Logistics loading and unloading operation early warning method, device and equipment and storage medium

Abstract

This invention relates to the field of intelligent logistics technology, and in particular to a method, device, equipment, and storage medium for early warning of loading and unloading operations. The method first acquires actual operation data and preset operation type matching rules, then performs consistency comparison and verification processing on the actual operation data based on the operation type matching rules to obtain anomaly warning records. Next, the anomaly warning records are input into a pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data. The cost difference data is then subjected to attribution analysis and statistical processing to obtain attribution statistical data. Finally, incentive and disposal suggestions are generated based on the attribution statistical data. This invention aims to solve the problems of wage mismatch and difficulty in tracing and controlling errors caused by loading and unloading violations, realize automatic verification and early warning of operational anomalies and intelligent cost accounting, and improve operational collaboration efficiency and service quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics technology, and in particular to a method, device, equipment and storage medium for early warning of logistics loading and unloading operations. Background Technology

[0002] In the express delivery industry's operational chain, the handover of goods between outlets and distribution centers is a key link affecting labor cost control. The industry generally divides the goods at outlets into four different loading and unloading operation types based on business attributes. Different types correspond to exclusive piece-rate pricing standards, and distribution centers calculate the salaries of front-line loading and unloading personnel according to the pre-agreed operation types.

[0003] Currently, most mainstream logistics management systems in use in the industry can only complete the labeling of work types at the document level, lacking the ability to dynamically verify the work execution process. During the actual loading and unloading operations, operators often do not follow the pre-set work specifications of the network. When work behavior deviates from the established type, the system still uses the original pricing standard to calculate wages, resulting in mismatched piece-rate prices and driving up unnecessary labor costs for distribution centers. Existing traditional technologies also have shortcomings such as delayed identification of violations, difficulty in quickly attributing errors after pricing errors occur, and inability to automatically correct erroneous wage data. Enterprises can only rely on manual spot checks for post-event verification, which is inefficient and has a high rate of missed detections, and cannot provide real-time early warnings for abnormal operations. These existing defects not only make it difficult to ensure that on-site loading and unloading operations comply with standardized specifications, but also continue to hinder logistics companies from achieving refined management of labor costs in the distribution process. The industry urgently needs an intelligent early warning solution for abnormal loading and unloading operations to solve the existing technical problems. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the present invention aims to provide a logistics loading and unloading operation early warning method, device, equipment and storage medium, which aims to solve the problems of wage mismatch and difficulty in tracing and controlling errors caused by loading and unloading violations, realize automatic verification and early warning of operation abnormalities and intelligent cost accounting, and improve operational collaboration efficiency and service quality.

[0005] The first aspect of this invention provides a method for early warning of logistics loading and unloading operations, comprising: acquiring actual operation data and preset operation type matching rules; performing consistency comparison and verification processing on the actual operation data based on the operation type matching rules to obtain anomaly warning records; inputting the anomaly warning records into a pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data; performing attribution analysis and statistical processing on the cost difference data to obtain attribution statistical data; and generating incentive disposal suggestions based on the attribution statistical data.

[0006] Optionally, in a first implementation of the first aspect of the present invention, before obtaining the actual operation data, the method further includes: obtaining the unique identifier of the goods and the worker's sweeping information; and using a data association tool to associate and bind the unique identifier and the worker's sweeping information to obtain the actual operation data.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of performing consistency comparison and verification processing on the actual operation data based on the operation type matching rule to obtain an anomaly warning record includes: obtaining the worker's shopping information and the unique identifier; determining the actual operation type based on the worker's shopping information; searching in the operation type matching rule based on the unique identifier to obtain a preset operation type corresponding to the unique identifier; comparing the actual operation type with the preset operation type; and obtaining the anomaly warning record when the actual operation type is inconsistent with the preset operation type.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the cost difference calculation model includes a warning feature extraction module, a wage recalculation module, and a cost difference calculation module connected in sequence; the step of inputting the abnormal warning record into the pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data includes: performing feature extraction processing on the abnormal warning record based on the warning feature extraction module to obtain key feature data; performing pricing recalculation processing on the key feature data based on the wage recalculation module to obtain pricing comparison data; and performing cost difference calculation processing on the pricing comparison data based on the cost difference calculation module to obtain the cost difference data.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the wage recalculation module includes a piece-rate wage restoration submodule, a compliant piece-rate wage calculation submodule, and an integration submodule. The integration submodule is connected to the piece-rate wage restoration submodule and the compliant piece-rate wage calculation submodule, respectively. The step of performing pricing recalculation processing on the key feature data based on the wage recalculation module to obtain pricing comparison data includes: performing abnormal operation pricing restoration processing on the key feature data based on the piece-rate wage restoration submodule to obtain the original piece-rate wage data; performing compliant wage calculation processing on the key feature data based on the compliant piece-rate wage calculation submodule to obtain compliant piece-rate wage data; and performing association integration processing on the original piece-rate wage data and the compliant piece-rate wage data based on the integration submodule to obtain the pricing comparison data.

