Intelligent early warning method and device for health degree of logistics network, medium and equipment

Abstract

The invention provides an intelligent early warning method and device for the health degree of a logistics network, a medium and equipment. The method comprises the following steps: constructing a network health scoring model; continuously updating the network health scoring model, and updating the health score of each network in real time based on the updated network health scoring model; when the health score of the website is smaller than a threshold value, intelligent early warning is triggered; when the health score of the website is smaller than a threshold value, the current website is regarded as a low-efficiency website, factors influencing the efficiency of the low-efficiency website are identified based on historical data analysis and a machine learning algorithm, and an improvement strategy is generated; constructing a prediction model, and predicting the change of at least one index of order processing time, inventory turnover rate, on-time delivery rate and transportation cost efficiency of the low-efficiency network after implementation of the improvement strategy by using the prediction model to obtain a changed index; based on the changed indexes, obtaining a health score of the current website by using a health scoring model of the current website; and evaluating the improvement effect based on the health score of the current website.

Description

Technical Field

[0001] The present invention relates to the field of intelligent early warning technology, and in particular to an intelligent early warning method, device, medium and equipment for the health of logistics outlets. Background Art

[0002] With the continuous and rapid development of the global logistics industry, as the artery of the modern economic system, it carries the important mission of commodity circulation, information transmission and market demand response. In this context, ensuring that logistics outlets all over the country can operate efficiently and stably is not only directly related to customer experience satisfaction, but also has a profound impact on the operational efficiency and cost control of logistics companies, and thus becomes the key to improving the service quality of the entire industry. However, traditional logistics outlet monitoring and management methods often rely on manual inspections and post-analysis, which is inadequate in the face of massive data, complex business scenarios and rapidly changing market environments. Manual monitoring is not only inefficient, difficult to achieve comprehensive coverage and real-time response, but also has significant limitations in identifying potential problems and predicting risks. In particular, in the face of complex and changeable internal and external environmental factors such as sudden weather changes, traffic congestion, and equipment failures, traditional methods often find it difficult to discover and solve problems in a timely and accurate manner, resulting in increased operational risks and reduced service quality. Summary of the invention

[0003] The main purpose of the present invention is to solve the technical problems in the prior art that manual monitoring is inefficient, difficult to achieve comprehensive coverage and real-time response, and has significant limitations in identifying potential problems and predicting risks.

[0004] The first aspect of the present invention provides an intelligent early warning method for the health of a logistics network, comprising:

[0005] Build a network health scoring model;

[0006] Continuously update the branch health scoring model, and update the health score of each branch in real time based on the updated branch health scoring model;

[0007] When the health score of the outlet is lower than the threshold, an intelligent warning is triggered.

[0008] Optionally, in a first implementation of the first aspect of the present invention, the constructing of the network health scoring model includes: constructing the network health scoring model based on the intrinsic efficiency index of logistics operation and external environmental influencing factors;

[0009] The intrinsic efficiency indicators of logistics operations include: order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency;

[0010] The external environmental influencing factors include: traffic congestion index, weather conditions and market demand fluctuations;

[0011] The outlet health scoring model includes:

[0012]

[0013] Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

[0014] Optionally, in a second implementation of the first aspect of the present invention, updating the health score of each outlet in real time based on the updated outlet health score model includes:

[0015] Continuously update the site health scoring model using methods including sliding window models or online learning;

[0016] Use the updated branch health scoring model to update the health score of each branch in real time.

[0017] Optionally, in a third implementation of the first aspect of the present invention, the step of updating the health score of each outlet in real time by using the updated outlet health score model includes:

[0018] The health score of the updated outlet = original score + η × (new data contribution - predicted value)

[0019] Wherein, η represents the learning rate; the new data contribution is the score change calculated based on the collected new data; and the predicted value is the health score of the outlet calculated by the updated outlet health score model based on the collected old data.

