Smart management method, device and equipment of main sub-account of logistics system and medium

Abstract

The present application relates to the technical field of intelligent collaboration, and in particular to a main-sub-account intelligent management method, device and equipment for a logistics system and a medium, which breaks through the technical limitations of the lack of collaborative management and control in the traditional single-person single-account mode, and realizes the intelligent management of main-sub-accounts and the accurate identification of abnormal operations in the express collaboration scenario through a multi-step process of dynamic configuration of the upper limit of the number of created accounts, multi-dimensional security verification of created information, association and binding of regions and work volume constraints, and operation data anomaly detection and reputation disposal. The method solves the problems of insufficient collaboration capacity during peak periods, idle and wasteful account resources, and the inability to promptly discover abnormal behavior in the existing management mode, reduces the human coordination cost and account management cost of express delivery and pick-up collaboration, improves collaboration efficiency and abnormal identification accuracy, provides technical support for the standardization and intelligentization of express delivery and pick-up collaboration, and is suitable for various account management scenarios that rely on multi-person collaboration.

Description

Technical Field

[0001] This invention relates to the field of intelligent collaboration technology, and in particular to a method, apparatus, equipment and medium for intelligent management of master and sub-accounts in a logistics system. Background Technology

[0002] In the current express delivery industry, during e-commerce promotions and large-item pickups, couriers often need temporary helpers to complete pickup and delivery operations. The existing management model only supports single-person, single-account operation, and the collaboration permissions are statically preset, lacking a flexible adjustment mechanism based on dynamic changes in business, resulting in insufficient collaboration capabilities during peak periods and idle account resources during off-peak periods.

[0003] Furthermore, the main account's management of assistants is limited to basic permission allocation, lacking full lifecycle control capabilities and failing to achieve real-time account status adjustments, operation record tracking, and proactive identification of abnormal behavior. Branch administrators' control over global main and sub-accounts is at a basic level, resulting in management gaps, unclear responsibility attribution, and data security risks.

[0004] Furthermore, the existing business system and account management module are isolated from each other. The operation data of assistants cannot be automatically attributed to the main account, and data entry requires manual intervention, resulting in insufficient data authenticity and timeliness. Issues such as assistants privately altering pickup and delivery routes, impersonating others, and frequently reporting anomalies have not been systematically resolved for a long time due to the lack of real-time data collection and intelligent identification mechanisms, which seriously affects pickup and delivery efficiency and service quality.

[0005] It is evident that existing technologies still need improvement and enhancement. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, the present invention aims to provide an intelligent management method for master and sub-accounts in a logistics system. It constructs a closed-loop system covering the entire process from quantity limit configuration, security verification, association binding to anomaly detection, and realizes dynamic configuration and refined management of master and sub-accounts in express delivery collaboration scenarios.

[0007] The first aspect of this invention provides a method for intelligent management of master and sub-accounts in a logistics system, comprising: parsing a received sub-account permission activation request to obtain request information, the request information including a collaboration scenario type, a requesting master account and its corresponding business volume data and express delivery data; performing a weighted calculation on the business volume data, the express delivery data and the collaboration scenario type using a preset weighted calculation rule to obtain an upper limit for the number of sub-accounts to be created corresponding to the requesting master account; if a sub-account creation request is received, obtaining creation information, performing quantity verification on the creation information based on the upper limit for the number of sub-accounts, and performing a security verification to obtain a creation verification result; if the creation verification result is successful, creating the corresponding sub-account according to the creation information, and associating and binding the created sub-account with the master account that sent the sub-account creation request; collecting logistics operation data of the sub-account, performing anomaly detection on the logistics operation data, generating anomaly detection results and sending them to the master account.

[0008] Optionally, in a first implementation of the first aspect of the present invention, the step of performing weighted calculation on the business volume data, the express delivery data, and the collaborative scenario type using a preset weighted calculation rule to obtain the upper limit of the number of creations corresponding to the requesting main account includes: calculating the ratio of the business volume data to preset standard business volume data to obtain a business volume ratio; performing weighted summation on the operation frequency, completion rate, abnormal reporting rate, and average single-ticket operation time in the express delivery data according to a preset first weight ratio to obtain a pickup and delivery efficiency score; querying the weight coefficient corresponding to the collaborative scenario type from a preset scenario weight mapping table to obtain a scenario weight coefficient; performing weighted summation on the business volume ratio, pickup and delivery efficiency score, and scenario weight coefficient according to a preset second weight ratio to obtain a weighted summation result; and correcting and rounding the weighted summation result to obtain the upper limit of the number of creations.

[0009] Optionally, in a second implementation of the first aspect of the present invention, the step of performing quantity verification and security verification on the creation information based on the creation quantity limit to obtain a creation verification result includes: determining whether the number of sub-accounts under the main account exceeds the creation quantity limit, obtaining a quantity verification result; if the quantity verification result is unsuccessful, generating a creation verification result that failed verification; if the quantity verification result is successful verification, verifying whether the mobile phone number information in the sub-account creation information conforms to a preset mobile phone number format and is not bound to other main accounts, obtaining a first verification result; verifying whether the name information in the sub-account creation information consists only of preset Chinese and English characters, and whether the length of the name information is within a preset name length range, obtaining a second verification result; verifying whether the sub-account creation information has logistics permission information, the logistics permission information including delivery permission or pickup permission, obtaining a third verification result; and generating a creation verification result that passed verification when the first verification result, the second verification result, and the third verification result all indicate successful verification.

[0010] Optionally, in a third implementation of the first aspect of the present invention, the step of associating the created sub-account with the main account that sent the sub-account creation request includes: associating the sub-account identifier of the sub-account with the identifier of the main account that sent the sub-account creation request to generate a courier account binding relationship table; extracting the historical parcel delivery trajectory of the main account within a preset delivery period, performing boundary calculation on the historical parcel delivery trajectory using a convex hull algorithm to generate a regular delivery area of ​​the main account, and setting the regular delivery area as the authorized delivery area of ​​the sub-account; extracting the maximum daily delivery volume and the maximum daily pickup volume of the main account within a preset statistical period, multiplying the maximum daily delivery volume by a preset delivery ratio coefficient to obtain the upper limit of the sub-account's delivery quantity, multiplying the maximum daily pickup volume by a preset pickup ratio coefficient to obtain the upper limit of the sub-account's pickup quantity; and writing the authorized delivery area, the upper limit of the delivery quantity, and the upper limit of the pickup quantity into the courier account binding relationship table.

[0011] Optionally, in the fourth implementation of the first aspect of the present invention, the step of collecting the logistics operation data of the sub-account, performing anomaly detection on the logistics operation data, and generating anomaly detection results includes: collecting the logistics operation data of the sub-account in real time, wherein the logistics operation data includes operation frequency, geographical location, equipment information, network address, and order deviation rate; determining whether the operation frequency, geographical location, equipment information, network address, and order deviation rate are within the corresponding preset safety threshold range, and marking the logistics operation data that are not within the preset safety threshold range as candidate abnormal data; obtaining the historical operation habit data of the sub-account and the commonly used operation range data of the main account creation, and performing secondary verification on the candidate abnormal data; determining the candidate abnormal data that fails the secondary verification as target abnormal data, and generating anomaly detection results based on the target abnormal data.

[0012] Optionally, in a fifth implementation of the first aspect of the present invention, generating an anomaly detection result based on the target anomaly data includes: identifying the type of operational feature to which the target anomaly data belongs, determining the corresponding anomaly type based on the type of operational feature; calculating the deviation between the actual value of the target anomaly data and the value exceeding the side boundary in the corresponding preset safety threshold range, comparing the deviation with multiple preset anomaly degree level thresholds to determine the corresponding anomaly degree; and integrating the anomaly type and the anomaly degree to generate the anomaly detection result.