[0010] Optionally, in the fifth implementation of the first aspect of the present invention, the step of performing attribution analysis and statistical processing on the cost difference data to obtain attribution statistics includes: obtaining preset statistical dimension parameters; performing ETL extraction, transformation, and loading processing on the cost difference data based on the statistical dimension parameters to obtain a multi-dimensional data cube; using a dual-mode calculation tool to perform indicator calculation on the multi-dimensional data cube to obtain full statistical indicator data; and using a Gini coefficient ranking algorithm to sort the full statistical indicator data into abnormally high-incidence subjects to obtain the attribution statistics.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the step of generating incentive treatment recommendations based on the attribution statistics includes: using a Pareto analysis algorithm to screen for high-incidence subjects of the attribution statistics to obtain target operating subjects and the anomalous rate corresponding to the target operating subjects; and generating the incentive treatment recommendations based on the target operating subjects and the anomalous rate.

[0012] A second aspect of the present invention provides a logistics loading and unloading operation early warning device, comprising: a data acquisition module for acquiring actual operation data and preset operation type matching rules; a comparison and verification module for performing consistency comparison and verification processing on the actual operation data based on the operation type matching rules to obtain anomaly early warning records; a cost accounting module for inputting the anomaly early warning records into a pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data; an attribution analysis module for performing attribution analysis and statistical processing on the cost difference data to obtain attribution statistical data; and a suggestion generation module for generating incentive disposal suggestions based on the attribution statistical data.

[0013] A third aspect of the present invention provides a logistics loading and unloading operation early warning device, the logistics loading and unloading operation early warning 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 loading and unloading operation early warning device to execute each step of the logistics loading and unloading operation early warning method described above.

[0014] 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 loading and unloading operation early warning method described in any of the preceding claims.

[0015] In the technical solution of this invention, actual operation data and preset operation type matching rules are first obtained. Then, the actual operation data is subjected to consistency comparison and verification processing based on the operation type matching rules to obtain anomaly warning records. Next, the anomaly warning records are input into a pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data. The cost difference data is subjected to attribution analysis and statistical processing to obtain attribution statistical data. Finally, incentive disposal suggestions are generated based on the attribution statistical data. This invention aims to solve the problems of wage mismatch and difficulty in tracing and controlling errors caused by loading and unloading violations, realize automatic verification and early warning of operation anomalies and intelligent cost accounting, and improve operational collaboration efficiency and service quality. Attached Figure Description

[0016] Figure 1 This is a first flowchart of a logistics loading and unloading operation early warning method provided in an embodiment of the present invention; Figure 2 This is a second flowchart of the logistics loading and unloading operation early warning method provided in an embodiment of the present invention; Figure 3 This is a third flowchart of the logistics loading and unloading operation early warning method provided in the embodiments of the present invention; Figure 4 This is a fourth flowchart of the logistics loading and unloading operation early warning method provided in this embodiment of the invention; Figure 5 The fifth flowchart of the logistics loading and unloading operation early warning method provided in the embodiments of the present invention; Figure 6 The sixth flowchart of the logistics loading and unloading operation early warning method provided in the embodiments of the present invention; Figure 7 The seventh flowchart of the logistics loading and unloading operation early warning method provided in the embodiments of the present invention; Figure 8 This is a schematic diagram of the structure of the logistics loading and unloading operation early warning device provided in an embodiment of the present invention; Figure 9 A schematic diagram of the structure of the logistics loading and unloading operation early warning device provided in an embodiment of the present invention. Detailed Implementation

[0017] This invention provides a method, apparatus, device, and storage medium for early warning of logistics loading and unloading operations. 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 data can be interchanged 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 includes 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.

[0018] 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 loading and unloading operation early warning method in this invention includes: 101. Obtain actual operation data and preset operation type matching rules; In this embodiment, the operation type matching rules are set based on the pre-established basic process. Four operation attributes are pre-defined for each branch: self-loading, self-unloading, self-loading and self-unloading, and non-self-loading and self-unloading. Branch codes, distribution center codes, and corresponding operation attributes are bound and stored to establish a mapping benchmark. Then, according to a unified data standard, all data is cleaned, deduplicated, and formatted to finally form the operation type matching rules. Actual operation data is standardized operation data formed by binding the unique identifier of the goods and the worker's scanning information through a data association tool. This type of data is obtained by deploying intelligent scanning equipment at loading and unloading ports. The data acquisition components of the equipment automatically collect the unique identifier corresponding to each piece of goods. When workers perform loading and unloading operations on-site, they generate corresponding scanning information by scanning the code with their terminals. The two types of raw information are then linked and bound by the data association tool to obtain the actual operation data. Simultaneously, a transmission link built using an event-driven architecture and message queues ensures reliable, second-level flow of scanning information, guaranteeing that the raw information is delivered completely and orderly to the data processing stage. By simultaneously acquiring actual operational data and matching rules for operational types, subsequent compliance verification of operations can be carried out based on unified judgment criteria and real on-site operation records. The entire data preparation and acquisition method relies on automated hardware collection to reduce data omissions caused by manual intervention. The standardized rule system unifies the judgment criteria for operations, and the real-time data transmission mechanism ensures the efficiency of on-site operation data synchronization. This not only improves the integrity and standardization of the two types of target data, but also lays a solid and accurate data foundation for subsequent anomaly verification, cost accounting, and multi-dimensional attribution statistics. It reduces payroll calculation errors caused by information mismatch from the data source and helps to implement refined management of human resource costs in the distribution process.

