics operation feedback information, and pushing the logistics operation feedback information to a target user client. Therefore, logistics operation accurate identification, dynamic authority management, operation monitoring and real-time feedback are realized, logistics interaction data processing efficiency and security are improved, and user experience is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of smart Internet of Things, and in particular to a logistics interaction data processing method and system based on smart Internet of Things. Background Art

[0002] In today's rapidly developing logistics industry, the processing of interactive logistics data plays a key role in improving logistics efficiency, ensuring logistics safety, and enhancing user experience. With the development of information technology, some logistics systems have begun to use digital methods to process data. However, these systems often lack flexibility and accuracy in identifying operation types. For example, they often rely on fixed rules or simple keyword matching to determine user needs, making them difficult to adapt to complex and changing logistics scenarios. For example, their ability to identify and process diverse operations such as delivery time slot reservations, package access authorizations, and return requests is limited.

[0003] In terms of permission management, existing technologies mostly use static permission setting modes, which are unable to dynamically adjust permissions according to the actual logistics operation type, which puts the security of logistics data and the compliance of operations at risk. Unauthorized personnel may perform sensitive operations, causing potential losses to logistics companies and users.

[0004] In the monitoring phase of logistics operations, monitoring equipment lacked effective coordination with logistics operational processes, preventing real-time monitoring during operations. This made it difficult to quickly locate and trace responsibility when problems arose. Furthermore, monitoring data was not fully utilized, and operational status feedback was not provided to users in a timely manner, resulting in a lack of clear understanding of the progress of logistics operations and reduced user satisfaction. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a logistics interaction data processing method based on smart Internet of Things, the method comprising:

[0006] Obtaining a logistics interaction request initiated by a target user through a logistics service terminal, wherein the logistics interaction request includes logistics data to be processed and a corresponding logistics operation instruction set;

[0007] Parsing the logistics operation instruction set in the logistics interaction request to determine a target operation type corresponding to the logistics data to be processed, where the target operation type includes at least one of a delivery time slot reservation, a package access authorization, and a return request;

[0008] Based on the target operation type, dynamic authority verification is performed on the logistics data to be processed, and a logistics interaction instruction set associated with the logistics service terminal is generated;

[0009] Sending the logistics interaction instruction set to the IoT lock control module and wide-angle monitoring module corresponding to the logistics service terminal, triggering the IoT lock control module to perform the access unlocking operation, and simultaneously starting the wide-angle monitoring module to collect video streams of the logistics operation process;

[0010] Receive the real-time monitoring data returned by the wide-angle monitoring module, generate logistics operation feedback information based on the real-time monitoring data, and push the logistics operation feedback information to the client corresponding to the target user.

[0011] On the other hand, an embodiment of the present invention also provides a logistics interaction data processing system based on smart Internet of Things, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0012] Based on the above aspects, the embodiment of the present invention obtains a logistics interaction request containing the logistics data to be processed and an operation instruction set, then parses the operation instruction set to determine the target operation type, and based on this, performs dynamic permission verification to generate a logistics interaction instruction set. This allows precise permission control according to different logistics operation requirements, ensuring that only authorized operations can be executed, greatly improving the security and standardization of logistics operations, and effectively preventing unauthorized access and operations from interfering with or disrupting the logistics process. Next, the logistics interaction instruction set is sent to the IoT lock control module and the wide-angle monitoring module to simultaneously achieve access control unlocking and video stream acquisition, not only ensuring the smooth progress of logistics operations, but also recording the operation process through real-time monitoring, and then receiving real-time monitoring data to generate logistics operation feedback information and push it to the target user, thus realizing closed-loop communication of logistics interaction. Users can timely understand the execution status of logistics operations, enhance user experience and trust in logistics services, and significantly improve the intelligence level and efficiency of logistics interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the execution flow of the logistics interactive data processing method based on smart Internet of Things provided by an embodiment of the present invention.

[0014] Figure 2 It is a schematic diagram of exemplary hardware and software components of a logistics interactive data processing system based on smart Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1This is a flow chart of a logistics interaction data processing method based on smart Internet of Things provided by an embodiment of the present invention. The logistics interaction data processing method based on smart Internet of Things is introduced in detail below.

[0016] Step S110: obtaining a logistics interaction request initiated by a target user through a logistics service terminal, wherein the logistics interaction request includes logistics data to be processed and a corresponding logistics operation instruction set.

[0017] In this embodiment, multiple logistics service terminals can be set up for an e-commerce logistics distribution center to facilitate different logistics operations. For example, a target user named Mr. Li purchases an item on an e-commerce platform. The e-commerce platform has integrated the logistics information related to Mr. Li's purchase with his identity information. Mr. Li wants to reserve a suitable delivery time slot. He initiates a logistics interaction request through the interactive interface of the logistics service terminal. The pending logistics data in this logistics interaction request includes basic information such as Mr. Li's name, contact information, and delivery address, as well as relevant information about the item, such as the item number and name. The corresponding logistics operation instruction set includes instructions related to reserving a delivery time slot. For example, Mr. Li wishes to receive the item between 3:00 PM and 5:00 PM on a specific date. Meanwhile, if a package has already arrived at the logistics service terminal and the courier wants to access the package, the pending logistics data can include relevant identification information for the package, and the logistics operation instruction set will include instructions related to accessing the package. Alternatively, if Mr. Li wants to return the item, the pending logistics data will include information such as the item's purchase record and current item status, and the logistics operation instruction set will include instructions related to the return request.

[0018] Step S120, parsing the logistics operation instruction set in the logistics interaction request, and determining the target operation type corresponding to the logistics data to be processed, wherein the target operation type includes at least one of a delivery time period reservation, a package access authorization, and a return request.

[0019] For example, in the scenario of Mr. Li's delivery time slot reservation request, the delivery time slot reservation request included in Mr. Li's request can be extracted from the logistics operation instruction set. The target delivery time range in this delivery time slot reservation request is 3:00 PM to 5:00 PM, and the target user identity is Mr. Li's ID number or the unique user ID registered on the e-commerce platform. Therefore, the binding relationship between Mr. Li's identity and the logistics service terminal is first verified to see if it meets the preset permission conditions, such as whether Mr. Li is a legitimate user within the service area of ​​the logistics service terminal and whether he has a good credit record on the e-commerce platform. If so, the target delivery time range is matched with the e-commerce platform's logistics data, which includes the estimated transportation time from the shipping point to the destination, the logistics center's delivery schedule, and so on. Assuming the e-commerce platform indicates that the product is expected to arrive at the logistics service terminal at 2:00 PM that day, then the delivery time slot reservation is feasible, and the corresponding logistics time slot reservation data is generated. After conflict detection, if no conflict is found, the target operation type is determined to be a delivery time period appointment, and the logistics time period appointment data is associated with the access control unlocking strategy of the logistics service terminal, and an unlocking permission tag containing a timestamp is generated. For example, the unlocking permission tag may indicate that between 3 pm and 5 pm, the access control of the storage area where Mr. Li's package is located can be unlocked for delivery.

[0020] For another example, in a package access authorization scenario, suppose courier Xiao Wang arrives at a logistics service terminal to pick up a package for delivery. Then, the courier Xiao Wang's biometric code (such as fingerprint or facial recognition features) and package size information contained in the logistics data to be processed can be extracted from the logistics operation instruction set. At this point, it can be verified whether the match between Xiao Wang's biometric code and the delivery personnel database stored in the logistics platform meets the first security threshold. If Xiao Wang's biometric code does not meet the required match for some reason (for example, a dirty finger affects fingerprint recognition), then alternative verification information is extracted from the logistics operation instruction set. The alternative verification information includes the dynamic work permit number and the current geographic location. Next, it can be determined whether the dynamic work permit number is associated with the authorized company list of the logistics service terminal, and whether the distance between the current geographic location and the logistics service terminal is less than a preset radius. Assuming that Xiao Wang's dynamic work permit number indicates that he belongs to an authorized courier company and his current geographic location is near the logistics service terminal, the target operation type is determined to be package access authorization. The weighing sensor of the logistics service terminal is activated according to the package size information. Assuming that the package size is small, a storage compartment number matching the package volume (such as storage compartment No. 001) can be generated and written into the logistics interaction instruction set.

[0021] For another example, for a return request scenario, assuming that the product purchased by Mr. Li does not meet his expectations and he wants to return it, the return reason code (such as the product quality problem code) and the original order number contained in the logistics data to be processed can be extracted from the logistics operation instruction set. Then, the corresponding product receipt timestamp and return policy validity period in the e-commerce platform can be retrieved based on the original order number. Assuming that the product receipt time is three days ago, the return policy is valid for seven days. Verify whether the difference between the current time and the product receipt timestamp is less than the return policy validity period, which is satisfied. If it exceeds, for example, Mr. Li applied for a return on the eighth day after receipt, then extract the abnormality certificate document uploaded by the user from the logistics operation instruction set, identify the digital signature of the abnormality certificate document and the location watermark of the logistics service terminal. When the digital signature is consistent with Mr. Li’s registration information and the location watermark covers the identification area of ​​the logistics service terminal, the target operation type is determined to be a return request. Finally, the barcode scanner of the logistics service terminal is activated based on the return reason code. Assuming that the barcode of the returned product is 123456, a unique reverse logistics number of the return package, such as RL123, can be generated and written into the logistics interaction instruction set.