[0013] Optionally, in the sixth implementation of the first aspect of the present invention, after integrating the anomaly type and the anomaly degree to generate the anomaly detection result, the method further includes: matching the corresponding deduction score from a preset deduction mapping table according to the anomaly type and anomaly degree in the anomaly detection result, deducting the deduction score from the preset initial reputation score to obtain an updated reputation score; determining whether the updated reputation score is lower than a preset disabling threshold, and if it is lower, switching the sub-account to a reputation-disabled state and sending a reputation alarm notification to the main account that created the account.

[0014] A second aspect of the present invention provides an intelligent management device for master and sub-accounts in a logistics system, comprising: a parsing module, configured to parse a received sub-account permission activation request to obtain request information, the request information including a collaboration scenario type, a requesting master account and its corresponding business volume data and express delivery data; a weighting module, configured to perform weighted calculation on the business volume data, the express delivery data and the collaboration scenario type according to a preset weighted calculation rule to obtain an upper limit for the number of sub-accounts to be created corresponding to the requesting master account; a judgment module, configured to, if a sub-account creation request is received, obtain creation information, perform quantity verification on the creation information based on the upper limit for the number of sub-accounts to be created, and perform security verification to obtain a creation verification result; an association module, configured to, if the creation verification result is successful, create a corresponding sub-account according to the creation information, and associate and bind the created sub-account with the master account that sent the sub-account creation request; and a checking module, configured to collect logistics operation data of the sub-account, perform anomaly detection on the logistics operation data, generate anomaly detection results and send them to the master account.

[0015] A third aspect of the present invention provides a master-sub-account intelligent management device for a logistics system, the master-sub-account intelligent management device for a logistics system comprising: a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to cause the master-sub-account intelligent management device for the logistics system to execute the various steps of the master-sub-account intelligent management method for the logistics system described in any of the preceding claims.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of the master-sub-account intelligent management method for the logistics system described in any of the preceding claims.

[0017] The technical solution of this invention overcomes the limitations of traditional single-person, single-account models, such as static preset collaboration permissions, lack of management and control, and isolated data ownership. Through a multi-step process involving dynamic configuration of the creation limit, multi-dimensional security verification of creation information, association with bound regions and workload constraints, and anomaly detection and reputation handling of operational data, it achieves intelligent management of master and sub-accounts and accurate identification of abnormal operations in express delivery collaboration scenarios. This method not only specifically addresses core technical problems in existing management models, such as insufficient collaboration capacity during peak periods, idle and wasted account resources, unclear management responsibilities, and inability to promptly detect abnormal behavior, but also significantly reduces the manpower coordination and account management costs of express delivery pickup and delivery collaboration. It effectively shortens the time required for sub-account configuration and anomaly handling, improves collaboration efficiency and anomaly identification accuracy, and enhances the fairness and traceability of management. It provides systematic technical support for the standardization and intelligentization of express delivery pickup and delivery collaboration and is applicable to various service industry account management scenarios that rely on multi-person collaboration. Attached Figure Description

[0018] Figure 1 A first flowchart of the intelligent management method for master and sub-accounts of a logistics system provided in an embodiment of the present invention; Figure 2 A second flowchart of the master-sub-account intelligent management method for a logistics system provided in this embodiment of the invention; Figure 3 A third flowchart of the intelligent management method for master and sub-accounts of a logistics system provided in this embodiment of the invention; Figure 4 A fourth flowchart of the intelligent management method for master and sub-accounts of a logistics system provided in this embodiment of the invention; Figure 5 A fifth flowchart of the intelligent management method for master and sub-accounts of a logistics system provided in an embodiment of the present invention; Figure 6 A sixth flowchart of the intelligent management method for master and sub-accounts of a logistics system provided in an embodiment of the present invention; Figure 7 A seventh flowchart of the intelligent management method for master and sub-accounts of a logistics system provided in an embodiment of the present invention; Figure 8 A schematic diagram of the structure of the master-sub-account intelligent management device for a logistics system provided in an embodiment of the present invention; Figure 9 A schematic diagram of the structure of the master-sub-account intelligent management device for the logistics system provided in an embodiment of the present invention. Detailed Implementation

[0019] This invention provides a method, apparatus, device, and medium for intelligent management of master and sub-accounts in a logistics system. In this invention, the terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent management method for master and sub-accounts in a logistics system according to the present invention includes: 101. Parse the received sub-account permission activation request to obtain request information, which includes the collaboration scenario type, the requesting main account and its corresponding business volume data and express delivery data; In this embodiment, the sub-account permission activation request is a collaboration permission application submitted by the main account through the logistics system. The collaboration scenario type is the scenario that the main account wants the sub-account to apply, such as large item pickup, remote area delivery, and daily collaboration scenarios. The business volume data is the average daily pickup and delivery volume of the main account's branch within a preset time period (e.g., 7 days). The parcel delivery data refers to the main account's historical operation records over a period of time (e.g., 30 days), including operation frequency, completion rate, anomaly reporting rate, and average single-parcel operation time.

[0021] Specifically, upon receiving a sub-account permission activation request, the request is parsed to extract the requesting main account and the collaboration scenario type selected by the requesting main account. Then, based on the main account identifier, the business volume data of the branch to which the main account belongs and the historical parcel delivery data of the main account are retrieved from a preset database, thus obtaining all request information. Obtaining request information based on the sub-account permission activation request provides a data foundation for the subsequent dynamic calculation of the creation quantity limit.

[0022] 102. By using preset weighted calculation rules, the business volume data, the express delivery data, and the collaboration scenario type are weighted to obtain the maximum number of creations corresponding to the requesting main account; In this embodiment, the preset weighted calculation rule refers to a calculation method that assigns different weights to multiple input parameters, sums them, and then rounds them down. Specifically, the business volume data is compared with a preset baseline business volume to calculate the business volume ratio. Then, the express delivery data is comprehensively calculated using multi-dimensional indicators to generate a pickup and delivery efficiency score. For example, indicators such as operation frequency, completion rate, and abnormal reporting rate are extracted from the express delivery data. The extracted indicators are weighted according to their respective weights to obtain the pickup and delivery efficiency score. Scenario weight coefficients are matched according to the type of collaboration scenario. Finally, the business volume ratio, pickup and delivery efficiency score, and scenario weight coefficients are weighted and summed according to the preset weight coefficients to obtain an initial quantity limit. Then, the initial quantity limit is rounded to the nearest integer to obtain the final creation quantity limit.

[0023] By considering data from multiple dimensions to generate an upper limit on the number of creations, dynamic and intelligent configuration of the upper limit on the number of creations is achieved, so that the upper limit on the number of creations matches the actual business needs, avoiding the problems of insufficient collaboration capabilities during peak periods or waste of account resources during off-peak periods caused by static configuration.

[0024] 103. If a sub-account creation request is received, the creation information is obtained, the creation information is validated based on the maximum number of creations, and a security check is performed to obtain the creation verification result. In this embodiment, the sub-account creation request is sent by the main account with sub-account creation permissions, wherein the main account has a maximum number of sub-accounts it can create. The creation information is the main information for generating the sub-account, including the sub-account's mobile phone number, name, and operation permissions entered by the main account, wherein the operation permissions include at least one of delivery permissions and pickup permissions.

