Performance risk prediction method and device for high-value piece, equipment and medium

Abstract

The invention provides a performance risk prediction method and device for a high-value document, equipment and a medium. The performance risk prediction method comprises the steps of obtaining performance data, external environment data and regional feature data of the high-value document meeting a preset requirement in historical data; preprocessing the acquired performance data, the external environment data and the regional feature data of the high-value piece meeting the preset requirements; constructing a performance risk prediction model based on a long short-term memory (LSTM) network and a Prophet model; training a performance risk prediction model by using the preprocessed performance data of the high-value piece meeting the preset requirement, the external environment data and the regional feature data to obtain a trained performance risk prediction model; marking the target cargo as a high-value piece or a common piece based on a preset requirement; and performing performance risk dynamic prediction on the high-value piece meeting a preset requirement by using the trained performance risk prediction model, and performing graded early warning according to a prediction result and the preset requirement.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and more specifically, to a method, apparatus, equipment, and medium for predicting fulfillment risks for high-value items. Background Technology

[0002] In the modern logistics industry, high-value shipments are subject to stringent customer requirements for delivery timeliness and service quality, making risk management in the transportation process directly impact supply chain stability and customer satisfaction. However, current risk prediction for high-value shipments faces numerous challenges. Traditional methods often rely on manual experience or single-dimensional data, such as focusing solely on the acceptance rate, making it difficult to integrate multi-source heterogeneous information such as delivery data, external environment, and regional resources. This results in a lack of comprehensiveness in predicting risks such as delays and complaints.

[0003] Meanwhile, the fulfillment risks of high-value shipments are affected by multiple factors, including long-term trends, cyclical fluctuations, and sudden disruptions. A single prediction model cannot adequately account for the long-term dependencies and cyclical characteristics of time-series data, resulting in insufficient prediction accuracy and poor dynamic adaptability. Furthermore, existing solutions do not conduct precise screening and targeted analysis of the specific attributes of high-value shipments, making them easily confused with ordinary goods, further reducing the pertinence and effectiveness of risk warnings.

[0004] Therefore, there is an urgent need for a performance risk prediction technology that integrates multi-source data, adapts to complex time-series characteristics, and focuses on the attributes of high-value items, so as to achieve accurate risk prediction and graded early warning, and improve the performance management level of the entire logistics chain. Summary of the Invention

[0005] The main objective of this invention is to address the technical problems in existing technologies, such as insufficient prediction accuracy, poor dynamic adaptability, lack of precise screening and targeted analysis of the specific attributes of high-value items, easy confusion with ordinary goods, and further reduction of the pertinence and effectiveness of risk warning.

[0006] The first aspect of this invention provides a method for predicting performance risks for high-value items, comprising: Acquire performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements from historical data; The acquired performance data, external environment data, and regional characteristic data of high-value parts that meet the preset requirements are preprocessed to obtain preprocessed performance data, external environment data, and regional characteristic data of high-value parts that meet the preset requirements. A performance risk prediction model is constructed based on the Long Short-Term Memory (LSTM) network and the Prophet model. The performance risk prediction model is trained using preprocessed performance data of high-value items that meet preset requirements, external environment data, and regional characteristic data, resulting in the trained performance risk prediction model. Based on preset requirements, the target goods are labeled as high-value items or ordinary items; The trained performance risk prediction model is used to dynamically predict the performance risk of high-value items that meet the preset requirements, and the prediction results are used to issue graded warnings according to the preset requirements.

[0007] Optionally, in a first implementation of the first aspect of the present invention, the acquisition of performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements from historical data includes: The high-value items that meet the preset requirements include: customer qualifications, cargo attributes, and transportation area characteristics all meet the preset requirements; The performance data includes: daily receipt rate, delay rate, and complaint rate; The external environment data includes: weather, traffic congestion index, and holiday data; The regional feature data includes: distribution center capacity and service capacity of service outlets.