[0020] Optionally, in a fourth implementation of the first aspect of the present invention, triggering an intelligent early warning when the health score of the network point is less than a threshold value includes:

[0021] Set scoring thresholds based on business needs and historical data;

[0022] When the health score of the outlet is less than the score threshold, the early warning mechanism is triggered, including: generating early warning reports and adjusting strategies;

[0023] The early warning report includes: the current operating status of the outlets and analysis of the reasons for the reduction in scores;

[0024] The adjustment strategy includes: sending a request to the regional operation center to dispatch a professional team and / or triggering a high-level management intervention process.

[0025] Optionally, in a fifth implementation of the first aspect of the present invention, the method further comprises: when the health score of the outlet is less than a threshold, the current outlet is regarded as an inefficient outlet, factors affecting the efficiency of the inefficient outlet are identified based on historical data analysis and a machine learning algorithm, and an improvement strategy is generated;

[0026] When the health score of the outlet is less than the threshold, the current outlet is regarded as an inefficient outlet, and the factors affecting the efficiency of the inefficient outlet are identified based on historical data analysis and machine learning algorithms, and improvement strategies are generated, including:

[0027] Obtain historical operational data for each inefficient site, including order processing time, inventory turnover, on-time delivery rate, and transportation cost efficiency;

[0028] Use clustering algorithms to cluster inefficient outlets based on their historical operating data to generate different groups;

[0029] For each group, a machine learning algorithm is used to analyze the factors that affect the efficiency of the outlets;

[0030] Generate corresponding improvement strategies based on the factors affecting outlet efficiency obtained through analysis;

[0031] The improvement strategies include at least one of adjusting inventory strategies, optimizing distribution routes, and improving warehouse automation levels.

[0032] Optionally, in a sixth implementation of the first aspect of the present invention, the method further includes:

[0033] Construct a prediction model, and use the prediction model to predict the change of at least one indicator of the inefficient outlets after the implementation of the improvement strategy, including order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency, to obtain the changed indicator;

[0034] Based on the changed indicators, the health score of the current outlet is obtained using the current outlet health score model; and the improvement effect is evaluated based on the health score of the current outlet.

[0035] The second aspect of the present invention provides an intelligent early warning device for the health of a logistics network, comprising:

[0036] Model building module, used to build a network health scoring model;

[0037] The scoring module is used to continuously update the outlet health scoring model and update the health score of each outlet in real time based on the updated outlet health scoring model;

[0038] The early warning module is used to trigger an intelligent early warning when the health score of the outlet is less than the threshold.

[0039] Optionally, in a first implementation of the second aspect of the present invention, the model building module includes: building a network health scoring model based on the intrinsic efficiency index of logistics operation and external environmental influencing factors;

[0040] The intrinsic efficiency indicators of logistics operations include: order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency;

[0041] The external environmental influencing factors include: traffic congestion index, weather conditions and market demand fluctuations;

[0042] The outlet health scoring model includes:

[0043]

[0044] Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

[0045] Optionally, in a second implementation of the second aspect of the present invention, the scoring module includes:

[0046] Continuously update the site health scoring model using methods including sliding window models or online learning;

[0047] Use the updated branch health scoring model to update the health score of each branch in real time.

[0048] Optionally, in a third implementation of the second aspect of the present invention, the step of updating the health score of each outlet in real time by using the updated outlet health score model includes:

[0049] The health score of the updated outlet = original score + η × (new data contribution - predicted value)

[0050] Wherein, η represents the learning rate; the new data contribution is the score change calculated based on the collected new data; and the predicted value is the health score of the outlet calculated by the updated outlet health score model based on the collected old data.

[0051] Optionally, in a fourth implementation of the second aspect of the present invention, the early warning module includes:

[0052] Set scoring thresholds based on business needs and historical data;

[0053] When the health score of the outlet is less than the score threshold, the early warning mechanism is triggered, including: generating early warning reports and adjusting strategies;

[0054] The early warning report includes: the current operating status of the outlets and analysis of the reasons for the reduction in scores;

[0055] The adjustment strategy includes: sending a request to the regional operation center to dispatch a professional team and / or triggering a high-level management intervention process.

