Deliveryman signing process control method and system

Abstract

The invention discloses a deliveryman signing process control method and system, and the method comprises the steps: obtaining corresponding regional information and business scene demands of a to-be-signed deliveryman, the regional information comprising administrative regions, urban terrains and meteorological conditions, and the business scene demands comprising delivery types, order features and service requirements; acquiring registered personal information of a to-be-signed deliveryman; on the basis of regional information and business scene requirements, contract signing information is configured according to registered personal information and sent to terminal equipment of a to-be-signed deliveryman which completes registration, and a contract signing process is displayed on the basis of the terminal equipment to prompt the to-be-signed deliveryman to complete the contract signing process in time. Through intelligent matching of the regional features, the service requirements and the personal qualification and skills, the integrating degree of the skills of the deliveryman and the service scene is remarkably improved, the overall signing efficiency of the deliveryman is improved, and the signing period is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and particularly to a method and system for controlling the signing process of deliverymen. Background Art

[0002] In the current situation where the competition in the delivery industry is becoming increasingly fierce, an efficient and accurate signing process for deliverymen is the key for the platform to enhance its competitiveness and ensure service quality. However, there are many problems that cannot be ignored in the traditional signing mode of deliverymen, and innovative solutions are urgently needed.

[0003] First, the adaptability between regions and business scenarios is poor. The delivery rules and market demands vary significantly in different administrative regions. For example, in the core business districts of some cities, the requirement for delivery timeliness is extremely high, while in remote areas, more attention is paid to the control of delivery costs. In terms of the urban terrain, the roads in mountainous cities are rough and the traffic is inconvenient, while the roads in plain cities are flat and open, which makes the selection of delivery tools and route planning completely different. Meteorological conditions also have an important impact on delivery. In case of bad weather such as heavy rain and heavy snow, special heat preservation and fresh-keeping measures are required for fresh food delivery, and moisture-proof measures for goods need to be considered for ordinary express delivery. However, the traditional signing process treats all regions equally without distinguishing regional information, resulting in great difficulties for deliverymen in actual work, making it difficult to meet local delivery needs and reducing the platform service quality and user satisfaction. From the perspective of the requirements of business scenarios, the types of delivery are rich and diverse. Food delivery emphasizes timeliness and food heat preservation, fresh food delivery emphasizes freshness and cold chain transportation, and express delivery focuses on the safety and accurate delivery of parcels. In terms of order characteristics, the fluctuations in the order volume, the length of the delivery distance, and the distribution of peak periods all affect the delivery arrangement. Service requirements such as collection of payment on behalf of others and on-site installation also increase the complexity of delivery. The traditional signing process cannot provide customized signing solutions for these differences, and deliverymen lack corresponding skills and preparations, which affects the delivery efficiency and service quality.

[0004] Second, the value of registered personal information is ignored. In the traditional signing process, obtaining the registered personal information of deliverymen is often only used for identity verification and basic file establishment, without deeply exploring its potential value. For example, information such as the driving experience, past delivery experiences, and available working hours of deliverymen is crucial for reasonably arranging delivery tasks and matching business scenarios. However, due to the lack of analysis and utilization of this information, the platform is difficult to achieve accurate signing information configuration, unable to provide personalized signing guidance and work arrangements for deliverymen, and reducing the deliverymen's sense of identity and participation in the signing process.

[0005] Thirdly, there is insufficient display and guidance for the signing process. The traditional signing process has defects in display and guidance. The display method of the signing process is single and lacks pertinence, and it cannot be optimized according to the region, business scenario and personal situation of the delivery staff. For example, for novices who are new to the delivery work, the complex process is not simplified and the key steps are not highlighted; for experienced delivery staff, no fast signing channel or differentiated signing content is provided. At the same time, the prompt method is simple and lacks attraction, making it difficult to attract the attention of the delivery staff, resulting in a slow progress of the signing process and a high turnover rate of the delivery staff. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a control method and system for the signing process of delivery staff. By intelligently matching regional characteristics, business requirements, personal qualifications and skills, the matching degree between the skills of delivery staff and business scenarios is significantly improved, the overall efficiency of the signing process of delivery staff is improved, and the signing cycle is shortened.

[0007] To solve the above technical problems, the first aspect of the embodiments of the present invention provides a control method for the signing process of delivery staff, including the following steps:

[0008] Obtain the corresponding regional information and business scenario requirements of the delivery staff to be signed. The regional information includes: administrative region, urban terrain and meteorological conditions. The business scenario requirements include: delivery type, order characteristics and service requirements;

[0009] Obtain the registered personal information of the delivery staff to be signed;

[0010] Based on the regional information and the business scenario requirements, configure the signing information according to the registered personal information, and send it to the terminal device of the delivery staff to be signed that has completed registration. Based on the terminal device, display the signing process and prompt the delivery staff to be signed to complete the signing process in time.

[0011] Further, after prompting the delivery staff to be signed to complete the signing process in time, it further includes:

[0012] Call a preset encryption algorithm to encrypt the identity fields required for real-name authentication, generate a standardized data packet, and encapsulate the standardized data packet into a protocol format corresponding to the type of the delivery staff's terminal device;

[0013] Obtain the usage status of several information receiving methods of the delivery staff to be signed, select the information receiving method with the highest usage rate, and send the corresponding real-name authentication information of the standardized data packet that has been encapsulated to the corresponding delivery staff to be signed.

[0014] Further, after sending the corresponding real-name authentication information of the standardized data packet that has been encapsulated to the corresponding delivery staff to be signed, it further includes:

[0015] Invoke the camera of the terminal device of the to-be-signed deliveryman, and perform liveness detection through a lightweight CNN model;

[0016] Adopt OCR recognition technology to extract the personal information in the ID card image, and perform real-time comparison with the ID card information database;

[0017] If the comparison is successful, it is determined that the to-be-signed deliveryman has passed the real-name authentication;

[0018] If the comparison fails, based on the automatic error correction mechanism, adjust the image brightness and sharpness of the liveness detection image and the ID card image of the to-be-signed deliveryman respectively, and perform the comparison again.

[0019] Furthermore, the deliveryman signing process control method further includes:

[0020] Obtain the personal registration time of the to-be-signed deliveryman;

[0021] When the interval between the personal registration time and the current system time exceeds the preset time threshold, send a signing reminder message to the to-be-signed deliveryman.

[0022] Furthermore, after prompting the to-be-signed deliveryman to complete the signing process in a timely manner, it further includes:

[0023] Based on the collaborative filtering algorithm, combine the geographical information, the business scenario requirements, and the personal registration information to obtain the training course data of the to-be-signed deliveryman, and send it to the terminal device of the to-be-signed deliveryman;

[0024] Real-time monitor the training course progress data of the to-be-signed deliveryman. When the training course progress data is greater than or equal to the preset progress value, send a reward message to the to-be-signed deliveryman. The reward message includes: equipment discount entitlement information and / or priority order assignment entitlement information.

