Logistics goods sorting and counting statistical method, device and equipment and storage medium

Abstract

The invention relates to the technical field of information processing, in particular to a logistics cargo sorting and counting statistical method, device and equipment and a storage medium, cargo weight data from a sorting assembly line and forklift ticket counting data from a forklift vehicle-mounted terminal are obtained in real time, multi-dimensional integration is performed on the cargo weight data and the forklift ticket counting data, and the logistics cargo sorting and counting statistical method and device are obtained. The method comprises the steps of obtaining a complete piece counting transaction record, correcting heavy abnormal data of the piece counting transaction record, constructing an employee sorting piece counting statistical table according to employee basic information, checking the piece counting transaction record based on a preset piece counting rule, and updating the employee sorting piece counting statistical table according to checking information. According to the sorting efficiency, the sorting cost and the employee satisfaction degree, the piece counting rule is dynamically adjusted, the manual accounting time and the related labor cost can be remarkably reduced, the accuracy and fairness of piece counting accounting are improved, and systematic and precise management and control of logistics enterprise cost management are achieved.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a method, apparatus, equipment, and storage medium for the sorting and counting of logistics goods. Background Technology

[0002] With the increasing penetration of e-commerce, the volume of express parcels is growing exponentially. The massive and fragmented nature of these orders places unprecedented demands on the efficiency, speed, and accuracy of logistics sorting. Traditional, inefficient manual management has become a bottleneck in the industry. Currently, manual sorting operations and piece-rate calculations in logistics transfer suffer from the following drawbacks: 1. Traditional manual statistics are prone to errors: Relying on paper documents and manual data entry, errors in copying and calculation are inevitable with massive amounts of data, leading to salary disputes and affecting employee morale. 2. There is a risk of cheating and ethical hazard: Manual recording may result in false or excessive reporting of piece-rate quantities, especially under complex employment models involving outsourcing and temporary workers, making management more difficult and causing direct economic losses to the company. 3. Inability to achieve accurate cost accounting: The inability to accurately distinguish the specific workload of regular employees, probationary employees, and outsourced workers leads to vague labor cost accounting, making scientific cost analysis and optimization difficult. 4. Lack of basis for management decisions: Managers cannot quickly and accurately understand key issues such as which distribution center has high sorting efficiency, which goods have high sorting costs, and whether staffing is reasonable, relying on experience rather than data for decision-making. 5. Long accounting cycle: At the end of the month or cycle, a large amount of manpower is required for data collection and verification, which is time-consuming and labor-intensive, making it impossible to achieve accurate daily performance analysis. 6. Lack of process transparency: From sorting operations to payroll calculation, there are many intermediate links and a lack of transparent data flow, making it difficult to supervise the process and optimize the workflow. Summary of the Invention

[0003] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method, device, equipment and storage medium for sorting and calculating logistics goods in a piece-rate system that can significantly reduce manual calculation time and related labor costs, improve the accuracy and fairness of piece-rate calculation, and realize systematic and precise control of logistics enterprise cost management.

[0004] The first aspect of this invention provides a method for calculating piecework statistics in logistics cargo sorting, comprising: acquiring cargo weight data from a sorting line and forklift ticket piecework data from a forklift vehicle terminal in real time, wherein the forklift ticket piecework data includes a sub-order number, scanning time, and operator ID; integrating the cargo weight data and the forklift ticket piecework data in multiple dimensions to obtain a complete piecework transaction record; using a preset anomaly detection model to identify anomalies in the piecework transaction record, and correcting the anomaly data when anomalies are identified; acquiring basic employee information and constructing an employee sorting piecework statistics table based on the basic employee information; calculating the piecework transaction record based on preset piecework rules to obtain calculation information, and updating the employee sorting piecework statistics table based on the calculation information; uploading the employee sorting piecework statistics table to a blockchain, and dynamically adjusting the piecework rules based on sorting efficiency, sorting cost, and employee satisfaction.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the real-time acquisition of goods weight data from the sorting line and forklift ticket piecework data from the forklift vehicle terminal, wherein the forklift ticket piecework data includes a sub-order number, scanning time, and operator ID, and the multi-dimensional integration of the goods weight data and the forklift ticket piecework data to obtain a complete piecework transaction record, includes: real-time acquisition of goods weight data through weight sensors on the sorting line; acquisition of forklift ticket piecework data through the scanning device of the forklift vehicle terminal, wherein the forklift ticket piecework data includes a sub-order number, scanning time, and operator ID; removal of duplicate data from the goods weight data and the forklift ticket piecework data, and filling of missing values ​​in the goods weight data and the forklift ticket piecework data respectively; association of the goods weight data and the forklift ticket piecework data of the same transaction using timestamp alignment technology; and aggregation of the associated goods weight data and the forklift ticket piecework data to obtain a complete piecework transaction record.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the step of using a preset anomaly detection model to identify anomalies in the piecework transaction records, and correcting the anomaly data when anomalies are identified, includes: constructing an anomaly detection model using an isolated forest algorithm; using the anomaly detection model to identify weight anomalies, scan time conflicts, and missing operator IDs in the piecework transaction records to perform multi-dimensional anomaly identification and obtain identification results; when the identification result is a weight anomaly, smoothing the anomaly data according to the historical weight data distribution; when the identification result is a scan time conflict, timestamp correction of the anomaly data according to the scan time sequence; and when the identification result is a missing operator ID, correlating and completing the anomaly data according to the scan time and shift schedule.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the step of obtaining employee basic information and constructing an employee sorting piece-rate statistics table based on the employee basic information includes: obtaining employee basic information from the enterprise human resources system, wherein the employee basic information includes employee ID, name, position, work group, and piece-rate coefficient; initializing and constructing an employee sorting piece-rate statistics table based on the employee basic information, wherein the statistics table includes employee ID, total number of sorted goods, total weight, piece-rate wage, and efficiency score; and persistently storing the employee sorting piece-rate statistics table using SQL database operations and establishing an index to support fast querying and updating.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the step of calculating the piecework transaction records based on preset piecework rules to obtain calculation information, and updating the employee sorting piecework statistics table according to the calculation information, includes: identifying the current preset piecework rules, which include weight-based tiered billing, cumulative billing by number of pieces, time-based weighted billing, and quality reward and penalty rules; calculating the base piecework wage for each operator based on the piecework rules, combined with the weight distribution of goods and the number of sub-order numbers in the piecework transaction records, to obtain calculation information; and updating the employee sorting piecework statistics table according to the calculation information.