[0056] Optionally, in a fifth implementation of the second aspect of the present invention, the device further includes: an optimization module, configured to, when the health score of the outlet is less than a threshold, regard the current outlet as an inefficient outlet, identify factors affecting the efficiency of the inefficient outlet based on historical data analysis and a machine learning algorithm, and generate an improvement strategy;

[0057] When the health score of the outlet is less than the threshold, the current outlet is regarded as an inefficient outlet, and the factors affecting the efficiency of the inefficient outlet are identified based on historical data analysis and machine learning algorithms, and improvement strategies are generated, including:

[0058] Obtain historical operational data for each inefficient site, including order processing time, inventory turnover, on-time delivery rate, and transportation cost efficiency;

[0059] Use clustering algorithms to cluster inefficient outlets based on their historical operating data to generate different groups;

[0060] For each group, a machine learning algorithm is used to analyze the factors that affect the efficiency of the outlets;

[0061] Generate corresponding improvement strategies based on the factors affecting outlet efficiency obtained through analysis;

[0062] The improvement strategies include at least one of adjusting inventory strategies, optimizing distribution routes, and improving warehouse automation levels.

[0063] Optionally, in a sixth implementation of the second aspect of the present invention, the device further comprises: an evaluation module, configured to construct a prediction model, and use the prediction model to predict changes in at least one of the following indicators of the inefficient outlets after the implementation of the improvement strategy, including order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency, to obtain the changed indicator;

[0064] Based on the changed indicators, the health score of the current outlet is obtained using the current outlet health score model; and the improvement effect is evaluated based on the health score of the current outlet.

[0065] A third aspect of the present invention provides an electronic device, the electronic device comprising a memory and at least one processor, the memory storing instructions;

[0066] The at least one processor calls the instructions in the memory so that the electronic device executes each step of the intelligent early warning method for the health of logistics outlets as described above.

[0067] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the intelligent early warning method for the health of logistics outlets as described above.

[0068] Compared with the prior art, the present invention has the following beneficial effects: the present invention significantly improves the identification speed and processing efficiency of logistics network operation problems in an intelligent and automated manner, effectively prevents express delivery backlogs and service quality degradation, ensures the overall health of the logistics network and customer satisfaction, and provides a powerful operation management tool for the logistics industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0070] Figure 1 A first flow chart of the intelligent early warning method for the health of logistics outlets provided in an embodiment of the present invention.

[0071] Figure 2 A second flow chart of the intelligent early warning method for the health of logistics outlets provided in an embodiment of the present invention.

[0072] Figure 3 A third flow chart of the intelligent early warning method for the health of logistics outlets provided in an embodiment of the present invention.

[0073] Figure 4 A schematic diagram of the structure of an intelligent early warning device for the health of logistics outlets provided in an embodiment of the present invention.

[0074] Figure 5 Another structural schematic diagram of the intelligent early warning device for the health of logistics outlets provided in an embodiment of the present invention.

[0075] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The embodiment of the present invention provides an intelligent early warning method, device, medium and equipment for the health of logistics outlets. By constructing a multi-dimensional evaluation framework, the intrinsic efficiency indicators of logistics operations are combined with external environmental influencing factors to form an evaluation system that comprehensively reflects the health status of outlets; a dynamic scoring algorithm is developed to regularly collect the latest data, calculate and update the health scores of each outlet in real time, and ensure the timeliness and accuracy of the evaluation results. A configurable scoring threshold is set. When the health score of a certain outlet falls below the preset threshold, the system automatically triggers the early warning process, including but not limited to sending a detailed early warning report to the logistics headquarters, activating built-in process optimization suggestions, dispatching professional teams for on-site support, and even starting the management personnel adjustment program to quickly respond to and solve potential problems. Based on historical data analysis and machine learning algorithms, customized improvement suggestions and operation guidelines are provided for inefficient outlets to promote the continuous optimization of internal processes. The present invention solves the technical problems of low efficiency of manual monitoring, difficulty in achieving comprehensive coverage and real-time response, and significant limitations in identifying potential problems and predicting risks.