[0008] Optionally, in a second implementation of the first aspect of the present invention, the preprocessing of the acquired performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements to obtain preprocessed performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements includes: Normalize the performance data, external environment data, and regional characteristic data of high-value items that meet the preset requirements to obtain normalized performance data, external environment data, and regional characteristic data. The normalized continuous time series performance data, external environment data, and regional feature data are divided into time series segments using a sliding window. Feature extraction was performed on time-series performance data, external environment data, and regional characteristic data to obtain features including performance trends, external environment risks, regional resource matching, and periodicity and seasonality.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the step of training a performance risk prediction model using preprocessed performance data of high-value components that meet preset requirements, external environment data, and regional feature data, to obtain a trained performance risk prediction model, includes: When training the performance risk prediction model, the hyperparameters of the Long Short-Term Memory (LSTM) network are optimized by network search, and the parameters of the Prophet model are adjusted by cross-validation to obtain the trained performance risk prediction model.

[0010] Optionally, in a fourth implementation of the first aspect of the present invention, the step of marking the target goods as high-value items or ordinary items based on preset requirements includes: marking the target goods that meet the preset requirements in terms of customer qualifications, goods attributes, and transportation area characteristics as high-value items; and marking the target goods that do not meet any one or more of the preset requirements in terms of customer qualifications, goods attributes, and transportation area characteristics as ordinary items. Among them, the customer qualification meeting the preset requirements means that the customer has passed the high-value service review; The cargo attributes meet the preset requirements: the cargo weight and the single-side length of the cargo meet the preset requirements. The transportation area characteristics meet the preset requirements that the origin and destination are both within the same province or meet the preset requirements.

[0011] Optionally, in a fifth implementation of the first aspect of the present invention, the step of dynamically predicting the performance risk of high-value items that meet preset requirements using a trained performance risk prediction model includes: Long-term dependencies are captured using the Long Short-Term Memory (LSTM) network in the trained performance risk prediction model. The Prophet model in the trained performance risk prediction model was used to extract periodic patterns and seasonal fluctuations. The long-term dependencies captured by the Long Short-Term Memory (LSTM) network and the periodic patterns extracted by the Prophet model are fused with seasonal fluctuations through a weighted average or attention mechanism to generate the final prediction results.

[0012] A second aspect of the present invention provides a performance risk prediction device for high-value items, comprising: The data acquisition module is used to acquire performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements from historical data. The data preprocessing module is used to preprocess the acquired performance data, external environment data, and regional characteristic data of high-value parts that meet the preset requirements, so as to obtain the preprocessed performance data, external environment data, and regional characteristic data of high-value parts that meet the preset requirements. The model building module is used to build a performance risk prediction model based on the Long Short-Term Memory (LSTM) network and the Prophet model. The model training module is used to train the performance risk prediction model with preprocessed performance data of high-value parts that meet preset requirements, external environment data, and regional feature data to obtain the trained performance risk prediction model. The marking module is used to mark target goods as high-value items or ordinary items based on preset requirements; The prediction and early warning module is used to dynamically predict the performance risk of high-value parts that meet preset requirements using a trained performance risk prediction model, and to issue graded early warnings based on the prediction results and preset requirements.

[0013] Optionally, in a first implementation of the second aspect of the present invention, the data acquisition module includes: The high-value items that meet the preset requirements include: customer qualifications, cargo attributes, and transportation area characteristics all meet the preset requirements; The performance data includes: daily receipt rate, delay rate, and complaint rate; The external environment data includes: weather, traffic congestion index, and holiday data; The regional feature data includes: distribution center capacity and service capacity of service outlets.

[0014] Optionally, in a second implementation of the second aspect of the present invention, the data preprocessing module includes: Normalize the performance data, external environment data, and regional characteristic data of high-value items that meet the preset requirements to obtain normalized performance data, external environment data, and regional characteristic data. The normalized continuous time series performance data, external environment data, and regional feature data are divided into time series segments using a sliding window. Feature extraction was performed on time-series performance data, external environment data, and regional characteristic data to obtain features including performance trends, external environment risks, regional resource matching, and periodicity and seasonality.