[0019] 102. Based on the operation type matching rules, perform consistency comparison and verification on the actual operation data to obtain anomaly warning records; In this embodiment, the consistency comparison and verification process relies on the worker's scanning information and the unique identifier of the goods, which are decomposed from the actual operation data, to be implemented step by step. First, the on-site operation behavior is analyzed based on the worker's scanning information to determine the actual operation type. Then, the operation type matching rules are retrieved based on the unique identifier of the goods to extract the preset operation type bound to the unique identifier. After both types of parameters are obtained, a two-way verification and comparison are completed with reference to the logical truth table. The verification executes standardized judgment logic. If the preset operation type is self-loading but the actual operation deviates from the self-loading category, or the preset operation type is self-unloading but the actual operation deviates from the self-unloading category, or if the goods have been registered on-site but there is a lack of loading and unloading scanning records within the limited period, the actual operation type can be determined. When deviations occur from the preset operation type, an anomaly warning record is generated. This anomaly warning record is a standardized data carrier that collects all business elements related to operational anomalies. It fully retains information related to the network points, operators, waybills, and anomaly categories corresponding to operation mismatches or missing operations. The entire verification process relies on a rule matching engine to achieve fully automated calculation and identification, replacing the traditional manual spot check verification mode. It unifies the judgment benchmark across the entire chain, effectively avoiding omissions and judgment biases caused by manual verification. The generated anomaly warning record can directly deliver accurate data sources to the downstream cost accounting process, supporting automatic recalculation of salary pricing and multi-dimensional attribution of errors, and timely intercepting the inflated labor costs caused by illegal pricing from the front end of control.

[0020] 103. Input the abnormal early warning record into the pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data; In this embodiment, the cost difference calculation model relies on a multi-input fully connected neural network to build the overall training architecture. The network is divided into an input layer, a feature extraction hidden layer, a sub-module mapping hidden layer, and a calculation output layer. The input layer imports historical anomaly warning records, historical pricing parameters of each branch, and manually verified cost calculation results as training samples in batches. The feature extraction hidden layer completes the standardization of sample data and the selection of effective fields. The sub-module mapping hidden layer is divided into three groups of independent neuron units, which correspond to the business operation logic of the piece-rate wage restoration sub-module, the compliant piece-rate wage calculation sub-module, and the integration sub-module, respectively. The calculation output layer completes parameter convergence by benchmarking the standard cost difference after manual calculation. The training phase adopts a supervised learning mode, using the cost results generated by offline manual verification as the ground truth label, and iteratively optimizing the network weight parameters to continuously reduce the error between the model output data and the label data until the model loss function converges and the pre-training is completed.

[0021] In this embodiment, after the abnormal warning record is sent to the pre-trained cost difference calculation model, the cost accounting process is officially started. The warning feature extraction module first completes feature extraction processing from the abnormal warning record to generate key feature data. The key feature data is then sent to the wage recalculation module for pricing recalculation processing. The piece-rate wage restoration submodule uses the key feature data to restore the abnormal operation pricing to obtain the original piece-rate wage data. The compliant piece-rate wage calculation submodule matches the network's preset operation specifications with the compliant piece-rate unit price to calculate the compliant piece-rate wage data. The original piece-rate wage data and the compliant piece-rate wage data are associated and integrated by the integration submodule to generate pricing comparison data. After the pricing comparison data flows into the cost difference calculation module, the numerical solution is completed according to the calculation rules of deducting the compliant operation unit price from the erroneous operation unit price, and finally the cost difference data is obtained. The cost difference data is used to quantify the change in labor costs caused by erroneous operations and intuitively reflect the increase or decrease in costs caused by pricing mismatch. The entire automated accounting chain relies on a modular neural network model to replace traditional manual accounting operations, avoiding the inefficiency and calculation errors caused by manual statistics. It accurately quantifies the cost loss corresponding to a single abnormal document, and the output cost difference data can provide quantitative data basis for subsequent multi-dimensional attribution statistics and control measures, helping enterprises to accurately locate the source of cost loss.