[0009] Optionally, in the fifth implementation of the first aspect of the present invention, the step of uploading the employee sorting piecework statistics table to the blockchain and dynamically adjusting the piecework rules according to sorting efficiency, sorting cost, and employee satisfaction includes: using the SHA-256 hash algorithm to perform digital fingerprint calculation on the employee sorting piecework statistics table to generate a unique data identifier; uploading the data identifier and the employee sorting piecework statistics table to the blockchain and verifying data integrity through a smart contract deployed on the blockchain; collecting sorting efficiency indicators, sorting cost data, and employee satisfaction survey results; inputting the sorting efficiency indicators, the sorting cost data, and the employee satisfaction survey results into a preset dynamic piecework rule optimization model, and using the dynamic piecework rule optimization model to output adjusted rule adjustment parameters based on linear regression and multi-objective optimization algorithms; and adjusting the piecework rules according to the rule adjustment parameters.

[0010] Optionally, in the sixth implementation of the first aspect of the present invention, after uploading the employee sorting piecework statistics table to the blockchain and dynamically adjusting the piecework rules according to sorting efficiency, sorting cost, and employee satisfaction, the method further includes: summarizing the piecework statistics and rule adjustment records of all operators; generating a sorting piecework analysis report based on the piecework statistics and rule adjustment records; encrypting the sorting piecework analysis report using the public key in a preset key pair to obtain an encrypted report; and sending the encrypted report and the private key in the key pair to a management terminal so that the management terminal can decrypt the encrypted report using the private key.

[0011] A second aspect of the present invention provides a piece-rate statistics device for logistics cargo sorting, comprising: an acquisition and integration module, used to acquire cargo weight data from the sorting line and forklift ticket piece-rate data from a forklift vehicle terminal in real time, wherein the forklift ticket piece-rate data includes a sub-order number, scanning time, and operator ID, and to integrate the cargo weight data and the forklift ticket piece-rate data in multiple dimensions to obtain a complete piece-rate transaction record; an identification and correction module, used to identify anomalies in the piece-rate transaction record using a preset anomaly detection model, and to correct the anomaly data when anomalies are identified; an acquisition and construction module, used to acquire basic employee information and construct an employee sorting piece-rate statistics table based on the basic employee information; an accounting and update module, used to calculate the piece-rate transaction record based on preset piece-rate rules to obtain accounting information, and to update the employee sorting piece-rate statistics table based on the accounting information; and an upload and adjustment module, used to upload the employee sorting piece-rate statistics table to a blockchain and dynamically adjust the piece-rate rules based on sorting efficiency, sorting cost, and employee satisfaction.

[0012] Optionally, in a first implementation of the second aspect of the present invention, the acquisition and integration module includes: a first acquisition unit, used to acquire cargo weight data in real time through a weight sensor on the sorting line; a first acquisition unit, used to acquire forklift ticket piece data through a scanning device on the forklift vehicle terminal, the forklift ticket piece data including a sub-order number, scanning time, and operator ID; a removal and filling unit, used to remove duplicate data from the cargo weight data and the forklift ticket piece data respectively, and fill in missing values ​​for the cargo weight data and the forklift ticket piece data respectively; an association unit, used to associate the cargo weight data and the forklift ticket piece data of the same transaction through timestamp alignment technology; and an aggregation unit, used to aggregate the associated cargo weight data and the forklift ticket piece data to obtain a complete piece transaction record.

[0013] Optionally, in a second implementation of the second aspect of the present invention, the identification and correction module includes: a first construction unit, used to construct an anomaly detection model using an isolated forest algorithm; a first identification unit, used to perform multi-dimensional anomaly identification by using the anomaly detection model to identify weight anomalies, scan time conflicts, and missing operator IDs in the piecework transaction record, and obtain identification results; a correction unit, used to smooth and correct the anomaly data according to the historical weight data distribution when the identification result is a weight anomaly; a correction unit, used to perform timestamp correction on the anomaly data according to the scan time sequence when the identification result is a scan time conflict; and a completion unit, used to complete the anomaly data by associating it with the scan time and shift schedule when the identification result is a missing operator ID.

[0014] Optionally, in a third implementation of the second aspect of the present invention, the acquisition and construction module includes: a second acquisition unit, configured to acquire basic employee information from an enterprise human resources system, the basic employee information including employee ID, name, position, work group, and piece-rate coefficient; a second construction unit, configured to initialize and construct an employee sorting piece-rate statistics table based on the basic employee information, the statistics table including employee ID, total number of sorted goods, total weight, piece-rate wage, and efficiency score; and a storage establishment unit, configured to persistently store the employee sorting piece-rate statistics table using SQL database operations and establish an index to support fast querying and updating.

[0015] Optionally, in a fourth implementation of the second aspect of the present invention, the accounting update module includes: a second identification unit, used to identify the currently preset piece-rate rules, the piece-rate rules including weight-based tiered billing, cumulative billing by number of pieces, time-based weighted billing, and quality reward and penalty rules; a calculation unit, used to calculate the base piece-rate wage for each operator based on the piece-rate rules, combined with the weight distribution of goods and the number of sub-order numbers in the piece-rate transaction record, to obtain accounting information; and an update unit, used to update the employee sorting piece-rate statistics table according to the accounting information.

[0016] Optionally, in a fifth implementation of the second aspect of the present invention, the upload adjustment module includes: a calculation generation unit, used to perform digital fingerprint calculation on the employee sorting piecework statistics table using the SHA-256 hash algorithm to generate a unique data identifier; an upload verification unit, used to upload the data identifier and the employee sorting piecework statistics table to the blockchain, and verify the data integrity through a smart contract deployed on the blockchain; a second collection unit, used to collect sorting efficiency indicators, sorting cost data, and employee satisfaction survey results; an input-output unit, used to input the sorting efficiency indicators, the sorting cost data, and the employee satisfaction survey results into a preset dynamic piecework rule optimization model, and use the dynamic piecework rule optimization model to output adjusted rule adjustment parameters based on linear regression and multi-objective optimization algorithms; and an adjustment unit, used to adjust the piecework rules according to the rule adjustment parameters.