[0015] Optionally, in a third implementation of the second aspect of the present invention, the model training module includes: When training the performance risk prediction model, the hyperparameters of the Long Short-Term Memory (LSTM) network are optimized by network search, and the parameters of the Prophet model are adjusted by cross-validation to obtain the trained performance risk prediction model.

[0016] Optionally, in a fourth implementation of the second aspect of the present invention, the marking module includes: marking target goods that meet preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics as high-value items; and marking target goods that do not meet any one or more preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics as ordinary items. Among them, the customer qualification meeting the preset requirements means that the customer has passed the high-value service review; The cargo attributes meet the preset requirements: the cargo weight and the single-side length of the cargo meet the preset requirements. The transportation area characteristics meet the preset requirements that the origin and destination are both within the same province or meet the preset requirements.

[0017] Optionally, in a fifth implementation of the second aspect of the present invention, the prediction and early warning module includes: Long-term dependencies are captured using the Long Short-Term Memory (LSTM) network in the trained performance risk prediction model. The Prophet model in the trained performance risk prediction model was used to extract periodic patterns and seasonal fluctuations. The long-term dependencies captured by the Long Short-Term Memory (LSTM) network and the periodic patterns extracted by the Prophet model are fused with seasonal fluctuations through a weighted average or attention mechanism to generate the final prediction results.

[0018] Optionally, in the sixth implementation of the second aspect of the present invention, the device further includes: a deployment module, used for containerized deployment via Docker, combined with Kubernetes to achieve elastic scaling, with the front end displaying early warning information through ECharts dynamic charts, and a mobile APP supporting branch staff to receive notifications in real time.

[0019] A third aspect of the present invention provides an electronic device, the electronic device comprising a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the performance risk prediction method for high-value items as described above.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of the performance risk prediction method for high-value items as described above.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention breaks through the limitations of traditional single data prediction by deeply integrating performance data of high-value components, external environment data and regional characteristic data, comprehensively covering the three core dimensions of "internal performance - external interference - regional resources" that affect performance risk, avoiding prediction bias caused by one-sided data, and making risk prediction more in line with the actual performance scenario. 2. The performance risk prediction model of this invention, based on LSTM and Prophet, not only accurately captures the long-term dependency relationship of performance data of high-value parts through LSTM, but also efficiently extracts periodic patterns and seasonal fluctuations with the help of Prophet. It also achieves deep fusion of the outputs of the two types of models through weighted averaging or attention mechanism, which solves the technical problem that a single model cannot take into account both "long-term trend" and "periodic fluctuation", and significantly improves the prediction accuracy. 3. This invention accurately classifies target goods into high-value items and ordinary items by pre-setting requirements of "customer qualifications + cargo attributes + transportation area characteristics". High-value items must meet the core conditions of "customer passing high-value service review, cargo weight and single-side length meeting the standard, and transportation area within the province or a pre-set special area", thus avoiding resource waste caused by confusing the management of high-value items and ordinary items. Attached Figure Description

[0022] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a first flowchart of a method for predicting performance risks for high-value items provided in an embodiment of the present invention.

[0023] Figure 2 This is a second flowchart of a method for predicting performance risks for high-value items provided in an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of a performance risk prediction device for high-value items provided in an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] This invention provides a method, apparatus, device, and medium for predicting performance risks of high-value items. The method involves acquiring performance data, external environment data, and regional feature data of high-value items that meet preset requirements from historical data; preprocessing the acquired performance data, external environment data, and regional feature data to obtain preprocessed performance data, external environment data, and regional feature data of high-value items that meet preset requirements; constructing a performance risk prediction model based on a Long Short-Term Memory (LSTM) network and a Prophet model; training the performance risk prediction model using the preprocessed performance data, external environment data, and regional feature data of high-value items that meet preset requirements to obtain a trained performance risk prediction model; labeling target goods as high-value items or ordinary items based on preset requirements; dynamically predicting the performance risk of high-value items that meet preset requirements using the trained performance risk prediction model; and issuing graded early warnings based on the prediction results and preset requirements. This invention solves the technical problems of insufficient prediction accuracy, poor dynamic adaptability, and lack of precise screening and targeted analysis for the exclusive attributes of high-value items in the prior art, which makes them easy to be confused with ordinary goods and further reduces the pertinence and effectiveness of risk warning.