[0022] Step S130: Based on the target operation type, dynamic authority verification is performed on the logistics data to be processed, and a logistics interaction instruction set associated with the logistics service terminal is generated.

[0023] For example, in a package access authorization scenario, assuming the case of courier Xiao Wang, Xiao Wang's identity (e.g., employee number) and the type of package access (here, retrieval for delivery) can be extracted from the pending logistics data. Then, based on the package access type, the corresponding historical access records of the logistics service terminal are retrieved to determine whether Xiao Wang's identity is on a preset whitelist. If Xiao Wang is a newly hired courier and his identity is not on the preset whitelist, an identity verification request can be sent to the target user (here, the shipper of the e-commerce platform or the administrator of the logistics service terminal). A verification request page containing Xiao Wang's identity information is generated. This verification request page displays Xiao Wang's photo, the logistics platform (e.g., SF Express), and the type of the current operation (package retrieval for delivery). Next, the verification request page can be pushed to the target user through the client's interactive interface, and a countdown mechanism can be initiated, for example, with a 60-second countdown. If a confirmation action is received from the target user before the countdown expires, the timestamp and device fingerprint information of the confirmation action are extracted to verify whether the device fingerprint information matches the target user's historical login device. Assuming the device fingerprint information matches, a temporary authorization confirmation message containing a one-time unlock key is generated and linked to a dynamic unlock command. The dynamic unlock command includes an unlock time period (e.g., 10 minutes) and a range of operation restrictions (e.g., only opening parcel shelves in a specific area). Finally, the dynamic unlock command can be bound to the startup parameters of the wide-angle monitoring module to form a set of logistics interaction commands.

[0024] For another example, for the delivery time period reservation scenario, the target delivery time range (Mr. Li's 3pm to 5pm) and the target user identity (Mr. Li's relevant identity) can be extracted from the logistics data to be processed. Then, the target user identity is reverse geocoded and matched with the registered address of the logistics service terminal to generate a geographic location coincidence parameter. Assume that Mr. Li's delivery address completely coincides with the service area of ​​the logistics service terminal, and the geographic location coincidence parameter is greater than the preset threshold. Then, the number of scheduled orders of the logistics service terminal within the target delivery time range can be retrieved. Assume that the logistics service terminal already has 5 scheduled orders between 3pm and 5pm. Next, it can be determined whether the number of scheduled orders exceeds the concurrent processing capacity of the logistics service terminal. Assume that the concurrent processing capacity of the logistics service terminal is 10 orders, which is not exceeded. Then, a dynamic permission token containing the target delivery time range and geographic location overlap parameters can be generated, and the dynamic permission token can be bound to the appointment time period whitelist of the IoT lock control module to generate an access control unlocking time period sequence in the logistics interaction instruction set. For example, the access control unlocking time period sequence indicates that the access control of the area where Mr. Li's package is located can be unlocked for delivery between 3 pm and 5 pm.

[0025] For another example, in a return request scenario, the return package's weight threshold (assuming it's 2 kg) and product category code (such as an electronics code) can be extracted from the pending logistics data. The product category code is then matched against the logistics service terminal's prohibited goods list to detect whether the return package contains liquid or fragile labels. Assume the returned electronics do not contain these labels. Next, the logistics service terminal's pressure sensing unit is activated to monitor the return package's weight fluctuations in real time. For example, when Mr. Li places the return package on the pressure sensing unit, assuming the package weight remains stable at 1.8 kg and the weight fluctuations remain within the positive and negative deviation range of the weight threshold, a return package stability verification mark is generated. This return package stability verification mark is then cross-validated against the package outline comparison results from the wide-angle monitoring module. Assume the wide-angle monitoring module identifies that the package outline matches the return package description submitted by Mr. Li, indicating a satisfactory profile match. Finally, a dynamic verification instruction containing a return operation countdown parameter (e.g., requiring the return operation to be completed within 24 hours) can be generated and incorporated into the logistics interaction instruction set.

[0026] Step S140: Send the logistics interaction instruction set to the IoT lock control module and wide-angle monitoring module corresponding to the logistics service terminal, trigger the IoT lock control module to perform the access unlocking operation, and simultaneously start the wide-angle monitoring module to collect video streams of the logistics operation process.

[0027] Continuing with Mr. Li's delivery time slot reservation as an example, a logistics interaction instruction set containing a sequence of access control unlocking time slots can be sent to the IoT lock control module and wide-angle monitoring module corresponding to the logistics service terminal. An encrypted unlocking signal is sent to the IoT lock control module, containing a dynamic verification code (e.g., 123456) and an operation time window (3:00 PM to 5:00 PM). Upon receiving the signal, the IoT lock control module verifies it and returns an unlocking status confirmation signal. Based on this unlocking status confirmation signal, the wide-angle monitoring module's camera function is activated. The wide-angle monitoring module is controlled to capture a real-time video stream of the logistics service terminal's surroundings according to preset angle adjustment rules. For example, the wide-angle monitoring module adjusts the viewing angle every 10 degrees, starting from the horizontal direction, to fully cover the surrounding area. Frame parsing of the real-time video stream identifies the movement trajectory of the logistics operator (here, the delivery person) and changes in the package's position. If the package's position is detected to be outside the preset safety zone (e.g., if the package is placed in a non-designated storage area of ​​the logistics service terminal), an abnormality alarm signal is generated, interrupting the unlocking operation of the IoT lock control module.

[0028] For example, in the package access authorization scenario for courier Xiao Wang, a set of logistics interaction instructions is sent, including a dynamic unlock command bound to the wide-angle monitoring module's startup parameters. Upon receiving the encrypted unlock signal (containing information such as a one-time unlock key), the IoT lock control module performs the unlock operation and returns an unlock status confirmation signal upon successful unlocking. This signal activates the wide-angle monitoring module to begin capturing video streams. When courier Xiao Wang accesses the package, the wide-angle monitoring module collects the video stream according to the rules and performs relevant frame parsing and other operations.

[0029] Step S150: receiving the real-time monitoring data returned by the wide-angle monitoring module, generating logistics operation feedback information based on the real-time monitoring data, and pushing the logistics operation feedback information to the client corresponding to the target user.

[0030] Taking Mr. Li's delivery time slot appointment as an example, the system receives real-time monitoring data from the wide-angle monitoring module. Keyframes are extracted from the real-time monitoring data to generate a logistics operation summary video with a time stamp. Next, the system identifies the package status information from the logistics operation summary video. This information includes the package integrity tag (e.g., the package is externally intact) and the storage location coordinates (e.g., placed at the designated location outside Mr. Li's home). The package status information is compared with the expected operation result in the logistics interaction request (the package is delivered to the designated location intact between 3:00 PM and 5:00 PM) to generate an operation compliance assessment result. Assuming the package is intact and delivered on time, the operation compliance assessment result meets the preset threshold. The logistics operation feedback information is pushed to Mr. Li's client via an encrypted channel. A data digest corresponding to the logistics operation feedback information is generated and encrypted using Mr. Li's public key. The encrypted data digest is concatenated with the logistics operation summary video to form a feedback information packet. After receiving the feedback information packet, Mr. Li's client decrypts the data digest using its private key and performs an integrity check against the locally cached data. If the verification passes, the logistics operation summary video and operation compliance assessment results will be displayed in split screen on the message notification interface.

[0031] For another example, for the package access authorization scenario of courier Xiao Wang, key information is extracted from the real-time monitoring data of the wide-angle monitoring module. If it is found in the video that Xiao Wang operates in accordance with the regulations during the package collection process, and the package is taken out intact. The generated operation compliance assessment result is qualified. The logistics operation feedback information containing information such as the operation compliance assessment result is sent to the relevant parties (such as the logistics platform or the shipper's client) according to the above encryption and push process. If Xiao Wang is found to have an abnormality in the operation, such as slight damage to the package or the operation action does not comply with the regulations, and the operation compliance assessment result does not reach the preset threshold, then an exception handling suggestion is generated (such as re-checking the package or standardizing the operation process) and attached to the logistics operation feedback information before being pushed.

