CN116523429B - Intelligent logistics monitoring system and method based on Internet of things - Google Patents
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Abstract
The invention discloses an intelligent logistics monitoring system and method based on the Internet of things, and belongs to the technical field of logistics monitoring. The intelligent logistics characteristic information acquisition system comprises a real-time acquisition module, a real-time acquisition module and a real-time acquisition module, wherein the real-time acquisition module is used for acquiring intelligent logistics characteristic information based on the Internet of things in real time; the data processing module is used for preprocessing intelligent logistics characteristic information based on the Internet of things, which is acquired in real time; the comparison analysis module is used for carrying out comparison analysis on intelligent logistics characterization data based on the Internet of things; and the monitoring management and control module is used for intelligently monitoring and controlling the intelligent logistics based on the Internet of things. The intelligent logistics process monitoring system solves the problems that the current technology cannot monitor the intelligent logistics process based on the Internet of things in real time, so that the latest progress of the whole logistics process cannot be mastered in real time, and the intelligent logistics monitoring effect is poor.
Description
Technical Field
The invention relates to the technical field of logistics monitoring, in particular to an intelligent logistics monitoring system and method based on the Internet of things.
Background
The internet of things is an important component of a new generation of information technology, and as the name implies, the internet of things is the internet connected with things, and has two layers of meanings: firstly, the core and the foundation of the Internet of things are still the Internet, and the Internet is an extended and expanded network based on the Internet; secondly, the user side extends and expands to any article to article, and information exchange and communication are carried out. The internet of things technology is applied to the logistics system, so that logistics management is more accurate, intelligent and perfect.
The Chinese patent with publication number of CN204044876U discloses a logistics system based on the Internet of things, which relates to logistics materials and comprises: the system comprises an internet of things host, a material warehouse-in system and a material warehouse-out system; labeling the logistics materials; the material warehousing system is connected with the internet of things host; the material delivery system is connected with the internet of things host, and the internet of things technology is combined with the logistics system, so that the intelligent logistics system is truly realized. However, the above patent has the following drawbacks in practical use:
the intelligent logistics process based on the Internet of things cannot be monitored in real time, so that the latest progress of the whole logistics process cannot be mastered in real time, and the intelligent logistics monitoring effect is poor.
Disclosure of Invention
The invention aims to provide an intelligent logistics monitoring system and method based on the Internet of things, which can monitor the intelligent logistics process based on the Internet of things in real time, grasp the latest progress of the whole logistics process in real time, improve the intelligent logistics monitoring and controlling effect and solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
intelligent logistics monitoring system based on thing networking includes
The real-time acquisition module is used for acquiring intelligent logistics characteristic information based on the Internet of things in real time, wherein the intelligent logistics characteristic information comprises logistics vehicle ambient temperature information, logistics vehicle ambient humidity information, logistics vehicle ambient illumination intensity information, logistics vehicle running inertia information and logistics vehicle running track information;
the data processing module is used for preprocessing intelligent logistics characteristic information based on the Internet of things, acquiring the intelligent logistics characteristic information based on the Internet of things, converting the acquired intelligent logistics characteristic information into a form which can be received by the intelligent logistics monitoring system, searching, grouping and calculating the converted intelligent logistics characteristic information, and determining intelligent logistics characteristic data based on the Internet of things;
The comparison analysis module is used for carrying out comparison analysis on the intelligent logistics characterization data based on the Internet of things, acquiring the intelligent logistics characterization data based on the Internet of things, finding out intelligent logistics standard data corresponding to the intelligent logistics characterization data, carrying out comparison analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics comparison analysis report based on the Internet of things;
the monitoring management and control module is used for carrying out intelligent monitoring and control on the intelligent logistics based on the Internet of things, acquiring an intelligent logistics comparison analysis report based on the Internet of things, carrying out association analysis on the intelligent logistics comparison analysis report based on a data mining technology, determining the intelligent logistics fault cause, and based on the intelligent logistics fault cause, preparing a corresponding intelligent logistics monitoring and control strategy and carrying out intelligent monitoring and control on the intelligent logistics according to the intelligent logistics monitoring and control strategy;
the data storage module is used for storing intelligent logistics standard data and providing reference and comparison basis for intelligent logistics monitoring based on the Internet of things, and comprises a plurality of data storage units, wherein different types of intelligent logistics standard data are stored in each data storage unit.
