CN116976778A - Monitoring system and method based on intelligent logistics transportation platform - Google Patents
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Abstract
The application discloses a monitoring system and a monitoring method based on an intelligent logistics transportation platform, and relates to the technical field of logistics monitoring. The system comprises: the supervision model construction module is used for constructing a target supervision model; the track supervision module is used for supervising the track of the logistics transportation path; the goods monitoring module is used for collecting goods information in the logistics transportation process according to a preset goods monitoring period and monitoring the goods condition; the anomaly monitoring module is used for collecting personnel and vehicle information in the logistics transportation process, analyzing according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle; and the early warning reporting module is used for analyzing all abnormal conditions in the logistics transportation process based on the target supervision model, and generating and reporting early warning information. The application can accurately and comprehensively monitor the logistics transportation process, monitor the cargo transportation condition in real time, and timely report abnormal monitoring, thereby ensuring the logistics transportation effect.
Description
Technical Field
The application relates to the technical field of logistics monitoring, in particular to a monitoring system and method based on an intelligent logistics transportation platform.
Background
In recent years, the demand scale of large commodity logistics such as coal, ore, iron and steel, grains, building materials and the like is continuously created and increased, the total commodity logistics amount and the transportation scale are huge, and the problem of large commodity logistics transportation is also gradually presented. Therefore, the supervision of bulk commodity logistics transportation is particularly important.
The existing logistics monitoring system generally provides and records the main circulation condition of corresponding cargoes through logistics or express companies, corresponding users can only see the condition of the cargoes transportation places of some time nodes of the logistics or express company input system, the specific condition of the cargoes cannot be mastered, and the cargo transportation quality cannot be effectively monitored.
Disclosure of Invention
In order to overcome the problems or at least partially solve the problems, the application provides a monitoring system and a monitoring method based on an intelligent logistics transportation platform, which can accurately and comprehensively monitor the logistics transportation process, monitor the cargo transportation condition in real time, and timely report abnormal monitoring so as to ensure the logistics transportation effect.
In order to solve the technical problems, the application adopts the following technical scheme:
in a first aspect, the application provides a monitoring system based on an intelligent logistics transportation platform, which comprises a supervision model construction module, a track supervision module, a goods supervision module, an abnormality monitoring module and an early warning reporting module, wherein:
the supervision model construction module is used for inputting the target basic logistics transportation information, acquiring and constructing a target supervision model according to the historical logistics transportation data and the target basic logistics transportation information;
the track supervision module is used for acquiring and importing logistics transportation track information into the target supervision model in real time and supervising the logistics transportation path track;
the goods supervision module is used for collecting goods information in the logistics transportation process according to a preset goods supervision period, guiding the goods information into the target supervision model and supervising the goods condition;
the anomaly monitoring module is used for collecting personnel and vehicle information in the logistics transportation process, carrying out anomaly analysis according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle;
and the early warning reporting module is used for analyzing all abnormal conditions in the logistics transportation process based on the target supervision model, and generating and reporting early warning information.
The system carries out comprehensive and accurate supervision on the conditions of multiple aspects of track, goods and personnel vehicles in the logistics transportation process through the cooperation of a plurality of modules such as a supervision model construction module, a track supervision module, a goods supervision module, an abnormal monitoring module, an early warning reporting module and the like, monitors the goods transportation condition in real time, carries out abnormal monitoring reporting in time, and further guarantees the logistics transportation effect. Constructing a reasonable target supervision model by combining target basic logistics transportation information and historical logistics transportation conditions, and realizing targeted transportation management; based on the target supervision model, the follow-up accurate monitoring on multiple aspects such as track, cargo condition, personnel and vehicle condition and the like is realized, and abnormal early warning and reporting are timely carried out.
Based on the first aspect, further, the supervision model construction module includes a transportation track management node setting unit, a cargo supervision node setting unit, an object management node setting unit, an initial model construction unit, and a model training unit, wherein:
the transportation track management node setting unit is used for extracting and setting transportation track management nodes according to the transportation address information in the target basic logistics transportation information;
the goods supervision node setting unit is used for extracting and setting goods supervision nodes according to the goods information in the target basic logistics transportation information;
the object management node setting unit is used for extracting and setting an object management node according to the transport party information in the target basic logistics transport information;
the initial model building unit is used for building an initial supervision model based on the transportation track management node, the goods supervision node and the object management node;
and the model training unit is used for training the initial supervision model based on the historical logistics transportation data so as to construct a target supervision model.