[0077] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0078] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the intelligent early warning method for the health of logistics outlets in the embodiment of the present invention includes:

[0079] 101. Build a network health scoring model;

[0080] In this embodiment, the construction of the network health scoring model includes: constructing the network health scoring model based on the intrinsic efficiency index of logistics operation and the external environmental influencing factors;

[0081] The intrinsic efficiency indicators of logistics operations include: order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency;

[0082] The external environmental influencing factors include: traffic congestion index, weather conditions and market demand fluctuations;

[0083] The outlet health scoring model includes:

[0084]

[0085] Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

[0086] 102. Continuously update the outlet health scoring model, and update the health score of each outlet in real time based on the updated outlet health scoring model;

[0087] In this embodiment, a sliding window model or an online learning method is used to continuously update the network point health scoring model; and the health score of each network point is updated in real time using the updated network point health scoring model.

[0088] The health score of the updated outlet = original score + η × (new data contribution - predicted value)

[0089] Wherein, η represents the learning rate; the new data contribution is the score change calculated based on the collected new data; and the predicted value is the health score of the outlet calculated by the updated outlet health score model based on the collected old data.

[0090] 103. When the health score of an outlet is lower than the threshold, an intelligent warning is triggered.

[0091] In this embodiment, the scoring threshold is set according to business requirements and historical data;

[0092] When the health score of the outlet is less than the score threshold, the early warning mechanism is triggered, including: generating early warning reports and adjusting strategies;

[0093] The early warning report includes: the current operating status of the outlets and analysis of the reasons for the reduction in scores;

[0094] The adjustment strategy includes: sending a request to the regional operation center to dispatch a professional team and / or triggering a high-level management intervention process.

[0095] This embodiment uses a combination of internal efficiency indicators and external environmental factors to score the health of outlets, and then makes corresponding adjustments based on the health scores. This not only improves the operating efficiency and adaptability of the outlets, but also reduces potential risks through an intelligent early warning mechanism, optimizes resource allocation, and thus enhances competitiveness.

[0096] See also Figure 2 The second embodiment of the intelligent early warning method for the health of logistics outlets in the embodiment of the present invention includes:

[0097] 201. Build a multi-dimensional evaluation framework that combines the internal efficiency indicators of logistics operations with external environmental influencing factors to form an evaluation system that comprehensively reflects the health status of outlets;

[0098] In this embodiment, when constructing a multi-dimensional evaluation framework, it is first necessary to identify and quantify the key performance indicators and external environmental factors of logistics operations. These indicators may include, but are not limited to: internal efficiency indicators and external environmental influencing factors;

[0099] Among them, the internal efficiency indicators include: Order processing time (TPT): the average time required to process a single order. Inventory turnover (ITO): the ratio of the sales volume of inventory goods in a certain period to the average inventory volume. On-time delivery rate (OTD): the proportion of orders delivered on time to total orders. Transportation cost efficiency (TCE): the ratio of the transportation cost per unit of goods to the transportation distance or weight.

[0100] The external environmental influencing factors include: Traffic congestion index (TDI): the impact of traffic conditions on distribution efficiency based on geographical location. Weather condition impact (WCI): the degree of obstruction of bad weather on logistics operations. Market demand fluctuation (MDV): changes in market demand caused by seasonality or emergencies.

[0101] These indicators are integrated through a weighted comprehensive evaluation model, such as the analytic hierarchy process (AHP) or principal component analysis (PCA), to form a comprehensive evaluation system.

[0102]

[0103] Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

[0104] 202. Develop a dynamic scoring algorithm, regularly collect the latest data, calculate and update the health score of each outlet in real time, and ensure the timeliness and accuracy of the evaluation results.