Preferably, the real-time acquisition module comprises
The temperature sensor is used for acquiring the ambient temperature of the logistics vehicle in real time, and acquiring the ambient temperature information of the logistics vehicle in real time through the temperature sensor arranged on the logistics vehicle;
the humidity sensor is used for acquiring ambient humidity of the logistics vehicle in real time, and acquiring ambient humidity information of the logistics vehicle in real time through the humidity sensor arranged on the logistics vehicle;
the illumination sensor is used for acquiring the ambient illumination intensity of the logistics vehicle in real time, and acquiring the ambient illumination intensity information of the logistics vehicle in real time through the illumination sensor arranged on the logistics vehicle;
the inertia sensor is used for acquiring the running inertia of the logistics vehicle in real time, and acquiring the running inertia information of the logistics vehicle, such as acceleration, inclination, impact, vibration, rotation and multi-degree-of-freedom motion of the logistics vehicle in real time through the inertia sensor arranged on the logistics vehicle;
and the Beidou positioning unit is used for acquiring the running track of the logistics vehicle in real time and acquiring the running track information of the logistics vehicle in real time through the Beidou positioning unit arranged on the logistics vehicle.
Preferably, the real-time acquisition module further comprises:
the first time interval extraction module is used for extracting the data acquisition time interval of the temperature sensor;
The second time interval extraction module is used for extracting the data acquisition time interval of the humidity sensor;
the third time interval extraction module is used for extracting the data acquisition time interval of the illumination sensor;
a fourth time interval extraction module for extracting a data acquisition time interval of the inertial sensor;
the fifth time interval extraction module is used for extracting the data acquisition time interval of the Beidou positioning unit;
the time comparison module is used for comparing the time intervals among the temperature sensor, the humidity sensor, the illumination sensor, the inertial sensor and the Beidou positioning unit to obtain a plurality of time interval time differences;
the time interval time difference comparison module is used for comparing the time interval time difference with a preset time difference threshold value to obtain a comparison result; the time difference threshold is obtained through the following formula:
wherein,T y representing a time difference threshold;T i represent the firstiData acquisition time intervals corresponding to the sensors;nrepresenting the total number of sensors;T 5 andT 4 respectively representing data acquisition time intervals of the Beidou positioning unit and the inertial sensor;T 0 representing a reference time value;
and the time interval setting module is used for determining the time interval for transmitting the data to the data processing module by using the comparison result.
Preferably, the time interval setting module includes:
when the comparison result shows that the time difference of all the time intervals does not exceed the preset time difference threshold, setting the time interval for transmitting the data to the data processing module by using a first time interval setting model, wherein the first time interval setting model is as follows:
wherein,T 1 representing the time interval of data transmission to the data processing module, which is acquired by the first time interval setting model;rrepresenting the number of time interval time differences;T gi represent the firstiTime values corresponding to the time differences of the time intervals;T y representing a time difference threshold;T 5 representing the data acquisition time interval of the Beidou positioning unit;
when the comparison result shows that the time interval time difference exceeds the preset time difference threshold, setting a time interval for data transmission to the data processing module by using a second time interval setting model, wherein the second time interval setting model is as follows:
wherein,T 2 representing the time interval of data transmission to the data processing module, which is acquired by the second time interval setting model;rrepresenting the number of time interval time differences;T gi represent the firstiTime values corresponding to the time differences of the time intervals; T y Representing a time difference threshold;T 5 representing the data acquisition time interval of the Beidou positioning unit;mrepresenting the number of time interval time differences exceeding the time difference threshold;kindicating that the time interval time difference does not exceed the time difference thresholdThe number of the inter-differences;T 0 representing a reference time value.
Preferably, the data processing module comprises
The data conversion unit is used for converting the intelligent logistics characteristic information based on the Internet of things acquired in real time, acquiring the intelligent logistics characteristic information based on the Internet of things, and converting the acquired intelligent logistics characteristic information into a form which can be received by the intelligent logistics monitoring system;
the data retrieval unit is used for retrieving the converted intelligent logistics feature information, retrieving the intelligent logistics feature information based on a sequential retrieval method, filtering out the intelligent logistics feature information which is valuable for intelligent logistics monitoring, and determining the intelligent logistics feature information which is valuable for intelligent logistics monitoring;
the data grouping unit is used for grouping the searched intelligent logistics feature information, and based on the mutual exclusion principle, grouping the determined intelligent logistics feature information which is valuable for intelligent logistics monitoring, and determining intelligent logistics feature information with distribution features;
The data calculation unit is used for calculating the grouped intelligent logistics feature information, calculating the intelligent logistics feature information with the distribution feature through arithmetic and logic operation, and determining intelligent logistics characterization data based on the Internet of things.
Preferably, the contrast analysis module comprises
The search engine unit is used for searching the intelligent logistics standard data out of the engine, acquiring intelligent logistics characterization data based on the Internet of things, searching the intelligent logistics standard data corresponding to the intelligent logistics characterization data out of the stored intelligent logistics standard data in different categories, and determining and searching the intelligent logistics standard data;
and the contrast analysis unit is used for carrying out contrast analysis on the intelligent logistics characterization data, acquiring the intelligent logistics characterization data and the intelligent logistics standard data, carrying out contrast analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics contrast analysis report based on the Internet of things.