[0032] Based on the above steps, the embodiment of the present invention obtains a logistics interaction request containing the logistics data to be processed and an operation instruction set, then parses the operation instruction set to determine the target operation type, and based on this, performs dynamic permission verification to generate a logistics interaction instruction set. This allows precise permission control according to different logistics operation requirements, ensuring that only authorized operations can be executed, greatly improving the security and standardization of logistics operations, and effectively preventing unauthorized access and operations from interfering with or disrupting the logistics process. Next, the logistics interaction instruction set is sent to the IoT lock control module and the wide-angle monitoring module, synchronously realizing access control unlocking and video stream acquisition, not only ensuring the smooth progress of logistics operations, but also recording the operation process through real-time monitoring, and then receiving real-time monitoring data to generate logistics operation feedback information and push it to the target user, realizing closed-loop communication of logistics interaction. Users can timely understand the execution status of logistics operations, enhance user experience and trust in logistics services, and significantly improve the intelligence level and efficiency of logistics interaction.

[0033] In a possible implementation, step S120 includes:

[0034] A delivery period reservation request included in the logistics data to be processed is extracted from the logistics operation instruction set, where the delivery period reservation request includes a target delivery time range and a target user identity.

[0035] Verify whether the binding relationship between the target user identity and the logistics service terminal meets the preset authority conditions.

[0036] If so, the target delivery time range in the delivery time period reservation request is matched with the logistics data of the e-commerce platform to generate corresponding logistics time period reservation data.

[0037] If the conflict detection result between the logistics time period reservation data and the e-commerce platform logistics data is no conflict, the target operation type is determined to be delivery time period reservation.

[0038] The logistics time period reservation data is associated with the access control unlocking strategy of the logistics service terminal to generate an unlocking authority tag containing a timestamp.

[0039] In the logistics delivery scenario, taking the previously mentioned Mr. Li, courier Xiao Wang, and the related logistics interactions as an example, Mr. Li's delivery time slot reservation request can be extracted from the logistics operation instruction set. This delivery time slot reservation request includes the target delivery time range (for example, 3:00 PM to 5:00 PM) and the target user ID (Mr. Li's unique ID number registered on the e-commerce platform). Next, the binding between Mr. Li's ID and the logistics service terminal is verified to see if it meets the preset permission conditions. These preset permission conditions include factors such as whether Mr. Li is a legitimate user within the logistics service terminal's service area and his credit status on the e-commerce platform. Assuming that the logistics service terminal's database contains a list of legitimate users and their corresponding credit scores, a search of Mr. Li's ID indicates that he is within the service area and has a satisfactory credit score, thus satisfying the preset permission conditions. The target delivery time range in the delivery time slot reservation request is then matched against the e-commerce platform's logistics data. The e-commerce platform's logistics data includes information such as the goods' transportation route and the estimated arrival time at each node (including the logistics service terminal). For example, the goods are expected to arrive at the logistics service terminal at 2 pm on the same day, and the delivery time period scheduled by Mr. Li from 3 pm to 5 pm is within the reasonable time range of logistics operations, thereby generating the corresponding logistics time period reservation data. Afterwards, a conflict detection is performed between the logistics time period reservation data and the logistics data of the e-commerce platform. The detection process is to check whether there are other uncoordinated logistics arrangements within the time period, such as whether the logistics service terminal has large-scale inventory of goods or other special operations within the time period that make it impossible to perform delivery operations. After detection, there is no conflict, so the target operation type is determined to be a delivery time period reservation. Finally, the logistics time period reservation data is associated with the access control unlocking strategy of the logistics service terminal, and an unlocking permission label containing a timestamp (for example, the timestamp corresponding to the exact time point of 3 pm on the same day) is generated, which means that at that time point the access control system will perform the corresponding unlocking operation based on this label in order to carry out delivery.

[0040] For example, step S120 may further include:

[0041] The courier's biometric code and package size information included in the logistics data to be processed are extracted from the logistics operation instruction set.

[0042] Verify whether the matching degree between the courier's biometric code and the delivery personnel database pre-stored in the logistics platform reaches a first security threshold.

[0043] If not, then the backup verification information is extracted from the logistics operation instruction set, and the backup verification information includes the dynamic work permit number and the current geographic location.

[0044] Determine whether the dynamic work permit number is associated with the authorized enterprise list of the logistics service terminal, and detect whether the distance between the current geographical location and the logistics service terminal is less than a preset radius.

[0045] If both conditions are met, the target operation type is determined to be package access authorization.

[0046] The weighing sensor of the logistics service terminal is activated according to the package size information, a storage compartment number matching the package volume is generated and written into the logistics interaction instruction set.

[0047] In detail, the biometric code (e.g., fingerprint information) and package size information (assuming the package size is 30 cm long, 20 cm wide, and 15 cm high) of the courier Xiao Wang contained in the logistics data to be processed can be extracted from the logistics operation instruction set. In this way, it can be verified whether the matching degree between Xiao Wang's biometric code and the delivery personnel database pre-stored in the logistics platform reaches the first security threshold. Assume that the delivery personnel database pre-stored in the logistics platform stores the biometric code of each courier and the corresponding matching standard. The matching standard is measured in a numerical range, for example, between 0 and 1, with 0.8 as the first security threshold. After testing, Xiao Wang's biometric code matching value is 0.7 due to finger injury or other reasons, which does not reach the first security threshold of 0.8. Therefore, the backup verification information is extracted from the logistics operation instruction set. The backup verification information includes the dynamic work permit number (e.g., SF001) and the current geographic location (located as 50 meters away from the logistics service terminal). The dynamic work permit number is determined to be associated with the logistics service terminal's authorized enterprise list. The authorized enterprise list contains the names of multiple courier companies that have partnered with the logistics service terminal and their corresponding work permit number ranges. A search reveals that numbers beginning with SF belong to the authorized SF Express company. The user also checks whether the distance between the current location and the logistics service terminal is less than a preset radius. Assuming the preset radius is 100 meters, and 50 meters is less than 100 meters, if both conditions are met, the target operation type is determined to be package access authorization. The logistics service terminal's weighing sensor is activated based on the package's dimensions. The weighing sensor calculates the appropriate storage compartment number based on the package's dimensions. Assuming the storage compartments of the logistics service terminal are divided into different number ranges based on volume, the package volume is calculated to be 30 × 20 × 15 = 9000 cubic centimeters. Based on this volume, the package is matched to storage compartment 001, and the compartment number is written into the logistics interaction instruction set.

[0048] For example, step S120 may further include:

[0049] The return reason code and the original order number included in the logistics data to be processed are extracted from the logistics operation instruction set.

[0050] According to the original order number, retrieve the corresponding product receipt timestamp and return policy validity period in the e-commerce platform.

[0051] Verify whether the difference between the current time and the product receipt timestamp is less than the return policy validity period.

[0052] If it exceeds, the abnormality certification document uploaded by the user is extracted from the logistics operation instruction set, and the digital signature of the abnormality certification document and the location watermark of the logistics service terminal are identified.

[0053] When the digital signature is consistent with the registration information of the target user and the location watermark covers the identification area of ​​the logistics service terminal, the target operation type is determined to be a return request.

[0054] The barcode scanner of the logistics service terminal is activated based on the return reason code, and a unique reverse logistics number of the return package is generated and written into the logistics interaction instruction set.

[0055] In detail, the return reason code (assuming it is 001, indicating a product quality problem) and the original order number (for example, 123456789) contained in the logistics data to be processed can be extracted from the logistics operation instruction set. According to the original order number, the corresponding product receipt timestamp and return policy validity period in the e-commerce platform are retrieved. Assume that the database of the e-commerce platform records that the product receipt timestamp of order 123456789 was 10 days ago, and the return policy is valid for 15 days. Verify whether the difference between the current time and the product receipt timestamp is less than the return policy validity period, calculate the difference between the current time and 10 days ago, assuming that the current time is 12 days away from the receipt time, 12 days is less than 15 days, and the difference is within the return policy validity period. If it exceeds, for example, 16 days after receipt, it exceeds the 15-day return policy validity period, then the abnormal certificate document uploaded by the user is extracted from the logistics operation instruction set, and the digital signature of the abnormal certificate document and the location watermark of the logistics service terminal are identified. When the digital signature matches the registration information of the target user (Mr. Li) and the location watermark covers the identification area of ​​the logistics service terminal, the target operation type is determined to be a return request. Based on the return reason code, the logistics service terminal's barcode scanner is activated, and the barcode of the returned item is scanned to obtain product information. Based on this information and the algorithm related to the return process, a unique reverse logistics number (e.g., RL123) is generated for the return package and written into the logistics interaction instruction set. This completes the operational process of determining the target operation type by parsing the logistics operation instruction set in different scenarios. Each process is strictly judged and operated according to its own rules and data to ensure the accuracy and security of logistics operations.

[0056] In a possible implementation, step S130 includes:

[0057] When the target operation type is package access authorization, the courier identity and package access type are extracted from the logistics data to be processed.

[0058] According to the package access type, the historical access record corresponding to the logistics service terminal is retrieved to determine whether the courier identity identifier exists in a preset whitelist.