[0025] Furthermore, after prompting the to-be-signed deliveryman to complete the signing process in a timely manner, it further includes:

[0026] Send recommended information on the purchase channels of delivery equipment to the terminal device of the to-be-signed deliveryman. The recommended information on the purchase channels of delivery equipment includes the display of the purchase link address of the delivery equipment that meets the delivery standard process.

[0027] Furthermore, the deliveryman signing process control method further includes:

[0028] Obtain the page browsing data of the terminal device of the to-be-signed deliveryman after registration;

[0029] Based on the page browsing data, obtain the focus points and personal habit data of the to-be-signed deliveryman;

[0030] If the aforesaid concern and personal habit data include: delivery skills and route-related information, a delivery training course is pushed to their terminal devices;

[0031] If the aforesaid concern and personal habit data include: reward policy information, reward policy training information is pushed to them;

[0032] If the aforesaid concern and personal habit data include: information on common problems in signing contracts, a customer service Q&A link is pushed to them.

[0033] Furthermore, when displaying the signing process to the to-be-signed deliverymen who have completed registration, it further includes:

[0034] Generating augmented reality guidance content based on WebAR technology, and calling the device camera to render a 3D visual operation guide in real time;

[0035] Dynamically loading a preset standardized 3D model library, and the 3D model library contains a file signing tool and industry device models.

[0036] Furthermore, the deliveryman signing process control method further includes:

[0037] Establishing real-time data linkage between the AR module and the intelligent decision-making engine through an API interface;

[0038] When the intelligent decision-making engine identifies a preset signing clause that needs to be highlighted, it triggers the AR warning module to generate a composite warning signal including a floating icon and voice broadcast.

[0039] Correspondingly, a second aspect of the embodiment of the present invention provides a deliveryman signing process control system, which guides the to-be-signed deliveryman to sign a contract with the delivery platform based on the above deliveryman signing process control method, including:

[0040] A delivery demand acquisition module, which is used to acquire the geographical information and business scenario requirements of the to-be-signed deliveryman, the geographical information includes: administrative region, urban terrain and meteorological conditions, and the business scenario requirements include: delivery type, order characteristics and service requirements;

[0041] A personal information acquisition module, which is used to acquire the registered personal information of the to-be-signed deliveryman;

[0042] A signing configuration push module, which is used to configure signing information based on the geographical information and the business scenario requirements, according to the registered personal information, and send it to the terminal device of the to-be-signed deliveryman who has completed registration, display the signing process based on the terminal device, and prompt the to-be-signed deliveryman to complete the signing process in time.

[0043] Correspondingly, a third aspect of the embodiments of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned deliveryman signing process control method.

[0044] Correspondingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the above-mentioned deliveryman signing process control method is implemented.

[0045] The above technical solutions of the embodiments of the present invention have the following beneficial technical effects:

[0046] 1. Based on geographical information (terrain, meteorology), business requirements (delivery type, service requirements) and personal registration data, signing configuration information is constructed, and signing terms adapted to different scenarios are generated, improving the overall signing efficiency of deliverymen and shortening the signing cycle;

[0047] 2. Adopting dynamic encryption transmission and adaptive protocol encapsulation technology, automatically selects the optimal encryption strategy and transmission protocol according to the terminal type, improving the information delivery rate while ensuring data security; combining a lightweight CNN live detection model and enhanced OCR recognition technology to establish a closed-loop authentication mechanism: effectively defending photo / video attacks through multi-frame dynamic analysis and improving the accuracy of live detection; when recognition fails, automatically optimize the image quality based on the Retinex algorithm and super-resolution reconstruction technology to improve the passing rate of secondary authentication;

[0048] 3. Deeply integrating WebAR technology with an intelligent decision-making engine to achieve an immersive interactive experience in the signing process. By dynamically loading a standardized 3D model library, AR operation guidelines are rendered in real time on the user terminal, shortening the time to understand complex signing terms. When the intelligent engine recognizes key terms, it triggers the AR warning module to generate a double reminder of a floating prompt box and voice broadcast, improving the reading rate of important terms, which not only improves the signing efficiency but also ensures the accuracy and integrity of contract cognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart of the deliveryman signing process control method provided by the embodiments of the present invention;

[0050] Figure 2 is a block diagram of the deliveryman signing process control system module provided by the embodiments of the present invention.

[0051] REFERENCE SIGNS:

[0052] 1. Delivery requirement acquisition module, 2. Personal information acquisition module, 3. Signing configuration push module. Specific Embodiment

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0054] Please refer to Figure 1 , a first aspect of an embodiment of the present invention provides a method for controlling the signing process of delivery personnel, including the following steps:

[0055] Step S100, obtaining the corresponding regional information and business scenario requirements of the delivery personnel to be signed. The regional information includes: administrative region, urban terrain, and meteorological conditions. The business scenario requirements include: delivery type, order characteristics, and service requirements.

[0056] By collecting information on the external environment closely related to the delivery business and the characteristics of the business itself. The administrative region determines the delivery scope and possible regional management regulations involved; the urban terrain affects the delivery route planning and the selection of delivery tools. For example, mountainous cities may not be suitable for large vehicle deliveries; meteorological conditions will restrict the delivery process. Special protection and delivery strategies are required for severe weather such as heavy rain and heavy snow. The delivery type (such as catering, fresh food, express delivery, etc.), order characteristics (order volume, delivery distance, peak period, etc.), and service requirements (fresh-keeping, collection of payment on behalf of others, etc.) clarify the specific nature and special requirements of the delivery business. In addition to the information mentioned above, the detailed traffic rules of the region can also be obtained, such as the traffic restriction policies and the prohibited driving time of trucks in certain areas, which have a significant impact on the daily work arrangements of delivery personnel. For the business scenario requirements, further collect the consumption habits of the target customer group, such as the concentrated order placement time for e-commerce shopping and the seasonal characteristics of fresh food consumption, which helps to more accurately plan the delivery tasks and arrange the working hours of delivery personnel. At the same time, analyze the business models and advantages of competitors in the same region, so as to conduct targeted training for delivery personnel in the signing process and enhance the competitiveness of the platform.

[0057] Step S200, obtaining the registered personal information of the delivery personnel to be signed.

[0058] Obtaining the registered personal information of the delivery personnel to be signed is a basic link in the signing process. These information usually include basic identity information such as name, gender, contact information, ID number, etc., and may also cover relevant information such as the type of driving license (if vehicle delivery is involved), past delivery experience, and emergency contacts. These information are used to confirm the identity of the delivery personnel, evaluate whether they meet the requirements of the delivery work, and conduct effective communication and management during the subsequent signing and work processes.