Based on the first aspect, the monitoring system based on the intelligent logistics transportation platform further comprises an abnormal transportation marking module, wherein the abnormal transportation marking module is used for acquiring and carrying out evaluation analysis on transportation capacity and quality of the logistics transportation party according to basic information and transportation condition data of the logistics transportation party, and generating and marking the corresponding logistics transportation party according to an evaluation result.
In a second aspect, the application provides a monitoring method based on an intelligent logistics transportation platform, comprising the following steps:
inputting target basic logistics transportation information, and acquiring and constructing a target supervision model according to the historical logistics transportation data and the target basic logistics transportation information;
acquiring and importing logistics transportation track information into a target supervision model in real time, and supervising the logistics transportation path track;
according to a preset cargo supervision period, acquiring cargo information in the logistics transportation process, and importing the cargo information into a target supervision model to supervise cargo conditions;
collecting personnel and vehicle information in the logistics transportation process, carrying out anomaly analysis according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle;
and analyzing all abnormal conditions in the logistics transportation process based on the target supervision model, and generating and reporting early warning information.
According to the method, a reasonable target supervision model is built by combining target basic logistics transportation information and historical logistics transportation conditions, so that targeted transportation management is realized; based on the target supervision model, the follow-up accurate monitoring on multiple aspects such as track, cargo condition, personnel and vehicle condition and the like is realized, and abnormal early warning and reporting are timely carried out. The application can comprehensively and accurately monitor the track, goods and personnel and vehicles in the logistics transportation process, monitor the goods transportation condition in real time, and timely report abnormal monitoring, thereby ensuring the logistics transportation effect.
Based on the second aspect, the method for constructing the target supervision model according to the historical logistics transportation data and the target base logistics transportation information further comprises the following steps:
extracting and setting a transportation track management node according to transportation address information in the target basic logistics transportation information;
extracting and setting cargo supervision nodes according to cargo information in the target basic logistics transportation information;
extracting and setting an object management node according to the transport party information in the target basic logistics transport information; constructing an initial supervision model based on the transportation track management node, the goods supervision node and the object management node;
training the initial supervision model based on the historical logistics transportation data to construct a target supervision model.
Based on the second aspect, the method for importing the logistics transportation track information into the target supervision model and supervising the logistics transportation path track further comprises the following steps:
importing the logistics transportation track information into a transportation track management node of a target supervision model;
and analyzing the logistics transportation track information based on the corresponding transportation track management node, and judging whether the transportation path is normal or not.
Based on the second aspect, further, the method for collecting cargo information in the logistics transportation process and guiding the cargo information into the target supervision model to supervise the cargo condition comprises the following steps:
sequentially importing cargo information acquired in each cargo supervision period into cargo supervision nodes of a target supervision model;
and (3) analyzing and comparing the cargo image and the cargo weight according to the corresponding cargo information based on the cargo supervision node, and judging whether cargo conditions in each cargo supervision period are normal or not.
Based on the second aspect, the monitoring method based on the intelligent logistics transportation platform further comprises the following steps:
and acquiring and carrying out evaluation analysis on the transportation capacity and the quality of the logistics transportation party according to the basic information and the transportation condition data of the logistics transportation party, and generating and marking the corresponding logistics transportation party according to the evaluation result.
In a third aspect, the present application provides an electronic device comprising a memory for storing one or more programs; a processor; the method of any of the second aspects described above is implemented when one or more programs are executed by a processor.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as in any of the second aspects above.