Preferably, the monitoring and controlling module comprises
The fault locking unit is used for determining the intelligent logistics fault cause, acquiring an intelligent logistics comparison analysis report based on the Internet of things, performing association analysis on the intelligent logistics comparison analysis report based on a data mining technology, and determining the intelligent logistics fault cause;
The strategy making unit is used for making an intelligent logistics monitoring and controlling strategy, obtaining intelligent logistics fault reasons, carrying out multidimensional analysis on the intelligent logistics fault reasons, and making a corresponding intelligent logistics monitoring and controlling strategy based on the intelligent logistics fault reasons;
and the monitoring control unit is used for carrying out intelligent monitoring control on the intelligent logistics, acquiring an intelligent logistics monitoring control strategy and carrying out intelligent monitoring control on the intelligent logistics according to the intelligent logistics monitoring control strategy.
According to another aspect of the invention, an intelligent logistics monitoring method based on the internet of things is provided, and the intelligent logistics monitoring system based on the internet of things is realized, and comprises the following steps:
s1: acquiring intelligent logistics characteristic information based on the Internet of things in real time, preprocessing the intelligent logistics characteristic information acquired in real time, and determining intelligent logistics characterization data based on the Internet of things based on conversion, retrieval, grouping and calculation;
s2: performing contrast analysis on the intelligent logistics characterization data to acquire intelligent logistics characterization data based on the Internet of things, finding out intelligent logistics standard data corresponding to the intelligent logistics characterization data, performing contrast analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics contrast analysis report based on the Internet of things;
S3: based on the data mining technology, carrying out association analysis on the intelligent logistics comparison analysis report, determining the intelligent logistics fault reason, and based on the intelligent logistics fault reason, preparing a corresponding intelligent logistics monitoring and controlling strategy, and carrying out intelligent monitoring and controlling on the intelligent logistics according to the intelligent logistics monitoring and controlling strategy.
Preferably, in the step S1, the intelligent logistics feature information collected in real time is preprocessed, and the following operations are executed:
acquiring intelligent logistics characteristic information based on the Internet of things, which is acquired in real time;
converting the acquired intelligent logistics characteristic information into a form which can be received by an intelligent logistics monitoring system;
searching the intelligent logistics characteristic information based on a sequential searching method;
filtering out intelligent logistics characteristic information which is valuable for intelligent logistics monitoring, and determining the intelligent logistics characteristic information which is valuable for intelligent logistics monitoring;
based on the mutual exclusivity principle, the determined intelligent logistics characteristic information valuable for intelligent logistics monitoring is grouped, and intelligent logistics characteristic information with distribution characteristics is determined;
and calculating intelligent logistics characteristic information with distribution characteristics through arithmetic and logical operation, and determining intelligent logistics characterization data based on the Internet of things.
Preferably, in the step S2, the comparison analysis is performed on the intelligent logistics characterization data, and the following operations are performed:
acquiring intelligent logistics characterization data based on the Internet of things;
searching engine from stored intelligent logistics standard data of different categories to obtain intelligent logistics standard data corresponding to intelligent logistics characterization data, and determining and searching the intelligent logistics standard data;
based on the intelligent logistics standard data, performing comparative analysis on the intelligent logistics characterization data to determine an intelligent logistics comparative analysis report based on the Internet of things;
aiming at the condition that the intelligent logistics characterization data is in the intelligent logistics standard data range, determining an intelligent logistics comparison analysis report based on the Internet of things as intelligent logistics monitoring is normal;
aiming at the condition that the intelligent logistics characterization data is not in the intelligent logistics standard data range, the determined intelligent logistics comparison analysis report based on the Internet of things is abnormal intelligent logistics monitoring.
Preferably, the intelligent logistics monitoring management and control strategy is formulated correspondingly by carrying out association analysis on the intelligent logistics comparison analysis report based on the data mining technology to determine the intelligent logistics fault reason and carrying out multidimensional analysis on the intelligent logistics fault reason, and intelligent monitoring management and control is carried out on the intelligent logistics according to the intelligent logistics monitoring management and control strategy.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the intelligent logistics feature information processing method, intelligent logistics feature information based on the Internet of things is collected in real time, the intelligent logistics feature information is preprocessed, intelligent logistics feature data based on the Internet of things is determined, intelligent logistics standard data corresponding to the intelligent logistics feature data is found out, and based on the intelligent logistics standard data, intelligent logistics feature data is subjected to comparative analysis, and an intelligent logistics comparative analysis report based on the Internet of things is determined.
2. According to the intelligent logistics management and control method, the intelligent logistics fault reasons are determined by carrying out association analysis on the intelligent logistics comparison analysis report, corresponding intelligent logistics monitoring and control strategies are made based on the intelligent logistics fault reasons, and intelligent monitoring and control are carried out on the intelligent logistics according to the intelligent logistics monitoring and control strategies.