[0017] Optionally, in the sixth implementation of the second aspect of the present invention, it further includes: a summary generation module, used to summarize the piece-rate statistics and rule adjustment records of all operators, and generate a sorting piece-rate analysis report based on the piece-rate statistics and rule adjustment records; an encryption module, used to encrypt the sorting piece-rate analysis report using the public key in a preset key pair to obtain an encrypted report; and a sending module, used to send the encrypted report and the private key in the key pair to a management terminal, so that the management terminal can decrypt the encrypted report using the private key.

[0018] A third aspect of the present invention provides a logistics goods sorting and piece counting statistics device, the logistics goods sorting and piece counting statistics device comprising: a memory and at least one processor, the memory storing instructions; at least one processor calling the instructions in the memory to cause the logistics goods sorting and piece counting statistics device to perform the various steps of the logistics goods sorting and piece counting statistics method described above.

[0019] 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 logistics goods sorting and counting method described in any of the preceding claims.

[0020] In the technical solution of this invention, by acquiring real-time cargo weight data from the sorting line and forklift ticket piecework data from the forklift terminal, the cargo weight data and forklift ticket piecework data are integrated from multiple dimensions to obtain a complete piecework transaction record. Abnormal data in the piecework transaction record is corrected. An employee sorting piecework statistics table is constructed based on employee basic information. The piecework transaction record is calculated based on preset piecework rules. The employee sorting piecework statistics table is updated based on the calculation information. The piecework rules are dynamically adjusted based on sorting efficiency, sorting cost, and employee satisfaction. This can significantly reduce manual calculation time and related labor costs, improve the accuracy and fairness of piecework calculation, and achieve systematic and precise control over logistics enterprise cost management. Attached Figure Description

[0021] Figure 1 This is a first flowchart of a logistics cargo sorting and piece counting statistical method provided in an embodiment of the present invention; Figure 2 This is a second flowchart of the logistics cargo sorting and piece counting statistics method provided in an embodiment of the present invention; Figure 3 This is a third flowchart of the logistics cargo sorting and piece counting statistics method provided in the embodiments of the present invention; Figure 4 This is a fourth flowchart of the logistics cargo sorting and piece counting statistics method provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of a logistics cargo sorting and counting device provided in an embodiment of the present invention; Figure 6 This is another structural schematic diagram of the logistics cargo sorting and counting device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of a logistics cargo sorting and counting equipment provided in an embodiment of the present invention. Detailed Implementation

[0022] This invention provides a method, apparatus, equipment, and storage medium for the piece-rate statistics of logistics goods sorting, which can significantly reduce manual calculation time and related labor costs, improve the accuracy and fairness of piece-rate calculation, and realize systematic and precise control over the cost management of logistics enterprises.

[0023] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those 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 apparatus 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 apparatus.

[0024] 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 logistics cargo sorting and piece counting method of the present invention includes: 101. Real-time acquisition of cargo weight data from the sorting line and forklift ticket piece data from the forklift vehicle terminal. The forklift ticket piece data includes sub-order number, scanning time and operator ID. The cargo weight data and forklift ticket piece data are integrated from multiple dimensions to obtain a complete piece transaction record. In this embodiment, the system collects the weight data of each item in real time through high-precision dynamic weight sensors on the sorting line and records the timestamp. Simultaneously, the scanning device (such as a barcode scanner or RFID reader) on the forklift terminal scans the item order during the sorting operation, automatically obtaining the item order number, scanning time, and operator ID, etc., as forklift ticket piecework data. The system performs preliminary cleaning on these two types of data, removing duplicate records (based on item order number and timestamp deduplication), and filling in missing values ​​(such as using historical averages to fill in missing weights and using interpolation to complete timestamps). Subsequently, the system associates the item weight data and forklift ticket piecework data of the same transaction using timestamp alignment technology, allowing for small time deviations (such as ±5 seconds). Finally, the associated data is aggregated to generate a complete piecework transaction record containing fields such as item order number, item weight, scanning time, and operator ID, which is stored in a distributed database for subsequent processing.

[0025] 102. Use a preset anomaly detection model to identify anomalies in piecework transaction records. When anomaly data is identified, correct the anomaly data. In this embodiment, the system uses an anomaly detection model built with the isolated forest algorithm to perform multi-dimensional anomaly identification on piecework transaction records. This model is trained on historical data and can quickly identify anomalies such as weight anomalies (e.g., weight values ​​of zero or exceeding a reasonable range), scan time conflicts (e.g., timestamps in reverse order or with excessively long intervals), and missing operator IDs. When a weight anomaly is identified, the system uses a sliding window averaging method to perform smooth correction based on the distribution of historical weight data. For scan time conflicts, the system performs timestamp correction based on the scan time series (e.g., linear interpolation or logical time adjustment). For missing operator IDs, the system associates the scan time with the employee shift table and automatically completes the operator ID (e.g., assigning the most recent operator based on the shift table). The corrected data is then rewritten into the piecework transaction records to ensure data quality.

[0026] 103. Obtain basic employee information and construct an employee sorting piece-rate statistics table based on the basic employee information; In this embodiment, the system obtains basic employee information through the API interface of the enterprise human resources system, including employee ID, name, position (such as sorter, administrator), shift (such as morning shift, evening shift), and piece rate coefficient (such as weights set based on position and experience). Based on this information, the system initializes and constructs an employee sorting piece rate statistics table. The table structure includes fields such as employee ID, total number of sorted goods, total weight, piece rate wage, and efficiency score. This statistics table is persistently stored using an SQL database (such as MySQL), and indexes are created on the employee ID and shift fields to support efficient querying and real-time updates.

