CN-121981631-A - Logistics management method and system based on data collaboration

CN121981631ACN 121981631 ACN121981631 ACN 121981631ACN-121981631-A

Abstract

The invention discloses a logistics management method and system based on data collaboration, and relates to the field of logistics management, wherein the logistics management method comprises a sensing module, a storage module and a control module, wherein the sensing module is used for establishing a dynamic association sensing link of a cargo body and a refrigerating environment in a cabin, synchronously collecting multidimensional state data of the cargo body and the refrigerating environment in the cabin and marking association attributes; the invention improves the data integrity, timeliness and precision by the cooperative association and dynamic fusion of multiple types of data, and simultaneously accurately identifies the potential risk of the refrigeration environment and reasonably classifies the potential risk based on the fused data, and combines the real-time transportation working condition to generate an adaptability regulation scheme, thereby realizing the rapid regression of the refrigeration environment to a preset range and considering the optimal energy consumption.

Inventors

WAN FEN
BAI YUFENG
XIONG FEI
XU JIE

Assignees

湖北邮电规划设计有限公司

Dates

Publication Date: 20260505
Application Date: 20260114

Claims (9)

1. A data collaboration-based logistics management system, comprising: The sensing module is used for establishing a dynamic association sensing link of the cargo body and the refrigerating environment in the cabin, synchronously collecting multidimensional state data of the cargo body and the refrigerating environment in the cabin and marking association attributes; the fusion module is used for constructing a railway cold chain cross-link data interaction channel, receiving the perception data, and carrying out cooperative association and dynamic fusion with the transportation scheduling dynamic data and the cabin equipment operation data; The pre-judging module is used for constructing a pre-judging model based on the fused data, identifying potential risks of the refrigerating environment in the cabin deviating from a preset threshold value and classifying risk grades; the generation module is used for combining the risk level and the real-time transportation working condition data to generate a cabin refrigeration environment regulation and control instruction with dynamic suitability; the execution module is used for receiving the regulation and control instruction, driving the cabin regulation and control equipment and the transportation scheduling unit to execute based on the regulation and control instruction, and returning execution state data in real time; The recording module is used for recording sensing, fusion, prejudging, generating and executing all-link data in a time dimension association mode, and constructing a traceable data chain with a time sequence index.
2. The logistics management system based on data collaboration according to claim 1, wherein the sensing module establishes a dynamic association sensing link stage, deploys M cargo sensing nodes on the surface of a cargo body, deploys N environment sensing nodes in a refrigerating environment in a cabin according to preset distribution, and constructs an undirected association graph based on space-time coordinates and data transmission suitability of each node; The association strength of the dynamic association sensing link is as follows: ; wherein: the association strength coefficient of the ith cargo sensing node and the jth environment sensing node is obtained; is a space-time distance weight coefficient; is the distance attenuation coefficient; The three-dimensional space linear distance between the ith cargo sensing node and the jth environment sensing node is set; Synthesizing weight coefficients for transmission reliability and data correlation; The success rate of data transmission between two nodes; the pearson correlation coefficients of the data are collected for both nodes.
3. The logistics management system based on data collaboration of claim 1, wherein the multi-dimensional status data collected by the sensing module comprises cargo body core status data and in-cabin refrigeration environment status data; The core state data of the cargo body at least comprises real-time cargo temperature, cargo surface humidity, cargo respiration intensity and cargo package integrity coefficient; the cold storage environment state data in the cabin at least comprises average temperature in the cabin, uniformity of temperature in the cabin, relative humidity in the cabin, airflow speed in the cabin and running power of refrigeration equipment; the association attribute marking applies four-dimensional coding rules of goods ID, environment sensing area ID, acquisition time stamp and data type identification, and synchronously calculates data acquisition credibility in the marking process Wherein Representing the precision level coefficient of the sensor, Representing the ratio of the data acquisition frequency to the preset standard acquisition frequency, Representing the ambient interference correction factor, And the environmental interference intensity in the cabin at the time of acquisition is represented.
4. The data collaboration-based logistics management system of claim 1, wherein the operation of collaborative association and dynamic fusion in the fusion module is as follows: abnormal value rejection and data standardization processing are respectively carried out on the sensing data, the transportation scheduling dynamic data and the cabin equipment operation data; Constructing a three-dimensional weight distribution model based on the data timeliness weight, the data importance weight and the data integrity weight; Obtaining fused data through a dynamic fusion formula : ; K=1, 2,3, correspond to the perception data, transport and dispatch dynamic data, cabin equipment operation data separately; the data timeliness weight, the data importance weight and the data integrity weight of the kth class of data; the data after the k-th preprocessing is adopted; A deviation correction coefficient for the k-th class data; is the historical statistical mean value of the k-th data.