[0059] If not, an identity authentication request is sent to the target user, and temporary authorization confirmation information returned by the target user is received.

[0060] A dynamic unlocking instruction is generated based on the temporary authorization confirmation information, where the dynamic unlocking instruction includes an unlocking time period and an operation range restriction.

[0061] The dynamic unlocking instruction is bound to the startup parameters of the wide-angle monitoring module to form the logistics interaction instruction set.

[0062] In the previously described logistics scenario, for the target operation type of package access authorization, the identity of courier Xiao Wang (e.g., his employee number is SF001) and the package access type (here, retrieving a package for delivery) are extracted from the pending logistics data. Based on the package access type, the corresponding historical access records are retrieved from the logistics service terminal. For example, the logistics service terminal's database stores historical package access records, including information about package access operations performed by different couriers at different times. This allows for determination of whether courier Xiao Wang's identity is on a preset whitelist. Assuming the preset whitelist is a list of trusted couriers pre-set by the logistics service terminal, a query reveals that Xiao Wang's identity is not on the preset whitelist, as he is a new employee. An identity verification request is then sent to the target user, who in this case is the administrator of the logistics service terminal or a person responsible for logistics operations. A verification request page is then generated, containing courier Xiao Wang's identity information. This page displays Xiao Wang's photo, the logistics platform he belongs to (SF Express), and the type of operation (package retrieval and delivery). The verification request page is pushed to the target user through the client's interactive interface, and a countdown mechanism is initiated, set to 60 seconds. If a confirmation action is received from the target user before the countdown expires, the timestamp of the confirmation action (the time the action occurred, accurate to the second) and the device fingerprint information are extracted. The device fingerprint information is then verified to see if it matches the target user's historical login device. Assuming the target user's device has a unique device fingerprint identifier, which is recorded during previous logins, a comparison is performed to determine if the device fingerprint matches the historical login device. A temporary authorization confirmation message is then generated, containing a one-time unlock key. This one-time unlock key is a randomly generated, unique character combination. Based on the temporary authorization confirmation message, a dynamic unlock command is generated. The dynamic unlock command includes an unlock time period (set to 10 minutes) and an access range restriction (limiting access to parcel shelves in a specific area). The dynamic unlock command is then bound to the wide-angle monitoring module's startup parameters, which include parameters such as monitoring angle and resolution. These two parameters are combined to form a logistics interaction command set, which can then be used to control parcel access operations.

[0063] For example, when the target operation type is delivery time slot reservation, step S130 may further include:

[0064] The target delivery time range and target user identity are extracted from the logistics data to be processed.

[0065] The target user identity identifier is matched with the registered address of the logistics service terminal by reverse geocoding to generate a geographic location coincidence parameter.

[0066] If the geographic location coincidence parameter is greater than a preset threshold, the number of reserved orders for the logistics service terminal within the target delivery time range is retrieved.

[0067] Determine whether the number of reserved orders exceeds the concurrent processing capacity of the logistics service terminal.

[0068] If not, a dynamic authorization token including the target delivery time range and geographic location coincidence parameters is generated.

[0069] The dynamic authority token is bound to the reservation time period whitelist of the Internet of Things lock control module to generate the access control unlocking time period sequence in the logistics interaction instruction set.

[0070] For example, in the case of a target operation type such as a delivery time appointment, the target delivery time range (e.g., Mr. Li's appointment of 3:00-5:00 p.m.) and the target user identity (Mr. Li's registered identity on the e-commerce platform) are extracted from the logistics data to be processed. Next, the target user identity is reverse geocoded and matched with the registered address of the logistics service terminal. Reverse geocoding matching is the process of converting the address information in the identity into geographic coordinate information and then comparing it with the geographic coordinates of the logistics service terminal. For example, the geographic coordinates corresponding to Mr. Li's delivery address are calculated with the geographic coordinates of the logistics service terminal, and the proportion of the overlapping part of the two to the total area of ​​Mr. Li's delivery address is calculated to generate a geographic location coincidence parameter. Assume Mr. Li's delivery address is a rectangular area, 100 meters long and 80 meters wide. The overlap with the logistics service terminal is 80 meters long and 60 meters wide. The area of ​​the overlap is calculated to be 80 × 60 = 4,800 square meters. The total area of ​​Mr. Li's delivery address is 100 × 80 = 8,000 square meters, and the geographic overlap parameter is 4,800 ÷ 8,000 = 0.6. If the geographic overlap parameter is greater than the preset threshold (assuming the preset threshold is 0.5), the number of scheduled orders at the logistics service terminal within the target delivery time range is retrieved. The logistics service terminal's database records the scheduled orders for each time period. The query reveals that there are already five scheduled orders for the time period of 3:00 PM to 5:00 PM.

[0071] Next, we can determine whether the number of scheduled orders exceeds the concurrent processing capacity of the logistics service terminal. Assuming the logistics service terminal has a concurrent processing capacity of 10 orders, and after comparison, we find that 5 orders do not exceed the concurrent processing capacity of 10 orders. A dynamic authorization token is then generated, containing the target delivery time range and geographic location overlap parameters. This dynamic authorization token contains permission information related to the delivery time period and address.

[0072] Then, the dynamic permission token is bound to the appointment time whitelist of the IoT lock control module. The appointment time whitelist of the IoT lock control module is a list of time periods during which access control unlocking is allowed. The access control unlocking time sequence in the logistics interaction instruction set is generated through the binding operation. In this way, corresponding operations can be performed according to the access control unlocking time sequence during the delivery period, such as unlocking the access control at the appropriate time for delivery.

[0073] For example, when the target operation type is a return request, step S130 may further include:

[0074] The weight threshold and product category code of the return package are extracted from the logistics data to be processed.

[0075] The prohibited goods list of the logistics service terminal is matched according to the commodity category code to detect whether the return package contains liquids or fragile labels.

[0076] If not included, activate the pressure sensing unit of the logistics service terminal to monitor the weight fluctuation of the return package in real time.

[0077] When the weight fluctuation amplitude continues to be within the positive and negative deviation range of the weight threshold, a return package stability certification mark is generated.

[0078] The return package stability authentication mark is cross-validated with the package outline comparison result of the wide-angle monitoring module.

[0079] If the profile matching degree meets the requirements, a dynamic verification instruction including the return operation countdown parameter is generated and written into the logistics interaction instruction set.

[0080] For example, for a target operation type such as a return request, the weight threshold (assuming it is 2 kilograms) and the commodity category code (for example, the electronic product code is 001) of the return package can be extracted from the logistics data to be processed. Match the prohibited goods list of the logistics service terminal according to the commodity category code. The prohibited goods list contains various types of goods that are not allowed to be processed at the logistics terminal and their related identifications. Detect whether the return package contains liquids or fragile identifications. Assume that the electronic product package does not contain liquids or fragile identifications through package appearance inspection and commodity information query. Activate the pressure sensing unit of the logistics service terminal to monitor the weight fluctuation amplitude of the return package in real time. The pressure sensing unit detects the weight of the package at a set frequency (for example, once every 5 seconds) and records the weight data. When the weight fluctuation amplitude is continuously within the positive and negative deviation range of the weight threshold, the weight fluctuation amplitude is calculated by comparing the weight detected each time with the set weight threshold. Assuming that the set positive and negative deviation range is ±0.1 kg, after multiple tests (such as 10 tests), the weight detected each time is 1.92 kg, 1.95 kg, 1.98 kg, 2.01 kg, 1.97 kg, 1.99 kg, 2.02 kg, 1.96 kg, 1.94 kg, and 1.97 kg respectively. These weight values ​​are all between 1.9 kg (2-0.1) and 2.1 kg (2+0.1), which meets the condition that the weight fluctuation amplitude is within the positive and negative deviation range, and generates a return package stability certification mark. The return package's stability certification mark is cross-validated against the package outline comparison results from the wide-angle monitoring module. The wide-angle monitoring module uses image recognition technology to obtain the package's outline information and compares it with a pre-stored package outline template. Assuming the comparison result shows a 95% contour match (the preset standard is 90%), the contour match meets the standard. A dynamic verification instruction containing a return operation countdown parameter (for example, setting the return operation to be completed within 24 hours) is then generated and written into the logistics interaction instruction set. This allows for the control and management of the return operation based on this logistics interaction instruction set.

[0081] In a possible implementation, sending the identity authentication request to the target user and receiving temporary authorization confirmation information returned by the target user includes:

[0082] Step S210: Generate a verification request page containing the courier's identity information. The verification request page displays the courier's photo, the logistics platform to which he belongs, and the type of this operation.

[0083] For example, when courier Xiao Wang accesses a package and his identity is not on the pre-set whitelist of the logistics service terminal, an authentication request can be sent to the target user. First, a verification request page containing the courier's identity information is generated. This page displays a detailed photo of Xiao Wang, clearly identifiable and stored after review during the courier's registration. The logistics platform is clearly displayed as SF Express, which helps the target user confirm the courier's affiliation. The operation type is accurately displayed as package retrieval and delivery, clearly informing the target user of the courier's purpose.