[0105] In this embodiment, the dynamic scoring algorithm needs to be designed to be able to capture the latest business data (such as order data, cost data, customer feedback, etc.) in real time and calculate it instantly. You can consider using a sliding window model or an online learning method, such as stochastic gradient descent (SGD), to continuously update the scoring model.

[0106] Updated score = original score + η × (new data contribution - predicted value)

[0107] Among them, η is the learning rate, which controls the magnitude of each update; the new data contribution is the score change calculated based on the new data, and the predicted value is the model's prediction of the current state based on the old data.

[0108] 203. Set configurable scoring thresholds. When the health score of a branch drops below the preset threshold, the system automatically triggers the early warning process, including but not limited to sending a detailed early warning report to the logistics headquarters, activating built-in process optimization suggestions, dispatching professional teams for on-site support, and even initiating management personnel adjustment procedures to quickly respond to and resolve potential problems.

[0109] In this embodiment, a configurable scoring threshold is set. When the health score of a certain outlet drops below the preset threshold, the system automatically triggers the early warning process, including but not limited to sending a detailed early warning report to the logistics headquarters, activating built-in process optimization suggestions, dispatching a professional team for on-site support, and even initiating a management personnel adjustment procedure to quickly respond to and resolve potential problems.

[0110] This embodiment significantly improves the speed of identifying and handling logistics network operation problems in an intelligent and automated manner, effectively prevents express delivery backlogs and service quality degradation, ensures the overall health of the logistics network and customer satisfaction, and provides a powerful operation management tool for the logistics industry.

[0111] See also Figure 3 The third embodiment of the intelligent early warning method for the health of logistics outlets in the embodiment of the present invention includes:

[0112] 301. Construct a network health scoring model;

[0113] 302. Continuously update the outlet health scoring model, and update the health score of each outlet in real time based on the updated outlet health scoring model;

[0114] 303. When the health score of the outlet is less than the threshold, an intelligent early warning is triggered;

[0115] 304. When the health score of an outlet is less than a threshold, the current outlet is considered an inefficient outlet, and factors affecting the efficiency of the inefficient outlet are identified based on historical data analysis and machine learning algorithms, and improvement strategies are generated;

[0116] In this embodiment, historical operation data of each inefficient outlet is obtained, including: order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency;

[0117] Use clustering algorithms to cluster inefficient outlets based on their historical operating data to generate different groups;

[0118] For each group, a machine learning algorithm is used to analyze the factors that affect the efficiency of the outlets;

[0119] Generate corresponding improvement strategies based on the factors affecting outlet efficiency obtained through analysis;

[0120] The improvement strategies include at least one of adjusting inventory strategies, optimizing distribution routes, and improving warehouse automation levels.

[0121] 305. Construct a prediction model and use the prediction model to predict the change of at least one indicator of inefficient outlets after the implementation of improvement strategies, including order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency, to obtain the changed indicator; based on the changed indicator, use the current outlet health score model to obtain the health score of the current outlet; and evaluate the improvement effect based on the health score of the current outlet.

[0122] This embodiment can not only effectively identify and improve inefficient outlets, but also continuously optimize logistics operations through real-time monitoring and improvement strategies, thereby enhancing overall business performance and competitive advantage.

[0123] The above describes the intelligent early warning method for the health of logistics outlets in the embodiment of the present invention. The following describes the intelligent early warning device for the health of logistics outlets in the embodiment of the present invention. Figure 4 In one embodiment of the present invention, an intelligent early warning device for the health of a logistics network point includes:

[0124] A model building module 401 is used to build a network health scoring model;

[0125] In this embodiment, the model building module 401 includes:

[0126] Construct a network health scoring model based on the internal efficiency indicators of logistics operations and external environmental influencing factors;

[0127] The intrinsic efficiency indicators of logistics operations include: order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency;

[0128] Among them, the order processing time is the average time required to process a single order; the inventory turnover rate is the ratio of the sales quantity of inventory goods in a certain period to the average inventory volume; the on-time delivery rate is the proportion of orders delivered on time to the total orders; the transportation cost efficiency is the ratio of the transportation cost per unit of goods to the transportation distance or weight.