Drawings
FIG. 1 is a schematic block diagram of an intelligent logistics monitoring system based on the Internet of things;
FIG. 2 is a block diagram of the intelligent logistics monitoring system based on the Internet of things of the present invention;
FIG. 3 is a flow chart of the intelligent logistics monitoring method based on the Internet of things of the present invention;
FIG. 4 is a flowchart of an algorithm for performing comparative analysis and monitoring control on intelligent logistics characterization data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problem that the current intelligent logistics process based on the internet of things cannot be monitored in real time, so that the latest progress of the whole logistics process cannot be mastered in real time, the intelligent logistics monitoring effect is poor, please refer to fig. 1-4, the embodiment provides the following technical scheme:
intelligent logistics monitoring system based on thing networking includes
The real-time acquisition module is used for acquiring intelligent logistics characteristic information based on the Internet of things in real time, wherein the intelligent logistics characteristic information comprises logistics vehicle ambient temperature information, logistics vehicle ambient humidity information, logistics vehicle ambient illumination intensity information, logistics vehicle running inertia information and logistics vehicle running track information;
The data processing module is used for preprocessing intelligent logistics characteristic information based on the Internet of things, acquiring the intelligent logistics characteristic information based on the Internet of things, converting the acquired intelligent logistics characteristic information into a form which can be received by the intelligent logistics monitoring system, searching, grouping and calculating the converted intelligent logistics characteristic information, and determining intelligent logistics characteristic data based on the Internet of things;
the comparison analysis module is used for carrying out comparison analysis on the intelligent logistics characterization data based on the Internet of things, acquiring the intelligent logistics characterization data based on the Internet of things, finding out intelligent logistics standard data corresponding to the intelligent logistics characterization data, carrying out comparison analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics comparison analysis report based on the Internet of things;
the monitoring management and control module is used for carrying out intelligent monitoring and control on the intelligent logistics based on the Internet of things, acquiring an intelligent logistics comparison analysis report based on the Internet of things, carrying out association analysis on the intelligent logistics comparison analysis report based on a data mining technology, determining the intelligent logistics fault cause, and based on the intelligent logistics fault cause, preparing a corresponding intelligent logistics monitoring and control strategy and carrying out intelligent monitoring and control on the intelligent logistics according to the intelligent logistics monitoring and control strategy;
The data storage module is used for storing intelligent logistics standard data and providing reference and comparison basis for intelligent logistics monitoring based on the Internet of things, and comprises a plurality of data storage units, wherein different types of intelligent logistics standard data are stored in each data storage unit.
The real-time acquisition module comprises
The temperature sensor is used for acquiring the ambient temperature of the logistics vehicle in real time, and acquiring the ambient temperature information of the logistics vehicle in real time through the temperature sensor arranged on the logistics vehicle;
the humidity sensor is used for acquiring ambient humidity of the logistics vehicle in real time, and acquiring ambient humidity information of the logistics vehicle in real time through the humidity sensor arranged on the logistics vehicle;
the illumination sensor is used for acquiring the ambient illumination intensity of the logistics vehicle in real time, and acquiring the ambient illumination intensity information of the logistics vehicle in real time through the illumination sensor arranged on the logistics vehicle;
the inertia sensor is used for acquiring the running inertia of the logistics vehicle in real time, and acquiring the running inertia information of the logistics vehicle, such as acceleration, inclination, impact, vibration, rotation and multi-degree-of-freedom motion of the logistics vehicle in real time through the inertia sensor arranged on the logistics vehicle;
and the Beidou positioning unit is used for acquiring the running track of the logistics vehicle in real time and acquiring the running track information of the logistics vehicle in real time through the Beidou positioning unit arranged on the logistics vehicle.
Specifically, the real-time acquisition module further includes:
the first time interval extraction module is used for extracting the data acquisition time interval of the temperature sensor;
the second time interval extraction module is used for extracting the data acquisition time interval of the humidity sensor;
the third time interval extraction module is used for extracting the data acquisition time interval of the illumination sensor;
a fourth time interval extraction module for extracting a data acquisition time interval of the inertial sensor;
the fifth time interval extraction module is used for extracting the data acquisition time interval of the Beidou positioning unit;
the time comparison module is used for comparing the time intervals among the temperature sensor, the humidity sensor, the illumination sensor, the inertial sensor and the Beidou positioning unit to obtain a plurality of time interval time differences;
the time interval time difference comparison module is used for comparing the time interval time difference with a preset time difference threshold value to obtain a comparison result; the time difference threshold is obtained through the following formula:
wherein,T y representing a time difference threshold;T i represent the firstiData acquisition time intervals corresponding to the sensors;nrepresenting the total number of sensors; T 5 AndT 4 respectively representing data acquisition time intervals of the Beidou positioning unit and the inertial sensor;T 0 representing a reference time value;
and the time interval setting module is used for determining the time interval for transmitting the data to the data processing module by using the comparison result.