[0084] Step S220: Push the verification request page to the target user through the interactive interface of the client, and start the countdown mechanism.

[0085] In this embodiment, the client can be an application on the target user's device used to manage logistics operations-related matters. The interactive interface ensures that the information can be pushed to the target user accurately. Assume that the countdown is set to 60 seconds, and the countdown starts from the moment the verification request page is pushed.

[0086] Step S230: If a confirmation operation triggered by the target user is received before the countdown ends, the timestamp and device fingerprint information of the confirmation operation are extracted.

[0087] For example, first, the timestamp of the confirmation operation is extracted. This timestamp is accurate to the second and records the specific time when the target user triggered the confirmation operation, such as 10:30:20 on October 10, 2023. At the same time, the device fingerprint information is extracted. The device fingerprint information is a unique identifier of the target user's device, including the device's hardware information, operating system information, and some set configuration information.

[0088] Step S240: Verify whether the device fingerprint information matches the target user's historical login device.

[0089] In this embodiment, the target user can record the fingerprint information of the device when logging in for logistics operations in the past, forming a fingerprint information database of historically logged-in devices. A detailed comparison of the currently extracted device fingerprint information with the information in the historically logged-in device fingerprint information database is performed to check whether the two are completely consistent. For example, the hardware serial number of the device is checked to see if it is the same, the operating system version number matches, and the configured device configuration parameters are consistent.

[0090] Step S250: If there is a match, a temporary authorization confirmation message including a one-time unlocking key is generated, and the one-time unlocking key is associated with the dynamic unlocking instruction.

[0091] In this embodiment, the one-time unlock key is randomly generated and unique, consisting of a series of numbers and letters, such as "abc123def456." This one-time unlock key is then linked to a dynamic unlock instruction, which includes information such as the unlock time period (e.g., 10 minutes) and the operating range restriction (e.g., only parcel storage shelves in a specific area can be opened). This association ensures that when courier Xiao Wang performs a parcel access operation, he can accurately perform the operation based on the temporary authorization confirmation information. Within the specified unlock time period, the corresponding parcel storage shelf can be opened according to the operating range restriction, completing the parcel access operation. This ensures that the parcel access authorization process in special circumstances is implemented while ensuring the safety of logistics operations.

[0092] In a possible implementation, step S140 includes:

[0093] Step S141: Send an encrypted unlocking signal to the IoT lock control module, where the encrypted unlocking signal includes a dynamic verification code and an operation time window.

[0094] For example, in the previously set logistics scenarios, such as the courier Xiao Wang performing package access operations or Mr. Li's delivery time appointment, when logistics operations are required, taking the courier Xiao Wang's package access operations as an example, the dynamic verification code is a string of character combinations randomly generated according to a preset algorithm, such as "x7y9a2b3", and the operation time window is set to 10 minutes, which means that within these 10 minutes, the IoT lock control module can perform the corresponding unlocking operation based on this signal.

[0095] Step S142: receiving an unlock status confirmation signal returned by the IoT lock control module, and activating the shooting function of the wide-angle monitoring module based on the unlock status confirmation signal.

[0096] Step S143 , controlling the wide-angle monitoring module to collect a real-time video stream of the surrounding environment of the logistics service terminal according to a preset angle adjustment rule.

[0097] For example, if the preset angle adjustment rule is to start from the horizontal direction, first capture a picture at 0 degrees, and then adjust the shooting angle every 10 degrees for a total of 36 times, thus achieving full coverage of the 360-degree environment around the logistics service terminal. This ensures that all areas around the logistics service terminal that may be involved in logistics operations are monitored.

[0098] Step S144: Frame analysis is performed on the real-time video stream to identify the movement trajectory of the logistics operator and the change in the location of the package.

[0099] For example, to identify motion trajectories, the video stream is first divided into multiple continuous video segments, each corresponding to a preset time interval, such as one second per video segment. Key points of the human skeleton are detected for each video segment to generate a sequence of posture changes of the logistics operator. A three-dimensional motion trajectory model is constructed based on this posture change sequence, and the movement path of the logistics operator around the logistics service terminal is marked to obtain the motion trajectory. For package position changes, package features are simultaneously identified for each video segment to extract the color, shape, and barcode information of the package's outer packaging. The package's movement speed and direction are calculated based on the position offset of the package features in the continuous video segments. For example, by comparing the change in the package's position in the previous and next video segments, the distance the package moves in one second is calculated. If the distance is one meter, then the package's movement speed is 1 meter per second. The package's movement direction is also determined, such as from left to right, to generate a heat map of the package's position changes.

[0100] In step S145 , if it is detected that the location of the package exceeds the preset safety area, an abnormal alarm signal is generated and the unlocking operation of the IoT lock control module is interrupted.

[0101] For example, if a package is detected outside a pre-defined safe zone—for example, a specific storage rack within a logistics service terminal—and is moved to an aisle outside the rack, an abnormal alarm signal is generated and the unlocking operation of the IoT lock control module is interrupted. This ensures that logistics operations are carried out within a safe and controllable range, preventing abnormal situations such as package loss or misplacement, and ensuring the accuracy and security of the entire logistics process.

[0102] In a possible implementation, step S144 includes:

[0103] Step S1441 : Segment the real-time video stream into a plurality of continuous video segments, each video segment corresponding to a preset time interval.

[0104] For example, in the logistics scenario previously described, when courier Xiao Wang is accessing a package at a logistics service terminal, or when a delivery person is delivering a package to Mr. Li, the wide-angle monitoring module will capture a real-time video stream of the logistics service terminal's surroundings. Assuming the preset interval is 1 second, the real-time video stream will be segmented into 1-second segments. This facilitates subsequent detailed analysis of the video content within each time period.

[0105] Step S1442 : Detect key points of the human skeleton for each video clip to generate a posture change sequence of the logistics operator.

[0106] Taking the example of courier Xiao Wang, in each one-second video clip, a pre-defined image recognition algorithm can detect multiple key skeletal points on Xiao Wang's body, such as those on the head, shoulders, elbows, hands, waist, knees, and feet. These key points have corresponding coordinate positions on the two-dimensional plane of the video image. Furthermore, because video streams contain time information, each video clip has a corresponding timestamp, forming a posture change sequence consisting of multiple video clips.

[0107] Step S1443: construct a three-dimensional motion trajectory model based on the posture change sequence, mark the movement path of the logistics operator around the logistics service terminal, and obtain the motion trajectory of the logistics operator.

[0108] For example, step S1443 includes:

[0109] Step S1443-1, extracting the three-dimensional coordinates and timestamps of multiple human skeleton key points corresponding to each video clip from the posture change sequence.

[0110] For example, since the coordinates previously detected were on a two-dimensional plane, they can be converted to three-dimensional coordinates based on some prior knowledge and algorithms, such as by analyzing the same key point from different perspectives. Assume that at a certain timestamp, the two-dimensional coordinates of the key point on the shoulder of courier Xiao Wang are detected in the video image as (x1, y1). By analyzing the relationship with other related points and the parameters of the video acquisition device, its three-dimensional coordinates are converted to (x1', y1', z1'). In this way, the three-dimensional coordinates of multiple human skeletal key points and the corresponding timestamps are obtained in each video clip.

[0111] Step S1443-2: Connect the three-dimensional coordinates of the multiple human skeleton key points at the same time stamp according to a preset skeleton connection order to generate a single-frame posture skeleton.

[0112] For example, according to the physiological connection order of the human skeleton, the key points of the head are connected to the key points of the neck, the key points of the neck are connected to the key points of the shoulders, and so on. In this way, a single-frame posture skeleton of the courier Xiao Wang at a certain moment is constructed.

[0113] Step S1443-3: Arrange the different single-frame posture skeletons into a time-continuous posture sequence according to the order of the timestamps.

[0114] As time goes by, there is a corresponding single-frame posture skeleton for every 1 second. Arranging these single-frame posture skeletons at different times in chronological order forms a sequence that reflects the posture changes of the courier Xiao Wang over a period of time.

[0115] Step S1443-4, performing differential calculation on the three-dimensional coordinates of the same human skeleton key points of adjacent single-frame posture skeletons in the time-continuous posture sequence to generate a motion vector for each key point.

[0116] For example, for the shoulder keypoint, the 3D coordinates at second t are (x_t, y_t, z_t), and the 3D coordinates at second t+1 are (x_{t+1}, y_{t+1}, z_{t+1}). Then the motion vector of the shoulder keypoint is ((x_{t+1}-x_t), (y_{t+1}-y_t), (z_{t+1}-z_t)). In this way, the motion vector of each keypoint between adjacent frames is calculated.

