Intelligent early warning and efficient management and control method and device for damage, equipment and medium

Abstract

The invention relates to the technical field of logistics transportation and supply chain management, and provides a damage intelligent early warning and efficient management and control method, device, equipment and medium, and the method comprises the steps: carrying out the standardization processing of waybill data, damage data, customer complaint data and claim settlement data, and forming a basic data set in a unified format; setting a first monitoring rule for the operation management and control dimension, identifying an abnormal damage behavior based on the basic data set and the first monitoring rule, and generating an organization hit task including an organization current situation, a rectification target and a rectification description if an abnormality is identified; setting a second monitoring rule for the customer management and control dimension, identifying an abnormal customer damage behavior based on the basic data set and the second monitoring rule, and generating a management and control task if an abnormality is identified; early warning information is recorded according to the organization hit task and the management and control task, the early warning information comprises execution time, threshold adjustment, hit rate and approval information, historical statistics and whether checking schemes are generated, and a management and control result closed loop is formed.

Description

Technical Field

[0001] The present application relates to the technical field of logistics, transportation and supply chain management, and in particular to a method, device, equipment and medium for intelligent damage warning and efficient control. Background Art

[0002] In the logistics and transportation industry, cargo damage is a core issue that affects customer experience and business operating costs. Existing technologies for managing and controlling cargo damage have the following shortcomings:

[0003] 1. Fragmented data processing: Traditional solutions lack unified standards for processing waybill data, damage data, customer complaint data, and claims data. Incompatible data formats and confusing field definitions make it difficult to correlate and analyze multi-source data, making it impossible to form a complete damage risk assessment system.

[0004] 2. Single monitoring dimension: Existing monitoring methods typically focus on single-dimensional indicators on the operational side (such as branch damage rate statistics) or the client side (such as customer complaint records). They lack coordinated monitoring of operational control dimensions (organizational goals, trend thresholds) and customer control dimensions (customer characteristics, cargo properties), making it difficult to accurately locate the root cause of abnormal damage behavior.

[0005] 3. Lack of closed-loop management: After identifying anomalies, existing early warning systems often only generate simple warning information. They lack structured definitions of rectification tasks (such as organizational status, rectification goals, and execution instructions) and closed-loop analysis of historical data. They are unable to form a full-process management and control of "identification-processing-optimization", resulting in the recurrence of similar problems.

[0006] 4. Poor rule reusability: Monitoring rules are configured independently between regions, and there is a lack of a rule migration mechanism based on business similarities. When launching business in a new region, monitoring rules need to be rebuilt, which consumes a lot of manpower and resources and results in low rule adaptation efficiency.

[0007] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0008] The present application provides a method, device, equipment and medium for intelligent damage warning and efficient control, aiming to solve the problems in the existing technology of damage control methods, mainly including data processing fragmentation, single monitoring dimension, lack of closed-loop management and poor rule reusability.

[0009] In a first aspect, the present application provides a method for intelligent damage warning and efficient management and control, including:

[0010] Standardize waybill data, damage data, customer complaint data, and claims data to form a basic data set in a unified format;

[0011] For the operational control dimension, set basic indicators, organizational goals, and trend monitoring thresholds. Configure the first monitoring rule for identifying abnormal and disruptive behavior. Identify abnormal and disruptive behavior based on the basic data set and the first monitoring rule. If an anomaly is identified, generate an organizational hit task that includes the organizational status, rectification goals, and rectification instructions.

[0012] For the customer management dimension, set customer characteristics including the shipping company, customer type, delivery address, and cargo nature. Configure a second monitoring rule to identify abnormal customer damage behavior. Based on the basic data set and the second monitoring rule, identify abnormal customer damage behavior. If an anomaly is identified, generate a management task.

[0013] Early warning information is recorded based on the organization's hit tasks and management and control tasks. The early warning information includes execution time, threshold adjustment, hit rate and approval information, and historical statistics and verification plans are generated to form a closed loop of management and control results.

[0014] In some embodiments, after the generation of historical statistics and verification plans to form a closed loop of management and control results, it also includes: according to the statistical dimensions of shipping time, shipping point, customer code and organizational level, with a sliding week or month as a cycle, statistics are collected on the problem type, number of days failing to meet the standards, average daily breakage rate, improvement results and ROI to generate statistical results; according to the statistical results, the rule application effects corresponding to the hit rate, false alarm rate and improvement achievement rate of the first monitoring rule and the second monitoring rule in the original area are recorded; according to the rule application effects, the target first monitoring rule and the target second monitoring rule are determined; based on the target area whose indicators of rule application effect, business scale, route structure, cargo characteristics and customer distribution are similar to those of the source area, the target first monitoring rule and the target second monitoring rule are migrated to the target area and fine-tuning parameter information is generated, and the migrated target first monitoring rule and the target second monitoring rule are formed into new versions in the target area for version management.

[0015] In some embodiments, the waybill data, damage data, customer complaint data and claims data are standardized to form a basic data set in a unified format, including: uniformly defining the field name, data type and value range of each type of data according to a preset data dictionary, and aligning the waybill number, shipping time, shipping point, receiving address, cargo type and weight and volume in the waybill data, the damage location, damage degree and damage responsibility link in the damage data, the customer complaint time, customer complaint content and processing status in the customer complaint data, and the claim amount, claim reason and processing result in the claims data; using data cleaning tools to deduplicate duplicate data, complete missing data, and correct erroneous data, and correlating and matching the waybill data, damage data, customer complaint data and claims data based on the waybill number to generate a unified structured data record containing multi-dimensional information to form a basic data set.

[0016] In some embodiments, the basic indicators, organizational goals and trend monitoring thresholds are set for the operational control dimension, and the first monitoring rule for determining abnormal damage behavior is configured, including: setting the breakage rate, customer complaint rate, claim rate and proportion of damage responsibility links as basic indicators, and setting the breakage rate control target, customer complaint response time target and claim processing cycle target of each level as the organizational goal according to the organizational level and business type; calculating the mean and standard deviation based on the basic indicator data of the past six months, and setting the rising warning threshold, falling warning threshold and trend fluctuation threshold of each indicator in combination with the industry benchmark value; logically combining the comparison conditions of the basic indicators and the organizational goals, and the deviation conditions of the basic indicators and the trend monitoring threshold to form a first monitoring rule containing at least one monitoring condition combination.

[0017] In some embodiments, the abnormal damage behavior is identified based on the basic data set and the first monitoring rule. If an abnormality is identified, an organizational hit task containing the organizational status, rectification goals, and rectification instructions is generated, including: inputting the real-time indicator data in the basic data set into the first monitoring rule for condition matching, and when any rule condition corresponding to the first monitoring rule is met, extracting the current indicator values ​​such as the breakage rate and customer complaint rate corresponding to the organizational level of the triggering rule as the organizational status; determining the rectification goals according to the organizational goals corresponding to the organizational level, and generating rectification instructions including operation specification optimization requirements, equipment maintenance plans, and personnel training arrangements according to the responsible links corresponding to the abnormal damage behavior, and encapsulating the organizational status, rectification goals, and rectification instructions into an organizational hit task.

[0018] In some embodiments, the customer characteristics set for the customer management dimension include the shipping company, customer type, receiving address, and nature of the goods, and a second monitoring rule is configured to identify abnormal customer damage behavior, including: taking the shipping company's cooperation level, customer type, the region to which the receiving address belongs, and the nature of the goods as customer characteristic dimensions, and extracting historical damage data, customer complaint data, and claims data corresponding to each customer characteristic; setting a damage risk assessment model for different customer feature combinations, which is used to logically associate customer feature combination conditions with damage risk assessment conditions, to form a second monitoring rule including multi-dimensional customer feature matching and risk threshold judgment.

[0019] In some embodiments, the identification of abnormal customer damage behavior based on the basic data set and the second monitoring rules, and the generation of a management and control task if an anomaly is identified, include: matching the customer-related data in the basic data set with the second monitoring rules, and when the customer characteristics meet the risk assessment conditions in the rules, determining it as abnormal customer damage behavior; generating a management and control task including the customer risk level, management and control measure recommendations and response time requirements based on the type and severity of the customer's abnormal behavior.