The technical effects of the technical scheme are as follows: and comparing the time intervals among the sensors through a time comparison module so as to obtain a plurality of time interval data differences. And then, the time interval data difference comparison module compares the data differences with a preset time difference threshold value to obtain a comparison result.
The time interval setting module determines a time interval for transmitting data to the data processing module by using the comparison result. In particular, based on the comparison, the module may adjust the operating frequency of the data processing module to decrease the frequency of data processing when the time interval difference exceeds a threshold value, and to increase the frequency of data processing when the time interval difference is small or satisfactory.
The technical scheme realizes the extraction and comparison of the data acquisition time interval of the sensor, and the frequency of data processing can be effectively controlled by dynamically adjusting the time interval of data processing. The performance and efficiency of the system are effectively improved, and meanwhile, energy sources are saved and the pressure of data processing is reduced. In addition, by setting the time difference threshold, data acquisition can be optimized according to specific requirements, so that the system is more flexible and adapts to different application scenes. Meanwhile, the time interval threshold value set in the mode can effectively improve the accuracy of time interval comparison and the matching between the time interval comparison and actual Beidou positioning and inertial data acquisition. Other data acquisition is performed around Beidou positioning and inertial data acquisition, so that the problem that the current position data and the inertial data of the vehicle are lack in single data processing and conversion is prevented.
Specifically, the time interval setting module includes:
when the comparison result shows that the time difference of all the time intervals does not exceed the preset time difference threshold, setting the time interval for transmitting the data to the data processing module by using a first time interval setting model, wherein the first time interval setting model is as follows:
wherein,T 1 representing the time interval of data transmission to the data processing module, which is acquired by the first time interval setting model;rrepresenting the number of time interval time differences;T gi represent the firstiTime values corresponding to the time differences of the time intervals;T y representing a time difference threshold;T 5 representing the data acquisition time interval of the Beidou positioning unit;
when the comparison result shows that the time interval time difference exceeds the preset time difference threshold, setting a time interval for data transmission to the data processing module by using a second time interval setting model, wherein the second time interval setting model is as follows:
wherein,T 2 representing the time interval of data transmission to the data processing module, which is acquired by the second time interval setting model;rrepresenting the number of time interval time differences;T gi represent the firstiTime values corresponding to the time differences of the time intervals; T y Representing a time difference threshold;T 5 representing the data acquisition time interval of the Beidou positioning unit;mrepresenting the number of time interval time differences exceeding the time difference threshold;kindicating the number of time interval time differences that the time interval time difference does not exceed the time difference threshold;T 0 representing a reference time value.
The technical effects of the technical scheme are as follows: the time interval of data processing is dynamically adjusted according to the actual condition of sensor data acquisition. When the data difference of all the time intervals does not exceed the preset time difference threshold, a first time interval setting model is used, which means that the stability of the data acquisition of the sensor is good, and the data can be sent to the data processing module according to the preset time interval.
However, when there is a time interval data difference exceeding a preset time difference threshold, the second time interval setting model is used. The stability of the sensor data acquisition may be affected to some extent, possibly with anomalies or variations in sensor performance. By using the second time interval setting model, the time interval of data transmission can be dynamically adjusted according to the actual situation so as to adapt to the unstable data acquisition situation.
The technical scheme provides a flexible way to dynamically adjust the time interval of data processing according to the stability of sensor data acquisition. The adaptability and the robustness of the system are effectively improved, and the accuracy and the reliability of data processing are ensured. Meanwhile, the time interval obtained through the formula can ensure that the comprehensiveness of the data acquisition and transmission of the positioning data is ensured, the comprehensiveness of other data in the single data transmission process is furthest improved, and the data transmission time interval is furthest reduced while the comprehensiveness of the other data in the single data transmission process is improved, so that the data transmission efficiency is improved.
The data processing module comprises
The data conversion unit is used for converting the intelligent logistics characteristic information based on the Internet of things acquired in real time, acquiring the intelligent logistics characteristic information based on the Internet of things, and converting the acquired intelligent logistics characteristic information into a form which can be received by the intelligent logistics monitoring system;
The data retrieval unit is used for retrieving the converted intelligent logistics feature information, retrieving the intelligent logistics feature information based on a sequential retrieval method, filtering out the intelligent logistics feature information which is valuable for intelligent logistics monitoring, and determining the intelligent logistics feature information which is valuable for intelligent logistics monitoring;
the data grouping unit is used for grouping the searched intelligent logistics feature information, and based on the mutual exclusion principle, grouping the determined intelligent logistics feature information which is valuable for intelligent logistics monitoring, and determining intelligent logistics feature information with distribution features;
the data calculation unit is used for calculating the grouped intelligent logistics feature information, calculating the intelligent logistics feature information with the distribution feature through arithmetic and logic operation, and determining intelligent logistics characterization data based on the Internet of things.