[0027] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of the performance risk prediction method for high-value items in this invention includes: 101. Obtain performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements from historical data; In this embodiment, the high-value items that meet the preset requirements include: customer qualifications, cargo attributes, and transportation area characteristics all meeting the preset requirements; The performance data includes: daily receipt rate, delay rate, and complaint rate; The external environment data includes: weather, traffic congestion index, and holiday data; The regional feature data includes: distribution center capacity and service capacity of service outlets.

[0029] 102. The acquired performance data, external environment data, and regional characteristic data of high-value parts that meet the preset requirements are preprocessed to obtain preprocessed performance data, external environment data, and regional characteristic data of high-value parts that meet the preset requirements. In this embodiment, the performance data, external environment data, and regional feature data of high-value items that meet the preset requirements are normalized to obtain normalized performance data, external environment data, and regional feature data. The normalization process solves the problem of inconsistent dimensions and different units among the three types of data, and avoids model training deviation due to differences in data range. More specifically, for high-value shipment fulfillment data, including daily receipt rate, delay rate, and complaint rate, Min-Max normalization is used to map the percentage data to the [0,1] range; For weather data in the external environment data, the following steps are taken: converting qualitative weather types such as "heavy rain" and "heavy snow" into quantitative values, for example, heavy rain = 5, heavy rain = 4, and sunny = 1, and then mapping them to [0,1] through Min-Max normalization; The traffic congestion index in the external environment is directly normalized to [0,1] based on the existing numerical range of 0-10; For holiday data in the external environment, a "0-1 encoding" is used, for example: holidays = 1, non-holidays = 0; The utilization rate of the distribution center capacity in the regional feature data is calculated based on "actual processing volume / maximum capacity", and then converted into a percentage value in the range [0,1]. For the service capacity of outlets in the regional feature data, the "average delivery volume per person" is calculated, and then normalized to [0,1] by the industry's historical extreme values.

[0030] Normalized continuous time series performance data, external environment data, and regional feature data are divided into time series segments using a sliding window to adapt to the input requirements of the LSTM model. Feature extraction was performed on time-series performance data, external environment data, and regional characteristic data to obtain features including performance trends, external environmental risks, regional resource matching, and periodicity and seasonality. Specifically, performance trend features were extracted based on basic data such as daily acceptance rate, delay rate, and complaint rate of high-value items, reflecting long-term / short-term changes in performance risk, which were used by LSTM to capture short-term / long-term dependencies. External environmental risk features were extracted based on basic data such as weather, traffic congestion index, and holidays, extracting external interference features affecting performance efficiency, which served as regression factors and risk superposition criteria for the Prophet model. Regional resource matching features were extracted based on basic data such as distribution center capacity and network service capabilities. Features reflecting the supply and demand balance of regional logistics resources are used to pinpoint the root causes of fulfillment risks, such as overloaded distribution centers and insufficient network capacity. Periodic and seasonal features are extracted primarily by the Prophet model, combined with the time series patterns of three types of basic data to capture the periodic fluctuations in fulfillment risks, including: weekly periodic features: such as "the average increase in the delay rate of high-value items on Saturdays and Sundays compared to weekdays" and "the duration of the low collection efficiency on Mondays"; seasonal fluctuation features: such as "the fluctuation range of the complaint rate of high-value items before and after quarterly e-commerce promotions" and "the difference coefficient of fulfillment risk between winter and summer"; time node correlation features: such as "the change in the fulfillment compliance rate of high-value item delivery peaks at the end of the month / year" and "the attenuation rate of network service capacity in the two weeks before the Spring Festival".