[0027] 104. Calculate piecework transaction records based on preset piecework rules to obtain calculation information, and update the employee sorting piecework statistics table according to the calculation information; In this embodiment, the system loads the currently preset piece-rate rules from the configuration library, including weight-based tiered pricing (e.g., 1 yuan per piece for 0-10kg, 1.5 yuan per piece for 10-20kg), cumulative pricing by number of pieces (e.g., 0.1 yuan increase per piece for every 100 pieces), time-weighted pricing (e.g., a peak-hour pricing coefficient of 1.2), and quality reward and penalty rules (e.g., a reward for sorting error rate below 1%, and a deduction for error rate above 5%). The system combines the weight distribution of goods and the number of sub-order numbers in the piece-rate transaction records to calculate the base piece-rate wage for each operator: first, the basic cost is calculated based on the weight tier; then, the unit price is adjusted according to the cumulative number of pieces and time-weighted pricing; finally, the quality reward and penalty rules are applied. The accounting information includes the total number of goods sorted, total weight, piece-rate wage, and efficiency score (calculated based on sorting speed and accuracy) for each operator. The system updates the employee sorting piece-rate statistics table in real time based on the accounting information to ensure data accuracy and timeliness.

[0028] 105. Upload the employee sorting piecework statistics table to the blockchain and dynamically adjust the piecework rules based on sorting efficiency, sorting cost and employee satisfaction. In this embodiment, the system uses the SHA-256 hash algorithm to calculate a digital fingerprint for the employee sorting piecework statistics table, generating a unique data identifier. Subsequently, the data identifier and statistics table data are uploaded to a blockchain network (such as an Ethereum private chain or Hyperledger Fabric), and the deployed smart contract automatically verifies the data integrity to ensure that the records cannot be tampered with. At the same time, the system collects sorting efficiency indicators (such as average sorting time, number of pieces sorted per hour), sorting cost data (such as unit sorting cost), and employee satisfaction survey results (collected through mobile questionnaires). These data are input into a preset dynamic piecework rule optimization model. This model analyzes the balance between efficiency, cost, and satisfaction based on linear regression and multi-objective optimization algorithms (such as NSGA-II), and outputs rule adjustment parameters (such as gradient threshold, billing unit price, weighting coefficient). The system dynamically adjusts the piecework rules according to the parameters and updates the configuration file to optimize overall sorting performance and employee incentives.

[0029] In this embodiment of the invention, by integrating sorting line weight data and forklift ticket piecework data in real time, a complete and reliable piecework transaction record is constructed. An anomaly detection model is used to automatically identify and correct data anomalies, ensuring the accuracy and reliability of the original data. Then, based on preset piecework rules, the cleaned data is automatically calculated, and the employee piecework statistics table is dynamically updated. Finally, blockchain technology is used to ensure the immutability and traceability of the statistical results. The piecework rules are dynamically optimized based on multi-dimensional indicators such as efficiency, cost, and satisfaction. This achieves full-process automation and intelligence from data collection, processing, and calculation to rule optimization, significantly improving the efficiency, accuracy, and fairness of piecework calculation, and providing reliable data support for the refined cost management and scientific decision-making of logistics enterprises.

[0030] Please see Figure 2 The second embodiment of the logistics cargo sorting and piece counting method in this invention includes: 201. Collect cargo weight data in real time through weight sensors on the sorting line; In this embodiment, high-precision dynamic weight sensors (such as strain gauge or piezoelectric sensors) are deployed on the sorting line. When goods pass through the conveyor belt, the sensors measure the weight of each item in real time and transmit the weight data and timestamp to the central processing system via Internet of Things protocols (such as MQTT). The system performs preliminary verification of the data and eliminates noise caused by equipment vibration.

[0031] 202. Obtain forklift ticket piece data through the scanning device of the forklift vehicle terminal. The forklift ticket piece data includes the sub-ticket number, scanning time and operator ID. In this embodiment, the forklift is equipped with an on-board terminal (such as an industrial PDA or tablet computer) and a barcode scanning device. When the operator is sorting goods, the operator scans the sub-order barcode. The terminal automatically records the sub-order number and scanning time, and associates the operator ID through the operator login session. The data is uploaded to the server in real time via Wi-Fi or 5G network.

[0032] 203. Remove duplicate data from the cargo weight data and forklift ticket piecework data respectively, and fill in missing values ​​for the cargo weight data and forklift ticket piecework data respectively. In this embodiment, the system performs deduplication on the cargo weight data and forklift ticket piece count data based on the sub-order number and timestamp, deleting completely duplicate records. For missing weight data, the system fills it in according to the historical average weight of similar goods. For missing scan time, linear interpolation is performed using the timestamps of the previous and next records. For missing operator IDs, they are temporarily marked as pending processing and will be completed in subsequent steps.

[0033] 204. Use timestamp alignment technology to associate the weight data of goods in the same transaction with the piece count data of forklift tickets; In this embodiment, the system uses a timestamp alignment algorithm to match the recording time of cargo weight data with the scanning time of forklift ticket piece count data, allowing a reasonable time deviation (such as ±5 seconds). By using the sub-order number as a key field, the weight data and piece count data of the same cargo are associated to form a preliminary transaction record.

[0034] 205. Aggregate the associated cargo weight data with the forklift ticket piecework data to obtain a complete piecework transaction record; In this embodiment, the system aggregates the associated data by transaction. Each transaction record contains complete fields such as sub-order number, goods weight, scanning time, and operator ID. The aggregated piece-rate transaction records are stored in a NoSQL database (such as MongoDB) to support high-concurrency read and write operations and complex queries.

[0035] 206. Construct an anomaly detection model using the Isolation Forest algorithm; In this embodiment, the system uses the isolated forest algorithm to train an anomaly detection model on historical piecework transaction records. This algorithm identifies outliers by randomly dividing the feature space and is suitable for high-dimensional data. After training, an online detection module is deployed to monitor new input data in real time.

[0036] 207. Using an anomaly detection model, multi-dimensional anomaly identification was performed to identify weight anomalies, scan time conflicts, and missing operator IDs in piecework transaction records, and the identification results were obtained. In this embodiment, the anomaly detection model performs multi-dimensional scanning on the real-time incoming piecework transaction records to identify weight anomalies, scanning time conflicts (such as timestamps that are not monotonically increasing), and missing operator IDs. The model outputs the anomaly type, location, and confidence level, generating structured recognition results.