[0129] The external environmental influencing factors include: traffic congestion index, weather conditions and market demand fluctuations;

[0130] Among them, the traffic congestion index refers to the impact of traffic conditions based on geographical location on distribution efficiency; the weather condition impact refers to the degree of hindrance to logistics operations caused by bad weather; and the market demand fluctuation refers to changes in market demand caused by seasonality or emergencies.

[0131] Integrate these indicators through a weighted comprehensive evaluation model, such as analytic hierarchy process or principal component analysis, to form a comprehensive evaluation system;

[0132] The outlet health scoring model includes:

[0133]

[0134] Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

[0135] Scoring module 402, used to continuously update the network point health scoring model, and update the health score of each network point in real time based on the updated network point health scoring model;

[0136] In this embodiment, the scoring module 402 includes:

[0137] Continuously update the branch health scoring model using a sliding window model or online learning method; use the updated branch health scoring model to update the health score of each branch in real time;

[0138] The health score of the updated outlet = original score + η × (new data contribution - predicted value)

[0139] Wherein, η represents the learning rate; the new data contribution is the score change calculated based on the collected new data; and the predicted value is the health score of the outlet calculated by the updated outlet health score model based on the collected old data.

[0140] The early warning module 403 is used to trigger an intelligent early warning when the health score of the outlet is less than a threshold;

[0141] In this embodiment, the early warning module 403 includes:

[0142] Set scoring thresholds based on business needs and historical data;

[0143] When the health score of the outlet is less than the score threshold, the early warning mechanism is triggered, including: generating early warning reports and adjusting strategies;

[0144] The early warning report includes: the current operating status of the outlets and analysis of the reasons for the reduction in scores;

[0145] The adjustment strategy includes: sending a request to the regional operation center to dispatch a professional team and / or triggering a high-level management intervention process to consider strategy adjustments and personnel transfers.

[0146] The optimization module 404 is used to, when the health score of the outlet is less than a threshold, regard the current outlet as an inefficient outlet, identify factors affecting the efficiency of the inefficient outlet based on historical data analysis and machine learning algorithms, and generate improvement strategies;

[0147] In this embodiment, the optimization module 404 includes:

[0148] Obtain historical operational data for each inefficient site, including order processing time, inventory turnover, on-time delivery rate, and transportation cost efficiency;

[0149] Use clustering algorithms to cluster inefficient outlets based on their historical operating data to generate different groups;

[0150] For each group, a machine learning algorithm is used to analyze the factors that affect the efficiency of the outlets;

[0151] Generate corresponding improvement strategies based on the factors affecting outlet efficiency obtained through analysis;

[0152] The improvement strategies include at least one of adjusting inventory strategies, optimizing distribution routes, and improving warehouse automation levels.

[0153] Evaluation module 405, used to construct a prediction model, and use the prediction model to predict the change of at least one indicator of the inefficient outlets including order processing time, inventory turnover rate, on-time delivery rate and transportation cost efficiency after the implementation of the improvement strategy, and obtain the changed indicator;

[0154] Based on the changed indicators, the health score of the current outlet is obtained using the current outlet health score model; and the improvement effect is evaluated based on the health score of the current outlet.

[0155] See also Figure 5 Another embodiment of the intelligent early warning device for the health of logistics outlets in the embodiment of the present invention includes:

[0156] Evaluation system construction module 501 is used to construct a multi-dimensional evaluation framework, combining the internal efficiency indicators of logistics operations with external environmental influencing factors to form an evaluation system that comprehensively reflects the health status of outlets;

[0157] In this embodiment, when constructing a multi-dimensional evaluation framework, it is first necessary to identify and quantify the key performance indicators (KPIs) and external environmental factors of logistics operations. These indicators may include but are not limited to:

[0158] Intrinsic efficiency indicators:

[0159] Order processing time (TPT): the average time required to process a single order. Inventory turnover (ITO): the ratio of the number of sales of inventory items in a certain period to the average inventory. On-time delivery rate (OTD): the proportion of orders delivered on time to total orders. Transportation cost efficiency (TCE): the ratio of the transportation cost per unit of goods to the transportation distance or weight.