[0022] 104. Perform attribution analysis and statistical processing on the cost variance data to obtain attribution statistics; In this embodiment, the attribution analysis statistical processing first retrieves preset statistical dimension parameters, covering the operation time dimensions of distribution centers, outlets, operation teams, and hierarchical levels. Relying on the ETL extraction, transformation, and loading process, the cost difference data is standardized and integrated to construct a multi-dimensional data cube. Then, a dual-mode computing tool with both batch processing and real-time stream processing capabilities is used to perform quantitative indicator accounting on the multi-dimensional data cube. The accounting process is simultaneously implemented with shift dynamic monitoring and periodic report statistics, generating full statistical indicator data including the number of operation errors, error rate, and total cost difference. Then, combined with Pareto analysis logic and Gini coefficient ranking algorithm, the full statistical indicator data is used to perform hierarchical sorting and screening of the subjects with high incidence of anomalies, and finally, attribution statistics data are obtained. Attribution statistics data is a structured dataset that carries multi-dimensional statistical results and information on subjects with high incidence of errors, and completely retains the frequency of operation errors, error ratio, and corresponding cost loss details of each subject in different statistical periods. The entire automated statistical process relies on a hierarchical computing architecture to take into account both real-time data monitoring and offline aggregation needs. It eliminates the inefficiency and statistical bias caused by manual aggregation and statistics. It achieves accurate location of abnormal cost sources through multi-dimensional and three-dimensional statistics. It can directly drive the automated generation and targeted push of subsequent processing documents through attribution statistics. It standardizes on-site loading and unloading operations through supporting training and incentive measures, implements closed-loop management of the entire chain, and steadily reduces ineffective labor cost expenditures caused by violations.

[0023] 105. Generate incentive treatment recommendations based on the attribution statistics.

[0024] In this embodiment, Pareto analysis algorithm is used to screen entities with high anomaly rates based on attribution statistics. Target operational entities are identified from the full statistical data, and their corresponding anomaly rates are extracted. These rates are compared with pre-configured control thresholds to identify entities whose anomaly rates exceed the thresholds. Incentive recommendations are then generated based on these thresholds. These recommendations are standardized business documents for implementing operational rectification and personnel management. The documents consist of a secondary training task sheet and a negative incentive recommendation sheet. Once generated, these documents are pushed to the mobile application or web-based workbench used by distribution managers via a workflow engine. This entire mechanism for generating response plans based on quantitative statistical data abandons the traditional, crude management model that relies on managers' subjective experience to define rectification targets. It accurately identifies frequently error-prone branches and teams based on objective statistical data, enabling targeted skills training and performance evaluation. This establishes a closed-loop management system encompassing early warning, statistics, attribution, and feedback, continuously optimizing on-site loading and unloading operation standards and reducing the waste of human resources caused by mismatched pricing at the business source.

[0025] Please see Figure 2 In this embodiment of the invention, before obtaining the actual operation data, the following steps are also included: 201. Obtain the unique identifier of the goods and the worker's sweeping information; 202. Use a data association tool to associate and bind the unique identifier and the worker's shopping information to obtain the actual operation data.

[0026] In this embodiment, the unique identifier of the goods is specifically the waybill number and sub-item number. This type of identifier is collected by RFID reading and writing devices and high-speed barcode scanners deployed at the loading and unloading ports. After the device triggers the scanning action, it automatically captures the identifier information and generates the original operation log using the data acquisition SDK built into the terminal. The original data is uniformly stored in a relational database configured with a hash index. The worker's scanning information includes business fields such as the corresponding network point, distribution entity, operation team, operator, operation time, and loading and unloading operation type. The data is generated by the on-site loading and unloading personnel using handheld terminals to scan the code. Each scanning action is encapsulated as an immutable atomic event and written to a message queue. The real-time data pipeline built on the message queue completes the data transmission with a latency of seconds, and finally summarizes to form complete worker scanning information. The data association tool is a database association engine built on relational databases and hash indexes. The association binding process uses the unique identifier of the goods as the primary key. It retrieves the collected unique identifiers of the goods and worker sweeping information from the storage medium, and uses a pre-built mapping database of waybills and branch operation types to match and concatenate the fields of the two types of data. Then, according to established data specifications, the concatenated dataset undergoes cleaning, deduplication, field normalization, and formatting. After this full-process standardization, the actual operation data is obtained. The entire chain relies on automated hardware collection and automated engine association to replace the traditional manual data entry and organization mode, effectively avoiding data loss and input errors caused by manual registration. The underlying hash index significantly improves the association matching speed of massive amounts of operation data, and the real-time message transmission architecture ensures the timeliness of on-site operation data synchronization. The normalized, high-quality actual operation data provides a reliable data foundation for subsequent operation consistency verification, automatic cost calculation, and multi-dimensional statistical attribution, reducing payroll mismatch problems caused by data anomalies from the source.