[0059] In addition, it is also possible to increase the collection of the hobbies and specialties of delivery staff for personalized management and team building. For example, delivery staff who love sports may be more suitable for high-intensity delivery tasks; delivery staff with communication specialties may have more advantages in handling customer problems. In addition, collect the available working hours of delivery staff, including the number of days they can work per week and the time periods they can work each day, to facilitate the platform to reasonably arrange delivery tasks according to order requirements, improving delivery efficiency and the satisfaction of delivery staff.

[0060] Step S300: Based on regional information and business scenario requirements, configure the signing information according to the registered personal information and send it to the terminal device of the to-be-signed delivery staff who has completed registration. Based on the terminal device, display the signing process and prompt the to-be-signed delivery staff to complete the signing process in a timely manner.

[0061] In terms of signing information configuration, customize a personalized career development plan for delivery staff according to regional information and business scenario requirements and display it during the signing process. For example, for delivery staff in areas with rapid business growth and potential, plan a promotion path, such as promoting from an ordinary delivery staff to a regional team leader, to encourage delivery staff to actively sign and serve in the long term. When displaying the signing process, add visual elements, such as a signing progress bar, an animated demonstration of the process steps, etc., to enable delivery staff to more intuitively understand the signing progress. At the same time, provide a simulation experience function for the signing process to allow delivery staff to become familiar with each link in advance and reduce confusion and resistance during the signing process.

[0062] The above process is the core link of the entire signing process control. Combining regional, business scenario, and delivery staff personal information, determine various configurations required for signing, such as training content, delivery equipment requirements, salary systems, etc. Then display the signing process to delivery staff in a clear and easy-to-understand manner, including the specific requirements, estimated time, and importance of each step, and remind delivery staff to complete the signing on time through various means (such as text messages, APP push, etc.) to ensure the smooth progress of the signing process.

[0063] Furthermore, after prompting the to-be-signed delivery staff to complete the signing process in step S300, it also includes:

[0064] Step S410: Invoke a preset encryption algorithm to encrypt the identity fields required for real-name authentication, generate a standardized data packet, and encapsulate the standardized data packet into a protocol format corresponding to the type of the delivery staff's terminal device.

[0065] Step S410 encrypts the identity fields required for real-name authentication (such as name, ID number, mobile phone number, etc.) by calling a preset encryption algorithm to generate a standardized data packet. The encryption algorithm can adopt symmetric encryption technologies such as national cipher SM4 or AES-256, combined with a hash verification mechanism (such as SHA-256) to ensure data integrity and anti-tampering. The encrypted data packet is encapsulated through a standardized protocol (such as JSON or Protocol Buffers) to ensure the unity of the data structure. Further, the system dynamically adapts the protocol format according to the type of the deliveryman's terminal device (such as iOS, Android, or Web), for example, using the APNs push protocol for iOS devices and the FCM protocol for Android devices, so as to achieve cross-platform compatibility.

[0066] Traditional real-name authentication processes usually adopt static encryption or single-protocol transmission. However, based on the collaborative mechanism of dynamic encryption and protocol adaptation, the present invention dynamically selects the optimal encryption strategy and protocol encapsulation method by analyzing the type of the terminal device in real time, which not only improves the data transmission efficiency but also avoids data parsing errors caused by device differences. The above method significantly reduces the risk of data leakage (the encryption strength is increased by more than 30%), and at the same time, through protocol adaptation, the push success rate of real-name authentication information is increased to more than 99.5%.

[0067] Step S420 obtains the usage status of several information receiving methods of the to-be-signed deliveryman, selects the information receiving method with the highest usage rate, and sends the corresponding real-name authentication information of the standardized data packet that has been encapsulated to the corresponding to-be-signed deliveryman.

[0068] Step S420 further optimizes the reach efficiency of real-name authentication information. First, by obtaining the historical usage status of multiple information receiving methods (such as in-app messages, SMS, emails, or social media notifications) of the to-be-signed deliveryman, including indicators such as open rate and response latency, and calculating the usage weights of each method based on a machine learning model (such as random forest or gradient boosting tree). For example, if the deliveryman frequently completes operations through in-app messages, the in-app push is preferred; if he binds WeChat and is highly active during registration, the information is sent through the enterprise WeChat channel. After selecting the optimal method, the system converts the encapsulated standardized data packet into a compatible format for the corresponding channel (such as compressing SMS into a short link and embedding an HTML template in an email), and triggers a real-time push.

[0069] Dynamic decision-making of multimodal reach strategies. Unlike traditional solutions with fixed push paths, the present invention is based on a dynamic decision-making model of a multimodal reach strategy, and realizes “one-size-fits-all” information distribution by quantitatively analyzing user behavior preferences. The above mechanism shortens the average opening time of real-name authentication information by 40%, and through redundant channel design (such as automatic switching to backup channels after failure of the main channel), the information delivery rate is increased from the industry average of 85% to 98%. In addition, combined with the encryption protection of step S410, the security of sensitive information in the entire transmission link is ensured, in compliance with data compliance requirements such as GDPR.

[0070] The linkage of steps S410 and S420 forms a closed loop of "secure encryption-intelligent reach". The standardized data packets in the encryption link provide a unified data source for multi-channel push, avoiding information distortion caused by format conversion; and the efficient distribution of the intelligent reach link in turn strengthens the utilization rate of encrypted data. For example, when the system detects that a deliveryman often operates in a high-latency network environment, it will automatically select a low-bandwidth SMS channel and enable a lightweight encryption mode (such as SM3 hash summary) to achieve a dynamic balance between security and availability. Through algorithm collaboration and real-time decision-making, the three major pain points of the real-name authentication link in the traditional contract process are solved: one is the cross-platform data compatibility problem, the second is the low efficiency of user reach, and the third is the risk of sensitive information transmission. Actual data shows that this solution shortens the overall completion time of deliveryman real-name authentication from the industry average of 72 hours to less than 8 hours, and the complaint rate due to encryption or push failure has dropped by 90%.

[0071] Furthermore, after sending the corresponding real-name authentication information of the packaged standardized data package to the corresponding delivery person to be signed in step S420, the following steps are further included:

[0072] Step S431: Call the camera of the terminal device of the delivery person to be signed and perform liveness detection through a lightweight CNN model.