The contrast analysis module comprises
The search engine unit is used for searching the intelligent logistics standard data out of the engine, acquiring intelligent logistics characterization data based on the Internet of things, searching the intelligent logistics standard data corresponding to the intelligent logistics characterization data out of the stored intelligent logistics standard data in different categories, and determining and searching the intelligent logistics standard data;
And the contrast analysis unit is used for carrying out contrast analysis on the intelligent logistics characterization data, acquiring the intelligent logistics characterization data and the intelligent logistics standard data, carrying out contrast analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics contrast analysis report based on the Internet of things.
The monitoring and controlling module comprises
The fault locking unit is used for determining the intelligent logistics fault cause, acquiring an intelligent logistics comparison analysis report based on the Internet of things, performing association analysis on the intelligent logistics comparison analysis report based on a data mining technology, and determining the intelligent logistics fault cause;
the strategy making unit is used for making an intelligent logistics monitoring and controlling strategy, obtaining intelligent logistics fault reasons, carrying out multidimensional analysis on the intelligent logistics fault reasons, and making a corresponding intelligent logistics monitoring and controlling strategy based on the intelligent logistics fault reasons;
and the monitoring control unit is used for carrying out intelligent monitoring control on the intelligent logistics, acquiring an intelligent logistics monitoring control strategy and carrying out intelligent monitoring control on the intelligent logistics according to the intelligent logistics monitoring control strategy.
In order to better show the intelligent logistics monitoring flow based on the internet of things, the embodiment now provides an intelligent logistics monitoring method based on the internet of things, and the intelligent logistics monitoring system based on the internet of things is realized and comprises the following steps:
S1: acquiring intelligent logistics characteristic information based on the Internet of things in real time, preprocessing the intelligent logistics characteristic information acquired in real time, and determining intelligent logistics characterization data based on the Internet of things based on conversion, retrieval, grouping and calculation;
s2: performing contrast analysis on the intelligent logistics characterization data to acquire intelligent logistics characterization data based on the Internet of things, finding out intelligent logistics standard data corresponding to the intelligent logistics characterization data, performing contrast analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics contrast analysis report based on the Internet of things;
s3: based on the data mining technology, carrying out association analysis on the intelligent logistics comparison analysis report, determining the intelligent logistics fault reason, and based on the intelligent logistics fault reason, preparing a corresponding intelligent logistics monitoring and controlling strategy, and carrying out intelligent monitoring and controlling on the intelligent logistics according to the intelligent logistics monitoring and controlling strategy.
S1, preprocessing intelligent logistics characteristic information acquired in real time, and executing the following operations:
acquiring intelligent logistics characteristic information based on the Internet of things, which is acquired in real time;
converting the acquired intelligent logistics characteristic information into a form which can be received by an intelligent logistics monitoring system;
Searching the intelligent logistics characteristic information based on a sequential searching method;
filtering out intelligent logistics characteristic information which is valuable for intelligent logistics monitoring, and determining the intelligent logistics characteristic information which is valuable for intelligent logistics monitoring;
based on the mutual exclusivity principle, the determined intelligent logistics characteristic information valuable for intelligent logistics monitoring is grouped, and intelligent logistics characteristic information with distribution characteristics is determined;
and calculating intelligent logistics characteristic information with distribution characteristics through arithmetic and logical operation, and determining intelligent logistics characterization data based on the Internet of things.
S2, performing contrast analysis on the intelligent logistics characterization data, and executing the following operations:
acquiring intelligent logistics characterization data based on the Internet of things;
searching engine from stored intelligent logistics standard data of different categories to obtain intelligent logistics standard data corresponding to intelligent logistics characterization data, and determining and searching the intelligent logistics standard data;
based on the intelligent logistics standard data, performing comparative analysis on the intelligent logistics characterization data to determine an intelligent logistics comparative analysis report based on the Internet of things;
aiming at the condition that the intelligent logistics characterization data is in the intelligent logistics standard data range, determining an intelligent logistics comparison analysis report based on the Internet of things as intelligent logistics monitoring is normal;
Aiming at the condition that the intelligent logistics characterization data is not in the intelligent logistics standard data range, the determined intelligent logistics comparison analysis report based on the Internet of things is abnormal intelligent logistics monitoring.