[0117] Step S1443-5: Map the motion vector to the three-dimensional space coordinate system of the logistics service terminal, and determine the displacement trajectory of each key point in the three-dimensional space coordinate system.

[0118] The logistics service terminal itself has a defined three-dimensional coordinate system, for example, with a corner as the origin, the horizontal x-axis, the vertical y-axis, and the z-axis pointing upwards from the ground. By placing the previously calculated motion vectors of each key point into this three-dimensional coordinate system, we can determine the movement trajectory of each key point in this three-dimensional space over time.

[0119] Step S1443-6: performing noise filtering on the displacement trajectory to generate a smoothed key point motion path.

[0120] During the actual video capture and calculation process, various factors may generate some noise, resulting in small fluctuations and inaccuracies in the calculated displacement trajectory. Displacement trajectories are processed using filtering algorithms, such as mean filtering. If the original displacement trajectory of a key point exhibits small fluctuations, the filtering algorithm adjusts the position of that key point based on the average of several surrounding data points, resulting in a smoother and more accurate motion path.

[0121] Step S1443-7: superimpose and fuse the smoothed key point motion paths according to the topological relationship of human skeleton connections to generate a three-dimensional motion trajectory model of the logistics operator.

[0122] For example, based on the connection relationship of the human skeleton, the smoothed key point motion paths are combined together to construct a complete three-dimensional motion trajectory model that reflects the courier Xiao Wang's activities around the logistics service terminal.

[0123] Step S1443-8: Mark the boundary points of the logistics operator's movement path in the three-dimensional space coordinate system of the logistics service terminal according to the motion path endpoint coordinates of each key point in the three-dimensional motion trajectory model.

[0124] For example, the boundary points of the courier Xiao Wang's activity range around the logistics service terminal are determined from the starting and ending endpoint coordinates of the head key point motion path, as well as the starting and ending endpoint coordinates of the hand key point motion path.

[0125] Step S1443-9: Connect the boundary points of the moving path to form a continuous trajectory segment to obtain the movement trajectory of the logistics operator.

[0126] For example, the marked boundary points are connected in a set order to form a continuous trajectory segment, which accurately describes the movement path of the courier Xiao Wang around the logistics service terminal, that is, the movement trajectory of the logistics operator is obtained.

[0127] In step S1444, package feature recognition is performed on each video clip simultaneously to extract the color, shape, and barcode information of the package outer packaging.

[0128] For example, the image analysis algorithm determines the RGB value of a package's color. For example, if a package is red, the corresponding RGB value is (255, 0, 0). A shape might be recognized as a cuboid. A barcode image recognition algorithm accurately reads the barcode information, such as the barcode 1234567890.

[0129] Step S1445 , calculating the package movement speed and direction based on the position offset of the package features in the continuous video segments, and generating a heat map of the package position change.

[0130] Suppose that in the video clip at second t, a feature point on the package (such as a corner) has coordinates (x1, y1, z1) in the 3D coordinate system of the logistics service terminal. In the video clip at second t+1, the coordinates of this feature point change to (x2, y2, z2). Calculate the difference between these coordinates: the offset along the x-axis is (x2-x1), the offset along the y-axis is (y2-y1), and the offset along the z-axis is (z2-z1). Based on a 1-second interval, calculate the package's speed in each direction: the x-axis speed is (x2-x1) units / second, the y-axis speed is (y2-y1) units / second, and the z-axis speed is (z2-z1) units / second. These speed values ​​can be used to determine the package's direction of movement. For example, if x2-x1 > 0, the package is moving in the positive x-axis direction. Then, generate a heat map of the package's position change based on the package's movement at different times and locations. Heat maps can use different colors to represent information such as the frequency of packages appearing in different areas or the length of time they stay. For example, if a package stays near a storage rack for a long time, the color of the corresponding area on the heat map will be darker, thereby intuitively reflecting the changes in the package's position around the logistics service terminal.

[0131] In a possible implementation, step S150 includes:

[0132] Step S151 : extract key frames from the real-time monitoring data to generate a logistics operation summary video containing a time stamp.

[0133] In a previously defined logistics scenario, such as courier Xiao Wang's package access operations or Mr. Li's delivery time slot reservation, the wide-angle monitoring module collects real-time monitoring data. This data includes numerous video frames as courier Xiao Wang accesses the package. A predefined algorithm then identifies key frames. For example, key frames are identified when courier Xiao Wang approaches the package storage area, opens the storage rack, removes or places a package, closes the rack, and leaves. Each key frame is tagged with a precise time stamp, such as "October 10, 2023, 10:30:20 AM - Courier approaches the storage rack." These key frames are combined in chronological order to generate a logistics operation summary video, which concisely summarizes the entire logistics operation.

[0134] Step S152 : Identify package status information from the logistics operation summary video, where the package status information includes a package integrity tag and storage location coordinates.

[0135] For the identification of the package integrity label, image analysis technology can be used to check whether the outer packaging of the package is damaged or deformed. For example, it can be detected whether the outer packaging of the package has any signs of tearing, dents, or bulges, and other abnormalities. If the outer packaging of the package is not found to be damaged or deformed, the package integrity label is marked as "complete". For the determination of the storage location coordinates, the surrounding environment of the logistics service terminal has a pre-set three-dimensional space coordinate system, and the storage location coordinates can be determined based on the relationship between the position of the package in the video and the three-dimensional space coordinate system. Assuming that a corner of the logistics service terminal is the origin, the horizontal direction is the x-axis, the vertical direction is the y-axis, and the z-axis perpendicular to the ground is upward, after image analysis and calculation, it is determined that the package is stored at the coordinates (x = 2 meters, y = 3 meters, z = 1 meter), and the storage location coordinates are recorded as the storage location coordinates.

[0136] Step S153: Compare the package status information with the expected operation result in the logistics interaction request to generate an operation compliance assessment result.

[0137] Taking Mr. Li's delivery time slot appointment as an example, the expected operation result in the logistics interaction request is that the package be delivered intact to the designated storage location within the scheduled delivery time slot. If the package integrity label is identified as "intact" in the logistics operation summary video and the storage location coordinates match Mr. Li's specified storage location, the operation compliance assessment result is "compliant." If the package integrity label is "damaged" or the storage location coordinates do not match the specified location, the operation compliance assessment result is "non-compliant." To calculate whether the package's location meets the requirements, a precise comparison of the expected location coordinates and the actual storage location coordinates is required. For example, if the expected storage location coordinates are (x = 2 meters, y = 3 meters, z = 1 meter) and the actual storage location coordinates are (x = 2.1 meters, y = 3 meters, z = 1 meter), although there is a 0.1-meter deviation in the x-axis, if the preset position deviation tolerance is 0.2 meters, the location is still considered to meet the requirements.

[0138] Step S154: If the operation compliance assessment result does not reach the preset threshold, an exception handling suggestion is generated and attached to the logistics operation feedback information.

[0139] If the operational compliance assessment result does not meet the preset threshold—for example, if the preset threshold is "compliant" but the actual assessment result is "non-compliant"—an exception handling suggestion is generated and attached to the logistics operation feedback information. For example, if the package is damaged, the exception handling suggestion might be "re-inspect the package packaging and record the damage"; if the storage location is inconsistent, the exception handling suggestion might be "move the package to the correct storage location."

[0140] Step S155: Push the logistics operation feedback information to the message notification interface of the client through an encrypted channel, and simultaneously update the logistics status database of the e-commerce platform.

[0141] In a possible implementation, step S155 includes:

[0142] Step S1551: Generate a data summary corresponding to the logistics operation feedback information, and encrypt the data summary using the public key of the target user.

[0143] This process first generates a data digest corresponding to the logistics operation feedback information. This data digest is processed using a predefined hash algorithm to produce a unique, fixed-length digest value. For example, logistics operation feedback information may include operational compliance assessment results, package status information, and other content. After hashing, a hexadecimal data digest such as "123456789abcdef" is generated. This data digest is then encrypted using the target user's public key. When registering on the e-commerce platform, the target user generates a public and private key pair: the public key is used for encryption, and the private key is used for decryption. The encrypted digest becomes a string of ciphertext, such as "efghijklmnopqrst."

[0144] Step S1552: splice the encrypted data summary with the logistics operation summary video to form a feedback information data packet.

[0145] For example, the encrypted summary is placed at the beginning of the data packet, followed by the data content of the logistics operation summary video.

[0146] Step S1553: After receiving the feedback information data packet, the client uses a private key to decrypt the data digest and performs an integrity check with the locally cached data.