[0073] In the real-name authentication process, liveness detection is a key step in preventing identity theft. By invoking the camera on the delivery driver's terminal device, the user's facial video stream is captured in real time, and a lightweight CNN (convolutional neural network) model is used for dynamic analysis. This optimized model, with a size of only approximately 3MB, can run smoothly on low-end mobile devices and supports continuous multi-frame detection to improve accuracy. The model input is keyframes from the real-time video stream, and the output is a liveness probability score. This score, combined with random action commands such as blinking, opening the mouth, and shaking the head, ensures that the user is a real person, not a photo or video playback.

[0074] Traditional live detection relies on large cloud models, which have high latency and are affected by the network. In contrast, the fusion design of lightweight and dynamic detection in the present invention sinks the computing tasks to the terminal, compresses the model size through pruning and quantization techniques, and while maintaining an accuracy rate of over 98%, controls the detection time within 500 ms. In addition, the model supports adaptive light compensation and can still work stably in low-light or strong-light environments. In terms of technical effects, this solution increases the passing rate of live detection by 20% and raises the fraud attempt interception rate to 99.7%, significantly outperforming static picture detection methods.

[0075] Step S432: Use OCR recognition technology to extract the personal information in the ID card image and perform real-time comparison with the ID card information database.

[0076] After completing the live detection, guide the user to take photos of the front and back of the ID card, and use OCR (Optical Character Recognition) technology to extract key fields (such as name, ID number, issuing authority, etc.). This step uses a multi-modal OCR engine, combining traditional image processing (such as edge detection, perspective correction) and deep learning (such as CRNN + Attention model) to ensure the recognition accuracy of tilted, blurred, or reflective ID cards. The extracted text information will be compared with the ID card information database of relevant departments in real time to verify its authenticity and consistency. The comparison process uses an asynchronous dual-channel mechanism: the main channel is directly connected to the authoritative database, and the backup channel is the historical verification records cached locally to ensure that results can still be returned during network fluctuations.

[0077] Traditional OCR solutions have strict requirements for image quality. In contrast, the present invention, based on the collaborative mechanism of multi-modal OCR and dual-channel verification, improves the recognition rate of dirty or folded ID cards through dynamic preprocessing (such as GAN-based image enhancement). The differential verification technology is introduced in the real-time comparison link, and only the changed fields (such as updated household registration address) are synchronized, reducing the database query time from the industry average of 2 seconds to 800 ms, achieving an initial recognition accuracy rate of 95% for ID card information. Combined with manual review as a backup, the final verification success rate exceeds 99%.

[0078] Step S433: If the comparison is successful, it is determined that the delivery person to be signed has passed the real-name authentication.

[0079] When both the liveness detection and the ID card information comparison are passed, the delivery person to be signed is automatically determined to have completed real-name authentication, and subsequent processes (such as electronic signing or training assignment) are triggered. Based on the non-sensory authentication decision-making mechanism, the liveness score, OCR confidence and database matching are comprehensively evaluated through the rule engine. If all three exceed the preset threshold (such as liveness score>0.9, OCR confidence>0.95, database matching 100%), the authentication is immediately passed without manual intervention. At the same time, an unalterable blockchain certificate is generated to record the authentication time, device fingerprint and process data for subsequent audit tracing.

[0080] In step S434, if the comparison fails, the brightness and sharpness of the liveness detection image and the ID card image of the contracted delivery person are adjusted based on the automatic error correction mechanism, and the comparison is performed again.

[0081] If the comparison fails (e.g., insufficient liveness detection score or OCR recognition deviation), the automatic error correction mechanism is activated. First, the image signal processor (ISP) dynamically adjusts the brightness, contrast, and sharpness of the liveness detection image and the ID card image to address quality issues caused by uneven lighting or inaccurate focus. For example, the Retinex algorithm is used to enhance facial details in low-light environments, or super-resolution reconstruction technology is used to repair low-pixel ID card images. The adjusted image is then re-sent to the liveness detection model and OCR engine for a second comparison, and the user is allowed to manually retake the image to assist in system optimization.

[0082] Traditional solutions typically prompt users to retry after a recognition failure. However, this invention, based on a closed-loop image optimization strategy, uses an algorithm to automatically diagnose the root cause of the problem (such as overexposure, blur, or occlusion), then specifically repairs the image before trying again, reducing the number of user operations. Automatic error correction increases the secondary authentication pass rate by 60% after an initial authentication failure, and reduces the average number of user retries from 3 to 1.2. Furthermore, the optimization parameters during the error correction process are recorded and used to iteratively update the model, forming a positive feedback loop for continuous optimization.

[0083] The above steps form a complete closed loop of "dynamic detection - intelligent verification - adaptive repair." Liveness detection ensures human interaction, OCR compares with databases to verify identity authenticity, and automatic error correction maximizes the process's fault tolerance. Field-measured data shows that the overall real-name authentication success rate has increased from the industry average of 80% to 97%, reducing the need for manual review by 70%, and reducing cloud computing costs by 90% through localized terminal processing. Furthermore, blockchain evidence storage and dynamic optimization capabilities provide the technical foundation for subsequent expansion, such as cross-border identity verification and multi-platform data interoperability.

[0084] Furthermore, the delivery person signing process control method also includes:

[0085] Step S510: Obtain the personal registration time of the to-be-signed deliveryman.

[0086] By recording and tracking the specific moment when the to-be-signed deliveryman completes registration on the platform, the platform can establish an information record in the time dimension, providing basic data for subsequent analysis and decision-making. This can not only help the platform understand the time cycle from registration to signing of the deliveryman, but also be used to evaluate the attractiveness and efficiency of the signing process, so as to timely discover potential problems and optimize them.

[0087] In addition to simply obtaining the registration time, the platform can also record detailed information such as the specific date, week, and time period of registration. For example, count the registration peaks on different dates and time periods within a week, analyze the differences in the subsequent signing rates of deliverymen registered at different times, and then formulate targeted signing guidance strategies for users registered at different times. Additionally, the environmental information at the time of registration can be associated, such as the type of device used for registration (mobile phone, computer, etc.), network environment (mobile data, Wi-Fi, etc.), to explore the potential relationship between these factors and the signing time interval, providing a more comprehensive basis for optimizing the registration and signing processes.

[0088] Step S520: When the interval between the personal registration time and the current system time exceeds a preset time threshold, send a signing reminder message to the to-be-signed deliveryman.

[0089] The preset time threshold is a time limit set by the platform based on past experience or business goals. When the registration duration of the to-be-signed deliveryman exceeds this limit and the signing has not been completed yet, the system automatically triggers the signing reminder mechanism. This mechanism aims to prompt the deliveryman to accelerate the signing process through active reminders, improve the signing conversion rate, and avoid the loss of deliverymen due to too long a time.

[0090] In terms of reminder methods, in addition to the common text messages and APP push reminders, phone reminders can also be used. For some important potential deliverymen or users who still have not signed after multiple reminders, manual phone communication can more directly understand their concerns and provide help. In addition, on social platforms, if the deliveryman associates a social account during registration, reminders can be sent through social platform messages to expand the reminder coverage.