[0020] In a second aspect, the present application provides an intelligent damage warning and efficient control device, comprising:

[0021] The data processing unit is used to standardize the waybill data, damage data, customer complaint data, and claims data to form a basic data set in a unified format;

[0022] A first generation unit is configured to set basic indicators, organizational goals, and trend monitoring thresholds for the operational control dimension, configure a first monitoring rule for determining abnormal and damaging behavior, identify abnormal and damaging behavior based on the basic data set and the first monitoring rule, and generate an organizational hit task containing the organizational status, rectification goals, and rectification instructions if an abnormality is identified;

[0023] The second generation unit is configured to set customer characteristics including the shipping company, customer type, receiving address, and cargo nature for the customer management dimension, configure a second monitoring rule for identifying abnormal customer damage behavior, identify abnormal customer damage behavior based on the basic data set and the second monitoring rule, and generate a management task if an abnormality is identified;

[0024] The control closed-loop unit is used to record early warning information based on the organization's hit tasks and control tasks. The early warning information includes execution time, threshold adjustment, hit rate and approval information, and generates historical statistics and verification plans to form a closed-loop control result.

[0025] In a third aspect, the present application further provides a computer device, comprising:

[0026] memory and processor;

[0027] The memory is used to store computer programs;

[0028] The processor is used to execute the computer program and implement the steps of the intelligent damage warning and efficient management method as described in the first aspect when executing the computer program.

[0029] In a fourth aspect, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of the intelligent damage warning and efficient control method as described in the first aspect above.

[0030] The embodiments of the present application provide a method, device, equipment and medium for intelligent early warning and efficient control of damage. The method integrates multi-source heterogeneous data through unified data format and cleansing association to form a basic data set containing waybills, damages, customer complaints, and claims information, providing high-quality data support for subsequent analysis and improving the accuracy of anomaly identification. Organizational goals and trend thresholds are set for the operational control dimension, and combined with customer characteristics and risk assessment in the customer control dimension to achieve multi-perspective identification of abnormal damage behaviors, covering the entire chain of risk points from internal operations to external customers, and avoiding the one-sidedness of single-dimensional monitoring. By generating organizational hit tasks and customer control tasks that include organizational status, rectification goals, and rectification instructions, combined with historical statistics, rule effect evaluation, and cross-regional rule migration, a complete closed loop of "early warning-processing-optimization-reuse" is formed, improving control efficiency and the systematic nature of problem solving. By statistically analyzing the application effects of rules and migrating them to similar target areas, cross-regional reuse and versioning management of monitoring rules are achieved, reducing the rule configuration costs of new areas, and improving the flexibility and adaptability of the overall control strategy.

[0031] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 This is a schematic flow chart of the steps of a damage intelligent early warning and efficient control method provided by an embodiment of the present application;

[0034] Figure 2 This is a schematic diagram of the structure of a damage intelligent early warning and efficient control device provided in one embodiment of the present application;

[0035] Figure 3 This is a schematic block diagram of the structure of a computer device provided in one embodiment of the present application.

[0036] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0039] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0040] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0041] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0042] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0043] In the logistics and transportation industry, cargo damage is a core issue that affects customer experience and business operating costs. Existing technologies for managing and controlling cargo damage have the following shortcomings:

[0044] Fragmented data processing: Traditional solutions lack unified standards for processing waybill data, damage data, customer complaint data, and claims data. Incompatible data formats and confusing field definitions make it difficult to correlate and analyze multi-source data, making it impossible to form a complete damage risk assessment system.

[0045] Single monitoring dimension: Existing monitoring methods typically focus only on single-dimensional indicators on the operational side (such as branch damage rate statistics) or the client side (such as customer complaint records). They lack coordinated monitoring of operational control dimensions (organizational goals, trend thresholds) and customer control dimensions (customer characteristics, cargo properties), making it difficult to accurately locate the root cause of abnormal damage behavior.

[0046] Lack of closed-loop management: After identifying anomalies, existing early warning systems often only generate simple warning information. They lack structured definitions of rectification tasks (such as organizational status, rectification goals, and execution instructions) and closed-loop analysis of historical data. This makes it impossible to form a full-process management and control of "identification-processing-optimization", leading to the recurrence of similar problems.

[0047] Poor rule reusability: Monitoring rules between regions are configured independently, and there is a lack of a rule migration mechanism based on business similarities. When launching business in a new region, monitoring rules need to be rebuilt, which consumes a lot of manpower and material resources and has low rule adaptation efficiency.

[0048] Existing technologies lack a systematic approach to integrating data standardization, dual-dimensional monitoring rule configuration, structured task generation, and cross-regional rule migration to achieve intelligent early warning and efficient management of damage. This invention effectively addresses these technical issues by building a dual-dimensional monitoring system for both operations and customers, a structured task generation mechanism, and a closed-loop management process, providing a new technical approach for damage management in the logistics industry.

[0049] See also Figure 1 , Figure 1 This is a schematic flow chart of an intelligent damage warning and efficient management method provided by one embodiment of the present application. This intelligent damage warning and efficient management method can be implemented using a computer device, which can be deployed on a single server or a server cluster. Alternatively, it can be deployed on a handheld terminal, laptop computer, wearable device, or robot.

[0050] It should be noted that the acquisition of any information involved in the provided method complies with relevant regulations and is carried out with the user's consent. It will not infringe on the user's privacy and will not violate relevant laws and regulations.

[0051] Specifically, if Figure 1 As shown, the provided damage intelligent early warning and efficient control method includes steps S101 to S104, which are detailed as follows:

[0052] Step S101: Standardize the waybill data, damage data, customer complaint data, and claims data to form a basic data set in a unified format.

[0053] Specifically, to address the fragmentation of four types of data in logistics and transportation, namely waybills, damages, customer complaints, and claims, we will achieve compatibility and correlation of multi-source data by unifying data standards, cleaning heterogeneous data, and building basic data sets, providing a standardized data base for subsequent analysis.

[0054] Define a data dictionary and a unified format by establishing a cross-system data mapping table, unify field definitions (for example, the "shipping address" in waybill data and the "problem address" in customer complaint data are unified into standardized address codes), and standardize data types (for example, damage types use enumeration values: damaged outer packaging, damaged contents, liquid leakage, etc.).

[0055] Establish data quality standards, clarify required fields (such as waybill ID, damage occurrence time, customer complaint number), format verification rules (such as standardizing time fields to ISO 8601 format), and value ranges (such as damage levels are divided into levels 1-5).

[0056] Data cleaning and integration uses ETL (Extract-Transform-Load) tools or a data middle platform to extract raw data from various business systems (TMS transportation management system, customer service system, and claims system), remove duplicate records, fill in missing values ​​(such as completing ambiguous delivery addresses through the address database), and correct outliers (such as abnormal data with a damage rate exceeding 100% is marked as pending verification).

[0057] Build a data association relationship, using the waybill ID as the primary key, and associate damage records (damage ID), customer complaint records (customer complaint ID), and claim records (claim ID), forming a wide table containing 40+ fields (such as basic waybill information, damage details, customer complaint reasons, claim amounts, etc.).

[0058] Data storage and updates are achieved by storing standardized basic data sets in distributed databases (such as HBase and MySQL clusters). This supports real-time incremental updates (e.g., data synchronization triggered by changes in shipping order status) and batch offline processing (e.g., full updates at night). A data lineage tracking mechanism is configured to record data sources, processing logic, and update logs to ensure data traceability.

[0059] Step S102. Set basic indicators, organizational goals, and trend monitoring thresholds for the operational management and control dimensions, configure the first monitoring rule for determining abnormal and damaging behaviors, identify abnormal and damaging behaviors based on the basic data set and the first monitoring rule, and generate an organizational hit task containing the organizational status, rectification goals, and rectification instructions if an anomaly is identified.

[0060] Specifically, based on the damage control needs on the operational side, a three-layer monitoring system of "basic indicators-organizational goals-trend thresholds" is constructed. Through the rule engine, abnormal damage behaviors are identified, and structured rectification tasks are generated to solve the problem of lack of single-dimensional monitoring and closed-loop management.