[0025] If a sub-account creation request is parsed, specific fields or parameters are extracted from the request or data packet to obtain creation information including the sub-account's phone number, name, and operation permissions. The total number of sub-accounts currently created by the main account that initiated the creation request is then compared to the creation limit. If the current total has reached or exceeded the limit, the creation request is rejected, and a message indicating that the limit has been reached is returned to the main account. If the limit has not been exceeded, security checks continue. For example, the phone number is checked to ensure it conforms to an 11-digit format and is not bound to another main account; operation permissions include at least one of delivery or pickup permissions. If all security checks pass, a successful creation verification result is generated; otherwise, a corresponding verification failure message is returned.

[0026] With dual safeguards of quantity verification and security verification, compliance control of sub-account creation is achieved, avoiding irregular operations such as excessive creation, false identity, and lack of permissions, laying the foundation for the normal use and effective supervision of sub-accounts in the future.

[0027] 104. If the creation verification result is successful, then create the corresponding sub-account according to the creation information, and associate and bind the created sub-account with the main account that sent the sub-account creation request; In this embodiment, the "creating master account" refers to the master account that initiates the sub-account creation request, i.e., the master account to which the sub-account belongs. When the creation verification result is successful, a unique sub-account identifier is generated based on the mobile phone number in the creation information, a sub-account nickname is generated based on the name, the functional permissions of the sub-account are configured based on the operation permissions, an initial login password is assigned to the sub-account, and the initial login password is sent to the master account that created the sub-account. Finally, the sub-account identifier is associated and bound with the identifier of the master account that created the sub-account. Through the integrated operation of creation and binding, the automated creation of sub-accounts and the persistent storage of the master-sub-account relationship are realized.

[0028] 105. Collect logistics operation data of the sub-account, perform anomaly detection on the logistics operation data, generate anomaly detection results and send them to the main account that created the account; In this embodiment, the logistics operation data refers to the operation records generated by the sub-account during the pickup and delivery process, such as operation frequency, geographical location, and equipment information. Anomaly detection is performed on the logistics operation data. Specifically, the real-time collected logistics operation data is compared with a preset safety threshold range. For example, the safety threshold range for operation frequency is 10 to 120 times / day. If the current operation frequency does not fall within this range, it is identified as abnormal data, and a corresponding anomaly detection result is generated and sent to the monitoring page of the main account. By collecting the logistics operation data of the sub-account in real time and performing anomaly detection, real-time monitoring and accurate detection of abnormal operation behavior of the sub-account are achieved.

[0029] This invention discloses an intelligent management method for master and sub-accounts in a logistics system. This method overcomes the limitations of traditional single-person, single-account models, which suffer from static preset collaborative permissions, lack of management and control, and isolated data ownership. Through a multi-step process involving dynamic configuration of the creation quantity limit, multi-dimensional security verification of creation information, association with bound regions and workload constraints, and anomaly detection and reputation handling of operational data, it achieves intelligent management of master and sub-accounts and accurate identification of abnormal operations in express delivery collaboration scenarios. This method not only specifically addresses core technical problems in existing management models, such as insufficient collaboration capacity during peak periods, idle and wasted account resources, unclear management responsibilities, and inability to promptly detect abnormal behavior, but also significantly reduces the manpower coordination costs and account management costs of express delivery pickup and delivery collaboration. It effectively shortens the time required for sub-account configuration and anomaly handling, improves collaboration efficiency and anomaly identification accuracy, and enhances the fairness and traceability of management. It provides systematic technical support for the standardization and intelligentization of pickup and delivery collaboration in the express delivery industry and is applicable to account management scenarios in various service industries that rely on multi-person collaboration.

[0030] Please see Figure 2 In this embodiment of the invention, the step of performing weighted calculations on the business volume data, the express delivery data, and the collaboration scenario type using preset weighted calculation rules to obtain the maximum number of creations corresponding to the requesting main account includes: 201. Calculate the ratio between the business volume data and the preset standard business volume data to obtain the business volume ratio; In this embodiment, the business volume data is the average daily pickup and delivery volume of the branch to which the main account belongs over a preset period (e.g., seven days). The preset standard business volume data refers to the standard business volume value of the branch during normal off-peak hours (i.e., ordinary workdays without e-commerce promotions, severe weather, or regional emergencies), which can be determined based on the average business volume data of the branch over a past preset period (e.g., 30 days). The business volume ratio is a relative indicator reflecting the current level of business activity.

[0031] The system reads the transaction volume data of the main account from the database, and simultaneously reads the preset standard transaction volume data for the branch. Using the average daily transaction volume data as the numerator and the preset standard transaction volume data as the denominator, the ratio of the two is calculated to obtain the transaction volume ratio. For example, if the transaction volume is 1200 items and the preset standard transaction volume is 800 items, the transaction volume ratio is 1200 ÷ 800 = 1.5, indicating that the current transaction volume is 1.5 times the daily benchmark, indicating a high-load state. If the average daily transaction volume is 600 items and the preset standard transaction volume is 800 items, the transaction volume ratio is 600 ÷ 800 = 0.75, indicating that the current transaction volume is lower than the daily benchmark, indicating a low-load state. By comparing the current transaction volume with the daily benchmark, the system can understand the current level of business activity, providing reference data for subsequent weighted calculations to create quantity limits.

[0032] 202. Based on the preset first weight ratio, the operation frequency, completion rate, abnormal reporting rate, and average single-item operation time in the express delivery data are weighted and summed to obtain the pickup and delivery efficiency score. In this embodiment, the completion rate is the ratio of the number of pickup and delivery operations successfully completed by the main account to the total number of operations. The anomaly reporting rate is the ratio of the number of operations in which the main account reported anomalies to the total number of operations; this indicator is a contrarian indicator (the lower the value, the better). The average single-package operation time is the average time required for the main account to complete the pickup or delivery of a single package. The preset first weight percentage refers to the weight coefficient assigned to each of the above four indicators, and the sum of the four weight coefficients is 1. The pickup and delivery efficiency score is a comprehensive quantitative evaluation of the main account's historical operational capabilities.

[0033] Four indicators are extracted from the express delivery data: operation frequency, completion rate, anomaly reporting rate, and average single-package operation time for the main account. These four indicators are then normalized, mapping their values ​​to a uniform range. After normalization, a weighted sum is calculated based on a preset first weight percentage. For example, the preset first weight percentages are: operation frequency 30%, completion rate 40%, anomaly reporting rate 20%, and average single-package operation time 10%. If the normalized scores are: operation frequency 85 points, completion rate 95 points, anomaly reporting rate 70 points, and average single-package operation time 80 points, then the pickup and delivery efficiency score is 85×30%+95×40%+70×20%+80×10%=25.5+38+14+8=85.5 points. It is understood that the preset first weight percentages can be set according to specific needs and are not limited thereto.

[0034] By using a weighted summation of multi-dimensional indicators, a pickup and dispatch efficiency score is obtained, enabling an objective quantitative evaluation of the main account's historical pickup and dispatch capabilities.

[0035] 203. Query the weight coefficient corresponding to the collaborative scenario type from the preset scenario weight mapping table to obtain the scenario weight coefficient; In this embodiment, the preset scenario weight mapping table is a pre-established data table that corresponds to different collaboration scenario types and weight coefficients. The specific corresponding data can be set according to actual conditions. For example, e-commerce promotional scenarios are assigned higher weight coefficients to meet the efficient allocation needs during peak delivery periods. The scenario weight coefficient refers to a weight value that reflects the urgency or reasonable number of sub-accounts required in different collaboration scenarios; a larger coefficient indicates a stronger demand for sub-accounts in that scenario.