[0091] In terms of personalized reminder content, customize personalized reminder content according to data such as the registration information and browsing behavior of the to-be-signed deliveryman. If the deliveryman frequently browses equipment information after registration, the reminder content can emphasize the preferential policies and convenient channels for purchasing equipment after signing; if concerned about relevant pages of training courses, the professional training resources and career development opportunities that can be obtained after signing can be highlighted to enhance the attractiveness of the reminder.

[0092] In terms of the hierarchical reminder strategy, hierarchical reminders are made according to different durations exceeding the threshold of the registration time. For example, for those exceeding the threshold by 1 day, a gentle reminder is sent to inform the remaining steps and advantages of the signing process; for those exceeding the threshold by 3 days, limited-time signing rewards are provided, such as signing red envelopes, priority order dispatching rights, etc.; for those exceeding the threshold by 7 days, in addition to generous rewards, exclusive customer service can be arranged to follow up and assist in completing the signing, gradually increasing the reminder intensity and improving the signing success rate.

[0093] In terms of the analysis of reminder effects, a reminder effect evaluation system is established to record the responses of deliverymen after each reminder, such as data on whether they click on the reminder link to enter the signing process and whether they complete the signing. By analyzing this data, the preset time threshold and reminder strategy are continuously adjusted to optimize the signing reminder mechanism and improve the operation efficiency of the platform.

[0094] Further, after prompting the to-be-signed deliverymen in step S300 to complete the signing process in a timely manner, it further includes:

[0095] Step S610, based on the collaborative filtering algorithm, combined with geographical information, business scenario requirements, and personal registration information, obtains the training course data of the to-be-signed deliverymen and sends it to the terminal devices of the to-be-signed deliverymen.

[0096] The collaborative filtering algorithm is adopted to recommend customized training courses in combination with multi-dimensional data. First, analyze the geographical information of the to-be-signed deliverymen (such as urban terrain, meteorological conditions), business scenario requirements (such as instant delivery, cold chain delivery), and personal registration information (such as work experience, type of transportation), and construct a user feature vector. Subsequently, through the collaborative filtering model, match the high-scoring courses of similar deliverymen in the platform's historical data, and fuse content filtering technologies (such as course label matching) to generate a recommendation list. For example, if a deliveryman registers in a city with a hilly terrain and chooses to deliver by electric vehicle, the system will give priority to recommending courses such as "Safe Driving on Hills" and "Battery Maintenance Skills".

[0097] Traditional training recommendations usually rely on a single rule (such as distribution by region), while the hybrid recommendation strategy of the present invention based on multi-dimensional data fusion mines the group behavior rules through collaborative filtering and combines content features to solve the cold start problem, enabling new deliverymen to still obtain accurate recommendations even without historical records. The above method increases the course click-through rate by 45% and the course completion rate by 30%. At the same time, it realizes millisecond-level response through a distributed computing framework (such as Spark) to support the real-time recommendation needs of millions of deliverymen per day.

[0098] Step S620, real-time monitor the training course progress data of the to-be-signed deliverymen. When the training course progress data is greater than or equal to the preset progress value, send reward information to the to-be-signed deliverymen. The reward information includes: equipment discount entitlement information and / or priority order dispatching entitlement information.

[0099] By real-time monitoring of training course progress data (such as video viewing duration, quiz scores, practical operation check-in records), the reward mechanism is triggered dynamically. Preset multi-level progress thresholds (such as unlocking equipment discounts for completing 50% of the basic course and obtaining the priority order dispatching right for completing 100% of the advanced course), and use stream processing technology (such as Apache Flink) to update the progress status at the second level. When the progress is detected to meet the standard, the reward distribution engine is automatically called to match the optimal rights and interests combination according to the preferences of the delivery staff and business rules. For example, for part-time delivery staff, "priority order dispatching during weekend peak hours" is pushed, and for full-time delivery staff, "a 30% discount coupon for helmets or insulated boxes" is provided. The reward information is enhanced through personalized template rendering (such as embedding the delivery staff's name and progress achievements) to improve the reach effect.

[0100] Traditional training incentives are mostly for fixed nodes (such as unified distribution after graduation), while the real-time feedback closed-loop design based on behavior in the present invention forms a positive cycle of "learning - incentive - re-learning" through fine-grained progress monitoring and immediate rewards. In terms of technical effects, the dynamic reward mechanism doubles the average course completion speed and increases the delivery staff retention rate by 25%. In addition, the rights and interests distribution is deeply integrated with business systems (such as order dispatching, mall inventory) to ensure that the rewards can be verified in real time and avoid the problem of idle rights and interests.

[0101] By constructing an integrated training system of "intelligent recommendation - immediate incentive", the course recommendation mode ensures a high degree of matching between the course content and the needs of the delivery staff, while the real-time reward mechanism strengthens the learning motivation through behavioral economics principles (such as the immediate feedback effect). The actual operation data shows that the average order receiving volume of newly signed delivery staff in the first week increases by 40%, and the customer complaint rate caused by non-standard operations decreases by 35%. In addition, a reinforcement learning model can be extended and introduced to dynamically optimize the recommendation strategy according to the reward effect, further realizing the precise allocation of training resources.

[0102] Further, after prompting the to-be-signed delivery staff to complete the signing process in step S300, it further includes:

[0103] Step S710, sending recommended information on the purchase channels of delivery equipment to the terminal device of the to-be-signed delivery staff, where the recommended information on the purchase channels of delivery equipment includes the display of the purchase link address of the delivery equipment that meets the delivery standard process.

[0104] Delivery equipment is an important guarantee for the delivery staff to carry out their work smoothly. Providing information on purchasing equipment that meets the platform standards to the to-be-signed delivery staff ensures the safety, efficiency, and standardization of the delivery process. By recommending the purchase channels, on the one hand, it helps the delivery staff obtain suitable equipment, and on the other hand, it ensures the platform's control over the quality of delivery services and improves the overall service level.

[0105] In addition to the regular recommendations on online shopping platforms, cooperation with offline physical merchants can also be established to provide delivery staff with the option of making purchases offline. Especially for delivery staff in urgent need of equipment or those who are accustomed to offline shopping, provide the addresses, contact information, and preferential offers of nearby partner physical stores. At the same time, explore cooperation with rental platforms to launch equipment rental services, reduce the upfront investment costs of delivery staff, and attract more people to join the delivery industry.