[0061] Operational monitoring elements are defined as follows: Basic indicators: These include over 20 quantitative indicators, including damage rate (damaged waybills / total waybills), responsible parties (responsible outlets / transport routes), damage type distribution, and month-over-month growth rate. These indicators support stratified statistics by time (daily / weekly / monthly) and organizational level (headquarters-region-outlet). Organizational objectives: KPIs are set based on historical data and business plans (e.g., regional damage rate ≤ 0.5%, damage rate reduction of 10% on key routes), with support for dynamic adjustments (e.g., temporarily raising thresholds during peak season). Trend monitoring thresholds: Dynamic thresholds are calculated using time series analysis (e.g., moving average and exponential smoothing). For example, an alert is triggered when the damage rate exceeds the regional target by 1.5 times for three consecutive days or when the week-over-week growth rate is ≥ 20%.

[0062] Configure the first monitoring rule by defining a compound rule using a rule engine (such as Drools or Aviator). For example, the rule might look like this: IF (Branch A's today's breakage rate is > 1.2 times the regional target) AND (Breakage rate increased by ≥15% month-over-month over the past seven days) AND (Percentage of fragile items among breakage types >60%); THEN generate an organizational action. Rules support visual configuration, allowing operators to drag and drop indicators, set logical relationships (AND / OR / NOT), and set threshold parameters.

[0063] The task template for generating structured organizational tasks includes three elements: organizational status (current breakage rate, responsible branch, primary breakage types, and impact (e.g., a 20% increase in customer complaints); rectification objectives (clearly defined quantitative targets (e.g., reducing the breakage rate to below the target value within three days), timeframe (72 hours for rectification); rectification instructions (specific implementation measures (e.g., reviewing the responsible branch's operational procedures, increasing training on fragile goods reinforcement, temporarily changing transport routes), responsible individuals, and collaborating departments (branch managers are primarily responsible, with operations and training departments collaborating). Tasks are automatically assigned to the responsible individuals through the work order system, with mobile access and progress feedback supported.

[0064] Step S103. Set customer characteristics including the shipping company, customer type, receiving address, and nature of the goods for the customer management dimension, configure the second monitoring rule for identifying abnormal customer damage behavior, identify abnormal customer damage behavior based on the basic data set and the second monitoring rule, and generate a management task if an abnormality is identified.

[0065] Specifically, by focusing on the damage risk on the customer side, a second monitoring rule is constructed based on customer characteristics (shipping company, customer type, nature of goods, etc.), high-risk customer damage behavior is identified, and targeted management and control tasks are generated to solve the problem of disconnection between client-side and operation-side monitoring.

[0066] Define four types of customer characteristics: Shipping companies: distinguish between directly-operated customers, franchised customers, e-commerce platform customers, etc., and associate their historical damage rate preferences; Customer type: Divide into VIP customers (annual shipment volume ≥ 100,000 orders), ordinary customers, and new customers, and set differentiated risk weights (for example, VIP customers have a lower tolerance for damage); Receiving address: Mark the address risk level (such as remote mountainous areas, areas with frequent violent sorting), and whether it is a prohibited area; Nature of goods: including category (furniture, electronic equipment, fresh food), packaging type (cardboard boxes, wooden boxes, bare bags), value (whether insured), and fragility level (through NLP analysis of keywords such as "fragile" and "handle with care" in the goods description).

[0067] Configure the second monitoring rule, which focuses on abnormal customer scenarios. Example rules include: for high-value customers: "A VIP customer's breakage rate over the past 30 days is 1.5 times greater than the industry average, and the proportion of damaged goods is insured at least 30%." For special cargo: "Five consecutive orders of broken glassware from the same shipping company, without designated reinforced packaging." For address risk: "A remote address has a breakage rate of 10% or more over the past 30 days, and the complaint resolution period exceeds 48 hours." Rules support differentiated configuration based on customer stratification (e.g., by shipment volume or complaint history). For example, for e-commerce platform customers, add the correlation indicator "damage leading to a decrease in store ratings."

[0068] Generate customer-side control tasks, including: customer risk details (customer name, damage history, current abnormal indicators); control measures (contact the customer to recommend reinforced packaging, negotiate an insurance plan, adjust the shipping route to a dedicated logistics provider); and coordination requirements (customer service department to return to the customer within 24 hours, and operations department to evaluate the feasibility of optimized shipping solutions). These tasks are synchronized to the customer relationship management (CRM) system, linked to customer service records, and form a customer damage risk profile.

[0069] Step S104. Record the warning information according to the organization's hit tasks and management and control tasks. The warning information includes execution time, threshold adjustment, hit rate and approval information, and generate historical statistics and verification plans to form a closed loop of management and control results.

[0070] Specifically, by recording the entire warning execution process information, analyzing historical data and checking cases, a "identification-processing-optimization" closed loop is formed to solve the problems of the lack of closed loop and poor rule reusability in traditional warning systems.

[0071] The early warning information database includes core fields: basic information: warning time, rule trigger type (operation side / client side), associated task ID; execution information: responsible person receipt time, rectification measure execution time, threshold adjustment record (such as temporarily relaxing the breakage rate threshold by 5% due to seasonal factors); effect evaluation: task completion rate (on time / overtime), hit rate (earnings efficiency = actual number of anomalies / total number of warnings), customer feedback (changes in complaint volume, claim amount reduction rate); approval information: multi-level approval nodes (regional manager initial review, headquarters operations department review), approval opinions and timestamps.

[0072] The historical statistics module includes: analyzing warning trends by time dimension (for example, in Q3, warnings from the operational side accounted for 60%, and warnings from the client side accounted for 40%); evaluating the effectiveness of rules (for example, the hit rate of the "fragile goods breakage rate exceeds the standard" rule reached 85%, which is higher than the average level); and identifying high-frequency problems (a certain branch received 15 repeated warnings within the quarter due to aging sorting equipment).

[0073] The verification and rejection plan management records the warning cases that have not passed the review (for example, a warning was misjudged due to delayed data collection and marked as "verified"), analyzes the reasons for the misjudgment (field mapping error, overly strict threshold setting); establishes a rule optimization knowledge base, and converts the verification and rejection cases into the basis for rule iteration (such as adjusting the calculation method of the "remote address damage rate" to exclude force majeure factors).

[0074] Cross-region rule migration and reuse are based on business similarities (e.g., the cargo structure and transportation network of Region A and Region B are similar). The monitoring rules of mature regions are replicated through the rule engine, and localized parameters are automatically adapted (e.g., adjusting the regional damage rate target value by ±10%). A rule similarity evaluation model is constructed, and the cosine similarity is used to calculate the matching degree of indicator weights of different regions. Best practice rules are recommended to reduce the cost of configuring rules for new regions.

[0075] Closed-loop verification and continuous optimization are carried out through the generation of closed-loop reports every month, comparing the changes in damage rates before and after rectification (for example, the damage rate of a certain outlet dropped from 1.2% to 0.4% after rectification) and the decline in customer complaints (a 30% decrease month-on-month). Machine learning models (such as random forests) are used to analyze historical closed-loop data and predict high-risk scenarios (such as a 20% increase in the risk of damage to fresh produce in winter). Thresholds and monitoring rules are adjusted in advance to achieve an upgrade from passive response to active prevention.

[0076] In some embodiments, after the generation of historical statistics and verification plans to form a closed loop of management and control results, it also includes: according to the statistical dimensions of shipping time, shipping point, customer code and organizational level, with a sliding week or month as a cycle, statistics are collected on the problem type, number of days failing to meet the standards, average daily breakage rate, improvement results and ROI to generate statistical results; according to the statistical results, the rule application effects corresponding to the hit rate, false alarm rate and improvement achievement rate of the first monitoring rule and the second monitoring rule in the original area are recorded; according to the rule application effects, the target first monitoring rule and the target second monitoring rule are determined; based on the target area whose indicators of rule application effect, business scale, route structure, cargo characteristics and customer distribution are similar to those of the source area, the target first monitoring rule and the target second monitoring rule are migrated to the target area and fine-tuning parameter information is generated, and the migrated target first monitoring rule and the target second monitoring rule are formed into new versions in the target area for version management.