[0036] The system retrieves the collaboration scenario type selected by the main account in the sub-account permission activation application. Using this scenario type as the query key, it performs a matching query in a preset scenario weight mapping table to obtain the corresponding scenario weight coefficient. For example, if the main account selected "E-commerce Promotion" as the collaboration scenario type when submitting the sub-account permission activation application, the corresponding scenario weight coefficient retrieved from the preset scenario weight mapping table would be 1.5. By retrieving the corresponding weight coefficient from the preset scenario weight mapping table, the calculation of the creation limit can adapt to the urgency of different collaboration scenarios, avoiding the problem of insufficient collaboration capacity during promotional periods or wasted account resources during normal times due to uniform configuration.

[0037] 204. The business volume ratio, pickup and dispatch efficiency score and scenario weight coefficient are weighted and summed according to the preset second weight ratio to obtain the weighted summation result; In this embodiment, the preset second weight percentage is a weight coefficient assigned to each of the three parameters in advance according to business requirements, and the sum of the three weight coefficients is 1. First, the business volume ratio, pickup and dispatch efficiency score, and scenario weight coefficient are normalized so that all parameters are within the same range. For example, the pickup and dispatch efficiency score is normalized by dividing by 100, mapping it to a score range of 0 to 1, making it on the same order of magnitude as the other two parameters. After normalization, a weighted sum is calculated according to the preset second weight percentage to obtain the weighted sum result. For example, the weighted sum result is calculated as: Weighted sum result = W1×Q + W2×E + W3×C, where W1, W2, and W3 are the preset second weight percentages. For example, the preset percentage for the business volume ratio is 30%, the preset percentage for the pickup and dispatch efficiency score is 40%, and the preset percentage for the scenario weight coefficient is 30%, and Q, E, and C are the business volume ratio, pickup and dispatch efficiency score, and scenario weight coefficient, respectively. By using a weighted summation method, parameters reflecting three different dimensions—network load, individual capabilities, and scenario requirements—are integrated into a single comprehensive value, providing a comprehensive evaluation basis for the dynamic configuration of the quantity limit.

[0038] 205. The weighted summation result is corrected and rounded to obtain the upper limit of the number of creations; In this embodiment, the weighted summation result is obtained and rounded to the nearest integer. For example, if the weighted summation result is 3.7, it is rounded to the nearest integer to obtain 4. This embodiment also includes comparing the rounded result with a preset value range. If the rounded result is less than the minimum value of the preset value range, it is corrected to the minimum value. It should be noted that the minimum value is greater than 0. If the rounded result is greater than the maximum value of the range (e.g., 20), it is corrected to the maximum value (20). If the rounded result is within the range of 0 to 20, it is directly retained. Through the dual processing of rounding correction and range verification, it is ensured that the upper limit of the number of creations is always within the reasonable range preset by the system, avoiding the problem of exceeding the upper limit of the number due to abnormal calculation results.

[0039] Please see Figure 3 In this embodiment of the invention, the step of performing quantity verification on the creation information based on the maximum creation quantity and performing security verification to obtain the creation verification result includes: 301. Determine whether the number of sub-accounts under the main account exceeds the maximum number of accounts that can be created, and obtain the quantity verification result. If the quantity verification result is not passed, generate a creation verification result that fails the verification. In this embodiment, the quantity verification result is a judgment result on whether the number of sub-accounts created under the sub-account being created has reached or exceeded the upper limit, including two states: verification passed (not exceeding the upper limit) and verification failed (reached or exceeded the upper limit). Specifically, the total number of sub-accounts currently created by the main account is queried from the database. The query range includes sub-accounts in a normal state and disabled sub-accounts. The current total number of sub-accounts obtained from the query is compared with the upper limit of the creation quantity. If the current total number of sub-accounts is less than the upper limit of the creation quantity, a quantity verification result of passing the verification is generated; otherwise, a creation verification result of failing the verification is generated, and the creation request is rejected. A prompt message is returned to the main account, informing it that the sub-account creation limit has been reached and suggesting that the main account delete or disable sub-accounts that are no longer in use before trying to create another one.

[0040] By performing a quantity check before the main account initiates a creation request, the number of sub-accounts of the main account is ensured to always be within a reasonable range of dynamic configuration, preventing the disorderly expansion of account resources.

[0041] 302. If the quantity verification result is that the verification is passed, then verify whether the mobile phone number information in the sub-account creation information conforms to the preset mobile phone number format and is not bound to other main accounts, and obtain the first verification result; In this embodiment, the preset mobile phone number format is a pre-defined format, such as an 11-digit number conforming to the mobile phone number segment rules of China Mobile, China Unicom, and China Telecom. When the quantity verification result is successful, the mobile phone number information entered by the main account in the sub-account creation request is obtained. First, the format is verified according to the preset mobile phone number format, that is, it is determined whether the mobile phone number is an 11-digit pure number and whether the first three digits belong to the preset mobile phone number segment set (such as 130-139, 150-159, 180-189, etc.). If it is 11 digits and belongs to the preset mobile phone number segment set, the system database is searched to see if the mobile phone number has been bound to other main accounts as a sub-account. If no binding record is found, the verification is successful. Optionally, a verification code can be sent according to the mobile phone number information, and the mobile phone number can be verified again according to the verification code to verify the first verification result. If the preset mobile phone number format verification fails or the search result shows that a binding record already exists, a first verification result of failure is generated, and a prompt message "This mobile phone number has been bound to another main account" is returned to the main account. By verifying both the format and uniqueness of the phone number, the compliance of the sub-account's phone number is verified, avoiding the problem of the sub-account being unable to log in due to invalid phone number entries. At the same time, it prevents ownership conflicts and data chaos caused by a phone number being repeatedly bound to multiple main accounts.

[0042] 303. Verify whether the name information in the sub-account creation information consists only of preset Chinese and English characters, and whether the length of the name information is within the preset name length range, to obtain the second verification result; In this embodiment, the name information refers to the information entered by the main account in the sub-account creation request. The preset Chinese and English characters are a pre-defined set of characters allowed as name characters, including Chinese characters and English letters. The preset name length range is a pre-defined range of the number of name characters, for example, a lower limit of 1 character and an upper limit of 10 characters. When the quantity verification result is passed, the name information in the sub-account creation information is verified. Specifically, each character in the name string is checked one by one to see if it belongs to the preset Chinese and English character set. If all characters in the name string are Chinese characters or English letters, the character composition verification passes; if there are numbers, punctuation marks, special symbols (such as @, #, $, %, &, etc.) or spaces, the character composition verification fails.

[0043] The length of the name string is calculated (Chinese and English characters are counted as 1 character), and it is determined whether the length is within the preset name length range (e.g., 1 to 10 characters). If it is, the length validation passes; otherwise, the validation fails. Only when both character composition and length validations pass is a second validation result generated. This dual validation of character composition and length achieves standardized validation of the sub-account name field.

[0044] 304. Verify whether the sub-account creation information contains logistics permission information, including delivery permission or pickup permission, and obtain the third verification result; In this embodiment, the logistics permission information refers to the scope of operational permissions assigned to the sub-account by the main account when it is created, including delivery permission and pickup permission. Delivery permission authorizes the sub-account to perform parcel delivery operations; after obtaining this permission, the sub-account can perform operations such as parcel receipt confirmation and reporting delivery anomalies. Pickup permission authorizes the sub-account to perform parcel pickup operations; after obtaining this permission, the sub-account can perform operations such as order pickup and reporting pickup anomalies.