[0160] External environmental factors:

[0161] Traffic congestion index (TDI): the impact of traffic conditions on distribution efficiency based on geographical location. Weather conditions impact (WCI): the degree to which bad weather hinders logistics operations. Market demand fluctuations (MDV): changes in market demand caused by seasonality or emergencies.

[0162] Integrate these indicators through a weighted comprehensive evaluation model, such as the analytic hierarchy process (AHP) or principal component analysis (PCA), to form a comprehensive evaluation system;

[0163]

[0164] Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

[0165] Scoring module 502, used to develop a dynamic scoring algorithm, regularly collect the latest data, and calculate and update the health score of each outlet in real time to ensure the timeliness and accuracy of the evaluation results;

[0166] In this embodiment, the dynamic scoring algorithm needs to be designed to be able to capture the latest business data (such as order data, cost data, customer feedback, etc.) in real time and calculate it instantly. You can consider using a sliding window model or an online learning method, such as stochastic gradient descent (SGD), to continuously update the scoring model.

[0167] Updated score = original score + η × (new data contribution - predicted value)

[0168] Among them, η is the learning rate, which controls the magnitude of each update, the new data contribution is the score change calculated based on the new data, and the predicted value is the model's prediction of the current state based on the old data.

[0169] The early warning module 503 is used to set a configurable scoring threshold. When the health score of a certain outlet drops below the preset threshold, the system automatically triggers the early warning process, including but not limited to sending a detailed early warning report to the logistics headquarters, activating built-in process optimization suggestions, dispatching a professional team for on-site support, and even initiating a management personnel adjustment procedure to quickly respond to and resolve potential problems.

[0170] In this embodiment, a series of configurable scoring thresholds are set according to business needs and historical data, such as "warning threshold" and "emergency intervention threshold". When the outlet score is lower than these thresholds, the preset early warning mechanism is automatically triggered. The early warning process may include:

[0171] Generate early warning reports: record in detail the current operating status of the outlets and analyze the reasons for the reduction in scores.

[0172] Process optimization suggestions activated: The system recommends standardized improvement measures based on historical cases.

[0173] Onsite support dispatch: Automatically send requests to regional operations centers to dispatch professional teams.

[0174] Management intervention: If the score drops significantly, initiate a high-level management intervention process and consider strategy adjustments or personnel transfers.

[0175] The optimization module 504 is used to provide customized improvement suggestions and operation guidelines for inefficient outlets based on historical data analysis and machine learning algorithms, thereby promoting continuous optimization of internal processes.

[0176] In this embodiment, historical data analysis and machine learning algorithms (such as decision trees, neural networks, cluster analysis, etc.) are used to identify specific problem patterns of inefficient outlets, and customized improvement plans are generated accordingly. For example, cluster analysis is used to identify groups of outlets with similar problems, and then the most effective improvement measures are recommended for each group. The operation guide should include:

[0177] Specific action steps: such as adjusting inventory strategies, optimizing delivery routes, and improving warehouse automation levels.

[0178] Expected effect simulation: predict the improvement effect that may be achieved after implementing the suggestions based on historical data.

[0179] Monitoring and evaluation mechanism: Set up key monitoring points and periodic evaluation plans to ensure effective implementation and continuous tracking of improvement measures.

[0180] above Figure 4 and Figure 5 The intelligent early warning device for the health of logistics outlets in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0181] Figure 67 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 can be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0182] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 6 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0183] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of an intelligent early warning method for the health of logistics outlets.

[0184] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0185] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0186] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent early warning method for the health of logistics outlets, characterized in that: include: Build a network health scoring model; Continuously update the branch health scoring model, and update the health score of each branch in real time based on the updated branch health scoring model; When the health score of the outlet is lower than the threshold, an intelligent warning is triggered.