[0077] On the basis of the closed-loop management and control results, we add cross-cycle statistical analysis, rule effectiveness evaluation and cross-regional migration mechanisms, quantify the effectiveness of rules through multi-dimensional statistics, and achieve efficient reuse and version management of rules based on business similarity to solve the problem of poor rule reusability.

[0078] Statistics are calculated by shipping time (accurate to the day), shipping point (branch / distribution center), customer code (uniquely identifies the customer), and organizational hierarchy (headquarters, region, city, and branch). The statistical period uses a sliding window (weekly / monthly), for example, a "7-day sliding window" covering the last seven days, updated daily. Monthly statistics are based on the calendar month or business cycle month.

[0079] Statistical indicators include: problem type (classifications such as damaged outer packaging / damaged contents / liquid leakage); number of days failing to meet the target (number of days within a cycle when the damage rate exceeds the target value); average daily damage rate (number of damaged waybills within a cycle / total number of waybills / number of days); improvement results (the decrease in the damage rate compared to the previous cycle); and ROI (the ratio of rectification investment costs to the reduction in claims and customer retention earnings).

[0080] Rule effectiveness evaluation includes: Hit rate = number of valid alerts / total number of alerts (valid definition: triggers that are verified as true anomalies); false alarm rate = number of false alarm alerts / total number of alerts (false alarm definition: alerts that are subsequently denied); improvement achievement rate = number of tasks that achieve rectification goals / total number of rectification tasks (targets such as a ≥10% reduction in the breakage rate). Recording method: Add a "Rule Effectiveness" column to the historical statistical report and generate radar charts on a weekly / monthly basis to visualize the performance of each rule at different organizational levels / customer types.

[0081] Target rule screening and migration involves identifying target rules: rules with a hit rate ≥ 70%, a false alarm rate ≤ 20%, and an improvement achievement rate ≥ 60% are selected as the primary / secondary target monitoring rules, eliminating inefficient rules (e.g., hit rates < 50% for two consecutive months). Similarity matching: A matching model is established between the target and source regions. Dimensions include: business scale (number of waybills, number of customers), route structure (ratio of trunk / branch lines, proportion of remote routes), cargo characteristics (proportion of fragile goods / high-value goods), and customer distribution (proportion of VIP customers, proportion of e-commerce customers). Similarity is calculated using Euclidean distance, and regions with a similarity ≥ 80% trigger the rule migration process. Parameter fine-tuning: Thresholds are adjusted based on the characteristics of the target region (e.g., if the target region has a high proportion of mountainous road transportation, the threshold for the fragile goods breakage rate may be relaxed by 0.3%). A "Rule Migration Parameter Adjustment Note" is generated.

[0082] Versioning management includes marking migrated rules as "V1.1 (Region X Customized Version)", recording the source rule version, migration time, fine-tuning parameters, and effective regions; establishing a rule version log to support historical version rollback (if the false alarm rate of the new version increases, you can roll back to the old version and re-fine-tune it); and centrally managing the rule versions of each region through the configuration center to ensure that the online running rules are consistent with the registered versions.

[0083] In some embodiments, the waybill data, damage data, customer complaint data and claims data are standardized to form a basic data set in a unified format, including: uniformly defining the field name, data type and value range of each type of data according to a preset data dictionary, and aligning the waybill number, shipping time, shipping point, receiving address, cargo type and weight and volume in the waybill data, the damage location, damage degree and damage responsibility link in the damage data, the customer complaint time, customer complaint content and processing status in the customer complaint data, and the claim amount, claim reason and processing result in the claims data; using data cleaning tools to deduplicate duplicate data, complete missing data, and correct erroneous data, and correlating and matching the waybill data, damage data, customer complaint data and claims data based on the waybill number to generate a unified structured data record containing multi-dimensional information to form a basic data set.

[0084] Multi-source data fields are standardized through a preset data dictionary. After data cleaning and correlation matching, a unified structured data set containing waybills, damages, customer complaints, and claims information is generated to solve the problem of data fragmentation.

[0085] The unified field standards include: waybill data: waybill number (string, 32-bit unique code), shipping time (datetime, ISO 8601 format), shipping point (point code, such as "BJ001" for Beijing Point 1), receiving address (structured address: province-city-district-street-house number), cargo type (enumeration value: furniture / electronics / fresh food / general cargo), weight and volume (numeric type, weight in kg, volume in cm) 3 ). Damage data: location of damage (outer packaging / contents / accessories), degree of damage (level 1-5, level 1 minor scratches, level 5 complete damage), damage responsibility link (sorting / transportation / loading and unloading / warehousing). Customer complaint data: customer complaint time (same format as shipping time), customer complaint content (text field, supports NLP parsing), processing status (pending / processing / closed). Claim data: claim amount (numeric type, accurate to cents), claim reason (enumeration value: damaged / lost / delayed), processing result (compensation / rejection / partial compensation). Field alignment: Through the data dictionary mapping table, synonymous fields in different systems are unified (for example, the "order time" of system A and the "waybill creation time" of system B are both mapped to "shipping time").

[0086] The cleaning tool uses Apache NiFi for data stream processing, and is configured with a deduplication component (deduplicates by waybill number + damage record time), a missing value processing component (for waybills with missing "weight", the default value of the cargo type is used to fill in the missing weight, such as the default weight of "furniture" is 50kg), and an error correction component (corrects incorrect delivery addresses through the address verification API).

[0087] Association matching uses the waybill number as the primary key and SQL JOIN operations to link four types of data, generating a wide table with multiple fields in a single record. For example, the waybill number, shipping time, shipping point, receiving address, cargo type, weight, volume, damage location, damage extent, responsible link, customer complaint time, customer complaint content, processing status, claim amount, claim reason, and processing result.

[0088] Basic dataset generation includes: output format: Parquet file (supports efficient query) or storage in a relational database table (such as the base_dataset table in MySQL); quality verification: running the data verification script daily to check the association success rate (required ≥99%) and field integrity (missing rate ≤1%), and generating a data quality report.

[0089] In some embodiments, the basic indicators, organizational goals and trend monitoring thresholds are set for the operational control dimension, and the first monitoring rule for determining abnormal damage behavior is configured, including: setting the breakage rate, customer complaint rate, claim rate and proportion of damage responsibility links as basic indicators, and setting the breakage rate control target, customer complaint response time target and claim processing cycle target of each level as the organizational goal according to the organizational level and business type; calculating the mean and standard deviation based on the basic indicator data of the past six months, and setting the rising warning threshold, falling warning threshold and trend fluctuation threshold of each indicator in combination with the industry benchmark value; logically combining the comparison conditions of the basic indicators and the organizational goals, and the deviation conditions of the basic indicators and the trend monitoring threshold to form a first monitoring rule containing at least one monitoring condition combination.

[0090] By constructing a three-layer system of "basic indicators-organizational goals-trend thresholds" in the operational control dimension, the first monitoring rules are formed through logical combination to achieve multi-condition compound early warning.

[0091] Basic indicators include: core indicators: damage rate (number of damaged waybills / total number of waybills), customer complaint rate (number of customer complaint waybills / total number of waybills), claim settlement rate (number of claim settlement waybills / total number of waybills); responsibility indicators: proportion of damage responsibility links (such as the proportion of damage in the sorting link and the proportion of damage in the transportation link).

[0092] Organizational goal setting includes: by hierarchical division: the headquarters sets the network-wide damage rate to ≤0.8%, and regional companies further refine it on this basis (such as ≤0.6% in the North China region and ≤0.7% in the South China region); by business type: the response time for e-commerce customer complaints is ≤24 hours, and the processing cycle for large-scale logistics claims is ≤3 working days.

[0093] The trend monitoring threshold calculation includes: Data basis: Take the daily indicator data of the past 6 months (excluding abnormal periods such as the Spring Festival) and calculate the mean (μ) and standard deviation (σ); Threshold settings include: Rising warning threshold = μ + 1.5σ (exceeding for 2 consecutive days triggers a warning); Falling warning threshold = μ - 1.0σ (continuous decline may hide data anomalies); Trend fluctuation threshold = month-on-month growth rate ≥ 20% or ≤ -15% (to identify sudden fluctuations); Industry benchmark reference: Compare with the average damage rate of the logistics industry white paper (such as 0.9%). If the company's current average is lower than the benchmark, the threshold can be appropriately tightened.