[0031] 103. Construct a performance risk prediction model based on Long Short-Term Memory (LSTM) network and Prophet model; 104. The performance risk prediction model is trained using preprocessed performance data of high-value parts that meet the preset requirements, external environment data, and regional characteristic data to obtain the trained performance risk prediction model. In this embodiment, when training the performance risk prediction model, the hyperparameters of the Long Short-Term Memory (LSTM) network are optimized through network search, and the parameters of the Prophet model are adjusted through cross-validation to obtain the trained performance risk prediction model.

[0032] The trained performance risk prediction model is evaluated using metrics including mean squared error (MSE), mean absolute error (MAE), and F1 score. To improve the model's real-time performance, the system supports incremental learning, updating model parameters weekly to adapt to the latest trends.

[0033] 105. Based on preset requirements, mark the target goods as high-value items or ordinary items; In this embodiment, target goods that meet preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics are marked as high-value items; If any one or more of the customer qualifications, cargo attributes, and transportation area characteristics do not meet the preset requirements, the target cargo will be marked as a regular shipment. Among them, the customer qualification meeting the preset requirements means that the customer has passed the high-value service review; The cargo attributes meet the preset requirements: the cargo weight and the single-side length of the cargo meet the preset requirements. The transportation area characteristics meet the preset requirements that the origin and destination are both within the same province or meet the preset requirements.

[0034] 106. Utilize the trained performance risk prediction model to dynamically predict the performance risk of high-value items that meet the preset requirements, and issue graded early warnings based on the prediction results and preset requirements.

[0035] In this embodiment, the Long Short-Term Memory (LSTM) network in the trained performance risk prediction model is used to capture long-term dependencies. The Prophet model in the trained performance risk prediction model was used to extract periodic patterns and seasonal fluctuations. The long-term dependencies captured by the Long Short-Term Memory (LSTM) network and the periodic patterns extracted by the Prophet model are fused with seasonal fluctuations through a weighted average or attention mechanism to generate the final prediction results.

[0036] This embodiment, through multi-dimensional innovation in data, models, and management, not only solves the core problems of low accuracy in predicting fulfillment risks of high-value items in heavy cargo logistics, unreasonable resource allocation, and low operational efficiency, but also provides logistics companies with a feasible and scalable intelligent management solution, possessing significant economic and industry application value.

[0037] Please see Figure 2 The second embodiment of the performance risk prediction method for high-value items in this invention includes: 201. Obtain performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements from historical data; 202. The acquired performance data, external environment data and regional characteristic data of high-value parts that meet the preset requirements are preprocessed to obtain preprocessed performance data, external environment data and regional characteristic data of high-value parts that meet the preset requirements. 203. Construct a performance risk prediction model based on Long Short-Term Memory (LSTM) network and Prophet model; 204. The performance risk prediction model is trained using preprocessed performance data of high-value parts that meet the preset requirements, external environment data, and regional characteristic data to obtain the trained performance risk prediction model. 205. Based on preset requirements, mark the target goods as high-value items or ordinary items; In this embodiment, target goods that meet the preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics are marked as high-value items; target goods that do not meet any one or more of the preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics are marked as ordinary items. The customer qualification requirement is that the customer has passed the high-value service review. More specifically, the branch submits a high-value service application for the customer through the "Branch Manager" system. The core fields include: customer VIP code, goods information, rate plan, and associated performance conditions. Provincial special reviewers review the customer's qualifications in the "Branch Manager" system, focusing on verifying the value of the goods and the branch's service capabilities, and provide feedback within one business day. The branch management department reviews the application to confirm that the rate plan matches the regional rules. The review results are synchronized to the branch manager in real time, and the customer's high-value service privileges are automatically activated after approval.

[0038] The cargo attributes must meet the preset requirements: cargo weight and cargo side length must meet preset requirements; more specifically, cargo weight ≥ 3KG and side length < 1.8 meters. The transportation area characteristics meet the preset requirements that the origin and destination are both within the same province or meet the preset requirements, such as: Beijing-Tianjin-Hebei, Sichuan-Chongqing, etc. In special circumstances, this also includes manually entering heavy cargo tracking numbers at the branch office to directly mark the goods as high-value items.