[0037] 208. When the identification result is an abnormal weight, the abnormal data is smoothed and corrected according to the distribution of historical weight data. In this embodiment, for weight anomalies, the system queries the historical weight data distribution and uses the exponentially weighted moving average (EWMA) method to smooth and correct the outliers, so that the corrected weight values ​​conform to historical trends while preserving the dynamic characteristics of the data.

[0038] 209. When the identification result is a scan time conflict, the abnormal data is corrected by timestamp based on the scan time series; In this embodiment, for scan time conflicts, the system analyzes the scan time series and corrects the timestamps using time series interpolation methods (such as spline interpolation) or based on context logic (such as device clock synchronization) to ensure time sequence consistency and transaction continuity.

[0039] 210. When the identification result indicates that the operator ID is missing, the abnormal data should be correlated and completed based on the scanning time and shift schedule. In this embodiment, if an operator ID is missing, the system queries the employee shift table based on the scanning time to determine the operator on duty during that time period and automatically completes the operator ID. If there are multiple operators during the same time period, the nearest operation principle is adopted or a manual review process is triggered.

[0040] In this embodiment of the invention, weight sensors and forklift onboard terminals are used to accurately collect cargo weight and piecework operation data, respectively. After rigorous data deduplication, missing value filling, and timestamp alignment, effective association and aggregation of multi-source heterogeneous data are achieved, forming high-quality piecework transaction records. Furthermore, an anomaly detection model based on the isolated forest algorithm is used to accurately identify and specifically correct multi-dimensional anomalies in the records, such as weight anomalies, time conflicts, and missing operators (e.g., smoothing correction, timestamp correction, and association completion). This constructs a complete data cleaning and enhancement process, fundamentally improving the quality and consistency of input data and laying a solid data foundation for subsequent accurate accounting and statistical analysis.

[0041] Please see Figure 3 The third embodiment of the logistics cargo sorting and piece counting statistics method in this invention includes: 301. Obtain basic employee information from the enterprise's human resources system. Basic employee information includes employee ID, name, position, work group, and piece rate coefficient. In this embodiment, the system obtains basic employee information from the enterprise human resources system (such as SAP HR or Oracle HCM) through RESTful API or direct database connection. The information includes employee ID, name, position (such as sorter, quality inspector), work group (such as shift A, shift B) and piece rate coefficient (such as 1.0 for regular employees and 0.8 for temporary employees), and updates it regularly.

[0042] 302. Initialize and construct an employee sorting piece-rate statistics table based on the employee's basic information. The statistics table includes employee ID, total number of sorted goods, total weight, piece-rate wage, and efficiency score. In this embodiment, the system initializes the employee sorting piece-rate statistics table based on the employee's basic information, creates a record for each employee, and initially sets the total number of sorted goods, total weight, piece-rate wages, and efficiency score to zero. The table structure is designed as relational, supporting transaction operations and complex aggregation queries.

[0043] 303. Use SQL database operations to persist the employee sorting piecework statistics table and create an index to support fast querying and updating; In this embodiment, the system uses an SQL database (such as PostgreSQL) to store the employee sorting piecework statistics table, ensures data consistency through ACID transactions, and creates B-tree indexes on the employee ID, work group, and time fields to optimize query performance, especially in multi-condition filtering and join operations.

[0044] 304. Identify the currently preset piece-rate rules, which include weight-based tiered billing, cumulative billing by number of pieces, time-based weighted billing, and quality reward and penalty rules; In this embodiment, the system loads the current piecework rules from the rule configuration library (such as Redis or configuration files). The rules include weight gradients (such as different unit prices for 0-5kg, 5-10kg, etc.), piece count accumulation (such as tiered unit prices), time period weighting (such as night shift coefficient of 1.5), and quality rewards and penalties (such as deductions for errors and rewards for accuracy), and support dynamic loading and hot updates.

[0045] 305. Based on the piece-rate rules, and combined with the weight distribution of goods and the number of sub-order numbers in the piece-rate transaction records, calculate the base piece-rate wage for each operator to obtain the accounting information; In this embodiment, the system aggregates piece-rate transaction records by operator group, counts the weight distribution of goods and the number of sub-order numbers for each operator, and then applies piece-rate rules: first, the basic wage is calculated according to the weight gradient, then the unit price is adjusted according to the cumulative number of pieces, then weighted by time period, and finally rewards and penalties are applied in combination with quality data (such as error records). The accounting information includes the base wage, the adjusted wage and the efficiency score.

[0046] 306. Update the employee sorting piecework statistics table based on the accounting information; In this embodiment, the system updates the accounting information in batches to the employee sorting piece-rate statistics table. The updated fields include the total number of sorted goods, total weight, piece-rate wages, and efficiency scores. The update operation uses database transactions to ensure data atomicity and consistency, and at the same time triggers the audit log to record the change history.

[0047] In this embodiment of the invention, by integrating the enterprise human resources system, an employee statistics table containing key information such as job position, work group, and piece-rate coefficient is automatically acquired and constructed, realizing efficient linkage between employee information and piece-rate data. By parsing and executing preset composite piece-rate rules covering weight gradient, cumulative number of pieces, time period weighting, and quality rewards and penalties, piece-rate transaction records are automatically and meticulously calculated, accurately calculating the benchmark piece-rate wage and efficiency score for each operator, and updating the statistics table in real time. This process realizes the transformation of piece-rate calculation from manual to automatic, and from rough to precise, ensuring the accuracy and timeliness of salary calculation, while providing a quantitative basis for performance evaluation.

[0048] Please see Figure 4 The fourth embodiment of the logistics cargo sorting and piece counting method in this invention includes: 401. Use the SHA-256 hash algorithm to calculate the digital fingerprint of the employee sorting piecework statistics table and generate a unique data identifier; In this embodiment, the system serializes the employee sorting piecework statistics table into a byte stream, calculates its hash value using the SHA-256 hash algorithm, and generates a 64-bit hexadecimal digital fingerprint as a unique data identifier. This identifier is used for subsequent data integrity verification and anti-tampering checks.