[0027] Please see Figure 3 In this embodiment of the invention, the step of performing consistency comparison and verification processing on the actual operation data based on the operation type matching rule to obtain an anomaly warning record includes: 301. Obtain the worker's shopping information and the unique identifier; 302. Determine the actual operation type based on the worker's sweeping information; 303. Based on the unique identifier, search in the operation type matching rules to obtain the preset operation type corresponding to the unique identifier; 304. Compare the actual operation type with the preset operation type; 305. When the actual operation type is inconsistent with the preset operation type, the abnormal warning record is obtained.

[0028] In this embodiment, the worker's scanning information retains details of the on-site loading and unloading personnel's scanning operations. Based on the actual loading and unloading actions recorded in the information, the actual on-site operation behavior can be analyzed to determine the actual operation type. The actual operation type represents the loading and unloading operation attribute actually performed by the on-site operators. The work of retrieving the preset operation type through the unique identifier of the goods is implemented by the rule matching engine. The engine extracts the waybill number corresponding to the unique identifier and links it with the branch code. It retrieves the content bound to the corresponding branch from the pre-built mapping relationship database, which is the preset operation type. The preset operation type is a standardized operation benchmark of four categories in advance, which are defined based on the branch's business attributes: self-loading, self-unloading, self-loading and self-unloading, and non-self-loading and self-unloading. The pre-built mapping relationship library contains two layers of binding mapping relationships. The first layer is the mapping relationship between the unique identifier of the goods and the branch code. The data association is completed based on the branch affiliation information bound during the waybill filing stage. The second layer is the mapping relationship between the branch code and the preset operation type. During the basic data pre-setting stage, each branch code and distribution center code is pre-bound and archived with four types of operation attributes: self-loading, self-unloading, self-loading and self-unloading, and non-self-loading and self-unloading. The two layers of mapping are linked to form a complete data link. The preset operation type of the corresponding branch can be retrieved and matched step by step by using the unique identifier of the goods. After the two types of data are collected, automated comparison calculations are performed based on logical truth tables. The comparison process identifies data discrepancies according to fixed business logic. Discrepancies between actual and preset operation types are categorized into three typical scenarios: 1) The branch's baseline operation attribute is set as self-loading, but non-self-loading / unloading operations are actually performed on-site; 2) The branch's baseline operation attribute is set as self-unloading, but non-self-unloading / unloading operations are actually performed on-site; 3) Goods have completed on-site registration, but no loading or unloading barcode record has been generated within the specified time limit. Once any of these scenarios is triggered, the system generates an anomaly warning record. This anomaly warning record summarizes all dimensions of business information, including waybills, branches, personnel, and anomaly classifications, providing raw data to downstream cost accounting processes. The entire process relies on the engine for automatic calculation to complete the verification work, eliminating the traditional manual sampling control model, unifying the judgment criteria across all business lines, avoiding omissions and subjective judgment errors inherent in manual checks, and quickly identifying non-compliant operations at the operation stage, reducing the waste of manpower costs caused by mismatched pricing.

[0029] Please see Figure 4 In this embodiment of the invention, the cost difference calculation model includes a warning feature extraction module, a wage recalculation module, and a cost difference calculation module connected in sequence; the step of inputting the abnormal warning record into the pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data includes: 401. Based on the warning feature extraction module, perform feature extraction processing on the abnormal warning record to obtain key feature data; 402. Based on the wage recalculation module, the key feature data is recalculated to obtain pricing comparison data; 403. Based on the cost difference calculation module, the pricing comparison data is processed to calculate the cost difference, and the cost difference data is obtained.

[0030] In this embodiment, the cost difference calculation model relies on a multi-input fully connected neural network to complete supervised pre-training. After the abnormal warning record is input into the model, the feature extraction module first completes the feature extraction process. The feature extraction process refers to the field filtering rules built into the model to standardize and extract all fields of the abnormal warning record. The effective fields required for calculation are selected from the massive information included in the record, such as the warning number, error time, distribution and network point identification, waybill information, work team and personnel code, error classification, and pricing benchmark. After normalization and regularization, key feature data is formed. The key feature data is a structured dataset that gathers all the necessary parameters for payroll calculation, and fully includes the basic information related to the pricing standard for error operations and the compliant pricing of the network point. After the key feature data is transferred to the wage recalculation module, the pricing recalculation process is initiated. This process relies on three interconnected sub-modules within the module for step-by-step calculations. The piece-rate wage restoration sub-module uses the erroneous pricing parameters embedded in the key feature data to reconstruct the original piece-rate wage data. The compliant piece-rate wage calculation sub-module retrieves the preset work types at the network points and matches the compliant piece-rate unit price to calculate the compliant piece-rate wage data. The integration sub-module receives the two types of wage results and performs correlation integration, summarizing the two sets of wage information corresponding to the same waybill into pricing comparison data. This pricing comparison data is stored in parallel with the original pricing result and the compliant pricing result for each waybill, serving as the basis for cost difference calculation. After the pricing comparison data is input into the cost difference calculation module, cost difference calculation processing is performed. This module uses the established calculation logic of subtracting the compliant operation unit price from the erroneous operation unit price to complete the numerical calculation, ultimately generating cost difference data used to quantify the magnitude of cost changes. The entire process relies on a neural network model to achieve fully automated pricing and accounting, avoiding the inefficiencies and calculation errors caused by manual accounting. The quantitative output of cost difference data can support subsequent multi-dimensional data attribution and handling plan formulation, accurately pinpointing the source of abnormal labor cost losses.