[0094] The first monitoring rule constructs the corresponding rule logic combination including: basic condition: breakage rate > organizational target value; trend condition: breakage rate in the past three days > rising warning threshold and the month-on-month growth rate ≥ 20%; responsibility condition: breakage ratio in the sorting link > 50% (locating the responsible link); compound rule example: IF (breakage rate > regional target) AND (breakage rate in the past three days > rising warning threshold) AND (sorting link ratio > 50%).

[0095] THEN triggering abnormal warning in the sorting process includes: rule configuration: through the rule engine visual interface, drag indicator components (breakage rate, sorting ratio), conditional operators (>, AND), and threshold parameters (regional target, warning threshold) to generate a rule chain.

[0096] In some embodiments, the abnormal damage behavior is identified based on the basic data set and the first monitoring rule. If an abnormality is identified, an organizational hit task containing the organizational status, rectification goals, and rectification instructions is generated, including: inputting the real-time indicator data in the basic data set into the first monitoring rule for condition matching, and when any rule condition corresponding to the first monitoring rule is met, extracting the current indicator values ​​such as the breakage rate and customer complaint rate corresponding to the organizational level of the triggering rule as the organizational status; determining the rectification goals according to the organizational goals corresponding to the organizational level, and generating rectification instructions including operation specification optimization requirements, equipment maintenance plans, and personnel training arrangements according to the responsible links corresponding to the abnormal damage behavior, and encapsulating the organizational status, rectification goals, and rectification instructions into an organizational hit task.

[0097] The first monitoring rule is triggered based on real-time data, abnormal indicators are extracted as the organizational status, and structured rectification tasks are generated in combination with organizational goals to clarify the rectification direction and implementation details.

[0098] Real-time data input uses message queues (such as Kafka) to obtain updates to the underlying data set in real time, synchronizing data on waybill damage, customer complaints, and claims every minute. Rule matching involves inputting real-time calculated metrics like damage rate and customer complaint rate into the rule engine, matching each metric against the first monitoring rule. Alerts are triggered when any rule condition (such as "damage rate > 1.2 times the regional target and sorting link share > 60%) is met). Organizational status extraction extracts real-time metrics such as the current damage rate (1.1%), customer complaint rate (0.8%), and the proportion of responsible links (sorting 65%), along with comparative data from the previous week (damage rate increased by 25% month-over-month).

[0099] The rectification targets include: according to the organizational goal setting, for example, if the regional target is 0.6%, the rectification target is "the breakage rate within 3 days is reduced to below 0.6%, and the breakage ratio in the sorting process is reduced to below 40%"; the rectification instructions include: optimization of operating specifications: requiring the sorting process of outlets to add the "separate partition for fragile items" operation, and publicizing the operating manual at the daily morning meeting; equipment maintenance plan: arrange for the technical team to check the tightness of the conveyor belt of the sorting equipment and the wear of the sorting grid buffer pad within 48 hours; personnel training arrangements: organize practical training for sorters this week, focusing on the assessment of the sorting specifications of fragile items, and those who fail the assessment will be suspended from work.

[0100] The dispatch mechanism automatically pushes tasks, and the system records the response time after the responsible person signs for them. Failure to sign within the time limit will trigger a second reminder.

[0101] In some embodiments, the customer characteristics set for the customer management dimension include the shipping company, customer type, receiving address, and nature of the goods, and a second monitoring rule is configured to identify abnormal customer damage behavior, including: taking the shipping company's cooperation level, customer type, the region to which the receiving address belongs, and the nature of the goods as customer characteristic dimensions, and extracting historical damage data, customer complaint data, and claims data corresponding to each customer characteristic; setting a damage risk assessment model for different customer feature combinations, which is used to logically associate customer feature combination conditions with damage risk assessment conditions, to form a second monitoring rule including multi-dimensional customer feature matching and risk threshold judgment.

[0102] By building a customer feature label system and combining historical damage data to establish a risk assessment model, a second monitoring rule with multi-dimensional correlation is formed to accurately identify customer-side damage risks.

[0103] Feature dimensions include: Shipping company: Partnership level (strategic customers / core customers / regular customers), historical damage rate (average damage rate over the past year); Customer type: categorized by annual shipment volume (VIP customers: ≥100,000 orders, key customers: 50,000-100,000 orders, regular customers: <50,000 orders); Delivery address: Region (first-tier cities / third- and fourth-tier cities / rural areas), High damage risk area (marked by historical data, e.g., a township area with a long-term damage rate >1.5%); Cargo nature: Category (fragile goods / liquids / precision instruments), Packaging type (original packaging / simple packaging / customized wooden crates), Insured value (≥10,000 yuan). Data extraction: The basic dataset is filtered for damage records, customer complaints, and claims over the past 12 months, and the damage rate corresponding to each feature is calculated (e.g., a customer with a "fragile goods + simple packaging" damage rate of 20%).

[0104] Examples of feature combinations include: high-risk combination: "strategic customers + the delivery address is in a high-damage area + the nature of the goods is fragile and not insured"; medium-risk combination: "ordinary customers + the delivery address is in a third- or fourth-tier city + liquid goods are simply packaged."

[0105] Risk assessment criteria include: single feature thresholds, such as "customer type is VIP and damage rate > 0.5%"; and combined feature logic: "(shipping company is a strategic customer OR customer type is VIP) AND the delivery address is a high-damage area AND the goods are fragile." Model output: A risk level (high / medium / low) is generated for each customer feature combination, which serves as the trigger for the second monitoring rule.

[0106] Examples of rules corresponding to the second monitoring rule configuration include: Rule 1: IF Customer Type = VIP AND Damage Rate > 0.4% in the Last 30 Days AND The Percentage of Insured Cargo Among Damaged Goods ≥ 40%, THEN Trigger a High-Value Damage Alert for VIP Customers; Rule 2: IF Shipper Partnership Level = Core Customer AND Delivery Address ∈ High-Damage Area List AND Goods Type = Glassware AND Packaging Type = Simple Carton, THEN Trigger an Improper Packaging Risk Alert. Rules support dynamic weighting: VIP customer damage rules are given a higher priority (e.g., triggering manual intervention).

[0107] In some embodiments, the identification of abnormal customer damage behavior based on the basic data set and the second monitoring rules, and the generation of a management and control task if an anomaly is identified, include: matching the customer-related data in the basic data set with the second monitoring rules, and when the customer characteristics meet the risk assessment conditions in the rules, determining it as abnormal customer damage behavior; generating a management and control task including the customer risk level, management and control measure recommendations and response time requirements based on the type and severity of the customer's abnormal behavior.

[0108] Based on the matching of customer-related data with the second monitoring rules, abnormal customer damage behavior is identified, and management tasks including risk levels, control measures, and time requirements are generated to strengthen customer-side risk management.

[0109] Abnormal behavior identification includes: data matching: extracting customer feature fields (such as the cooperation level of the shipping company, the receiving address, and the type of goods) from the basic data set and comparing them one by one with the conditions in the second monitoring rule; rule triggering: when the customer data meets the rule conditions (such as "cooperation level = strategic customer and the damage rate in the past 7 days = 1.2% > target value 0.8%"), it is judged as abnormal customer damage behavior, and the triggering rule name, time, and feature details are recorded.

[0110] The generation of control tasks includes risk level determination: based on the severity of the triggering rules (e.g., damage to VIP customers is determined to be "high risk", while similar problems for ordinary customers are determined to be "medium risk");

[0111] Recommended control measures include: High risk: arrange a dedicated customer service follow-up within 24 hours, provide free reinforced packaging solutions, and coordinate priority delivery routes; Medium risk: send a damage prevention reminder SMS within 48 hours, attach a link to the "Packaging Reinforcement Guide", and record customer feedback; Response time requirements: clarify the execution time nodes of each measure (such as "customer service follow-up must be initiated within 2 hours after the warning" and "plan confirmation must be completed within 12 hours").