[0045] When the quantity verification result is successful, the permission verification process begins. Specifically, logistics permission information is extracted. This logistics permission refers to the permission options selected by the main account in the sub-account creation request, including two independent options: delivery permission and pickup permission. If the logistics permission information is empty, meaning no permissions have been assigned to the sub-account, a third verification result indicating failure is generated; otherwise, the verification passes. This permission verification ensures that each sub-account is explicitly assigned at least one operational function upon creation.

[0046] 305. When the first verification result, the second verification result, and the third verification result all indicate that the verification has passed, a creation verification result indicating that the verification has passed is generated; In this embodiment, a successful creation verification result is generated only when the first, second, and third verification results all pass. If any one or more of the three results fail, a successful creation verification result is not generated, the creation process is terminated, and a corresponding prompt message is returned to the main account. By comprehensively judging based on the three verification results, it is ensured that the created sub-account can be used normally.

[0047] Please see Figure 4 In this embodiment of the invention, associating and binding the created sub-account with the main account that sent the sub-account creation request includes: 401. Associate and bind the sub-account identifier of the sub-account with the main account identifier that sent the sub-account creation request to generate a courier account binding relationship table; In this embodiment, the express delivery account binding relationship table refers to a database table used to store records of master-sub-account binding relationships, serving as the data carrier for this relationship. When the creation verification result is successful, the sub-account is created and a unique sub-account identifier is generated. The sub-account identifier can be a mobile phone number or randomly generated. The sub-account identifier and the master account identifier that initiated the creation request are obtained and written as a pair of related data into the express delivery account binding relationship table. The express delivery account binding relationship table includes a binding relationship code (a string uniquely identifying this binding record), a sub-account identifier, a master account identifier, a binding timestamp, and a binding status (1 for bound, 0 for unbound). It also includes fields for controlling the sub-account's permissions, such as authorized delivery area, maximum delivery quantity, and maximum pickup quantity. It is understood that other fields can be added according to actual needs, and this is not limited. For example, a sub-account identifier "20241201_001" is generated for the newly created sub-account, the identifier of the main account that initiated the creation request is "MSTR_12345", a unique binding relationship code "BIND_20241201_0001" is generated, the current system timestamp "2025-12-01 10:30:00" is recorded, and the binding status is set to 1 (bound). The above information is then written into the express delivery account binding relationship table to complete the association binding.

[0048] By establishing a relationship table for binding express delivery accounts, a structured record of the subordinate relationship between sub-accounts and main accounts was completed.

[0049] 402. Extract the historical parcel delivery trajectory of the main account within the preset delivery period, perform boundary calculation on the historical parcel delivery trajectory using the convex hull algorithm, generate the regular delivery area of ​​the main account, and set the regular delivery area as the authorized delivery area of ​​the sub-account. In this embodiment, the preset delivery time period is a pre-defined time window for extracting historical trajectories, such as the delivery operation period from 06:00 to 20:00 daily within the past 30 calendar days. The historical parcel delivery trajectory is the trajectory left by the main account when completing delivery operations within the preset delivery time period, which can be represented by a set of geographic coordinate points, with each coordinate point corresponding to the delivery and receipt location of a parcel. The convex hull algorithm is an algorithm in computational geometry that finds the smallest convex polygon containing all given points on a plane, such as Graham's scan and Andrew's algorithm. The regular delivery area refers to the smallest convex polygon area obtained by calculating the historical delivery trajectory points of the main account using the convex hull algorithm, which reflects the main account's usual delivery operation range. The authorized delivery area is the area where sub-accounts are allowed to perform pickup and delivery operations.

[0050] The system extracts historical parcel delivery trajectories for the primary account within a preset delivery period from the logistics system's database. This includes, for example, the GPS coordinates of all parcel delivery operations performed by the primary account over the past 30 calendar days. Each delivery record contains both longitude and latitude values. Using a convex hull algorithm, the system calculates the smallest convex polygon encompassing all coordinate points within the historical parcel delivery trajectory. For example, all coordinate points are sorted by their x-coordinates from smallest to largest, and those with the same x-coordinate are sorted by their y-coordinates from smallest to largest. The upper and lower convex hulls are then calculated separately. Finally, the upper and lower convex hulls are merged to obtain a complete sequence of convex hull boundary points. Connecting these boundary points counter-clockwise forms a convex polygon, which represents the primary account's regular delivery area.

[0051] The calculated regular delivery area is directly set as the authorized delivery area for the sub-account. By using the convex hull algorithm to calculate the boundaries of the main account's historical delivery trajectories, the automatic generation of regular delivery areas and the intelligent inheritance of the sub-account's work range are achieved.

[0052] 403. Extract the maximum daily delivery volume and the maximum daily pickup volume of the main account within the preset statistical period. Multiply the maximum daily delivery volume by the preset delivery ratio coefficient to obtain the upper limit of the delivery volume of the sub-account. Multiply the maximum daily pickup volume by the preset pickup ratio coefficient to obtain the upper limit of the pickup volume of the sub-account. In this embodiment, the preset statistical period is the period for statistically analyzing the order dispatch and pickup volume of the main account, such as seven days as one period. The maximum daily delivery volume is the maximum number of orders completed by the main account in a single day within the preset statistical period. The maximum daily pickup volume is the maximum number of pickups completed by the main account in a single day within the preset statistical period. The preset delivery ratio coefficient and the preset pickup ratio coefficient are pre-created comparison coefficients.

[0053] Extract daily delivery and pickup records for the main account within a preset statistical period (e.g., the past 30 calendar days) from the logistics system database. Iterate through the data from these 30 days to obtain the maximum daily delivery and pickup volume. Multiply these maximum daily delivery and pickup volumes by their respective preset ratio coefficients to obtain the maximum delivery and pickup volume limits for the sub-account. The preset delivery and pickup ratio coefficients can be the same (e.g., both 0.7), or different values ​​can be set based on business characteristics. For example, delivery operations are relatively standardized, so a higher ratio coefficient of 0.8 can be set; pickup operations involve communication with customers and are more complex, so a lower ratio coefficient of 0.6 can be set.

[0054] By multiplying the historical peak capacity of the main account by a proportional coefficient, a quantitative mapping is achieved from the historical operational capacity of the main account to the operational quantity constraints of the sub-account, so that the delivery limit and pickup limit of the sub-account match the actual carrying capacity of the main account.

[0055] 404. Write the authorized delivery area, the maximum number of packages to be delivered, and the maximum number of packages to be picked up into the express account binding relationship table; In this embodiment, the authorized delivery area, the maximum number of deliveries, and the maximum number of pickups are written into the express account binding relationship table. After the writing is completed, the record of the sub-account in the express account binding relationship table contains complete binding information (master and sub-account identifiers, binding time, binding status) and operational constraint information (authorized delivery area, maximum number of deliveries, and maximum number of pickups). By writing the operational constraints into the binding relationship table, unified storage of the sub-account's authorized delivery area, maximum number of deliveries, maximum number of pickups, and binding relationship is achieved.

[0056] Please see Figure 5 In this embodiment of the invention, the step of collecting logistics operation data of the sub-account, performing anomaly detection on the logistics operation data, and generating anomaly detection results includes: 501. Collect logistics operation data of the sub-account in real time, including operation frequency, geographical location, equipment information, network address and order deviation rate; In this embodiment, the logistics operation data refers to the real-time behavioral data generated by the sub-account during the execution of pickup and delivery operations. The operation frequency refers to the total number of pickup and delivery operations completed by the sub-account within a unit of time (e.g., the most recent hour). The geographical location is the coordinates or latitude and longitude values ​​of the sub-account's location when performing pickup and delivery operations. The device information refers to the device model and unique identifier (e.g., IMEI or Android ID) of the mobile terminal used by the sub-account when logging into the logistics system. The network address is the IP address used by the sub-account during operations. The order deviation rate refers to the proportion of difference between the order information reported by the sub-account (e.g., delivery address, pickup weight, etc.) and the actual order information.