[0031] Please see Figure 5 In this embodiment of the invention, the wage recalculation module includes a piece-rate wage restoration submodule, a compliant piece-rate wage calculation submodule, and an integration submodule. The integration submodule is connected to both the piece-rate wage restoration submodule and the compliant piece-rate wage calculation submodule. The step of recalculating the pricing of the key feature data based on the wage recalculation module to obtain pricing comparison data includes: 501. Based on the piece-rate wage restoration submodule, perform abnormal operation pricing restoration processing on the key feature data to obtain the original piece-rate wage data; 502. Based on the compliant piece-rate wage calculation submodule, perform compliant wage calculation processing on the key feature data to obtain compliant piece-rate wage data; 503. Based on the integration submodule, the original piece-rate wage data and the compliant piece-rate wage data are correlated and integrated to obtain the pricing comparison data.

[0032] In this embodiment, the abnormal operation pricing restoration process relies on the pricing parameters corresponding to the erroneous operation retained in the key feature data to complete the calculation. It extracts the erroneous pricing unit price and corresponding work volume applicable to the on-site violation from the key feature data, recalculates the wage amount using the erroneous pricing rules, and generates the original piece-rate wage data. The original piece-rate wage data is the original settlement wage calculated according to the pricing standard of the actual erroneous operation. The compliant wage calculation process matches the preset operation type of the waybill-bound network from the key feature data, retrieves the corresponding compliant piece-rate unit price for that operation type, and performs standardized wage calculation based on the work volume corresponding to the waybill. Finally, it generates compliant piece-rate wage data, which is the standard wage result calculated according to the network's preset operation specifications and compliant pricing standards. The associated integration process selects the waybill number as the associated index field, concatenates and aggregates the original piece-rate wage data and compliant piece-rate wage data belonging to the same waybill, and achieves the binding and summarization of the two sets of wage data. After aggregation, a pricing comparison data is formed. The pricing comparison data uniformly includes the erroneous pricing results and compliant pricing results of a single waybill, serving as the basic carrier for cost difference calculation. Relying on a wage recalculation module pre-trained by a neural network, a three-stage automated wage calculation is realized, getting rid of the inefficient mode of traditional manual item-by-item calculation of documents, avoiding numerical deviations caused by manual calculation, and relying on the established pricing comparison data, cost differences can be quickly solved according to predetermined formulas, accurately quantifying cost fluctuations caused by a single violation.

[0033] Please see Figure 6 In this embodiment of the invention, the attribution analysis statistical processing of the cost difference data to obtain attribution statistics includes: 601. Obtain preset statistical dimension parameters, and perform ETL extraction, transformation and loading processing on the cost difference data based on the statistical dimension parameters to obtain a multi-dimensional data cube; 602. Use a dual-mode calculation tool to perform index calculation on the multi-dimensional data cube to obtain full statistical index data; 603. The Gini coefficient ranking algorithm is used to sort the subjects with high incidence of anomalies in the full statistical index data to obtain the attribution statistics data.