[0112] Example of task content: [Damage warning for high-risk customers]: Customer name: XX e-commerce (strategic customer); Risk level: High; Abnormal reason: The damage rate in the past 7 days was 1.2% (target 0.8%), and the damaged goods were all uninsured mobile phones.

[0113] Control measures include: 1. Customer Service will retrieve the customer's order history and prepare a compensation plan within 30 minutes; 2. Operations will evaluate shipping routes and recommend switching to dedicated air routes within 2 hours; 3. Sales will visit the customer within 4 hours to negotiate long-term packaging optimization cooperation. Response Time: All measures must be completed within 24 hours. Task Synchronization: Tasks will be pushed to Customer Service, Operations, and Sales departments through the CRM system, linked to customer files, and task processing logs (such as the time of customer service return visits and the results of solution negotiations) will be recorded.

[0114] In some embodiments, to address the problem of "hidden associated risks are difficult to identify" in customer damage behavior, a heterogeneous graph neural network (GNN) of customer-cargo-transportation routes is constructed to explore the transmission path of customer damage risks and identify potential high-risk customer groups in advance.

[0115] The heterogeneous graph model construction includes four types of nodes: customer (including cooperation level and historical damage rate), cargo (including fragility level and packaging type), transport route (including road condition level and damage history), and outlet (including operation specification score). Edge types include: "customer-cargo" (if a certain type of cargo has been shipped), "cargo-route" (if a certain route frequently transports this cargo), and "route-outlet" (if a route passes through a certain outlet). Edge weights are defined as the historical collaborative damage probability (for example, the damage rate for cargo B shipped by customer A via route C is 12%).

[0116] Graph neural network training uses the RGCN (Relational Graph Convolutional Network) model. Each layer aggregates features of different types of neighboring nodes (for example, a customer node aggregates the average fragility rating of all its shipped goods) to output the customer's "potential damage risk score" (0-10, with 7 or higher indicating high risk). The training objectives include labeling positive samples (customers with damage) with historical damage events, randomly sampling negative samples, and optimizing the loss function using binary cross entropy to achieve an AUC-ROC ≥ 0.92 on the validation set.

[0117] Real-time monitoring automatically triggers in-depth analysis when a customer's risk score increases by 20% or more for three consecutive days, uncovering the underlying path (e.g., "customer recently placed an order for fragile items → the corresponding route's breakage rate recently increased → the transit point's operation score decreased"). Rule enhancements incorporate the GNN predicted score as a condition in the second monitoring rule. For example, "customer risk score ≥ 8 AND recent order for fragile items" directly generates a pre-control task (proactively contacting the customer to provide packaging recommendations) without waiting for a breakage event to occur.

[0118] In some embodiments, to address the problem of data silos within the industry, a federated learning framework is designed to achieve "collaborative modeling under privacy protection" of damaged data among logistics companies, jointly optimize monitoring rules, and improve the level of damage control in the entire industry.

[0119] The federated learning architecture design includes the following participants: multiple logistics companies (data providers), industry associations (coordinators), and a model aggregation center (computing power provider). It utilizes horizontal federated learning (with consistent data feature spaces across all companies). Data processing involves each company cleaning its data locally, retaining statistical indicators such as customer complaint rates and damage rates (without transmitting original waybill data), and ensuring data privacy through homomorphic encryption.

[0120] The federated model training process includes: Local training: Each enterprise optimizes its model (such as XGBoost) using its own data training rules and uploads model parameters (not raw data) to the aggregation center. Federated aggregation: The aggregation center uses the FedAvg algorithm to weighted average the model parameters of each party to generate an industry-wide model. This is repeated 50 times to ensure cross-enterprise consistency (improving the average hit rate by over 15%). Model distribution: Each enterprise downloads the federated model and integrates it with its local model (e.g., the federated model accounts for 60% and the local model accounts for 40%) to generate personalized monitoring rules.

[0121] Security safeguards include: using differential privacy technology to add noise (ε=0.5) to prevent the model from inferring the original data; recording data contribution (such as training rounds and parameter validity) on the blockchain. Incentive mechanisms: Industry model upgrade privileges are allocated based on contribution (the top 30% of contributors receive priority access to the latest rules), and actual rewards such as insurance rate discounts are offered to encourage participation.

[0122] In some embodiments, natural language processing (NLP) and knowledge graph technology are used to extract deep semantic features from unstructured texts such as customer complaint data and damage descriptions, and to construct a "damage cause-responsibility link-corrective measures" association map to improve the semantic richness of the basic data set.

[0123] Deep analysis of text data includes: Named Entity Recognition (NER): Using the BERT model to train a logistics-specific NER, we identify "damaged parts" (e.g., "screen," "corner"), "responsible links" (e.g., "violent sorting," "bumpy transportation"), and "cargo types" (e.g., "laptop," "ceramic vase") in customer complaint texts, achieving an entity recognition accuracy of ≥90%. Relationship Extraction: Through dependency parsing, we extract semantic relationships such as "damage caused by XX link" and "XX measures can improve XX problem." For example, from a customer complaint: "Cartons arrived damaged, suspected to have been thrown during sorting," we can extract the relationship "sorting-cause-damaged carton."

[0124] The damage knowledge graph construction includes: Graph nodes: 100+ entities across five categories, including "Responsible Link" (sorting / transportation / warehousing), "Damage Type" (outer packaging / contents / accessories), "Corrective Measures" (equipment upgrade / process optimization / personnel training), and "Cargo Attributes" (fragility level / packaging type). Graph edges: Define relationships such as "cause," "improve," and "associate." Using the TransE algorithm for graph embedding training, each entity vector contains semantic association information (for example, the cosine similarity between the vectors for "violent sorting" and "damage during sorting" is greater than 0.9).

[0125] Data enhancement and rule optimization include: Feature fusion: Incorporating derived features from the graph, such as "fragile item damage risk factor" and "corrective measures for similar historical cases," into the base dataset, adding over 20 new dimensions (e.g., "highest historical damage rate for goods" and "optimal corrective solution ID for similar issues"). Rule enhancement: Adding semantic matching conditions to the second monitoring rule. For example, when keywords such as "multiple damage" and "high-value goods" appear in customer complaint text, higher-priority control tasks are automatically triggered, eliminating the need for manual rule pre-setting.

[0126] See also Figure 2 As shown, Figure 2 2 is a schematic diagram of the structure of an intelligent damage warning and efficient management device 200 provided in an embodiment of the present application. This intelligent damage warning and efficient management device 200 is used to execute the steps of the intelligent damage warning and efficient management method described in each of the above embodiments. This intelligent damage warning and efficient management device 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0127] like Figure 2 As shown, the intelligent damage warning and efficient control device 200 includes:

[0128] The data processing unit 201 is used to standardize the waybill data, damage data, customer complaint data, and claim data to form a basic data set in a unified format;

[0129] The first generation unit 202 is configured to set basic indicators, organizational goals, and trend monitoring thresholds for the operational control dimension, configure a first monitoring rule for determining abnormal and damaging behavior, identify abnormal and damaging behavior based on the basic data set and the first monitoring rule, and generate an organizational hit task containing the organizational status, rectification goals, and rectification instructions if an abnormality is identified;

[0130] The second generation unit 203 is configured to set customer characteristics including the shipping company, customer type, delivery address, and cargo nature for the customer management dimension, configure a second monitoring rule for identifying abnormal customer damage behavior, identify abnormal customer damage behavior based on the basic data set and the second monitoring rule, and generate a management task if an abnormality is identified;

[0131] The control closed-loop unit 204 is used to record warning information based on the organization's hit tasks and control tasks. The warning information includes execution time, threshold adjustment, hit rate and approval information, and generates historical statistics and verification plans to form a closed-loop control result.