The application has at least the following advantages or beneficial effects:
the application provides a monitoring system and a monitoring method based on an intelligent logistics transportation platform, which are used for constructing a reasonable target supervision model by combining target basic logistics transportation information and historical logistics transportation conditions so as to realize targeted transportation management; based on the target supervision model, the follow-up accurate monitoring on multiple aspects such as track, cargo condition, personnel and vehicle condition and the like is realized, and abnormal early warning and reporting are timely carried out. The application can comprehensively and accurately monitor the track, goods and personnel and vehicles in the logistics transportation process, monitor the goods transportation condition in real time, and timely report abnormal monitoring, thereby ensuring the logistics transportation effect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a monitoring system based on an intelligent logistics transportation platform in accordance with an embodiment of the present application;
FIG. 2 is a schematic block diagram of a monitoring method based on an intelligent logistics transportation platform according to an embodiment of the present application;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 100. a supervision model construction module; 200. a track supervision module; 300. a cargo supervision module; 400. an abnormality monitoring module; 500. an early warning reporting module; 101. a memory; 102. a processor; 103. a communication interface.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present application, "plurality" means at least 2.
Examples:
as shown in fig. 1, in a first aspect, an embodiment of the present application provides a monitoring system based on an intelligent logistics transportation platform, which includes a supervision model construction module 100, a track supervision module 200, a cargo supervision module 300, an anomaly monitoring module 400, and an early warning reporting module 500, wherein:
the supervision model construction module 100 is used for inputting the target basic logistics transportation information, acquiring and constructing a target supervision model according to the historical logistics transportation data and the target basic logistics transportation information; the target basic logistics transportation information comprises transportation company information, transportation dispatch personnel and vehicle information, cargo information and transportation address information; the historical logistics transportation data comprise transportation track condition data, transportation time, abnormal transportation behavior data and the like.
The track supervision module 200 is used for acquiring and importing logistics transportation track information into the target supervision model in real time and supervising the logistics transportation path track;
the cargo supervision module 300 is configured to collect cargo information in the logistics transportation process according to a preset cargo supervision period, and guide the cargo information into a target supervision model to supervise cargo conditions;
the anomaly monitoring module 400 is used for collecting personnel and vehicle information in the logistics transportation process, carrying out anomaly analysis according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle;
the early warning reporting module 500 is configured to analyze all abnormal situations in the logistics transportation process based on the target supervision model, and generate and report early warning information.
The system carries out comprehensive and accurate supervision on the conditions of multiple aspects of track, goods and personnel vehicles in the logistics transportation process through the cooperation of a plurality of modules such as the supervision model construction module 100, the track supervision module 200, the goods supervision module 300, the abnormality monitoring module 400 and the early warning reporting module 500, monitors the goods transportation condition in real time, carries out abnormality monitoring reporting in time, and further guarantees the logistics transportation effect. Constructing a reasonable target supervision model by combining target basic logistics transportation information and historical logistics transportation conditions, and realizing targeted transportation management; based on the target supervision model, the follow-up accurate monitoring on multiple aspects such as track, cargo condition, personnel and vehicle condition and the like is realized, and abnormal early warning and reporting are timely carried out.
Based on the first aspect, further, the supervision model construction module 100 includes a transportation track management node setting unit, a cargo supervision node setting unit, an object management node setting unit, an initial model construction unit, and a model training unit, wherein:
the transportation track management node setting unit is used for extracting and setting transportation track management nodes according to the transportation address information in the target basic logistics transportation information;
the goods supervision node setting unit is used for extracting and setting goods supervision nodes according to the goods information in the target basic logistics transportation information;
the object management node setting unit is used for extracting and setting an object management node according to the transport party information in the target basic logistics transport information;
the initial model building unit is used for building an initial supervision model based on the transportation track management node, the goods supervision node and the object management node;
and the model training unit is used for training the initial supervision model based on the historical logistics transportation data so as to construct a target supervision model.
In order to ensure the monitoring effect on the logistics transportation process, an initial supervision model is constructed by combining the conditions of various aspects of cargo information, a transportation party, objects (personnel and vehicles) in the transportation process and the like through the cooperation of a plurality of units such as a transportation track management node setting unit, a cargo supervision node setting unit, an object management node setting unit, an initial model construction unit, a model training unit and the like, so that the logistics transportation process is comprehensively supervised; and then training and optimizing the initial supervision model by combining the historical logistics transportation data to obtain a better target supervision model, and accurately controlling the subsequent logistics transportation.
Based on the first aspect, the monitoring system based on the intelligent logistics transportation platform further comprises an abnormal transportation marking module, wherein the abnormal transportation marking module is used for acquiring and carrying out evaluation analysis on transportation capacity and quality of the logistics transportation party according to basic information and transportation condition data of the logistics transportation party, and generating and marking the corresponding logistics transportation party according to an evaluation result.