[0039] This embodiment uses a dedicated risk prediction model to achieve dynamic early warning and special control for high-value items after they are tagged; while ordinary items maintain the basic management mode without occupying additional model computing resources and monitoring costs. This allows logistics companies to concentrate their limited "refined service resources" on high-value items, ensuring the service quality of high-value items while avoiding excessive investment in the management of ordinary items, achieving optimal allocation of resources across the entire chain and reducing overall operating costs.

[0040] 206. Use the trained performance risk prediction model to dynamically predict the performance risk of high-value items that meet the preset requirements, and issue graded early warnings based on the prediction results and preset requirements.

[0041] In this embodiment, the tiered early warning based on the prediction results includes: when the model detects that the delay rate of a certain branch is close to the threshold of 80% for three consecutive days, and the weather forecast shows that there will be heavy rain the next day, the system automatically triggers a yellow warning, prompting the management personnel to schedule transportation capacity or adjust the delivery strategy in advance; if the predicted delay rate will exceed the threshold and exceed 85% for four consecutive days, a red warning is triggered, and the causal inference model is linked to locate the root cause of the problem, such as: equipment failure in the distribution center or insufficient driver manpower.

[0042] In this embodiment, during the model training phase, LSTM hyperparameters are optimized through network search and Prophet parameters are adjusted through cross-validation to ensure the model's adaptability to different performance scenarios. At the same time, incremental learning is supported, and the model parameters can be updated weekly to adapt to the latest performance trends, avoiding the model's accuracy decay due to data lag.

[0043] The risk prediction and graded early warning mechanism in this embodiment can accurately link the service capabilities of the terminal outlets. When it is predicted that the fulfillment risk of high-value items at a certain outlet will increase, it will not only prompt the outlet to optimize its services in a timely manner through early warning, but also provide data support for subsequent "service quality assessment".

[0044] The above describes the method for modifying the delivery address of a parcel in an embodiment of the present invention. The following describes the device for modifying the delivery address of a parcel in an embodiment of the present invention. Please refer to [link / reference]. Figure 3 One embodiment of the express delivery address modification device in this invention includes: The data acquisition module 301 is used to acquire performance data, external environment data, and regional characteristic data of high-value items that meet preset requirements from historical data; In this embodiment, the data acquisition module 301 includes: The high-value items that meet the preset requirements include: customer qualifications, cargo attributes, and transportation area characteristics all meet the preset requirements; The performance data includes: daily receipt rate, delay rate, and complaint rate; The external environment data includes: weather, traffic congestion index, and holiday data; The regional feature data includes: distribution center capacity and service capacity of service outlets.

[0045] The data preprocessing module 302 is used to preprocess the acquired performance data, external environment data and regional characteristic data of high-value parts that meet the preset requirements, so as to obtain the preprocessed performance data, external environment data and regional characteristic data of high-value parts that meet the preset requirements. In this embodiment, the data preprocessing module 302 includes: Normalize the performance data, external environment data, and regional characteristic data of high-value items that meet the preset requirements to obtain normalized performance data, external environment data, and regional characteristic data. The normalized continuous time series performance data, external environment data, and regional feature data are divided into time series segments using a sliding window. Feature extraction was performed on time-series performance data, external environment data, and regional characteristic data to obtain features including performance trends, external environment risks, regional resource matching, and periodicity and seasonality.

[0046] Model building module 303 is used to build a performance risk prediction model based on the Long Short-Term Memory Network (LSTM) and the Prophet model. The model training module 304 is used to train the performance risk prediction model with preprocessed performance data of high-value parts that meet preset requirements, external environment data and regional feature data to obtain the trained performance risk prediction model. In this embodiment, the model training module 304 includes: When training the performance risk prediction model, the hyperparameters of the Long Short-Term Memory (LSTM) network are optimized by network search, and the parameters of the Prophet model are adjusted by cross-validation to obtain the trained performance risk prediction model.