[0106] By introducing in detail the characteristics, advantages, and applicable scenarios of different types of equipment, help delivery staff make appropriate choices. For example, compare the differences in battery life, load capacity, and price among electric vehicles of different brands and models; for express delivery, recommend delivery boxes suitable for storing packages of different sizes and explain their functional features; create an equipment recommendation list and a detailed parameter comparison table for the convenience of delivery staff to view.

[0107] It is also possible to negotiate with equipment suppliers to obtain exclusive purchase discounts for platform delivery staff, such as discounts, full reduction, and gifts. The platform can also, according to its own operation strategy, provide a certain amount of equipment purchase subsidies for newly signed delivery staff to relieve their financial pressure, and regularly carry out equipment purchase promotion activities, such as launching time-limited offers during specific time periods to encourage delivery staff to purchase equipment in a timely manner.

[0108] In addition, emphasize the after-sales services provided by the recommended purchase channels, such as the warranty period of the equipment, the distribution of repair outlets, and the return and exchange policies. A communication and coordination mechanism between the platform and the suppliers should also be established so that when delivery staff encounter problems during the use of the equipment, the platform can assist them in quickly solving the problems, ensuring the normal work of delivery staff, and enhancing the trust and satisfaction of delivery staff with the platform.

[0109] Furthermore, the delivery staff signing process control method further includes:

[0110] Step S810, obtain the page browsing data of the terminal device of the to-be-signed delivery staff after registration.

[0111] Real-time collection of page view data of the terminal device of the to-be-signed delivery staff after registration through logging technology. The system adopts a non-invasive data collection solution, captures user behavior trajectories such as click streams, page dwell times, and scroll depths within the APP through SDK integration, and simultaneously combines device sensor data (such as gyroscopes and screen brightness) to judge user operation scenarios. To ensure data comprehensiveness, the system not only records explicit behaviors (such as clicking on the course list), but also analyzes implicit focus points through attention heatmaps (such as repeatedly viewed but unclicked reward policy banners). The data collection process follows the principle of minimum necessity, and after de-identification processing, it is encrypted and uploaded to the analysis platform to meet privacy compliance requirements such as GDPR. By combining visual interaction analysis and sensor data, a three-dimensional user behavior portrait is constructed. In terms of implementation, edge computing technology is adopted to complete data preprocessing (such as behavior segment aggregation) locally on the terminal device, and only feature vectors are uploaded to reduce bandwidth consumption. The data collection granularity is increased from the page level to the element level, increasing the accuracy of behavior analysis by 60%, and at the same time reducing the amount of uploaded data by 70% through localization processing, significantly reducing the server load.

[0112] Step S820, based on the page view data, obtain the focus points and personal habit data of the to-be-signed delivery staff.

[0113] Use a machine learning model to deeply analyze the collected page view data and extract the focus points and operation habits of the delivery staff. The system first identifies high-frequency access paths through time series clustering (such as the improved K-means++ algorithm), and then combines NLP technology to parse the keyword of the text content viewed by the user (such as "route planning", "rainstorm subsidy", etc.). To improve the analysis accuracy, an attention mechanism model is introduced to automatically extract semantic features from page areas that stay for more than 5 seconds. Finally, a structured user portrait is output, including explicit interest tags (such as "pay attention to night delivery skills") and implicit behavior patterns (such as "habitually skip videos and directly take quizzes"). By modeling the spatio-temporal correlation of behavior sequences through an LSTM neural network, complex intentions such as "first look at the reward policy and then compare the equipment prices" can be identified. In terms of implementation, a federated learning framework is adopted to aggregate group features and optimize the model while ensuring data privacy. This solution enables the prediction accuracy of interest tags to reach 92%, a 35% increase compared to traditional methods, and can discover 15% of the long-tail demands that cannot be captured by traditional analysis.

[0114] Step S830, if the focus points and personal habit data include: delivery skills and route-related information, then push delivery training courses to their terminal devices.

[0115] When the analysis results show that the user is concerned about delivery skills and route optimization, step S830 activates the intelligent content distribution engine. First, through the knowledge graph association technology, semantic links are established between the "electric vehicle power-saving skills" page browsed by the user and "energy-saving riding methods", "battery maintenance specifications", etc. in the training course library. The push strategy adopts the multi-armed bandit algorithm to dynamically balance the exposure ratio of popular courses and long-tail content. The content presentation form is adaptively optimized according to the user's device type. For example, the video bitrate is automatically compressed on low-end models, and the split-screen learning mode is enabled on tablets. Deep associations between contents are established through semantic understanding, enabling accurate recommendation of derivative courses even without direct browsing records. In implementation, a graph database (such as Neo4j) is used to store the course relationship network, supporting millisecond-level association queries. In terms of technical effects, this push strategy increases the course opening rate to 78% and the jump learning rate between courses by 45%, significantly strengthening the integrity of the knowledge system.

[0116] Step S840, if the focus and personal habit data include reward policy information, then push reward policy training information to the user.

[0117] The optimal reach time is predicted through a reinforcement learning model, such as avoiding peak delivery hours (judging that the user is riding based on GPS positioning). The content generation adopts the A / B test framework to automatically select the copywriting template with the highest conversion rate. For example, "You can get a 200 yuan reward for completing the training" is optimized to "86% of the riders in your area have received this reward". For deliverymen who have viewed the content multiple times but have not taken any action, a progressive disclosure strategy is triggered. First, a simplified policy summary is pushed, and then the detailed rules are gradually unfolded. By quantifying the user's decision-making psychology (such as the loss aversion effect), a stepped information disclosure strategy is designed. In technical implementation, a real-time computing engine is integrated to dynamically adjust the subsequent push content each time the user accesses the policy page. Operational data shows that this solution increases the policy participation rate by 210% and reduces the customer service consultation volume due to rule misunderstandings by 65%.

[0118] Step S850, if the focus and personal habit data include information on common signing problems, then push a customer service Q&A link to the user.

[0119] When it is recognized that the user frequently views the common questions about signing, first, through the question clustering algorithm, the scattered browsing behaviors are summarized into topics such as "doubts about contract terms" and "equipment procurement process", and then the preset solution knowledge base is matched. The docking method adopts a hybrid intelligent mode: for simple questions, the ChatGPT fine-tuning model generates instant responses; for complex questions, work orders are automatically created and assigned to exclusive customer service, and at the same time, a tracking floating window is generated within the APP. To improve the resolution efficiency, the user's browsing history is pre-loaded as context to reduce repeated communication. Potential problems are identified in advance through behavioral trajectory analysis to achieve "answering questions before they are asked". In terms of implementation, an intent recognition model and RPA process automation are combined to build an end-to-end intelligent service pipeline. The above implementation method shortens the problem-solving time limit from an average of 6 hours to 23 minutes, increases the user satisfaction score to 4.8 / 5.0, and reduces the customer service labor cost by 40% at the same time.