In the logistics transportation link, a proper logistics transportation company is selected to be more beneficial to logistics transportation quality, basic information and historical transportation conditions of related logistics transportation parties are acquired through an abnormal transportation marking module, transportation capacity and transportation quality of the logistics transportation parties are estimated, personnel evaluation data can be combined to comprehensively estimate the logistics transportation capacity and the transportation quality according to the estimated transportation capacity and the transportation quality level, and the logistics transportation companies are ordered and marked so as to conveniently select corresponding logistics transportation parties for transporting goods.
As shown in fig. 2, in a second aspect, an embodiment of the present application provides a monitoring method based on an intelligent logistics transportation platform, including the following steps:
s1, inputting target basic logistics transportation information, and acquiring and constructing a target supervision model according to historical logistics transportation data and the target basic logistics transportation information; the target basic logistics transportation information comprises transportation company information, transportation dispatch personnel and vehicle information, cargo information and transportation address information; the historical logistics transportation data comprise transportation track condition data, transportation time, abnormal transportation behavior data and the like.
S2, acquiring and importing logistics transportation track information into a target supervision model in real time, and supervising the logistics transportation path track;
s3, acquiring cargo information in the logistics transportation process according to a preset cargo supervision period, and importing the cargo information into a target supervision model to supervise cargo conditions;
s4, acquiring personnel and vehicle information in the logistics transportation process, carrying out anomaly analysis according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle;
s5, analyzing all abnormal conditions in the logistics transportation process based on the target supervision model, and generating and reporting early warning information.
According to the method, a reasonable target supervision model is built by combining target basic logistics transportation information and historical logistics transportation conditions, so that targeted transportation management is realized; based on the target supervision model, the follow-up accurate monitoring on multiple aspects such as track, cargo condition, personnel and vehicle condition and the like is realized, and abnormal early warning and reporting are timely carried out. The application can comprehensively and accurately monitor the track, goods, personnel and vehicles in the logistics transportation process, and monitor the goods transportation condition in real time, and if the conditions of offline equipment, long-time stay of goods, abnormal personnel or vehicles, abnormal check-in and check-out of the goods and the like occur, the abnormal monitoring report is timely carried out, so that the logistics transportation effect is ensured.
Based on the second aspect, the method for constructing the target supervision model according to the historical logistics transportation data and the target base logistics transportation information further comprises the following steps:
extracting and setting a transportation track management node according to transportation address information in the target basic logistics transportation information;
extracting and setting cargo supervision nodes according to cargo information in the target basic logistics transportation information;
extracting and setting an object management node according to the transport party information in the target basic logistics transport information;
constructing an initial supervision model based on the transportation track management node, the goods supervision node and the object management node;
training the initial supervision model based on the historical logistics transportation data to construct a target supervision model.
In order to ensure the monitoring effect on the logistics transportation process, an initial supervision model is built by combining the conditions of cargo information, a transportation party, objects (personnel and vehicles) in the transportation process and the like so as to comprehensively supervise the logistics transportation process; and then training and optimizing the initial supervision model by combining the historical logistics transportation data to obtain a better target supervision model, and accurately controlling the subsequent logistics transportation.
Based on the second aspect, the method for importing the logistics transportation track information into the target supervision model and supervising the logistics transportation path track further comprises the following steps:
importing the logistics transportation track information into a transportation track management node of a target supervision model;
and analyzing the logistics transportation track information based on the corresponding transportation track management node, and judging whether the transportation path is normal or not.
In order to ensure the transportation efficiency and the timely delivery of cargoes, whether the transportation track is normal or not is judged based on the target supervision model and the logistics transportation track information acquired in real time, whether the transportation track is matched with a pre-planned path is judged, and if the transportation track is not matched with the pre-planned path, early warning is immediately reported.
Based on the second aspect, further, the method for collecting cargo information in the logistics transportation process and guiding the cargo information into the target supervision model to supervise the cargo condition comprises the following steps:
sequentially importing cargo information acquired in each cargo supervision period into cargo supervision nodes of a target supervision model;
and (3) analyzing and comparing the cargo image and the cargo weight according to the corresponding cargo information based on the cargo supervision node, and judging whether cargo conditions in each cargo supervision period are normal or not.