[0049] 402. Upload the data identifiers and employee sorting piecework statistics to the blockchain, and verify the data integrity through a smart contract deployed on the blockchain; In this embodiment, the system uploads the data identifier and statistical table data to the distributed ledger through a blockchain client. The smart contract automatically compares the hash value of the uploaded data with the data identifier. If they match, the system confirms the data is complete and writes the transaction record to the blockchain to ensure that the data is traceable and tamper-proof.

[0050] 403. Collect sorting efficiency indicators, sorting cost data, and employee satisfaction survey results; In this embodiment, the system obtains sorting efficiency indicators (such as sorting speed and accuracy) from the monitoring platform, sorting cost data (such as labor and energy costs) from the financial system, and collects employee satisfaction survey results (such as piece-rate fairness and workload score) through a mobile application. The data is cleaned and standardized before being stored in the data warehouse.

[0051] 404. Input the sorting efficiency index, sorting cost data and employee satisfaction survey results into the preset dynamic piece-rate rule optimization model, and use the dynamic piece-rate rule optimization model to output the adjusted rule adjustment parameters based on linear regression and multi-objective optimization algorithms; In this embodiment, the dynamic piece-rate rule optimization model uses linear regression analysis to analyze the correlation between sorting efficiency, cost and satisfaction, and uses a multi-objective optimization algorithm (such as NSGA-II) to solve for the Pareto optimal solution, outputting rule adjustment parameters, such as weight gradient threshold, billing unit price, time period weighting coefficient, etc. The model is periodically retrained to adapt to data changes.

[0052] 405. Adjust the piece-rate rules according to the adjusted parameters; In this embodiment, the system adjusts parameters according to the rules output by the model and automatically updates the piece-rate rule configuration file (such as YAML or JSON format). For example, it adjusts the weight gradient boundary, modifies the billing unit price, or updates the time period weighting coefficient. The new rules take effect immediately and notify the relevant systems and users.

[0053] 406. Summarize the piece-rate statistics and rule adjustment records of all operators, and generate a sorting piece-rate analysis report based on the piece-rate statistics and rule adjustment records; In this embodiment, the system periodically summarizes all operators' piece-rate statistics (such as sorting volume, wages, and efficiency) and rule adjustment history, and uses a reporting engine (such as JasperReports) to generate a sorting piece-rate analysis report. The report includes performance trends, cost analysis, rule adjustment effects, and suggestions.

[0054] 407. Encrypt the sorting and piecework analysis report using the public key from the preset key pair to obtain an encrypted report; In this embodiment, the system uses an asymmetric encryption algorithm (such as RSA) to generate a key pair, uses the public key to encrypt the sorting and piece-counting analysis report, generates an encrypted report, distributes the public key to the system, and keeps the private key in the management terminal to ensure the confidentiality of report transmission and storage.

[0055] 408. Send the encrypted report and the private key from the key pair to the management terminal so that the management terminal can use the private key to decrypt the encrypted report; In this embodiment, the system distributes the encrypted report and private key to the management terminal (such as an administrator's computer or mobile device) through a secure channel (such as HTTPS or SFTP). The management terminal uses the private key to decrypt the encrypted report, restore the original report content, and allows the administrator to view and make decisions.

[0056] In this embodiment of the invention, by uploading the hash value and data of the employee sorting piecework statistics table to the blockchain, and using smart contracts to ensure the integrity and immutability of the data, a very high level of data credibility and audit traceability is established. By collecting multi-source feedback data such as efficiency, cost, and satisfaction, and inputting it into a dynamic piecework rule optimization model based on linear regression and multi-objective optimization algorithms, closed-loop optimization and adaptive adjustment of piecework rules are achieved, enabling the piecework strategy to continuously align with business objectives and employee incentives. Finally, by generating, encrypting, and securely transmitting a comprehensive analysis report, the timeliness, integrity, and confidentiality of management decision-making information are ensured, comprehensively improving management efficiency and the system's intelligence level.

[0057] The above describes the logistics cargo sorting and piece counting method in the embodiments of the present invention. The following describes the logistics cargo sorting and piece counting device in the embodiments of the present invention. Please refer to [link / reference]. Figure 5 One embodiment of the logistics cargo sorting and counting device of the present invention includes: The integration module 501 is used to acquire in real time the weight data of goods from the sorting line and the piece count data of forklift tickets from the forklift vehicle terminal. The piece count data of forklift tickets includes the sub-order number, scanning time and operator ID. The module integrates the weight data of goods and the piece count data of forklift tickets in multiple dimensions to obtain a complete piece count transaction record. The identification and correction module 502 is used to identify anomalies in piecework transaction records using a preset anomaly detection model, and correct the anomaly data when anomalies are identified. Get the construction module 503, which is used to obtain basic employee information and build an employee sorting piecework statistics table based on the basic employee information; The accounting update module 504 is used to calculate the piece-rate transaction records based on the preset piece-rate rules, obtain the accounting information, and update the employee sorting piece-rate statistics table according to the accounting information. The upload adjustment module 505 is used to upload the employee sorting piecework statistics table to the blockchain and dynamically adjust the piecework rules based on sorting efficiency, sorting cost and employee satisfaction.

[0058] In this embodiment, by acquiring real-time cargo weight data from the sorting line and forklift ticket piecework data from the forklift terminal, the cargo weight data and forklift ticket piecework data are integrated from multiple dimensions to obtain a complete piecework transaction record. Abnormal data in the piecework transaction record is corrected, an employee sorting piecework statistics table is constructed based on employee basic information, the piecework transaction record is calculated based on preset piecework rules, the employee sorting piecework statistics table is updated based on the calculation information, and the piecework rules are dynamically adjusted based on sorting efficiency, sorting cost, and employee satisfaction. This can significantly reduce manual calculation time and related labor costs, improve the accuracy and fairness of piecework calculation, and achieve systematic and precise control over logistics enterprise cost management.