[0057] The system monitors sub-accounts in real time and collects data. Specifically, it records the timestamp of every operation performed by a sub-account within a preset collection time window (e.g., 1 hour). Each time a sub-account performs a new operation, the system calculates the total number of operations performed within the previous hour to obtain the current operation frequency. For example, if a sub-account completed 30 operations between 14:00 and 15:00, then the operation frequency at 15:00 is 30 operations per hour.

[0058] Each time a sub-account performs a pickup / delivery operation, it automatically calls the GPS positioning interface of the mobile terminal system to obtain the longitude and latitude coordinates of its current location, thus acquiring geographical location information. Furthermore, based on the sub-account's authorization, it automatically obtains the mobile terminal's device model (e.g., iPhone 14 Pro) and unique device identifier (e.g., Android ID), using these as device information. Each time a sub-account initiates an operation request, the logistics system's backend server extracts the client's IP address from the HTTP request header, using it as network address information. For example, the extracted IP address might be "223.104.41.25".

[0059] Finally, when a sub-account performs pickup or delivery operations, the order information reported by the sub-account (such as the recipient's name, delivery location coordinates, and package weight) is compared with the original order information, and the discrepancy ratio is calculated. For example, if the original weight of a package order is 5.0 kg, and the sub-account reports a weight of 3.5 kg, the weight deviation is 1.5 kg, and the deviation rate is 1.5 ÷ 5.0 × 100% = 30%. The collected data is stored in a structured format. Through multi-dimensional real-time data collection, automated real-time collection of sub-account logistics operation data is achieved, providing a data foundation for subsequent anomaly detection.

[0060] 502. Determine whether the operation frequency, geographical location, equipment information, network address and order deviation rate are within the corresponding preset safety threshold range, and mark the logistics operation data that are not within the preset safety threshold range as candidate abnormal data. In this embodiment, the preset safety threshold range is a normal value range pre-set for logistics operation data of each dimension. The safety threshold ranges for different dimensions are set according to the operational standards of the express delivery industry. For example, the upper limit of the safety threshold range for operation frequency is determined based on the statistical value of normal work efficiency for a single person; the safety threshold range for geographical location is determined based on the authorized delivery area boundary, superimposed with GPS positioning error and a reasonable range of operational fluctuations; since device information and network addresses are discrete data categories, their safety threshold ranges adopt a whitelist mechanism, using the union of the historical login devices and network addresses of the sub-account with the authorized devices and network addresses of the main account as the allowed set; the upper limit of the safety threshold range for order deviation rate is determined based on the tolerance for order information errors during normal operations (e.g., the order deviation rate range is 10% to 20%).

[0061] Each operation frequency, geographical location, device information, network address, and order deviation rate is individually determined to be within the corresponding preset security threshold range. For example, the collected operation frequency value is compared with the operation frequency range of the preset security threshold range (e.g., 10 to 120 times / hour). If the operation frequency is within the range, it is considered normal; if the operation frequency exceeds the upper limit of the range, it is marked as candidate abnormal data. The geographical location coordinates are compared with the authorized delivery area, and the shortest distance from the coordinates to the boundary of the authorized delivery area is calculated. This distance is compared with the upper limit of the geographical location security threshold range of the preset security threshold range (e.g., 0 to 500 meters from the boundary of the authorized delivery area). If the distance is less than or equal to the upper limit, it is considered normal; otherwise, it is marked as candidate abnormal data. The IP address of the current operation is matched with historical commonly used IP address ranges and the commonly used IP address ranges of the main account. If the current IP address belongs to any commonly used address range, it is considered normal; if it does not belong to any commonly used address range, it is marked as candidate abnormal data. By comparing data from multiple dimensions one by one with preset thresholds, preliminary screening of abnormal sub-account operations was achieved.

[0062] 503. Obtain the historical operation habit data of the sub-account and the commonly used operation range data of the main account, and perform secondary verification on the candidate abnormal data; In this embodiment, the historical operation habit data is a data set extracted from the historical logistics operation records of the sub-account itself, including the average and standard deviation of the sub-account's operation frequency over the past preset number of days (e.g., 30 days), the center point and distribution radius of commonly used geographical locations, the set of commonly used equipment models, and the set of commonly used IP address ranges. The commonly used operation range data is a data set extracted from the historical operation records of the main account and its other sub-accounts, including the average operation frequency of the main account's operation system, the range of commonly used operation areas, the set of commonly used equipment models, and the set of commonly used IP address ranges.

[0063] The candidate abnormal data undergoes secondary verification. Specifically, the candidate abnormal data is compared with the two sets of baseline data mentioned above to determine whether the candidate abnormal data matches the historical habits of the sub-account and falls within the commonly used range of the main account's operating system. If both conditions are met, the secondary verification passes. By performing secondary verification of abnormal data based on the sub-account's historical habits and the main account's commonly used range, the system effectively distinguishes between genuine and false anomalies, achieving in-depth verification of candidate abnormal data and avoiding negative impacts on the main account due to false alarms.

[0064] 504. Identify the candidate abnormal data that fails the secondary verification as the target abnormal data, and generate an anomaly detection result based on the target abnormal data; In this embodiment, candidate abnormal data that fails the secondary verification is identified as target abnormal data. Based on the specific data in the target abnormal data that exceeds the safety threshold range, a corresponding anomaly detection result is generated. For example, if the operation frequency is abnormal, an anomaly detection result for frequency abnormality is generated and sent to the monitoring page of the main account. By generating anomaly detection results based on the target abnormal data, the main account can quickly understand the anomalies of the sub-accounts.

[0065] Please see Figure 6 In this embodiment of the invention, generating anomaly detection results based on the target anomaly data includes: 601. Identify the type of operation feature to which the target abnormal data belongs, and determine the corresponding abnormality type based on the type of operation feature; In this embodiment, the operation feature category refers to the category of the target abnormal data, including operation frequency, geographical location, device information, network address, and order deviation rate. The anomaly type refers to the anomaly classification identifier corresponding to the operation feature category. The operation feature category to which the target abnormal data belongs is identified, the determined target abnormal data is obtained, and its operation feature category is determined according to the dimension of the target abnormal data, thereby mapping to the corresponding anomaly type. For example, if the target abnormal data is an operation frequency exceeding a safety threshold range, its operation feature category is operation frequency, and the corresponding anomaly type is frequency anomaly. If the target abnormal data is a geographical location, the operation feature category is geographical location, and the corresponding anomaly type is location anomaly. If multiple target abnormal data exist, multiple corresponding anomaly types are obtained. By determining the anomaly type of the target abnormal data, a rapid conversion from data dimension to anomaly category is achieved.

[0066] 602. Calculate the deviation between the actual value of the target abnormal data and the value exceeding the side boundary in the corresponding preset safety threshold range, and compare the deviation with multiple preset abnormality level thresholds to determine the corresponding abnormality level. In this embodiment, the actual value is the specific value in the target abnormal data that exceeds the safety threshold range. The exceeded side boundary value refers to the boundary value on the side of the preset safety threshold range that is exceeded by the actual value. Specifically, when the actual value is higher than the upper limit of the range, the exceeded side boundary value is the upper limit value; when the actual value is lower than the lower limit of the range, the exceeded side boundary value is the lower limit value. The deviation magnitude refers to the degree of difference between the actual value and the boundary value, expressed as a percentage. The anomaly severity level threshold refers to a pre-set percentage limit value used to classify anomalies into mild, moderate, and severe levels.