[0132] In some embodiments, after the generation of historical statistics and verification plans to form a closed loop of management and control results, it also includes: according to the statistical dimensions of shipping time, shipping point, customer code and organizational level, with a sliding week or month as a cycle, statistics are collected on the problem type, number of days failing to meet the standards, average daily breakage rate, improvement results and ROI to generate statistical results; according to the statistical results, the rule application effects corresponding to the hit rate, false alarm rate and improvement achievement rate of the first monitoring rule and the second monitoring rule in the original area are recorded; according to the rule application effects, the target first monitoring rule and the target second monitoring rule are determined; based on the target area whose indicators of rule application effect, business scale, route structure, cargo characteristics and customer distribution are similar to those of the source area, the target first monitoring rule and the target second monitoring rule are migrated to the target area and fine-tuning parameter information is generated, and the migrated target first monitoring rule and the target second monitoring rule are formed into new versions in the target area for version management.

[0133] In some embodiments, the waybill data, damage data, customer complaint data and claims data are standardized to form a basic data set in a unified format, including: uniformly defining the field name, data type and value range of each type of data according to a preset data dictionary, and aligning the waybill number, shipping time, shipping point, receiving address, cargo type and weight and volume in the waybill data, the damage location, damage degree and damage responsibility link in the damage data, the customer complaint time, customer complaint content and processing status in the customer complaint data, and the claim amount, claim reason and processing result in the claims data; using data cleaning tools to deduplicate duplicate data, complete missing data, and correct erroneous data, and correlating and matching the waybill data, damage data, customer complaint data and claims data based on the waybill number to generate a unified structured data record containing multi-dimensional information to form a basic data set.

[0134] In some embodiments, the basic indicators, organizational goals and trend monitoring thresholds are set for the operational control dimension, and the first monitoring rule for determining abnormal damage behavior is configured, including: setting the breakage rate, customer complaint rate, claim rate and proportion of damage responsibility links as basic indicators, and setting the breakage rate control target, customer complaint response time target and claim processing cycle target of each level as the organizational goal according to the organizational level and business type; calculating the mean and standard deviation based on the basic indicator data of the past six months, and setting the rising warning threshold, falling warning threshold and trend fluctuation threshold of each indicator in combination with the industry benchmark value; logically combining the comparison conditions of the basic indicators and the organizational goals, and the deviation conditions of the basic indicators and the trend monitoring threshold to form a first monitoring rule containing at least one monitoring condition combination.

[0135] In some embodiments, the abnormal damage behavior is identified based on the basic data set and the first monitoring rule. If an abnormality is identified, an organizational hit task containing the organizational status, rectification goals, and rectification instructions is generated, including: inputting the real-time indicator data in the basic data set into the first monitoring rule for condition matching, and when any rule condition corresponding to the first monitoring rule is met, extracting the current indicator values ​​such as the breakage rate and customer complaint rate corresponding to the organizational level of the triggering rule as the organizational status; determining the rectification goals according to the organizational goals corresponding to the organizational level, and generating rectification instructions including operation specification optimization requirements, equipment maintenance plans, and personnel training arrangements according to the responsible links corresponding to the abnormal damage behavior, and encapsulating the organizational status, rectification goals, and rectification instructions into an organizational hit task.

[0136] In some embodiments, the customer characteristics set for the customer management dimension include the shipping company, customer type, receiving address, and nature of the goods, and a second monitoring rule is configured to identify abnormal customer damage behavior, including: taking the shipping company's cooperation level, customer type, the region to which the receiving address belongs, and the nature of the goods as customer characteristic dimensions, and extracting historical damage data, customer complaint data, and claims data corresponding to each customer characteristic; setting a damage risk assessment model for different customer feature combinations, which is used to logically associate customer feature combination conditions with damage risk assessment conditions, to form a second monitoring rule including multi-dimensional customer feature matching and risk threshold judgment.

[0137] In some embodiments, the identification of abnormal customer damage behavior based on the basic data set and the second monitoring rules, and the generation of a management and control task if an anomaly is identified, include: matching the customer-related data in the basic data set with the second monitoring rules, and when the customer characteristics meet the risk assessment conditions in the rules, determining it as abnormal customer damage behavior; generating a management and control task including the customer risk level, management and control measure recommendations and response time requirements based on the type and severity of the customer's abnormal behavior.

[0138] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working processes of the intelligent damage warning and efficient control device and each module described above can refer to the corresponding processes in the embodiments of the intelligent damage warning and efficient control method described in the above embodiments, and will not be repeated here.

[0139] The above damage intelligent early warning and efficient control method can be implemented in the form of a computer program. The computer program can be used in Figure 2 Run on the device shown.

[0140] See also Figure 3 , Figure 3 1 is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0141] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the intelligent damage early warning and efficient control methods.

[0142] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0143] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the intelligent damage warning and efficient control methods.

[0144] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0146] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0147] Standardize waybill data, damage data, customer complaint data, and claims data to form a basic data set in a unified format;

[0148] For the operational control dimension, set basic indicators, organizational goals, and trend monitoring thresholds. Configure the first monitoring rule for identifying abnormal and disruptive behavior. Identify abnormal and disruptive behavior based on the basic data set and the first monitoring rule. If an anomaly is identified, generate an organizational hit task that includes the organizational status, rectification goals, and rectification instructions.

[0149] For the customer management dimension, set customer characteristics including the shipping company, customer type, delivery address, and cargo nature. Configure a second monitoring rule to identify abnormal customer damage behavior. Based on the basic data set and the second monitoring rule, identify abnormal customer damage behavior. If an anomaly is identified, generate a management task.

[0150] Early warning information is recorded based on the organization's hit tasks and management and control tasks. The early warning information includes execution time, threshold adjustment, hit rate and approval information, and historical statistics and verification plans are generated to form a closed loop of management and control results.

[0151] In some embodiments, after the generation of historical statistics and verification plans to form a closed loop of management and control results, it also includes: according to the statistical dimensions of shipping time, shipping point, customer code and organizational level, with a sliding week or month as a cycle, statistics are collected on the problem type, number of days failing to meet the standards, average daily breakage rate, improvement results and ROI to generate statistical results; according to the statistical results, the rule application effects corresponding to the hit rate, false alarm rate and improvement achievement rate of the first monitoring rule and the second monitoring rule in the original area are recorded; according to the rule application effects, the target first monitoring rule and the target second monitoring rule are determined; based on the target area whose indicators of rule application effect, business scale, route structure, cargo characteristics and customer distribution are similar to those of the source area, the target first monitoring rule and the target second monitoring rule are migrated to the target area and fine-tuning parameter information is generated, and the migrated target first monitoring rule and the target second monitoring rule are formed into new versions in the target area for version management.

[0152] In some embodiments, the waybill data, damage data, customer complaint data and claims data are standardized to form a basic data set in a unified format, including: uniformly defining the field name, data type and value range of each type of data according to a preset data dictionary, and aligning the waybill number, shipping time, shipping point, receiving address, cargo type and weight and volume in the waybill data, the damage location, damage degree and damage responsibility link in the damage data, the customer complaint time, customer complaint content and processing status in the customer complaint data, and the claim amount, claim reason and processing result in the claims data; using data cleaning tools to deduplicate duplicate data, complete missing data, and correct erroneous data, and correlating and matching the waybill data, damage data, customer complaint data and claims data based on the waybill number to generate a unified structured data record containing multi-dimensional information to form a basic data set.

[0153] In some embodiments, the basic indicators, organizational goals and trend monitoring thresholds are set for the operational control dimension, and the first monitoring rule for determining abnormal damage behavior is configured, including: setting the breakage rate, customer complaint rate, claim rate and proportion of damage responsibility links as basic indicators, and setting the breakage rate control target, customer complaint response time target and claim processing cycle target of each level as the organizational goal according to the organizational level and business type; calculating the mean and standard deviation based on the basic indicator data of the past six months, and setting the rising warning threshold, falling warning threshold and trend fluctuation threshold of each indicator in combination with the industry benchmark value; logically combining the comparison conditions of the basic indicators and the organizational goals, and the deviation conditions of the basic indicators and the trend monitoring threshold to form a first monitoring rule containing at least one monitoring condition combination.