5. The logistic management system based on data collaboration according to claim 1, wherein the pre-judgment model in the pre-judgment module comprises a time sequence feature extraction layer and a risk factor coupling layer: A time sequence feature extraction layer: short-period time sequence feature vector of fusion data is extracted through sliding time window , Representing a trend characteristic of the recent data, Representing the stationarity characteristics of long-term data; risk factor coupling layer: constructing a coupling strength model based on core influence factors of cold storage environment deviation risk to calculate a potential risk value: ; wherein: Is a potential risk value; a weight matrix which is a time sequence feature vector; is a risk factor coupling coefficient; Interaction weight of the m-th class and the n-th class risk factors; 、 m and n=1, 2 and 3 respectively corresponding to temperature deviation rate, humidity fluctuation amplitude and equipment operation load change rate; the risk level is based on And dividing a matching result with a preset risk threshold interval.
6. The logistics management system based on data collaboration according to claim 1, wherein the generating module determines a regulation priority based on a risk level in an operation stage, synchronously calculates a working condition adaptation coefficient based on real-time transportation working condition data including a driving speed, a road condition level, an energy consumption state and a remaining transportation mileage, and then constructs a regulation parameter optimization model, and calculates an adjustment amount of a core regulation parameter with a goal of quickly returning a refrigeration environment to a preset range and optimizing energy consumption: ; wherein: the working condition adaptation coefficient is adopted; respectively the weight coefficients of the driving speed, the road condition grade, the energy consumption state and the residual transportation mileage, Are positive numbers and the sum of the additions is 1; Normalizing the running speed; the road condition grade quantized value; is an energy consumption state coefficient; Normalizing the value for the remaining transportation mileage; Correcting coefficients for speed-road condition interaction; The adjustment quantity of the core regulation parameters; for regulating priority; The deviation amount of the current refrigeration environment and a preset threshold value is obtained; The allowable duration of the preset range is returned to the environment; optimizing a weight coefficient for energy consumption; The operation parameter value of the current regulation and control equipment; operating parameter values for optimal energy consumption of the regulating equipment; the regulation and control instruction comprises a core regulation and control parameter adjustment amount, an execution time sequence and a feedback check threshold, and the instruction format is adaptively matched with control protocols of cabin regulation and control equipment and a transportation scheduling unit.
7. The data collaboration-based logistics management system of claim 1, wherein the logging module builds a traceable data chain stage with timing indexes, applies a three-level timing index structure of time slice-module identification-data type: dividing time slices according to preset time intervals, and distributing unique time slice codes for each time slice; Unique module identifiers are distributed for sensing, fusing, prejudging, generating and executing modules; Distributing data type codes for different types of data output by each module; to construct index keys ; Wherein; encoding a time slice; the method comprises the steps of marking a module; A and b are respectively the coding digit of the module identification and the digit of the data type coding; In the traceable data chain, each link data is orderly stored according to an index key, and each data record is associated with a corresponding front data ID and a corresponding rear data ID.
8. The logistics management system based on data collaboration according to claim 1, wherein the sensing module is interactively connected with the fusion module through a wireless network, the fusion module is interactively connected with the sensing module through the wireless network with a pre-judgment module, the pre-judgment module is interactively connected with the generation module through the wireless network, and the generation module is interactively connected with the execution module and the recording module through the wireless network.
9. A method for data collaboration-based logistics management as claimed in claim 1, wherein the method is implemented by a data collaboration-based logistics management system as claimed in any one of claims 1 to 8, and comprises: Establishing a dynamic association sensing link of the cargo body and the refrigerating environment in the cabin, synchronously collecting multidimensional state data of the cargo body and the refrigerating environment in the cabin, marking association attributes according to four-dimensional coding rules, and calculating data collection credibility; constructing a railway cold chain cross-link data interaction channel, carrying out outlier rejection and standardized pretreatment on three types of data, and completing dynamic fusion based on a three-dimensional weight distribution model; Constructing a pre-judging model containing a time sequence feature extraction layer and a risk factor coupling layer based on the fusion data, identifying potential risks of the cold storage environment deviating from a preset threshold value, and classifying risk grades; determining a regulation priority by combining the risk level, calculating a working condition adaptation coefficient by linking the real-time transportation working condition data, and generating a cabin refrigeration environment regulation instruction of an adaptation control protocol; Receiving a regulation and control instruction, driving cabin regulation and control equipment and a transportation scheduling unit to execute operation, and returning complete execution state data in real time; and recording all-link data according to time dimension association, and constructing a traceable data chain by adopting a three-level time sequence index structure.