[0147] The client extracts the encrypted data digest from the received feedback packet and decrypts it using the target user's private key, obtaining the original data digest "123456789abcdef." Simultaneously, the client retrieves previously stored data related to the logistics operation from its local cache, such as basic information about previously received packages. The decrypted data digest is then compared with the locally cached data using a predefined integrity verification algorithm. This integrity verification algorithm might involve checking the data's checksum or comparing data features using a predefined calculation method. If the verification passes, meaning the client's calculation indicates that the received data matches the locally cached data, a logistics operation summary video and the compliance assessment results are displayed in a split-screen format on the notification interface. For example, the logistics operation summary video might be displayed in the upper half of the screen, allowing the target user to visually review the logistics operation process; the compliance assessment results, such as "compliant" or "non-compliant," along with related details, might be displayed in the lower half of the screen, allowing the target user to quickly understand the execution status of the logistics operation.

[0148] Step S1554: If the verification passes, the logistics operation summary video and the operation compliance assessment result are displayed in split screen on the message notification interface.

[0149] Throughout the entire process, the e-commerce platform's logistics status database is synchronously updated. If the operational compliance assessment results in "Compliant," the logistics status in the database is updated to a corresponding status, such as "Delivery Successfully Completed" or "Package Successfully Deposited and Retrieved." If the result is "Non-Compliant," the logistics status in the database is updated to a status such as "Exception - Package Damaged" or "Exception - Incorrect Storage Location," and relevant exception handling suggestions are recorded for subsequent logistics tracking and management. This series of operations, which generates logistics operation feedback information from real-time monitoring data and pushes it to the target user client, enables monitoring, evaluation, and information feedback of logistics operations, ensuring the accuracy and transparency of the logistics process.

[0150] In one possible implementation, the method further includes:

[0151] Step S310: Collect the target user's preferred operation mode and commonly used delivery time period in the historical logistics interaction request.

[0152] In this example, in the previously defined logistics scenario, using Mr. Li as an example, we can review all of his past logistics interaction request records on the e-commerce platform. Within these logistics interaction request records, we can focus on the various details of Mr. Li's behavior when receiving package deliveries, thereby identifying his preferred operation mode. For example, we can find that Mr. Li repeatedly selected packages for delivery to his home address rather than his work address, indicating that he prefers to receive packages at his home address. Furthermore, we can also count Mr. Li's frequently selected delivery time periods, finding that he receives packages most frequently between 3:00 PM and 5:00 PM, thus identifying this as his typical delivery time period.

[0153] Step S320: Analyze the correlation between the preferred operation mode and logistics distribution efficiency, and generate personalized distribution strategy optimization suggestions.

[0154] Step S330: integrating the personalized delivery strategy optimization suggestion with the e-commerce platform logistics data to dynamically adjust the default unlocking time period of the logistics service terminal.

[0155] Step S340: When it is detected that the new logistics interaction request conflicts with the adjusted default unlocking time period, a time period modification prompt is automatically pushed to the target user.

[0156] Step S350: updating the time allocation parameters in the access control unlocking strategy according to the target user's response to the time period modification prompt.

[0157] For example, if Mr. Li's new logistics interaction request conflicts with the adjusted default unlocking time period, for example, if Mr. Li's new appointment is for 9:00 AM to 10:00 AM, which is not within the adjusted priority appointment time period, a time period change notification can be automatically sent to Mr. Li. This time period change notification will detail the conflict, such as "Your appointment time period does not match your efficient delivery time period, which may result in reduced delivery efficiency. We recommend that you change it to 10:00 AM to 12:00 PM."

[0158] Based on Mr. Li's response to the time slot modification prompt, the time allocation parameters in the access control unlocking policy are updated. If Mr. Li accepts the modification suggestion and changes the appointment time slot to 10:00 AM - 12:00 PM, the time allocation parameters in the access control unlocking policy will be adjusted accordingly. For example, the access control system at the logistics service terminal will prepare the unlocking operation for the area where Mr. Li's package is located between 10:00 AM and 12:00 AM, ensuring that the courier can smoothly retrieve the package for delivery. If Mr. Li refuses the modification, this situation can be recorded, and Mr. Li's delivery efficiency can be continuously monitored in subsequent logistics operations to further optimize the delivery strategy.

[0159] Based on the above steps, we can better generate personalized distribution strategy optimization suggestions based on the relationship between the target users' preferred operation modes and logistics distribution efficiency, thereby improving the efficiency and user satisfaction of the entire logistics distribution process.

[0160] Step S320 includes:

[0161] Step S321: extract the delivery operation records that have been successfully completed in the historical logistics interaction requests, and calculate the average operation time and error rate of each delivery period.

[0162] For example, for each delivery period, the average operation time and error rate can be calculated. Suppose we previously counted Mr. Li's 10 delivery operations between 3:00 PM and 5:00 PM. To calculate the average operation time, add the total time the courier took to deliver the package to Mr. Li's designated location during these 10 deliveries, from the time they arrived at the logistics service terminal to the time they delivered the package, and then divide the total by 10. For example, the total delivery times for these 10 deliveries were 30 minutes, 25 minutes, 35 minutes, 28 minutes, 32 minutes, 27 minutes, 30 minutes, 33 minutes, 29 minutes, and 31 minutes, respectively. The total operation time is 30+25+35+28+32+27+30+33+29+31 = 300 minutes. The average operation time is 300 divided by 10, which equals 30 minutes. To calculate the error rate, count the number of problems (such as package delays, misplaced packages, etc.) that occurred during these 10 deliveries. Assuming there was only one problem, the error rate is 1 divided by 10, which equals 10%.

[0163] Step S322: construct a two-dimensional analysis model with the delivery period as the horizontal axis and the operation efficiency index as the vertical axis to identify the efficient time period of the target user.

[0164] In this two-dimensional analysis model, the horizontal axis represents different delivery time periods, and the vertical axis represents the operational efficiency index, which comprehensively considers average operation time and error rate. For example, the operational efficiency index can be defined as higher values ​​for shorter average operation times and lower error rates. Mr. Li's average operation time and error rate data for different delivery time periods can be plotted on this two-dimensional model. By analyzing the distribution of these data points, Mr. Li's most efficient time periods can be identified. Suppose, after analysis, it is found that between 10:00 AM and 12:00 PM, Mr. Li's average operation time is 25 minutes, with an error rate of 5%. This period has the highest operational efficiency index of all time periods, and this period is then identified as a highly efficient time period.

[0165] Step S323: Match the efficient time period with the estimated arrival time of the logistics data of the e-commerce platform to generate a set of recommended delivery time periods.

[0166] For example, the logistics data from an e-commerce platform shows that the estimated arrival time of a package at the logistics service terminal is between 9:00 AM and 10:00 AM. Since Mr. Li's most efficient time is between 10:00 AM and 12:00 PM, the recommended delivery time set can be set to 10:00 AM and 12:00 PM. This recommended delivery time set takes into account both Mr. Li's most efficient time and the estimated arrival time of the package at the logistics service terminal, ensuring that Mr. Li receives his package efficiently while also meeting the actual logistics delivery requirements.

[0167] Step S324: Optimize the optional range of reservation time periods for the logistics service terminal based on the recommended delivery time period set.

[0168] For example, the original reservation time range of a logistics service terminal might be from 8:00 AM to 8:00 PM, with reservations available for any time period. Now, to optimize delivery efficiency, the range of available time periods can be adjusted based on Mr. Li's recommended delivery time period. For example, when Mr. Li makes a reservation, the 10:00 AM to 12:00 PM time period will be prioritized and given more reservation weight or preferential treatment (such as discounted express delivery fees). For other time periods, the display frequency can be reduced or the reservation threshold can be increased.

[0169] Step S325: When the target user initiates a new delivery time period reservation, the candidate time periods in the recommended delivery time period set are displayed first.

[0170] For example, when Mr. Li initiates a new logistics interaction request, candidate time slots from the recommended delivery time set are prioritized. Suppose Mr. Li makes another package delivery reservation on an e-commerce platform. When he enters the reservation interface, the recommended delivery time slot of 10:00 AM to 12:00 PM is displayed first, and the advantages of this time slot may be highlighted on the interface, such as "This time slot is your most efficient delivery time slot. Choose this time slot for faster delivery service."

[0171] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a logistics interactive data processing system 100 based on the intelligent Internet of Things, which can implement the concepts of the present invention, according to some embodiments of the present invention. For example, a processor 120 can be used in the logistics interactive data processing system 100 based on the intelligent Internet of Things to perform the functions of the present invention.

[0172] The logistics interactive data processing system 100 based on the intelligent Internet of Things can be a general-purpose server or a special-purpose server, both of which can be used to implement the logistics interactive data processing method based on the intelligent Internet of Things of the present invention. Although only one server is shown in the present invention, for convenience, the functions described in the present invention can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0173] For example, the logistics interactive data processing system 100 based on the smart Internet of Things may include a network port 110 connected to the network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROM, or RAM, or any combination thereof. Exemplarily, the logistics interactive data processing system 100 based on the smart Internet of Things may also include program instructions stored in ROM, RAM, or other types of non-temporary storage media, or any combination thereof. The method of the present invention can be implemented according to these program instructions. The logistics interactive data processing system 100 based on the smart Internet of Things also includes an input / output (I / O) interface 150 between the computer and other input and output devices.