In order to better monitor the cargo condition, a reasonable cargo monitoring period is set according to the actual transportation condition, cargo information in the logistics transportation process is collected based on the set cargo monitoring period, the cargo information comprises cargo images and cargo weight data, and whether the appearance of the cargo is damaged is judged by comparing the collected cargo images with the initially recorded cargo images based on a target monitoring model; meanwhile, the weight of the goods is compared with the weight of the initial goods through the target supervision model, and whether the loss of the goods is missed or not is judged. The cargo transportation condition is monitored from two aspects of appearance and weight, and a better cargo monitoring effect is achieved.
Based on the second aspect, the monitoring method based on the intelligent logistics transportation platform further comprises the following steps:
and acquiring and carrying out evaluation analysis on the transportation capacity and the quality of the logistics transportation party according to the basic information and the transportation condition data of the logistics transportation party, and generating and marking the corresponding logistics transportation party according to the evaluation result.
In the logistics transportation link, a proper logistics transportation company is selected to be more beneficial to logistics transportation quality, basic information and historical transportation conditions of related logistics transportation parties are acquired to evaluate transportation capacity and transportation quality of the logistics transportation parties, and personnel evaluation data can be combined to comprehensively evaluate the logistics transportation parties, and the logistics transportation parties are ordered and marked according to the transportation capacity and the transportation quality level in the evaluation result so as to conveniently select corresponding logistics transportation parties for transporting goods.
As shown in fig. 3, in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The method of any of the second aspects described above is implemented when one or more programs are executed by the processor 102.
And a communication interface 103, where the memory 101, the processor 102 and the communication interface 103 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules that are stored within the memory 101 for execution by the processor 102 to perform various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other manners. The above-described method and system embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by the processor 102, implements a method as in any of the second aspects described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. The utility model provides a monitored control system based on intelligent commodity circulation transportation platform, its characterized in that includes supervision model construction module, orbit supervision module, goods supervision module, unusual monitoring module and early warning reporting module, wherein:
the supervision model construction module is used for inputting the target basic logistics transportation information, acquiring and constructing a target supervision model according to the historical logistics transportation data and the target basic logistics transportation information;
the track supervision module is used for acquiring and importing logistics transportation track information into the target supervision model in real time and supervising the logistics transportation path track;
the goods supervision module is used for collecting goods information in the logistics transportation process according to a preset goods supervision period, guiding the goods information into the target supervision model and supervising the goods condition;
the anomaly monitoring module is used for collecting personnel and vehicle information in the logistics transportation process, carrying out anomaly analysis according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle;
and the early warning reporting module is used for analyzing all abnormal conditions in the logistics transportation process based on the target supervision model, and generating and reporting early warning information.
2. The intelligent logistics transport platform-based monitoring system of claim 1, wherein the supervisory model construction module comprises a transportation track management node setting unit, a cargo supervisory node setting unit, an object management node setting unit, an initial model construction unit, and a model training unit, wherein:
the transportation track management node setting unit is used for extracting and setting transportation track management nodes according to the transportation address information in the target basic logistics transportation information;
the goods supervision node setting unit is used for extracting and setting goods supervision nodes according to the goods information in the target basic logistics transportation information;
the object management node setting unit is used for extracting and setting an object management node according to the transport party information in the target basic logistics transport information;
the initial model building unit is used for building an initial supervision model based on the transportation track management node, the goods supervision node and the object management node;
and the model training unit is used for training the initial supervision model based on the historical logistics transportation data so as to construct a target supervision model.
3. The monitoring system based on the intelligent logistics transportation platform according to claim 1, further comprising an abnormal transportation marking module, wherein the abnormal transportation marking module is used for acquiring and carrying out evaluation analysis on transportation capacity and quality of a logistics transportation party according to basic information and transportation condition data of the logistics transportation party, and generating and marking the corresponding logistics transportation party according to evaluation results.