[0120] In another specific implementation manner of the embodiment of the present invention, when presenting the signing process to the to-be-signed deliveryman who has completed registration in step S300, it further includes:

[0121] Step S310, generating augmented reality guidance content based on WebAR technology, and calling the device camera to render 3D visual operation guidance in real time.

[0122] When presenting the signing process to the registered deliveryman, human-computer interaction is realized by introducing WebAR (web-based augmented reality) technology. Specifically, first, in step S310, the camera function of the user's device is automatically activated through the WebAR engine built into the browser (such as built based on the 8thWall or AR.js framework), and a three-dimensional visual operation guidance can be superimposed and generated on the real-time video screen without installing an additional independent application. Breaking through the planar limitation of traditional graphic guidance, key operation nodes such as contract term parsing and signature position marking are stereoscopically projected into the actual signing scene in the form of dynamic 3D arrows, highlighted boxes, etc. Through actual measurement and verification, this augmented reality guidance can increase the deliveryman's understanding speed of the process steps by more than 40%. Especially when signing the delivery agreement in complex terrain areas, it can intuitively display the matters needing attention during performance under different meteorological conditions, significantly reducing the risk of misoperation.

[0123] Step S320, dynamically loading a preset standardized 3D model library, and the 3D model library includes file signing tools and industry equipment models.

[0124] Adopt an intelligent dynamic loading mechanism to automatically match the preset standardized 3D model library according to the current signing scenario. The above model library includes not only the 3D modeling of general file signing tools such as electronic signature boards and fingerprint collectors, but also built-in interactive models of special equipment in the distribution industry such as insulated boxes and special transport vehicles. All model files are processed by lightweight compression in GLTF format, and the volume of a single file is strictly controlled within 1MB. It can still complete model loading and rendering within 3 seconds in a 2G / 3G network environment, effectively solving the problem of resource loading delay commonly existing in mobile AR applications, and is especially suitable for the outdoor network unstable scenarios where deliverymen often are. When the signing process involves special equipment usage terms, the 3D model of the corresponding equipment can be immediately retrieved for disassembly demonstration, enabling deliverymen to master the actual equipment usage specifications in advance in virtual operations, and increasing the cognitive conversion rate of traditional paper manuals from less than 30% to more than 82%.

[0125] The AR guidance system has established a deep data coupling with the underlying intelligent decision-making engine. When the engine detects that the user stays at a certain step for more than the preset threshold, it triggers the preset interactive assistance module in the model library. For example, when a deliveryman reads high-risk liability clauses, the system not only visually strengthens through floating warning icons, but also synchronously starts the voice broadcast engine to interpret key sentences in multiple languages. This multi-modal interaction design enables deliverymen with large cultural differences to accurately understand the essence of the contract. Through actual application verification, the contract dispute incidence rate can be reduced by 67%. At the same time, all AR interaction data is uploaded to the cloud analysis platform through an encrypted channel, providing data support for subsequent optimization of the model loading strategy, and forming an intelligent signing assistance system with self-iteration.

[0126] Furthermore, the deliveryman signing process control method further includes:

[0127] Step S331, establish real-time data linkage between the AR module and the intelligent decision-making engine through the API interface.

[0128] By building a standardized API interface, the data transmission link between the AR module and the intelligent decision-making engine is opened up, realizing real-time intelligent response across system levels. In specific implementation, the intelligent decision-making engine continuously analyzes the reading trajectory of clauses in the contract signing process, the length of time the page stays, and historical dispute data. When it detects that the delivery person has browsed to the preset high-risk clauses (such as compensation liability, extreme weather performance rules), it immediately sends the instruction code to the AR module through the encrypted API channel. The interface uses a two-way verification mechanism to ensure the security of data transmission, and the response delay is controlled within 200 milliseconds, so that the system can complete the intervention preparation before the user has a cognitive bias. After stress testing, it was verified that the architecture can support thousands of concurrent data processing times per second. Compared with the traditional one-way communication mode, the accuracy of clause recognition has increased by 58%, especially the efficiency of capturing implicit risk points in multilingual contract texts has reached the industry-leading level.

[0129] In step S332, when the intelligent decision-making engine identifies a pre-set contract clause requiring a special reminder, it triggers the AR warning module to generate a composite warning signal consisting of a floating icon and a voice announcement. By integrating spatial perception with multimodal interaction technologies through the composite warning signal, upon receiving the command, the AR warning module first locates the optimal warning area in the user's current field of view using a SLAM (simultaneous localization and mapping) algorithm. It then generates a semi-transparent floating icon with an added depth projection effect, creating a visual depth fusion between the warning sign and the physical environment. The simultaneously activated voice announcement engine uses TTS (text-to-speech) technology to translate the clause into colloquial language, and uses a sound field localization algorithm to make the voice appear to be emanating from the floating icon, creating spatial consistency in audio-visual perception. Measured data shows that this warning method increases the attention capture rate for high-risk clauses from 43% with traditional pop-up windows to 91%, maintaining an 82% effective reminder rate even in noisy outdoor environments. More importantly, it can record user feedback on the warning signal (such as gaze dwell time and gesture interaction records), and optimize the warning intensity parameters through machine learning to form a dynamically adaptive intelligent warning strategy.

[0130] Please refer to Figure 2 A second aspect of an embodiment of the present invention provides a delivery person signing process control system, which guides delivery persons to be signed to sign contracts with a delivery platform based on the above-mentioned delivery person signing process control method, including:

[0131] The delivery demand acquisition module 1 is used to obtain the regional information and business scenario requirements of the delivery personnel to be signed. The regional information includes: administrative areas, urban terrain and meteorological conditions. The business scenario requirements include: delivery type, order characteristics and service requirements.

[0132] The personal information acquisition module 2 is used to obtain the registered personal information of the delivery person to be signed.

[0133] The signing configuration push module 3 is used to configure signing information based on regional information and business scenario requirements according to the registered personal information, and send it to the terminal device of the to-be-signed deliveryman who has completed registration. Based on the terminal device, the signing process is displayed to prompt the to-be-signed deliveryman to complete the signing process in a timely manner.

[0134] Correspondingly, a third aspect of the embodiments of the present invention provides an electronic device, including: at least one processor, and a memory connected to the at least one processor. Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned deliveryman signing process control method.

[0135] Correspondingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the above-mentioned deliveryman signing process control method is implemented.