[0154] In some embodiments, the abnormal damage behavior is identified based on the basic data set and the first monitoring rule. If an abnormality is identified, an organizational hit task containing the organizational status, rectification goals, and rectification instructions is generated, including: inputting the real-time indicator data in the basic data set into the first monitoring rule for condition matching, and when any rule condition corresponding to the first monitoring rule is met, extracting the current indicator values ​​such as the breakage rate and customer complaint rate corresponding to the organizational level of the triggering rule as the organizational status; determining the rectification goals according to the organizational goals corresponding to the organizational level, and generating rectification instructions including operation specification optimization requirements, equipment maintenance plans, and personnel training arrangements according to the responsible links corresponding to the abnormal damage behavior, and encapsulating the organizational status, rectification goals, and rectification instructions into an organizational hit task.

[0155] In some embodiments, the customer characteristics set for the customer management dimension include the shipping company, customer type, receiving address, and nature of the goods, and a second monitoring rule is configured to identify abnormal customer damage behavior, including: taking the shipping company's cooperation level, customer type, the region to which the receiving address belongs, and the nature of the goods as customer characteristic dimensions, and extracting historical damage data, customer complaint data, and claims data corresponding to each customer characteristic; setting a damage risk assessment model for different customer feature combinations, which is used to logically associate customer feature combination conditions with damage risk assessment conditions, to form a second monitoring rule including multi-dimensional customer feature matching and risk threshold judgment.

[0156] In some embodiments, the identification of abnormal customer damage behavior based on the basic data set and the second monitoring rules, and the generation of a management and control task if an anomaly is identified, include: matching the customer-related data in the basic data set with the second monitoring rules, and when the customer characteristics meet the risk assessment conditions in the rules, determining it as abnormal customer damage behavior; generating a management and control task including the customer risk level, management and control measure recommendations and response time requirements based on the type and severity of the customer's abnormal behavior.

[0157] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement the steps of the intelligent damage warning and efficient control method provided in the above-mentioned embodiments of the present application.

[0158] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped on the computer device.

[0159] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A damage intelligent early warning and efficient control method, characterized in that: include: Standardize waybill data, damage data, customer complaint data, and claims data to form a basic data set in a unified format; For the operational control dimension, set basic indicators, organizational goals, and trend monitoring thresholds. Configure the first monitoring rule for identifying abnormal and disruptive behavior. Identify abnormal and disruptive behavior based on the basic data set and the first monitoring rule. If an anomaly is identified, generate an organizational hit task that includes the organizational status, rectification goals, and rectification instructions. For the customer management dimension, set customer characteristics including the shipping company, customer type, delivery address, and cargo nature. Configure a second monitoring rule to identify abnormal customer damage behavior. Based on the basic data set and the second monitoring rule, identify abnormal customer damage behavior. If an anomaly is identified, generate a management task. Early warning information is recorded based on the organization's hit tasks and management and control tasks. The early warning information includes execution time, threshold adjustment, hit rate and approval information, and historical statistics and verification plans are generated to form a closed loop of management and control results.

2. The method according to claim 1, characterized in that After generating historical statistics and checking solutions to form a closed loop of control results, the following steps are also included: Based on the statistical dimensions of shipping time, shipping point, customer code, and organizational level, with a sliding weekly or monthly cycle, statistics are collected on problem types, number of days not meeting standards, average daily damage rate, improvement results, and ROI to generate statistical results. Record the rule application effects of the first monitoring rule and the second monitoring rule in the original area in terms of hit rate, false alarm rate, and improvement achievement rate according to the statistical results; Determine the target first monitoring rule and the target second monitoring rule according to the rule application effect; Based on the target area whose indicators of rule application effect, business scale, route structure, cargo characteristics and customer distribution are similar to those of the source area, the target first monitoring rule and the target second monitoring rule are migrated to the target area and fine-tuning parameter information is generated. The migrated target first monitoring rule and the target second monitoring rule are formed into new versions in the target area for versioning management.

3. The method according to claim 1, characterized in that The standardized processing of waybill data, damage data, customer complaint data, and claims data forms a basic data set in a unified format, including: The field names, data types, and value ranges of various data types are uniformly defined according to the preset data dictionary. Field alignment is performed for the waybill number, shipping time, shipping point, receiving address, cargo type, weight and volume in the waybill data; the damage location, damage extent, and damage responsibility link in the damage data; the customer complaint time, complaint content, and handling status in the customer complaint data; and the claim amount, claim reason, and handling result in the claim data. Data cleaning tools are used to deduplicate data, complete missing data, and correct erroneous data. Waybill data, damage data, customer complaint data, and claims data are correlated and matched based on the waybill number to generate unified structured data records containing multi-dimensional information, forming a basic data set.

4. The method according to claim 1, wherein The basic indicators, organizational goals, and trend monitoring thresholds are set for the operational control dimension, and the first monitoring rule for determining abnormal and damaged behaviors is configured, including: The damage rate, customer complaint rate, claim settlement rate, and the proportion of damage responsibility links are set as basic indicators. Based on the organizational level and business type, the damage rate control targets, customer complaint response time targets, and claim settlement cycle targets are set as organizational goals for each level. Calculate the mean and standard deviation based on the historical six-month basic indicator data, and set the rising warning threshold, falling warning threshold, and trend fluctuation threshold for each indicator in combination with the industry benchmark value; The comparison conditions between the basic indicators and the organizational goals and the deviation conditions between the basic indicators and the trend monitoring threshold are logically combined to form a first monitoring rule including at least one monitoring condition combination.

5. The method according to claim 1, wherein The method of identifying abnormal and damaging behaviors based on the basic data set and the first monitoring rule, and generating an organizational hit task including the organizational status, rectification goals, and rectification instructions if an abnormality is identified, includes: The real-time indicator data in the basic data set is input into the first monitoring rule for condition matching. When any rule condition corresponding to the first monitoring rule is met, the current indicator values ​​such as the breakage rate and customer complaint rate corresponding to the organizational level of the triggering rule are extracted as the organizational status; Determine rectification targets based on the organizational goals corresponding to the organizational hierarchy. Generate rectification instructions that include operating specification optimization requirements, equipment maintenance plans, and personnel training arrangements based on the responsibility links corresponding to abnormal and damaging behaviors. Encapsulate the organizational status, rectification targets, and rectification instructions into organizational hit tasks.

6. The method according to claim 1, wherein The customer characteristics of the customer management dimension including the shipping company, customer type, receiving address, and cargo nature are set, and the second monitoring rule for identifying abnormal customer damage behavior is configured, including: The cooperation level of the shipping company, customer type, delivery address region, and cargo nature are used as customer feature dimensions. The historical damage data, customer complaint data, and claims data corresponding to each customer feature are extracted. A damage risk assessment model is set for different customer feature combinations to logically associate customer feature combination conditions with damage risk assessment conditions, forming a second monitoring rule that includes multi-dimensional customer feature matching and risk threshold judgment.

7. The method according to claim 1, characterized in that The method identifies abnormal customer damage behavior based on the basic data set and the second monitoring rule, and generates a control task if an abnormality is identified, including: Match the customer-related data in the basic data set with the second monitoring rule. When the customer characteristics meet the risk assessment conditions in the rule, it is determined to be abnormal customer damage behavior; Based on the type and severity of abnormal customer behavior, a management and control task is generated that includes the customer risk level, management and control measure recommendations, and response time requirements.

8. A damage intelligent early warning and efficient control device, characterized in that: include: The data processing unit is used to standardize the waybill data, damage data, customer complaint data, and claims data to form a basic data set in a unified format; A first generation unit is configured to set basic indicators, organizational goals, and trend monitoring thresholds for the operational control dimension, configure a first monitoring rule for determining abnormal and damaging behavior, identify abnormal and damaging behavior based on the basic data set and the first monitoring rule, and generate an organizational hit task containing the organizational status, rectification goals, and rectification instructions if an abnormality is identified; The second generation unit is configured to set customer characteristics including the shipping company, customer type, receiving address, and cargo nature for the customer management dimension, configure a second monitoring rule for identifying abnormal customer damage behavior, identify abnormal customer damage behavior based on the basic data set and the second monitoring rule, and generate a management task if an abnormality is identified; The control closed-loop unit is used to record early warning information based on the organization's hit tasks and control tasks. The early warning information includes execution time, threshold adjustment, hit rate and approval information, and generates historical statistics and verification plans to form a closed-loop control result.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 7.

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