To the unusual condition of wisdom commodity circulation control, then based on the data mining technique, carry out the association analysis to wisdom commodity circulation contrast analysis report, confirm wisdom commodity circulation fault cause, carry out multidimensional analysis to wisdom commodity circulation fault cause, formulate corresponding wisdom commodity circulation monitoring management and control tactics, carry out intelligent monitoring management and control to wisdom commodity circulation according to wisdom commodity circulation monitoring management and control tactics.
In summary, the intelligent logistics monitoring system and method based on the Internet of things acquire intelligent logistics characteristic information based on the Internet of things in real time, preprocess the intelligent logistics characteristic information acquired in real time, determine intelligent logistics characterization data based on the Internet of things, find out intelligent logistics standard data corresponding to the intelligent logistics characterization data, compare and analyze the intelligent logistics characterization data based on the intelligent logistics standard data, determine an intelligent logistics comparison analysis report based on the Internet of things, perform correlation analysis on the intelligent logistics comparison analysis report based on the data mining technology, determine intelligent logistics fault reasons, determine corresponding intelligent logistics monitoring and controlling strategies based on the intelligent logistics fault reasons, monitor and control intelligent logistics process based on the Internet of things in real time, grasp the latest progress of the whole logistics process in real time, and improve intelligent logistics monitoring and controlling effects.
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.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. Intelligent logistics monitoring system based on thing networking, its characterized in that includes
The real-time acquisition module is used for acquiring intelligent logistics characteristic information based on the Internet of things in real time, wherein the intelligent logistics characteristic information comprises logistics vehicle ambient temperature information, logistics vehicle ambient humidity information, logistics vehicle ambient illumination intensity information, logistics vehicle running inertia information and logistics vehicle running track information;
The data processing module is used for preprocessing intelligent logistics characteristic information based on the Internet of things, acquiring the intelligent logistics characteristic information based on the Internet of things, converting the acquired intelligent logistics characteristic information into a form which can be received by the intelligent logistics monitoring system, searching, grouping and calculating the converted intelligent logistics characteristic information, and determining intelligent logistics characteristic data based on the Internet of things;
the comparison analysis module is used for carrying out comparison analysis on the intelligent logistics characterization data based on the Internet of things, acquiring the intelligent logistics characterization data based on the Internet of things, finding out intelligent logistics standard data corresponding to the intelligent logistics characterization data, carrying out comparison analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics comparison analysis report based on the Internet of things;
the monitoring management and control module is used for carrying out intelligent monitoring and control on the intelligent logistics based on the Internet of things, acquiring an intelligent logistics comparison analysis report based on the Internet of things, carrying out association analysis on the intelligent logistics comparison analysis report based on a data mining technology, determining the intelligent logistics fault cause, and based on the intelligent logistics fault cause, preparing a corresponding intelligent logistics monitoring and control strategy and carrying out intelligent monitoring and control on the intelligent logistics according to the intelligent logistics monitoring and control strategy;
The data storage module is used for storing intelligent logistics standard data and providing reference and comparison basis for intelligent logistics monitoring based on the Internet of things, and comprises a plurality of data storage units, wherein each data storage unit stores intelligent logistics standard data of different categories;
the real-time acquisition module comprises:
the temperature sensor is used for acquiring the ambient temperature of the logistics vehicle in real time, and acquiring the ambient temperature information of the logistics vehicle in real time through the temperature sensor arranged on the logistics vehicle;
the humidity sensor is used for acquiring ambient humidity of the logistics vehicle in real time, and acquiring ambient humidity information of the logistics vehicle in real time through the humidity sensor arranged on the logistics vehicle;
the illumination sensor is used for acquiring the ambient illumination intensity of the logistics vehicle in real time, and acquiring the ambient illumination intensity information of the logistics vehicle in real time through the illumination sensor arranged on the logistics vehicle;
the inertia sensor is used for acquiring the running inertia of the logistics vehicle in real time, and acquiring the running inertia information of the logistics vehicle, such as acceleration, inclination, impact, vibration, rotation and multi-degree-of-freedom motion of the logistics vehicle in real time through the inertia sensor arranged on the logistics vehicle;
The Beidou positioning unit is used for acquiring the running track of the logistics vehicle in real time and acquiring the running track information of the logistics vehicle in real time through the Beidou positioning unit arranged on the logistics vehicle;
the real-time acquisition module further comprises:
the first time interval extraction module is used for extracting the data acquisition time interval of the temperature sensor;
the second time interval extraction module is used for extracting the data acquisition time interval of the humidity sensor;
the third time interval extraction module is used for extracting the data acquisition time interval of the illumination sensor;
a fourth time interval extraction module for extracting a data acquisition time interval of the inertial sensor;
the fifth time interval extraction module is used for extracting the data acquisition time interval of the Beidou positioning unit;
the time comparison module is used for comparing the time intervals among the temperature sensor, the humidity sensor, the illumination sensor, the inertial sensor and the Beidou positioning unit to obtain a plurality of time interval time differences;