[0136] The embodiments of the present invention aim to protect a deliveryman signing process control method, including the following steps: obtaining the corresponding regional information and business scenario requirements of the to-be-signed deliveryman, where the regional information includes: administrative region, urban terrain and meteorological conditions, and the business scenario requirements include: delivery type, order characteristics and service requirements; obtaining the registered personal information of the to-be-signed deliveryman; based on the regional information and business scenario requirements, configuring signing information according to the registered personal information, and sending it to the terminal device of the to-be-signed deliveryman who has completed registration. Based on the terminal device, the signing process is displayed to prompt the to-be-signed deliveryman to complete the signing process in a timely manner. The above technical solutions have the following effects:

[0137] 1. Based on regional information (terrain, meteorology), business requirements (delivery type, service requirements) and personal registration data, construct signing configuration information, generate signing terms adapted to different scenarios, improve the overall signing efficiency of deliverymen, and shorten the signing cycle;

[0138] 2. Adopt dynamic encryption transmission and adaptive protocol encapsulation technology, automatically select the optimal encryption strategy and transmission protocol according to the terminal type, improve the information delivery rate while ensuring data security; combine the lightweight CNN live detection model and enhanced OCR recognition technology to establish a closed-loop authentication mechanism: effectively defend against photo / video attacks through multi-frame dynamic analysis, and improve the accuracy of live detection; when the recognition fails, automatically optimize the image quality based on the Retinex algorithm and super-resolution reconstruction technology to improve the passing rate of secondary authentication;

[0139] 3. Deeply integrating WebAR technology with the intelligent decision-making engine realizes an immersive interactive experience in the signing process. By dynamically loading a standardized 3D model library, AR operation guides are rendered in real time on the user terminal, shortening the time for understanding complex signing terms. When the intelligent engine identifies key terms, it triggers the AR warning module to generate a dual reminder of a floating prompt box and voice broadcast, improving the reading rate of important terms, enhancing the signing efficiency, and ensuring the accuracy and integrity of contract cognition.

[0140] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0142] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A control method for the signing process of delivery staff, characterized in that It includes the following steps: Obtain the corresponding regional information and business scenario requirements of the deliveryman to be signed. The regional information includes: administrative region, urban terrain, and meteorological conditions. The business scenario requirements include: delivery type, order characteristics, and service requirements; Obtain the registered personal information of the deliveryman to be signed; Based on the regional information and the business scenario requirements, configure the signing information according to the registered personal information and send it to the terminal device of the deliveryman to be signed who has completed registration. Based on the terminal device, display the signing process and prompt the deliveryman to be signed to complete the signing process in a timely manner.

2. The deliveryman signing process control method according to claim 1, wherein After prompting the deliveryman to be signed to complete the signing process in a timely manner, it further includes: Call a preset encryption algorithm to encrypt the identity fields required for real-name authentication, generate a standardized data packet, and encapsulate the standardized data packet into a protocol format corresponding to the type of the deliveryman's terminal device; Obtain the usage status of several information receiving methods of the deliveryman to be signed, select the information receiving method with the highest usage rate, and send the real-name authentication information corresponding to the completed encapsulated standardized data packet to the corresponding deliveryman to be signed.

3. The deliveryman signing process control method according to claim 2, wherein, After sending the real-name authentication information corresponding to the completed encapsulated standardized data packet to the corresponding deliveryman to be signed, it further includes: Call the camera of the deliveryman to be signed's terminal device and perform live detection through a lightweight CNN model; Use OCR recognition technology to extract the personal information in the ID card image and perform real-time comparison with the ID card information database; If the comparison is successful, it is determined that the deliveryman to be signed has passed the real-name authentication; If the comparison fails, based on the automatic error correction mechanism, adjust the image brightness and sharpness of the live detection image and the ID card image of the deliveryman to be signed respectively, and perform the comparison again.

4. The deliveryman signing process control method according to claim 1, wherein It further includes: Obtain the personal registration time of the deliveryman to be signed; When the interval between the personal registration time and the current system time exceeds the preset time threshold, send a signing reminder message to the deliveryman to be signed.

5. The deliveryman signing process control method according to claim 1, wherein After prompting the deliveryman to be signed to complete the signing process in a timely manner, it further includes: Based on the collaborative filtering algorithm, combine the regional information, the business scenario requirements, and the personal registration information to obtain the training course data of the deliveryman to be signed, and send it to the terminal device of the deliveryman to be signed; Real-time monitor the training course progress data of the deliveryman to be signed. When the training course progress data is greater than or equal to the preset progress value, send a reward message to the deliveryman to be signed. The reward message includes: equipment discount entitlement information and / or priority order assignment entitlement information.

6. The deliveryman signing process control method according to claim 1, characterized in that After prompting the deliveryman to be signed to complete the signing process in a timely manner, it further includes: Send delivery equipment purchase route recommendation information to the terminal device of the deliveryman to be signed. The delivery equipment purchase route recommendation information includes the display of the delivery equipment purchase link address that meets the delivery standard process.

7. The deliveryman signing process control method according to any one of claims 1-6, characterized in that, It further includes: Obtain the page browsing data of the terminal device of the deliveryman to be signed after registration; Based on the page browsing data, obtain the focus points and personal habit data of the deliveryman to be signed; If the concerned points and personal habit data include: distribution skills and route-related information, then push a distribution training course to their terminal devices; If the concerned points and personal habit data include: reward policy information, then push reward policy training information to them; If the concerned points and personal habit data include: information on common problems in signing contracts, then push a customer service Q&A link to them.

8. The deliveryman signing process control method according to any one of claims 1-6, characterized in that, When showing the signing process to the to-be-signed deliverymen who have completed registration, it further includes: Generate augmented reality guidance content based on WebAR technology, and call the device camera to render 3D visual operation guidance in real time; Dynamically load a preset standardized 3D model library, and the 3D model library contains file signing tools and industry equipment models.

9. The deliveryman signing process control method according to claim 8, wherein It further includes: Establish real-time data linkage between the AR module and the intelligent decision-making engine through the API interface; When the intelligent decision-making engine identifies preset signing terms that need to be highlighted, trigger the AR warning module to generate a composite warning signal including a floating icon and voice broadcast.

10. A signing process control system for deliverymen, characterized in that, Guide the to-be-signed deliverymen to sign contracts with the delivery platform based on the deliveryman signing process control method according to any one of claims 1-9, including: A delivery demand acquisition module, which is used to acquire the regional information and business scenario requirements of the to-be-signed deliverymen, where the regional information includes: administrative region, urban terrain, and meteorological conditions, and the business scenario requirements include: delivery type, order characteristics, and service requirements; A personal information acquisition module, which is used to acquire the registered personal information of the to-be-signed deliverymen; A signing configuration push module, which is used to configure signing information based on the regional information and the business scenario requirements according to the registered personal information, and send it to the terminal devices of the to-be-signed deliverymen who have completed registration, display the signing process based on the terminal devices, and prompt the to-be-signed deliverymen to complete the signing process in a timely manner.

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