4. The monitoring method based on the intelligent logistics transportation platform is characterized by comprising the following steps of:
inputting target basic logistics transportation information, and acquiring and constructing a target supervision model according to the historical logistics transportation data and the target basic logistics transportation information;
acquiring and importing logistics transportation track information into a target supervision model in real time, and supervising the logistics transportation path track;
according to a preset cargo supervision period, acquiring cargo information in the logistics transportation process, and importing the cargo information into a target supervision model to supervise cargo conditions;
collecting personnel and vehicle information in the logistics transportation process, carrying out anomaly analysis according to a preset anomaly behavior data set by combining a target supervision model, and carrying out anomaly monitoring on the personnel and the vehicle;
and analyzing all abnormal conditions in the logistics transportation process based on the target supervision model, and generating and reporting early warning information.
5. The method for monitoring and controlling an intelligent logistics transportation platform according to claim 4, wherein the method for constructing the target supervision model according to the historical logistics transportation data and the target base logistics transportation information comprises the following steps:
extracting and setting a transportation track management node according to transportation address information in the target basic logistics transportation information;
extracting and setting cargo supervision nodes according to cargo information in the target basic logistics transportation information;
extracting and setting an object management node according to the transport party information in the target basic logistics transport information;
constructing an initial supervision model based on the transportation track management node, the goods supervision node and the object management node;
training the initial supervision model based on the historical logistics transportation data to construct a target supervision model.
6. The monitoring method based on the intelligent logistics transportation platform according to claim 5, wherein the method for importing logistics transportation track information into the target supervision model and supervising the logistics transportation path track comprises the following steps:
importing the logistics transportation track information into a transportation track management node of a target supervision model;
and analyzing the logistics transportation track information based on the corresponding transportation track management node, and judging whether the transportation path is normal or not.
7. The monitoring method based on the intelligent logistics transportation platform according to claim 4, wherein the method for collecting the cargo information in the logistics transportation process and importing the cargo information into the target supervision model to supervise the cargo condition comprises the following steps:
sequentially importing cargo information acquired in each cargo supervision period into cargo supervision nodes of a target supervision model;
and (3) analyzing and comparing the cargo image and the cargo weight according to the corresponding cargo information based on the cargo supervision node, and judging whether cargo conditions in each cargo supervision period are normal or not.
8. The method for monitoring an intelligent logistics transport platform in accordance with claim 4, further comprising the steps of:
and acquiring and carrying out evaluation analysis on the transportation capacity and the quality of the logistics transportation party according to the basic information and the transportation condition data of the logistics transportation party, and generating and marking the corresponding logistics transportation party according to the evaluation result.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method of any of claims 4-8 is implemented when the one or more programs are executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 4-8.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117273573A (en) * | 2023-11-22 | 2023-12-22 | 北京中海通科技有限公司 | Cross-border trade logistics data tracing method and system |
| CN117611045A (en) * | 2024-01-22 | 2024-02-27 | 湖南创亚信息科技有限公司 | Cargo flow monitoring method and system based on cloud computing |
| CN118037164A (en) * | 2024-04-11 | 2024-05-14 | 创兴世纪(深圳)跨境网络技术有限公司 | Logistics track remote supervision system and method based on cloud platform |
| CN118333501A (en) * | 2024-03-05 | 2024-07-12 | 深圳市瑞迪优科技技术有限公司 | Logistics and supply chain management system based on RFID |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117273573A (en) * | 2023-11-22 | 2023-12-22 | 北京中海通科技有限公司 | Cross-border trade logistics data tracing method and system |
| CN117273573B (en) * | 2023-11-22 | 2024-03-01 | 北京中海通科技有限公司 | Cross-border trade logistics data tracing method and system |
| CN117611045A (en) * | 2024-01-22 | 2024-02-27 | 湖南创亚信息科技有限公司 | Cargo flow monitoring method and system based on cloud computing |
| CN117611045B (en) * | 2024-01-22 | 2024-04-19 | 湖南创亚信息科技有限公司 | Cargo flow monitoring method and system based on cloud computing |
| CN118333501A (en) * | 2024-03-05 | 2024-07-12 | 深圳市瑞迪优科技技术有限公司 | Logistics and supply chain management system based on RFID |
| CN118037164A (en) * | 2024-04-11 | 2024-05-14 | 创兴世纪(深圳)跨境网络技术有限公司 | Logistics track remote supervision system and method based on cloud platform |
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