[0059] Please see Figure 6 Another embodiment of the logistics cargo sorting and counting device of the present invention includes: The integration module 501 is used to acquire in real time the weight data of goods from the sorting line and the piece count data of forklift tickets from the forklift vehicle terminal. The piece count data of forklift tickets includes the sub-order number, scanning time and operator ID. The module integrates the weight data of goods and the piece count data of forklift tickets in multiple dimensions to obtain a complete piece count transaction record. The identification and correction module 502 is used to identify anomalies in piecework transaction records using a preset anomaly detection model, and correct the anomaly data when anomalies are identified. Get the construction module 503, which is used to obtain basic employee information and build an employee sorting piecework statistics table based on the basic employee information; The accounting update module 504 is used to calculate the piece-rate transaction records based on the preset piece-rate rules, obtain the accounting information, and update the employee sorting piece-rate statistics table according to the accounting information. The upload adjustment module 505 is used to upload the employee sorting piecework statistics table to the blockchain and dynamically adjust the piecework rules based on sorting efficiency, sorting cost and employee satisfaction. In this embodiment, the acquisition and integration module 501 includes: a first acquisition unit 5011, used to acquire cargo weight data in real time through a weight sensor on the sorting line; a first acquisition unit 5012, used to acquire forklift ticket piece data through a scanning device on the forklift vehicle terminal, the forklift ticket piece data including a sub-order number, scanning time, and operator ID; a removal and filling unit 5013, used to remove duplicate data from the cargo weight data and the forklift ticket piece data respectively, and fill in missing values ​​for the cargo weight data and the forklift ticket piece data respectively; an association unit 5014, used to associate the cargo weight data and the forklift ticket piece data of the same transaction through timestamp alignment technology; and an aggregation unit 5015, used to aggregate the associated cargo weight data and the forklift ticket piece data to obtain a complete piece transaction record.

[0060] In this embodiment, the identification and correction module 502 includes: a first construction unit 5021, used to construct an anomaly detection model using the isolated forest algorithm; a first identification unit 5022, used to perform multi-dimensional anomaly identification by using the anomaly detection model to identify weight anomalies, scan time conflicts, and missing operator IDs in piecework transaction records, and obtain identification results; a correction unit 5023, used to smooth and correct the abnormal data according to the historical weight data distribution when the identification result is a weight anomaly; a correction unit 5024, used to perform timestamp correction on the abnormal data according to the scan time sequence when the identification result is a scan time conflict; and a completion unit 5025, used to complete the abnormal data by associating it with the scan time and shift schedule when the identification result is a missing operator ID.

[0061] In this embodiment, the acquisition and construction module 503 includes: a second acquisition unit 5031, used to acquire basic employee information from the enterprise human resources system, the basic employee information including employee ID, name, position, work group and piece rate coefficient; a second construction unit 5032, used to initialize and construct an employee sorting piece rate statistics table based on the employee basic information, the statistics table including employee ID, total number of sorted goods, total weight, piece rate wage and efficiency score; and a storage and establishment unit 5033, used to persistently store the employee sorting piece rate statistics table using SQL database operations and establish an index to support fast query and update.

[0062] In this embodiment, the accounting update module 504 includes: a second identification unit 5041, used to identify the currently preset piece-rate rules, which include weight-based tiered billing, cumulative billing by number of pieces, time-based weighted billing, and quality reward and penalty rules; a calculation unit 5042, used to calculate the base piece-rate wage for each operator based on the piece-rate rules, combined with the weight distribution of goods and the number of sub-order numbers in the piece-rate transaction record, to obtain accounting information; and an update unit 5043, used to update the employee sorting piece-rate statistics table according to the accounting information.

[0063] In this embodiment, the upload adjustment module 505 includes: a calculation generation unit 5051, used to calculate the digital fingerprint of the employee sorting piecework statistics table using the SHA-256 hash algorithm to generate a unique data identifier; an upload verification unit 5052, used to upload the data identifier and the employee sorting piecework statistics table to the blockchain, and verify the data integrity through a smart contract deployed on the blockchain; a second collection unit 5053, used to collect sorting efficiency indicators, sorting cost data, and employee satisfaction survey results; an input / output unit 5054, used to input the sorting efficiency indicators, sorting cost data, and employee satisfaction survey results into a preset dynamic piecework rule optimization model, and use the dynamic piecework rule optimization model to output adjusted rule adjustment parameters based on linear regression and multi-objective optimization algorithms; and an adjustment unit 5055, used to adjust the piecework rules according to the rule adjustment parameters.

[0064] In this embodiment, the system further includes: a summary generation module 506, used to summarize the piece-rate statistics and rule adjustment records of all operators, and generate a sorting piece-rate analysis report based on the piece-rate statistics and rule adjustment records; an encryption module 507, used to encrypt the sorting piece-rate analysis report using the public key in a preset key pair to obtain an encrypted report; and a sending module 508, used to send the encrypted report and the private key in the key pair to the management terminal so that the management terminal can decrypt the encrypted report using the private key.

[0065] above Figure 5 and Figure 6 The logistics goods sorting and counting device in this embodiment of the invention is described in detail from the perspective of modular functional entities. The logistics goods sorting and counting device in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0066] Figure 7This is a schematic diagram of the structure of a logistics goods sorting and counting device 600 provided in an embodiment of the present invention. The logistics goods sorting and counting device 600 can vary considerably due to different configurations or performance. It may include one or more central processing units (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the logistics goods sorting and counting device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 630 on the logistics goods sorting and counting device 600 to implement the steps of the logistics goods sorting and counting method provided in the above-described method embodiments.

[0067] The logistics goods sorting and counting equipment 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 7 The illustrated structure of the logistics goods sorting and piece counting equipment does not constitute a limitation on logistics goods sorting and piece counting equipment. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0068] 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 a logistics goods sorting and counting method.

[0069] 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.

[0070] 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.

[0071] 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 statistical analysis of sorting and counting of goods in logistics, characterized in that, include: The system acquires real-time cargo weight data from the sorting line and forklift ticket piece count data from the forklift vehicle terminal. The forklift ticket piece count data includes the sub-order number, scanning time, and operator ID. The system also integrates the cargo weight data and the forklift ticket piece count data from multiple dimensions to obtain a complete piece count transaction record. The piecework transaction records are anomaly identified using a preset anomaly detection model. When anomaly data is detected, the anomaly data is corrected. Obtain basic employee information and construct an employee sorting piece-rate statistics table based on the basic employee information; The piece-rate transaction records are calculated based on the preset piece-rate rules to obtain calculation information, and the employee sorting piece-rate statistics table is updated according to the calculation information. The employee sorting piecework statistics table is uploaded to the blockchain, and the piecework rules are dynamically adjusted based on sorting efficiency, sorting cost, and employee satisfaction.