[0034] In this embodiment, the statistical dimension parameters include configuration parameters corresponding to distribution centers, outlets, operation teams, and hierarchical operation times. These parameters are preset according to the operational management requirements of express delivery distribution and unloading operations. The operation time dimension is refined into multi-level statistical granularities of hours, days, weeks, and months. The ETL extraction, transformation, and loading process first extracts scattered cost difference data from the upstream business chain. Redundant fields are removed, format is standardized, and dimension field associations are transformed on the extracted data. Then, the standardized data is loaded into the database according to preset dimension rules. A multi-dimensional data cube is built based on the full-process processing results. This multi-dimensional data cube is a multi-dimensional aggregated data carrier using four statistical dimensions as retrieval indexes and error and cost indicators as measures, flexibly supporting multi-caliber data drill-down and summary operations. The dual-mode calculation tool is implemented based on the Lambda architecture. The real-time stream computing stage uses the Flink engine for dynamic monitoring of shifts, while the batch computing stage uses the SparkSQL engine to complete the data statistics for daily and monthly reports. The indicator calculation relies on two types of computing engines to perform operations. The Flink engine summarizes the error frequency and immediate cost loss for each shift of the real-time data stream, while the SparkSQL engine periodically batches and collects full-cycle data, calculating the error rate and total cumulative cost difference. Finally, the data is summarized to form full statistical indicator data. The full statistical indicator data is a standardized summary dataset that collects the number of operational errors, error rate, and total cost difference of different distribution centers, outlets, and teams in each statistical period. The Gini coefficient ranking algorithm integrates the Pareto analysis's 80 / 20 distribution operation logic. It uses the error rate and cost loss amount in the full statistical indicator data as ranking weights to sort the entities with high anomaly incidence. The sorting process completes the hierarchical division of outlets and teams according to the indicator values ​​from high to low, and filters out the list of entities with high error incidence. After sorting, the entity information and corresponding statistical values ​​are integrated to form attribution statistics data. The attribution statistics data is a structured dataset that retains multi-dimensional statistical results, details of entities with high error incidence, and details of cost loss in each period. The entire automated statistical system relies on a dual-engine architecture to meet the business needs of real-time monitoring and periodic reports. It eliminates the efficiency defects and data biases caused by manual statistics, accurately identifies the sources of cost losses based on quantified attribution statistics, provides objective data support for the subsequent automated generation of training documents and negative incentive documents, and standardizes on-site loading and unloading operations based on a closed-loop management mechanism to continuously optimize the level of human cost control in the distribution process.

[0035] Please see Figure 7 In this embodiment of the invention, generating incentive treatment recommendations based on the attribution statistics includes: 701. The Pareto analysis algorithm is used to screen the subjects with high incidence of anomalies in the attribution statistics to obtain the target operating subjects and the anomaly rate corresponding to the target operating subjects; 702. Generate the incentive and handling recommendations based on the target operating entity and the anomaly rate.

[0036] In this embodiment, the Pareto analysis algorithm uses the ABC hierarchical classification algorithm based on the Pareto distribution law. It uses quantitative indicators such as error frequency, total cost difference, and error rate built into the attribution statistics to screen for entities with high anomalies. The screening process first uses the error contribution ratio of each entity as the sorting benchmark to sort the statistical data of all outlets, work teams, and operators in descending order, and screens out the entity entries that account for the majority of errors and cost losses. From these, the target operating entities and their corresponding anomaly rates are extracted. The target operating entities are outlets, work teams, and front-line loading and unloading personnel with concentrated and frequent errors during the statistical period. The anomaly rate is the ratio of the number of operational errors to the total number of operations for the corresponding entity during the statistical period. After obtaining the target operation subject and the anomaly rate, the system retrieves the pre-configured error control thresholds and compares the anomaly rate values ​​of each subject item by item. Operations exceeding the thresholds are then identified. Incentive and remedial suggestions are automatically generated based on the selected subject information. These suggestions consist of a secondary training task sheet and a negative incentive suggestion sheet, with complete subject identity information, error details, and corresponding rectification and control content recorded in each document. Once all documents are generated, they are pushed to the mobile application or web-based workbench used by managers via the workflow engine. This entire remedial generation mechanism uses quantitative statistical results as the basis for control, moving away from the traditional extensive management model that relies on managers' experience to define the scope of rectification. It leverages the Pareto algorithm to accurately pinpoint the core sources of cost losses, implementing targeted skills training and personnel constraint measures. This forms a closed-loop control system across the entire chain, continuously standardizing on-site loading and unloading operations and reducing the waste of human resources caused by mismatched pricing from the business source.

[0037] The above describes the logistics loading and unloading operation early warning method in the embodiments of the present invention. The following describes the logistics loading and unloading operation early warning device in the embodiments of the present invention. Please refer to [link / reference]. Figure 8 One embodiment of the logistics loading and unloading operation early warning device of the present invention includes: Data acquisition module 801: Used to acquire actual operation data and preset operation type matching rules; Comparison and verification module 802: used to perform consistency comparison and verification processing on the actual operation data based on the operation type matching rules, and obtain anomaly warning records; Cost accounting module 803: used to input the abnormal early warning record into the pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data; Attribution analysis module 804: used to perform attribution analysis and statistical processing on the cost difference data to obtain attribution statistics data; Recommendation generation module 805: Used to generate incentive treatment recommendations based on the attribution statistics.

[0038] Based on the same ideas as the methods in the above embodiments, the apparatus provided in this application can implement the methods in the above embodiments.

[0039] above Figure 8 The logistics loading and unloading operation early warning device in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. The logistics loading and unloading operation early warning device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0040] Figure 9 This is a schematic diagram of the structure of a logistics loading and unloading operation early warning device 900 provided in an embodiment of the present invention. The logistics loading and unloading operation early warning device 900 can vary considerably 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) 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 logistics loading and unloading operation early warning device 900. 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 logistics loading and unloading operation early warning device 900 to implement the steps of the logistics loading and unloading operation early warning method provided in the above-described method embodiments.