[0067] The deviation between the actual value of the target anomaly data and the corresponding exceeded side boundary value within the preset safety threshold range is calculated. Specifically, the exceeded side boundary value is determined based on the direction in which the anomaly dimension in the target anomaly data exceeds the safety threshold range. The formula for calculating the deviation is: Deviation = (Actual value - Exceeded side boundary value) / Exceeded side boundary value × 100%. Wherein, when the actual value is lower than the lower limit, the exceeded side boundary value is the lower limit. In this case, the difference between the actual value and the boundary value is negative, and the absolute value is taken before further calculation.

[0068] For example, the safe threshold range for operation frequency is 0 to 120 times / hour. If the actual value in a certain abnormal target data is 150 times / hour, which is higher than the upper limit of 120, exceeding the side boundary value of 120, the deviation is (150-120) / 120×100%=25%. The safe threshold range for geographical location is 0 to 500 meters from the boundary of the authorized delivery area. If the actual deviation distance in a certain abnormal target data is 800 meters, the deviation is (800-500) / 500×100%=60%.

[0069] Multiple anomaly severity thresholds are pre-set. For example, the threshold range for mild anomaly is a deviation of less than 10%, for moderate anomaly it is a deviation of 10% or more but less than 30%, and for severe anomaly it is a deviation of 30% or more. The calculated deviation is compared with these threshold ranges to determine the corresponding anomaly severity. It is important to note that if discrete categories such as device information and network addresses exist, because they lack continuous numerical ranges, the deviation cannot be calculated, and their anomaly severity is directly classified as severe.

[0070] By quantifying the deviation and comparing it with the level threshold, the original numerical deviation is transformed into a classification result of mild, moderate, and severe, providing a level basis for the main account to take differentiated handling strategies in the future.

[0071] 603. Integrate the anomaly type and the anomaly degree to generate the anomaly detection result; In this embodiment, the identified anomaly type and the determined anomaly severity are integrated to generate anomaly detection results according to a preset format. For example, the anomaly detection result might be: Frequency Anomaly - Moderate Anomaly. Simultaneously with generating the anomaly detection result, detailed target anomaly data information corresponding to the result is stored, including the timestamp of the anomaly occurrence, sub-account identifier, actual values ​​of each dimension, preset safety thresholds, deviation magnitude, etc., forming a complete anomaly record. After generating the anomaly detection result, it is sent to the monitoring page of the main account. The main account can see the anomaly detection result corresponding to each anomaly record on the monitoring page. By integrating anomaly type and anomaly severity, a concise and intuitive anomaly overview is provided to the main account.

[0072] Please see Figure 7 In this embodiment of the invention, after integrating the anomaly type and the anomaly degree to generate the anomaly detection result, the method further includes: 701. Based on the anomaly type and degree in the anomaly detection result, match the corresponding deduction score from the preset deduction mapping table, deduct the deduction score from the preset initial reputation score, and obtain the updated reputation score. In this embodiment, the preset deduction mapping table is a database table that records the deduction scores corresponding to different combinations of anomaly types and degrees. The preset initial credit score refers to the baseline credit score assigned to a sub-account by the system when it is associated with and bound to the main account. The anomaly type and degree from the anomaly detection result are obtained, and these two pieces of information are used as a joint query key to match the corresponding deduction score in the preset deduction mapping table. Then, the current credit score of the sub-account (the preset initial credit score at the time of the first deduction) is obtained, and the deduction score is deducted from the current credit score to obtain the updated credit score. For example, the preset initial credit score of a sub-account is 100 points. The first anomaly detection result is "frequency anomaly - moderate anomaly," and a deduction score of 3 points is matched from the preset deduction mapping table. These 3 points are deducted from the 100 points to obtain an updated credit score of 97 points. Finally, the updated credit score is written back to the credit score field in the express delivery account binding relationship table, replacing the original credit score.

[0073] By standardizing and mapping the types and degrees of anomalies to specific deduction values, each abnormal operation can be reflected in the reputation record of the sub-account in the form of a score.

[0074] 702. Determine whether the updated reputation score is lower than the preset disabling threshold. If it is lower, switch the sub-account to the reputation disabled state and send a reputation alarm notification to the main account that created it. In this embodiment, the preset disabling threshold is a pre-set minimum credit score threshold that triggers the disabling of a sub-account, for example, 60 points. The credit-disabling state refers to a special state where a sub-account is restricted from logging in and performing pickup / delivery operations due to a low credit score; a sub-account in this state cannot perform any pickup / delivery work. The credit alarm notification is sent to the main account, informing the sub-account that it has been disabled due to low credit.

[0075] The updated reputation score is compared with the preset disabling threshold. If the updated reputation score is lower than the preset disabling threshold, the sub-account's status is switched to a reputation-disabling state. At this time, the sub-account immediately loses its access to the logistics system, and any ongoing operations will be forcibly interrupted. It will be unable to initiate any pickup, delivery, or anomaly reporting requests. A reputation alarm notification is sent to the main account that created the sub-account, such as "[Logistics System Alarm] Your sub-account (Zhang San)'s reputation score has dropped to 57 points. This sub-account has been switched to a reputation-disabling state and cannot continue operations. Details of this deduction: Frequency abnormality - moderate abnormality deducts 3 points, location abnormality - severe abnormality deducts 10 points." If the updated reputation score is greater than or equal to the preset disabling threshold, the disabling process is not triggered, and the sub-account remains in its original normal state.

[0076] By setting preset disabling thresholds and determining the reputation disabling status, the business risks that may arise from low-reputation sub-accounts continuing to operate are effectively prevented.

[0077] The above describes the intelligent management method for master and sub-accounts of the logistics system in the embodiments of the present invention. The following describes the intelligent management device for master and sub-accounts of the logistics system in the embodiments of the present invention. Please refer to [link / reference]. Figure 8 One embodiment of the intelligent management device for master and sub-accounts of the logistics system in this invention includes: The parsing module 801 is used to parse the received sub-account permission activation request to obtain request information, which includes the collaboration scenario type, the requesting main account and its corresponding business volume data and express delivery data. Weighting module 802 is used to perform weighted calculation on the business volume data, the express delivery data and the collaboration scenario type according to preset weighted calculation rules to obtain the upper limit of the number of creations corresponding to the requesting main account; The judgment module 803 is used to obtain creation information if a sub-account creation request is received, perform quantity verification on the creation information based on the maximum number of creations, perform security verification, and obtain creation verification result; The association module 804 is used to create a corresponding sub-account based on the creation information if the creation verification result is successful, and to associate and bind the created sub-account with the main account that sent the sub-account creation request. The inspection module 805 is used to collect logistics operation data of the sub-account, perform anomaly detection on the logistics operation data, generate anomaly detection results, and send them to the main account that created the account.

[0078] Based on the same ideas as the methods in the above embodiments, the apparatus provided by the present invention can implement the methods in the above embodiments.

[0079] The above combination Figure 8The master-sub-account intelligent management device of the logistics system in this embodiment of the invention is described in detail from the perspective of modular functional entities. The master-sub-account intelligent management device of the logistics system in this embodiment of the invention is described in detail below from the perspective of hardware processing.