2. The method for statistical analysis of sorting and counting of logistics goods according to claim 1, characterized in that, The system acquires real-time cargo weight data from the sorting line and forklift ticket piecework data from the forklift vehicle terminal. The forklift ticket piecework data includes a sub-order number, scan time, and operator ID. The cargo weight data and forklift ticket piecework data are then integrated from multiple dimensions to obtain a complete piecework transaction record, including: The weight data of goods is collected in real time by weight sensors on the sorting line; Forklift ticket piece data is obtained through the scanning device of the forklift vehicle terminal. The forklift ticket piece data includes the sub-ticket number, scanning time and operator ID. Remove duplicate data from the cargo weight data and the forklift ticket piece count data respectively, and fill in missing values ​​for the cargo weight data and the forklift ticket piece count data respectively; The cargo weight data and forklift ticket piece count data of the same transaction are associated using timestamp alignment technology; The associated cargo weight data and the forklift ticket piece data are aggregated to obtain a complete piece transaction record.

3. The method for statistical analysis of sorting and counting logistics goods according to claim 1, characterized in that, The step involves using a preset anomaly detection model to identify anomalies in the piecework transaction records. When anomaly data is detected, the anomaly data is corrected, including: An anomaly detection model was built using the isolated forest algorithm. The anomaly detection model is used to identify weight anomalies, scan time conflicts, and missing operator IDs in the piecework transaction records to perform multi-dimensional anomaly identification and obtain the identification results. When the identification result is an abnormal weight, the abnormal data is smoothed and corrected according to the distribution of historical weight data. When the identification result is a scan time conflict, the abnormal data is timestamped according to the scan time sequence. When the identification result indicates that the operator ID is missing, the abnormal data is supplemented by associating it with the scan time and shift schedule.

4. The method for statistical analysis of sorting and counting of logistics goods according to claim 1, characterized in that, The process of obtaining basic employee information and constructing an employee sorting and piece-rate statistics table based on that information includes: Obtain basic employee information from the enterprise's human resources system. The basic employee information includes employee ID, name, position, work group, and piece rate coefficient. Based on the employee basic information, an employee sorting piece-rate statistics table is initialized and constructed. The statistics table includes employee ID, total number of sorted goods, total weight, piece-rate wage, and efficiency score. The employee sorting piecework statistics table is persisted using SQL database operations, and an index is created to support fast queries and updates.

5. The method for statistical analysis of sorting and counting of logistics goods according to claim 1, characterized in that, The step of calculating the piecework transaction records based on preset piecework rules to obtain calculation information, and updating the employee sorting piecework statistics table according to the calculation information, includes: Identify the currently preset piece-rate rules, which include weight-based tiered billing, cumulative billing by number of pieces, time-based weighted billing, and quality reward and penalty rules; Based on the piece-rate rules, the base piece-rate wage for each operator is calculated by combining the weight distribution of goods and the number of sub-order numbers in the piece-rate transaction record, thus obtaining the accounting information; The employee sorting piecework statistics table is updated based on the accounting information.

6. The method for statistical analysis of sorting and counting of logistics goods according to claim 1, characterized in that, The step of uploading the employee sorting piecework statistics table to the blockchain and dynamically adjusting the piecework rules based on sorting efficiency, sorting cost, and employee satisfaction includes: The SHA-256 hash algorithm is used to calculate the digital fingerprint of the employee sorting piecework statistics table to generate a unique data identifier. The data identifier and the employee sorting piecework statistics table are uploaded to the blockchain, and the data integrity is verified through a smart contract deployed on the blockchain. Collect sorting efficiency indicators, sorting cost data, and employee satisfaction survey results; The sorting efficiency index, the sorting cost data, and the employee satisfaction survey results are input into a preset dynamic piece-rate rule optimization model. The dynamic piece-rate rule optimization model is then used to output adjusted rule adjustment parameters based on linear regression and multi-objective optimization algorithms. The piece-counting rules are adjusted according to the adjusted parameters based on the rules.

7. The method for statistical analysis of sorting and counting of logistics goods according to claim 1, characterized in that, After uploading the employee sorting piecework statistics table to the blockchain and dynamically adjusting the piecework rules based on sorting efficiency, sorting cost, and employee satisfaction, the process also includes: Summarize the piece-rate statistics and rule adjustment records of all operators, and generate a sorting piece-rate analysis report based on the piece-rate statistics and rule adjustment records; The sorting and piece counting analysis report is encrypted using the public key from a preset key pair to obtain an encrypted report; The encrypted report and the private key in the key pair are sent to the management terminal so that the management terminal can use the private key to decrypt the encrypted report.

8. A logistics goods sorting and counting device, characterized in that, include: The acquisition and integration module is used to acquire in real time the weight data of goods from the sorting line and the piece count data of forklift tickets from the forklift vehicle terminal. The piece count data of forklift tickets includes the sub-order number, scanning time and operator ID. The module integrates the weight data of goods and the piece count data of forklift tickets in multiple dimensions to obtain a complete piece count transaction record. The identification and correction module is used to identify anomalies in the piecework transaction records using a preset anomaly detection model, and to correct the anomaly data when anomalies are identified. The acquisition module is used to acquire basic employee information and construct an employee sorting piece-rate statistics table based on the basic employee information. The accounting update module is used to calculate the piece-rate transaction records based on preset piece-rate rules to obtain accounting information, and update the employee sorting piece-rate statistics table according to the accounting information. The upload and adjustment module is used to upload the employee sorting piecework statistics table to the blockchain and dynamically adjust the piecework rules based on sorting efficiency, sorting cost, and employee satisfaction.

9. A logistics cargo sorting and piece counting statistical device, characterized in that, The logistics cargo sorting and counting equipment 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 logistics goods sorting and counting device to perform the steps of the logistics goods sorting and counting method 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 logistics cargo sorting and counting statistics method as described in any one of claims 1-7.

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