[0041] The logistics loading and unloading operation early warning device 900 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 structure of the logistics loading and unloading operation early warning device does not constitute a limitation on the logistics loading and unloading operation early warning device. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0042] 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 loading and unloading operation early warning method.

[0043] 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.

[0044] 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.

[0045] 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 early warning of logistics loading and unloading operations, characterized in that, include: Obtain actual operation data and match it with preset operation type rules; Based on the operation type matching rules, the actual operation data is subjected to consistency comparison and verification processing to obtain anomaly warning records; The abnormal early warning records are input into a pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data. Attribution analysis and statistical processing are performed on the cost variance data to obtain attribution statistics. Incentive action recommendations are generated based on the attribution statistics.

2. The logistics loading and unloading operation early warning method according to claim 1, characterized in that, Before obtaining the actual operation data, the process also includes: Obtain the unique identifier of the goods and the worker's sweeping information; The unique identifier and the worker's shopping information are linked and bound using a data association tool to obtain the actual operation data.

3. The logistics loading and unloading operation early warning method according to claim 2, characterized in that, The process of performing consistency comparison and verification on the actual operation data based on the operation type matching rule to obtain anomaly warning records includes: Obtain the worker's shopping information and the unique identifier; The actual operation type is determined based on the worker's shopping information; Based on the unique identifier, a search is performed in the operation type matching rule to obtain a preset operation type corresponding to the unique identifier; Compare the actual operation type with the preset operation type; When the actual operation type is inconsistent with the preset operation type, the abnormal warning record is obtained.

4. The logistics loading and unloading operation early warning method according to claim 1, characterized in that, The cost difference calculation model includes a warning feature extraction module, a wage recalculation module, and a cost difference calculation module connected in sequence. The step of inputting the abnormal early warning record into a pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data includes: Based on the aforementioned early warning feature extraction module, feature extraction processing is performed on the abnormal early warning records to obtain key feature data; Based on the wage recalculation module, the key feature data is recalculated to obtain pricing comparison data. The cost difference calculation module performs cost difference calculation on the pricing comparison data to obtain the cost difference data.

5. The logistics loading and unloading operation early warning method according to claim 4, characterized in that, The wage recalculation module includes a piece-rate wage restoration submodule, a compliant piece-rate wage calculation submodule, and an integration submodule. The integration submodule is connected to the piece-rate wage restoration submodule and the compliant piece-rate wage calculation submodule, respectively. The pricing recalculation process based on the key feature data by the wage recalculation module to obtain pricing comparison data includes: Based on the piece-rate wage restoration submodule, abnormal operation pricing restoration processing is performed on the key feature data to obtain the original piece-rate wage data; Based on the compliant piece-rate wage calculation submodule, the key feature data is processed to obtain compliant piece-rate wage data. The original piece-rate wage data and the compliant piece-rate wage data are correlated and integrated based on the integration submodule to obtain the pricing comparison data.

6. The logistics loading and unloading operation early warning method according to claim 1, characterized in that, The attribution analysis and statistical processing of the cost variance data to obtain attribution statistics includes: Obtain preset statistical dimension parameters, and perform ETL extraction, transformation and loading processing on the cost difference data based on the statistical dimension parameters to obtain a multi-dimensional data cube; A dual-mode calculation tool is used to perform index calculations on the multi-dimensional data cube to obtain full statistical index data. The Gini coefficient ranking algorithm is used to sort the subjects with high incidence of anomalies in the full statistical index data to obtain the attribution statistics data.

7. The logistics loading and unloading operation early warning method according to claim 1, characterized in that, The generation of incentive treatment recommendations based on the attribution statistics includes: The Pareto analysis algorithm is used to screen for high-incidence subjects of the attribution statistics to obtain the target operation subjects and the anomaly rate corresponding to the target operation subjects. The incentive and handling recommendations are generated based on the target operating entity and the anomaly rate.

8. A logistics loading and unloading operation early warning device, characterized in that, include: Data acquisition module: used to acquire actual operation data and preset operation type matching rules; Comparison and verification module: used to perform consistency comparison and verification processing on the actual operation data based on the operation type matching rules, and obtain anomaly warning records; Cost accounting module: used to input the abnormal early warning records into the pre-trained cost difference calculation model for cost accounting processing to obtain cost difference data; Attribution analysis module: used to perform attribution analysis and statistical processing on the cost variance data to obtain attribution statistics; Recommendation generation module: used to generate incentive action recommendations based on the attribution statistics.

9. A logistics loading and unloading operation early warning device, characterized in that, The logistics loading and unloading operation early warning device 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 loading and unloading operation early warning device to perform the steps of the logistics loading and unloading operation early warning method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements each step of the logistics loading and unloading operation early warning method as described in any one of claims 1-7.

Images