Description

Logistics management method and system based on data collaboration Technical Field The invention relates to the technical field of logistics management, in particular to a logistics management method and system based on data collaboration. Background The railway cold chain transportation relies on a special refrigerated carriage and a precise temperature control technology, is suitable for low-temperature fresh-keeping requirements of fresh agricultural products, medical products and the like, has the advantages of large transportation capacity, low energy consumption and stable long-distance transportation, ensures the quality of goods through whole-course temperature monitoring, and is an important and efficient transportation mode of cross-regional cold chain logistics. The invention patent application with the application number 202210596987.2 discloses a logistics management method and a logistics management system based on big data analysis, and aims to solve the problem that an existing intelligent logistics management system cannot know the flow state and the current transportation state of logistics items through the codes of the logistics items and cannot know the transportation state of the logistics items in time. However, for railway cold chain transportation scenes, when the environment in the cold chain cabin is dynamically regulated and controlled in the prior art, the environment information in the cabin is sensed based on a sensor as a unique regulation basis, and the state information of the cargo body is not linked to carry out cooperative regulation and control. Therefore, we propose a logistics management method and system based on data collaboration. Disclosure of Invention Aiming at the defects existing in the prior art, the invention provides a logistics management method and a logistics management system based on data collaboration, which can effectively solve the problems in the prior art. In order to achieve the above object, the present invention is achieved by the following technical scheme; The invention discloses a logistics management system based on data collaboration, which comprises: The system comprises a sensing module, a fusion module, a pre-judging module, a generation module, an execution module, a recording module and a timing sequence index forming module, wherein the sensing module is used for establishing a dynamic association sensing link of a cargo body and a refrigerating environment in a cabin, synchronously collecting multidimensional state data of the cargo body and the refrigerating environment in the cabin, marking association attributes, the fusion module is used for constructing a railway cold chain cross-link data interaction channel, receiving sensing data, carrying out cooperative association and dynamic fusion with transportation scheduling dynamic data and cabin equipment operation data, the pre-judging module is used for constructing a pre-judging model based on the fused data, identifying potential risks of the refrigerating environment in the cabin deviating from a preset threshold value and dividing risk grades, the generation module is used for combining the risk grades and real-time transportation working condition data, generating a cabin refrigerating environment regulation instruction with dynamic suitability, the execution module is used for receiving the regulation instruction, driving cabin regulation equipment and transportation scheduling unit to execute based on the regulation instruction, and returning execution state data in real time, and the recording module is used for recording sensing, fusion, pre-judging, generation and execution of full-link data according to time sequence index; The sensing module is interactively connected with a fusion module through a wireless network, the fusion module is interactively connected with a pre-judging module through the wireless network, the pre-judging module is interactively connected with a generating module through the wireless network, and the generating module is interactively connected with the executing module and the recording module through the wireless network. Furthermore, the sensing module establishes a dynamic association sensing link stage, M cargo sensing nodes are deployed on the surface of the cargo body, N environment sensing nodes are deployed in the refrigerating environment in the cabin according to preset distribution, and an undirected association graph is constructed based on space-time coordinates and data transmission suitability of each node; The association strength of the dynamic association sensing link is as follows: ; wherein: the association strength coefficient of the ith cargo sensing node and the jth environment sensing node is obtained; is a space-time distance weight coefficient; is the distance attenuation coefficient; The three-dimensional space linear distance between the ith cargo sensing node and the jth environment sensing node is set; Synt