2. The intelligent early warning method for the health of logistics outlets according to claim 1 is characterized in that: The construction of the network health scoring model includes: constructing the network health scoring model based on the internal efficiency index of logistics operation and the external environmental influencing factors; The intrinsic efficiency indicators of logistics operations include: order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency; The external environmental influencing factors include: traffic congestion index, weather conditions and market demand fluctuations; The outlet health scoring model includes: Among them, ω i represents the weight of the i-th indicator; X i represents the actual value of the ith indicator; n represents the total number of indicators.

3. The intelligent early warning method for the health of logistics outlets according to claim 1 is characterized in that: The real-time updating of the health score of each outlet based on the updated outlet health score model includes: Continuously update the site health scoring model using methods including sliding window models or online learning; Use the updated branch health scoring model to update the health score of each branch in real time.

4. The intelligent early warning method for the health of logistics outlets according to claim 3 is characterized in that: The method of using the updated network health scoring model to update the health score of each network point in real time includes: The health score of the updated outlet = original score + η × (new data contribution - predicted value) Wherein, η represents the learning rate; the new data contribution is the score change calculated based on the collected new data; and the predicted value is the health score of the outlet calculated by the updated outlet health score model based on the collected old data.

5. The intelligent early warning method for the health of logistics outlets according to claim 1 is characterized in that: When the health score of the outlet is less than the threshold, the intelligent early warning is triggered, including: Set scoring thresholds based on business needs and historical data; When the health score of the outlet is less than the score threshold, the early warning mechanism is triggered, including: generating early warning reports and adjusting strategies; The early warning report includes: the current operating status of the outlets and analysis of the reasons for the reduction in scores; The adjustment strategy includes: sending a request to the regional operation center to dispatch a professional team and / or triggering a high-level management intervention process.

6. The intelligent early warning method for the health of logistics outlets according to claim 1 is characterized in that: The method further includes: when the health score of the outlet is less than a threshold, the current outlet is regarded as an inefficient outlet, factors affecting the efficiency of the inefficient outlet are identified based on historical data analysis and machine learning algorithms, and improvement strategies are generated; When the health score of the outlet is less than the threshold, the current outlet is regarded as an inefficient outlet, and the factors affecting the efficiency of the inefficient outlet are identified based on historical data analysis and machine learning algorithms, and improvement strategies are generated, including: Obtain historical operational data for each inefficient site, including order processing time, inventory turnover, on-time delivery rate, and transportation cost efficiency; Use clustering algorithms to cluster inefficient outlets based on their historical operating data to generate different groups; For each group, a machine learning algorithm is used to analyze the factors that affect the efficiency of the outlets; Generate corresponding improvement strategies based on the factors affecting outlet efficiency obtained through analysis; The improvement strategies include at least one of adjusting inventory strategies, optimizing distribution routes, and improving warehouse automation levels.

7. The intelligent early warning method for the health of logistics outlets according to claim 6 is characterized in that: The method further comprises: Construct a prediction model, and use the prediction model to predict the change of at least one indicator of the inefficient outlets after the implementation of the improvement strategy, including order processing time, inventory turnover rate, on-time delivery rate, and transportation cost efficiency, to obtain the changed indicator; Based on the changed indicators, the health score of the current outlet is obtained using the current outlet health score model; and the improvement effect is evaluated based on the health score of the current outlet.

8. An intelligent early warning device for the health of logistics outlets, characterized in that: include: Model building module, used to build a network health scoring model; The scoring module is used to continuously update the outlet health scoring model and update the health score of each outlet in real time based on the updated outlet health scoring model; The early warning module is used to trigger an intelligent early warning when the health score of the outlet is less than the threshold.

9. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the intelligent early warning method for the health of logistics outlets as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, each step of the intelligent early warning method for the health of logistics outlets as described in any one of claims 1-7 is implemented.

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