[0174] For ease of explanation, only one processor is described in the logistics interactive data processing system 100 based on the smart Internet of Things. However, it should be noted that the logistics interactive data processing system 100 based on the smart Internet of Things in the present invention may also include multiple processors, so the steps performed by one processor described in the present invention may also be performed jointly or individually by multiple processors. For example, if the processor of the logistics interactive data processing system 100 based on the smart Internet of Things executes step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0175] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned logistics interaction data processing method based on smart Internet of Things is implemented.

[0176] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A logistics interactive data processing method based on smart Internet of Things, characterized by: The method comprises: Obtaining a logistics interaction request initiated by a target user through a logistics service terminal, wherein the logistics interaction request includes logistics data to be processed and a corresponding logistics operation instruction set; Parsing the logistics operation instruction set in the logistics interaction request to determine a target operation type corresponding to the logistics data to be processed, where the target operation type includes at least one of a delivery time slot reservation, a package access authorization, and a return request; Based on the target operation type, dynamic authority verification is performed on the logistics data to be processed, and a logistics interaction instruction set associated with the logistics service terminal is generated; Sending the logistics interaction instruction set to the IoT lock control module and wide-angle monitoring module corresponding to the logistics service terminal, triggering the IoT lock control module to perform the access unlocking operation, and simultaneously starting the wide-angle monitoring module to collect video streams of the logistics operation process; Receive the real-time monitoring data returned by the wide-angle monitoring module, generate logistics operation feedback information based on the real-time monitoring data, and push the logistics operation feedback information to the client corresponding to the target user.

2. The logistics interactive data processing method based on intelligent Internet of Things according to claim 1 is characterized in that: The parsing of the logistics operation instruction set in the logistics interaction request to determine the target operation type corresponding to the logistics data to be processed includes: Extracting a delivery time period reservation request included in the logistics data to be processed from the logistics operation instruction set, wherein the delivery time period reservation request includes a target delivery time range and a target user identity; Verify whether the binding relationship between the target user identity and the logistics service terminal meets the preset authority conditions; If the conditions are met, the target delivery time range in the delivery time slot reservation request is matched with the logistics data of the e-commerce platform to generate corresponding logistics time slot reservation data; If the conflict detection result between the logistics time slot reservation data and the e-commerce platform logistics data is no conflict, the target operation type is determined to be delivery time slot reservation; The logistics time period reservation data is associated with the access control unlocking strategy of the logistics service terminal to generate an unlocking authority tag containing a timestamp.

3. The logistics interactive data processing method based on intelligent Internet of Things according to claim 1 is characterized in that: The step of performing dynamic authority verification on the logistics data to be processed based on the target operation type and generating a logistics interaction instruction set associated with the logistics service terminal includes: When the target operation type is package access authorization, extracting the courier identity and package access type from the logistics data to be processed; According to the package access type, retrieve the historical access record corresponding to the logistics service terminal to determine whether the courier identity identifier exists in a preset whitelist; If not, send an identity authentication request to the target user and receive temporary authorization confirmation information returned by the target user; Generate a dynamic unlock instruction based on the temporary authorization confirmation information, the dynamic unlock instruction including an unlock time period and an operation range restriction; The dynamic unlocking instruction is bound to the startup parameters of the wide-angle monitoring module to form the logistics interaction instruction set.

4. The logistics interactive data processing method based on intelligent Internet of Things according to claim 3 is characterized in that: The sending of the identity authentication request to the target user and receiving temporary authorization confirmation information returned by the target user includes: Generate a verification request page containing the courier's identity information, the verification request page displays the courier's photo, the logistics platform to which they belong, and the type of this operation; Pushing the verification request page to the target user through the interactive interface of the client and starting a countdown mechanism; If a confirmation operation is received from the target user before the countdown ends, extract the timestamp and device fingerprint information of the confirmation operation; Verify whether the device fingerprint information matches the target user's historical login device; If they match, a temporary authorization confirmation message including a one-time unlocking key is generated, and the one-time unlocking key is associated with the dynamic unlocking instruction.

5. The logistics interactive data processing method based on intelligent Internet of Things according to claim 1 is characterized in that: The triggering of the IoT lock control module to execute the access unlocking operation and the synchronous activation of the wide-angle monitoring module to collect the video stream of the logistics operation process include: Sending an encrypted unlocking signal to the IoT lock control module, wherein the encrypted unlocking signal includes a dynamic verification code and an operation time window; receiving an unlocking status confirmation signal returned by the IoT lock control module, and activating a shooting function of the wide-angle monitoring module based on the unlocking status confirmation signal; Controlling the wide-angle monitoring module to collect a real-time video stream of the surrounding environment of the logistics service terminal according to a preset angle adjustment rule; Perform frame analysis on the real-time video stream to identify the movement trajectory of the logistics operator and the change of the package location; If it is detected that the package location exceeds the preset safety area, an abnormal alarm signal is generated and the unlocking operation of the Internet of Things lock control module is interrupted.

6. The logistics interactive data processing method based on intelligent Internet of Things according to claim 5 is characterized in that: The frame parsing of the real-time video stream to identify the movement trajectory of the logistics operator and the change in the location of the package includes: Segmenting the real-time video stream into a plurality of continuous video segments, each video segment corresponding to a preset time interval; Detect key points of the human skeleton in each video clip to generate a sequence of posture changes of the logistics operator; Constructing a three-dimensional motion trajectory model based on the posture change sequence, and marking the movement path of the logistics operator around the logistics service terminal to obtain the motion trajectory of the logistics operator; Synchronously perform package feature recognition on each video clip to extract the color, shape and barcode information of the package outer packaging; Based on the position offset of the package features in continuous video clips, the package movement speed and direction are calculated to generate a heat map of the package position change.

7. The logistics interactive data processing method based on intelligent Internet of Things according to claim 1 is characterized in that: Generating logistics operation feedback information based on the real-time monitoring data and pushing the logistics operation feedback information to the client corresponding to the target user includes: Extract key frames from the real-time monitoring data to generate a logistics operation summary video containing time stamps; identifying package status information from the logistics operation summary video, the package status information including a package integrity tag and storage location coordinates; Comparing the package status information with the expected operation result in the logistics interaction request to generate an operation compliance assessment result; If the operation compliance assessment result does not reach the preset threshold, an exception handling suggestion is generated and attached to the logistics operation feedback information; The logistics operation feedback information is pushed to the message notification interface of the client through an encrypted channel, and the logistics status database of the e-commerce platform is updated synchronously.

8. The logistics interactive data processing method based on intelligent Internet of Things according to claim 7 is characterized in that: The step of pushing the logistics operation feedback information to the message notification interface of the client through an encrypted channel includes: Generate a data summary corresponding to the logistics operation feedback information, and encrypt the data summary using the public key of the target user; splicing the encrypted data summary with the logistics operation summary video to form a feedback information data packet; After receiving the feedback information data packet, the client uses a private key to decrypt the data digest and performs an integrity check on the locally cached data; If the verification is passed, the logistics operation summary video and the operation compliance assessment results will be displayed in split screen on the message notification interface.

9. The logistics interactive data processing method based on intelligent Internet of Things according to claim 2 is characterized in that: The method further comprises: Collect the target user's preferred operation mode and commonly used delivery time period as described in historical logistics interaction requests; Analyze the correlation between the preferred operation mode and logistics distribution efficiency, and generate personalized distribution strategy optimization suggestions; Integrate the personalized delivery strategy optimization suggestions with the e-commerce platform logistics data to dynamically adjust the default unlocking time period of the logistics service terminal; When a new logistics interaction request is detected to conflict with the adjusted default unlocking time period, a time period modification prompt is automatically pushed to the target user; updating the time allocation parameters in the access control unlocking strategy according to the target user's response to the time period modification prompt; The analysis of the correlation between the preferred operation mode and logistics distribution efficiency to generate personalized distribution strategy optimization suggestions includes: Extract the delivery operation records that have been successfully completed in the historical logistics interaction requests, and calculate the average operation time and error rate for each delivery period; Construct a two-dimensional analysis model with the delivery period as the horizontal axis and the operation efficiency index as the vertical axis to identify the target user's efficient time period; Matching the efficient time period with the estimated arrival time of the e-commerce platform logistics data to generate a set of recommended delivery time periods; Optimizing the optional range of reservation time periods for the logistics service terminal based on the recommended delivery time period set; When the target user initiates a new delivery time period reservation, the candidate time periods in the recommended delivery time period set are displayed first.

10. A logistics interactive data processing system based on smart Internet of Things, characterized by: The logistics interaction data processing system based on smart Internet of Things includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the logistics interaction data processing method based on smart Internet of Things as described in any one of claims 1 to 9 above.

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