[0047] The marking module 305 is used to mark target goods as high-value items or ordinary items based on preset requirements; In this embodiment, the marking module 305 includes: Target goods that meet the preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics are marked as high-value items; target goods that do not meet any one or more of the preset requirements in terms of customer qualifications, cargo attributes, and transportation area characteristics are marked as ordinary items. Among them, the customer qualification meeting the preset requirements means that the customer has passed the high-value service review; The cargo attributes meet the preset requirements: the cargo weight and the single-side length of the cargo meet the preset requirements. The transportation area characteristics meet the preset requirements that the origin and destination are both within the same province or meet the preset requirements.

[0048] The prediction and early warning module 306 is used to dynamically predict the performance risk of high-value parts that meet preset requirements using the trained performance risk prediction model, and to issue graded early warnings based on the prediction results and preset requirements.

[0049] In this embodiment, the prediction and early warning module 306 includes: Long-term dependencies are captured using the Long Short-Term Memory (LSTM) network in the trained performance risk prediction model. The Prophet model in the trained performance risk prediction model was used to extract periodic patterns and seasonal fluctuations. The long-term dependencies captured by the Long Short-Term Memory (LSTM) network and the periodic patterns extracted by the Prophet model are fused with seasonal fluctuations through a weighted average or attention mechanism to generate the final prediction results.

[0050] Deployment module 307 is used for containerized deployment via Docker and elastic scaling with Kubernetes. The front end displays warning information through dynamic charts using ECharts, and the mobile APP supports branch staff to receive notifications in real time. This embodiment adopts a Docker containerization + Kubernetes elastic scaling architecture, which can dynamically adjust computing resources according to the order volume, balancing response speed in high-concurrency scenarios and resource utilization during off-peak periods. It also uses front-end ECharts dynamic charts to display real-time risk warnings for high-value items, and a mobile app allows branch staff to receive notifications in real time. This enables operations managers to intuitively grasp the risk status of high-value items at each branch without relying on manual statistics and analysis, significantly shortening anomaly response time. Simultaneously, the prediction results and tiered warning mechanism provide clear evidence for "pre-intervention," promoting the transformation of heavy cargo logistics from "post-event processing" to "pre-event prevention," significantly improving overall performance management efficiency and the scientific nature of decision-making.

[0051] above Figure 3 The performance risk prediction device for high-value components in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The electronic equipment in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0052] Figure 4 This is a schematic diagram of the structure of an electronic device 700 provided in an embodiment of the present invention. The electronic device 700 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 710 (e.g., one or more processors) and a memory 720, and one or more storage media 730 (e.g., one or more mass storage devices) for storing application programs 733 or data 732. The memory 720 and storage media 730 can be temporary or persistent storage. The program stored in the storage media 730 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 700. Furthermore, the processor 710 may be configured to communicate with the storage media 730 and execute the series of instruction operations in the storage media 730 on the electronic device 700.

[0053] 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 / output interfaces 750, and / or one or more operating systems 731, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 4The illustrated electronic device structure does not constitute a limitation on electronic devices and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0054] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a performance risk prediction method for high-value items.