[0080] Figure 9 This is a schematic diagram of the structure of a master-sub-account intelligent management device for a logistics system provided in an embodiment of the present invention. The master-sub-account intelligent management device 900 for the logistics system can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 910 (e.g., one or more processors) and a memory 920, and one or more storage media 930 (e.g., one or more mass storage devices) for storing application programs 933 or data 932. The memory 920 and storage media 930 can be temporary or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the master-sub-account intelligent management device 900 of the logistics system. Furthermore, the processor 910 may be configured to communicate with the storage media 930 and execute the series of instruction operations in the storage media 930 on the master-sub-account intelligent management device 900 of the logistics system to implement the steps of the master-sub-account intelligent management method for the logistics system provided in the above-described method embodiments.

[0081] The master-slave account intelligent management device 900 of the logistics system may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 9 The illustrated master-sub-account intelligent management device structure of the logistics system does not constitute a limitation on the master-sub-account intelligent management device of the logistics system. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0082] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the master-sub-account intelligent management method of the logistics system.

[0083] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0084] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0085] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent management of master and sub-accounts in a logistics system, characterized in that, include: The received sub-account permission activation request is parsed to obtain request information, which includes the collaboration scenario type, the requesting main account and its corresponding business volume data and express delivery data; The business volume data, the express delivery data, and the collaboration scenario type are weighted and calculated using preset weighted calculation rules to obtain the maximum number of creations corresponding to the requesting main account. If a sub-account creation request is received, the creation information is obtained, the creation information is validated based on the maximum number of creations, and a security check is performed to obtain the creation verification result. If the verification result is successful, then the corresponding sub-account is created according to the creation information, and the created sub-account is associated and bound with the main account that sent the sub-account creation request; The system collects logistics operation data from the sub-account, performs anomaly detection on the logistics operation data, generates anomaly detection results, and sends them to the main account that created the sub-account.

2. The intelligent management method for master and sub-accounts in a logistics system according to claim 1, characterized in that, The step of performing a weighted calculation on the business volume data, the express delivery data, and the collaboration scenario type using a preset weighted calculation rule to obtain the maximum number of creations corresponding to the requesting main account includes: The ratio of the business volume data to the preset standard business volume data is calculated to obtain the business volume ratio. Based on a preset first weighting ratio, the operation frequency, completion rate, abnormal reporting rate, and average single-item operation time in the express delivery data are weighted and summed to obtain the pickup and delivery efficiency score. The scenario weight coefficient is obtained by querying the weight coefficient corresponding to the collaborative scenario type from the preset scenario weight mapping table; The business volume ratio, pickup and dispatch efficiency score, and scenario weight coefficient are weighted and summed according to a preset second weight ratio to obtain a weighted summation result; The weighted summation result is corrected and rounded to obtain the upper limit of the number of creations.

3. The intelligent management method for master and sub-accounts in a logistics system according to claim 1, characterized in that, The process of performing quantity verification and security verification on the creation information based on the maximum creation quantity to obtain the creation verification result includes: Determine whether the number of sub-accounts under the main account exceeds the creation limit, and obtain the quantity verification result. If the quantity verification result is not passed, generate a creation verification result that fails the verification. If the quantity verification result is successful, then verify whether the mobile phone number information in the sub-account creation information conforms to the preset mobile phone number format and is not bound to other main accounts, and obtain the first verification result; The second verification result is obtained by verifying whether the name information in the sub-account creation information consists only of preset Chinese and English characters, and whether the length of the name information is within the preset name length range; Verify whether the sub-account creation information contains logistics permission information, which includes delivery permission or pickup permission, to obtain a third verification result; When the first verification result, the second verification result, and the third verification result all indicate that the verification has passed, a successful creation verification result is generated.

4. The intelligent management method for master and sub-accounts in a logistics system according to claim 1, characterized in that, The step of associating and binding the created sub-account with the main account that sent the sub-account creation request includes: Associate and bind the sub-account identifier of the sub-account with the main account identifier that sent the sub-account creation request to generate a courier account binding relationship table; Extract the historical parcel delivery trajectory of the main account within the preset delivery period, perform boundary calculation on the historical parcel delivery trajectory using the convex hull algorithm, generate the regular delivery area of ​​the main account, and set the regular delivery area as the authorized delivery area of ​​the sub-account. Extract the maximum daily delivery volume and maximum daily pickup volume of the main account within a preset statistical period. Multiply the maximum daily delivery volume by a preset delivery ratio coefficient to obtain the upper limit of the number of deliveries for the sub-account. Multiply the maximum daily pickup volume by a preset pickup ratio coefficient to obtain the upper limit of the number of pickups for the sub-account. The authorized delivery area, the maximum number of packages to be delivered, and the maximum number of packages to be picked up are written into the express account binding relationship table.

5. The intelligent management method for master and sub-accounts in a logistics system according to claim 1, characterized in that, The process of collecting logistics operation data from the sub-account, performing anomaly detection on the logistics operation data, and generating anomaly detection results includes: Real-time collection of logistics operation data for the sub-accounts, including operation frequency, geographical location, equipment information, network address, and order deviation rate; Each operation frequency, geographical location, device information, network address, and order deviation rate is determined to be within the corresponding preset safety threshold range. Logistics operation data that is not within the preset safety threshold range is marked as candidate abnormal data. Obtain historical operation habit data of the sub-account and common operation range data of the main account, and perform secondary verification on the candidate abnormal data; Candidate abnormal data that fails the secondary verification are identified as target abnormal data, and anomaly detection results are generated based on the target abnormal data.

6. The intelligent management method for master and sub-accounts in a logistics system according to claim 5, characterized in that, The step of generating anomaly detection results based on the target anomaly data includes: Identify the type of operational feature to which the target abnormal data belongs, and determine the corresponding anomaly type based on the type of operational feature; Calculate the deviation between the actual value of the target abnormal data and the value exceeding the side boundary in the corresponding preset safety threshold range, and compare the deviation with multiple preset abnormality level thresholds to determine the corresponding abnormality level. The anomaly type and the anomaly severity are integrated to generate the anomaly detection result.

7. The intelligent management method for master and sub-accounts in a logistics system according to claim 6, characterized in that, After integrating the anomaly type and the anomaly severity to generate the anomaly detection result, the method further includes: Based on the anomaly type and degree in the anomaly detection results, the corresponding deduction score is matched from the preset deduction mapping table, and the deduction score is deducted from the preset initial reputation score to obtain the updated reputation score. Determine whether the updated reputation score is lower than a preset disabling threshold. If it is, switch the sub-account to a reputation-disabled state and send a reputation alert notification to the main account that created it.

8. A master-sub-account intelligent management device for a logistics system, characterized in that, include: The parsing module is used to parse the received sub-account permission activation request to obtain request information, which includes the collaboration scenario type, the requesting main account and its corresponding business volume data and express delivery data. The weighting module is used to perform weighted calculations on the business volume data, the express delivery data, and the collaboration scenario type according to preset weighted calculation rules, so as to obtain the upper limit of the number of creations corresponding to the requesting main account. The judgment module is used to obtain creation information if a sub-account creation request is received, perform quantity verification on the creation information based on the maximum number of creations, perform security verification, and obtain creation verification result. The association module is used to create a corresponding sub-account based on the creation information if the creation verification result is successful, and to associate and bind the created sub-account with the main account that sent the sub-account creation request. The inspection module is used to collect logistics operation data of the sub-account, perform anomaly detection on the logistics operation data, generate anomaly detection results, and send them to the main account that created the account.

9. A master-sub-account intelligent management device for a logistics system, characterized in that, The master and sub-account intelligent management device of the logistics system includes: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the master-sub-account intelligent management device of the logistics system to perform the steps of the master-sub-account intelligent management method of the logistics system as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the master-sub-account intelligent management method for the logistics system as described in any one of claims 1-7.

Images