[0055] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0056] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the performance risk of high-value items, characterized in that, The method comprises the following steps: obtaining performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements in historical data; preprocessing the obtained performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements to obtain preprocessed performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements; constructing a performance risk prediction model based on a long short-term memory network (LSTM) and a Prophet model; training the performance risk prediction model using the preprocessed performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements to obtain a trained performance risk prediction model; labeling target goods as high-value pieces or ordinary pieces based on preset requirements; performing dynamic performance risk prediction on high-value pieces meeting preset requirements using the trained performance risk prediction model, and performing hierarchical early warning based on the prediction results according to preset requirements. 2.The method for predicting the performance risk of high-value items according to claim 1, characterized in that, The obtained performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements in historical data comprise: The high-value pieces meeting preset requirements comprise customer qualifications, goods attributes and transportation regional characteristics meeting preset requirements; The performance data comprise daily signing rate, delay rate and complaint rate; The external environment data comprise weather, traffic congestion index and holiday data; The regional characteristic data comprise distribution center capacity and network service capability. 3.The method for predicting the performance risk of high-value items according to claim 1, wherein, The preprocessing of the obtained performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements comprises: normalizing the performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements to obtain normalized performance data, external environment data and regional characteristic data; dividing the normalized continuous time series performance data, external environment data and regional characteristic data into time sequence segments through sliding windows; extracting features from the time sequence segment performance data, external environment data and regional characteristic data to obtain features including performance trend, external environment risk, regional resource matching and periodicity and seasonality. 4.The method for predicting the performance risk of high-value items according to claim 1, wherein, The training of the performance risk prediction model using the preprocessed performance data, external environment data and regional characteristic data of high-value pieces meeting preset requirements comprises: optimizing LSTM hyperparameters based on network search during training of the performance risk prediction model, and adjusting Prophet model parameters through cross-validation to obtain the trained performance risk prediction model. 5.The method for predicting the performance risk of high-value items according to claim 1, wherein, The labeling of target goods as high-value pieces or ordinary pieces based on preset requirements comprises: labeling target goods meeting preset requirements in customer qualifications, goods attributes and transportation regional characteristics as high-value pieces; and labeling target goods not meeting preset requirements in any one or more of customer qualifications, goods attributes and transportation regional characteristics as ordinary pieces. The customer qualification meets the preset requirement, that is, the customer has passed the high-value service audit. The cargo attribute meets the preset requirement, that is, the cargo weight and the cargo single-side length meet the preset requirement. The transportation area feature meets the preset requirement, that is, the origin and the destination belong to the same province or meet the preset requirement. 6.The method for predicting the performance risk of high-value items according to claim 1, wherein, The method further comprises the following steps of: The long short-term memory network LSTM in the trained fulfillment risk prediction model is used to capture long-term dependency relationships. The Prophet model in the trained fulfillment risk prediction model is used to extract periodic patterns and seasonal fluctuations. The long-term dependency relationships captured by the long short-term memory network LSTM and the periodic patterns and seasonal fluctuations extracted by the Prophet model are fused through weighted averaging or attention mechanism to generate a final prediction result.

7. A device for predicting the performance risk of high-value items, characterized by, The method further comprises the following steps of: a data acquisition module configured to acquire historical data of high-value pieces that meet the preset requirement, external environment data, and area feature data; a data preprocessing module configured to preprocess the acquired historical data of high-value pieces that meet the preset requirement, external environment data, and area feature data to obtain preprocessed historical data of high-value pieces that meet the preset requirement, external environment data, and area feature data; a model construction module configured to construct a fulfillment risk prediction model based on a long short-term memory network LSTM and a Prophet model; a model training module configured to train the preprocessed historical data of high-value pieces that meet the preset requirement, external environment data, and area feature data with the fulfillment risk prediction model to obtain a trained fulfillment risk prediction model; a marking module configured to mark target cargo as a high-value piece or an ordinary piece based on the preset requirement; a prediction and early warning module configured to use the trained fulfillment risk prediction model to dynamically predict the fulfillment risk of high-value pieces that meet the preset requirement, and to perform hierarchical early warning according to the prediction result according to the preset requirement.

8. The device for predicting the performance risk of high-value items according to claim 7, wherein, The device further comprises a deployment module configured to be deployed through Docker containerization, to realize elastic scaling through Kubernetes, to display early warning information through ECharts dynamic charts on the front end, and to support real-time receipt of notifications by personnel at outlets through a mobile APP. 9.An electronic device, comprising a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to enable the electronic device to perform each step of the method for predicting the fulfillment risk of high-value pieces according to any one of claims 1-6.

10. A computer-readable storage medium having stored thereon instructions, the instructions comprising, The instructions are executed by the processor to implement each step of the method for predicting the fulfillment risk of high-value pieces according to any one of claims 1-6.

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