the time interval time difference comparison module is used for comparing the time interval time difference with a preset time difference threshold value to obtain a comparison result; the time difference threshold is obtained through the following formula:
;
Wherein,T y representing a time difference threshold;T i represent the firstiData acquisition time intervals corresponding to the sensors;nrepresenting the total number of sensors;T 5 andT 4 respectively representing data acquisition time intervals of the Beidou positioning unit and the inertial sensor;T 0 representing a reference time value;
the time interval setting module is used for determining the time interval for transmitting data to the data processing module by utilizing the comparison result;
a time interval setting module comprising:
when the comparison result shows that the time difference of all the time intervals does not exceed the preset time difference threshold, setting the time interval for transmitting the data to the data processing module by using a first time interval setting model, wherein the first time interval setting model is as follows:
;
wherein the method comprises the steps of，T 1 Representing the time interval of data transmission to the data processing module, which is acquired by the first time interval setting model;rrepresenting the number of time interval time differences;T gi represent the firstiTime values corresponding to the time differences of the time intervals;T y representing a time difference threshold;T 5 representing the data acquisition time interval of the Beidou positioning unit;
when the comparison result shows that the time interval time difference exceeds the preset time difference threshold, setting a time interval for data transmission to the data processing module by using a second time interval setting model, wherein the second time interval setting model is as follows: ;
Wherein,T 2 representing the time interval of data transmission to the data processing module, which is acquired by the second time interval setting model;rrepresenting the number of time interval time differences;T gi represent the firstiTime values corresponding to the time differences of the time intervals;T y representing a time difference threshold;T 5 representing the data acquisition time interval of the Beidou positioning unit;mrepresenting the number of time interval time differences exceeding the time difference threshold;kindicating the number of time interval time differences that the time interval time difference does not exceed the time difference threshold;T 0 representing a reference time value;
the data processing module comprises:
the data conversion unit is used for converting the intelligent logistics characteristic information based on the Internet of things acquired in real time, acquiring the intelligent logistics characteristic information based on the Internet of things, and converting the acquired intelligent logistics characteristic information into a form which can be received by the intelligent logistics monitoring system;
the data retrieval unit is used for retrieving the converted intelligent logistics feature information, retrieving the intelligent logistics feature information based on a sequential retrieval method, filtering out the intelligent logistics feature information which is valuable for intelligent logistics monitoring, and determining the intelligent logistics feature information which is valuable for intelligent logistics monitoring;
The data grouping unit is used for grouping the searched intelligent logistics feature information, and based on the mutual exclusion principle, grouping the determined intelligent logistics feature information which is valuable for intelligent logistics monitoring, and determining intelligent logistics feature information with distribution features;
the data calculation unit is used for calculating the grouped intelligent logistics feature information, calculating the intelligent logistics feature information with the distribution feature through arithmetic and logic operation, and determining intelligent logistics characterization data based on the Internet of things;
the contrast analysis module comprises:
the search engine unit is used for searching the intelligent logistics standard data out of the engine, acquiring intelligent logistics characterization data based on the Internet of things, searching the intelligent logistics standard data corresponding to the intelligent logistics characterization data out of the stored intelligent logistics standard data in different categories, and determining and searching the intelligent logistics standard data;
the comparison analysis unit is used for carrying out comparison analysis on the intelligent logistics characterization data to obtain intelligent logistics characterization data and intelligent logistics standard data, carrying out comparison analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics comparison analysis report based on the Internet of things;
The monitoring management and control module comprises:
the fault locking unit is used for determining the intelligent logistics fault cause, acquiring an intelligent logistics comparison analysis report based on the Internet of things, performing association analysis on the intelligent logistics comparison analysis report based on a data mining technology, and determining the intelligent logistics fault cause;
the strategy making unit is used for making an intelligent logistics monitoring and controlling strategy, obtaining intelligent logistics fault reasons, carrying out multidimensional analysis on the intelligent logistics fault reasons, and making a corresponding intelligent logistics monitoring and controlling strategy based on the intelligent logistics fault reasons;
and the monitoring control unit is used for carrying out intelligent monitoring control on the intelligent logistics, acquiring an intelligent logistics monitoring control strategy and carrying out intelligent monitoring control on the intelligent logistics according to the intelligent logistics monitoring control strategy.
2. The intelligent logistics monitoring method based on the Internet of things is realized based on the intelligent logistics monitoring system based on the Internet of things as claimed in claim 1, and is characterized in that: the method comprises the following steps:
s1: acquiring intelligent logistics characteristic information based on the Internet of things in real time, preprocessing the intelligent logistics characteristic information acquired in real time, and determining intelligent logistics characterization data based on the Internet of things based on conversion, retrieval, grouping and calculation;
S2: performing contrast analysis on the intelligent logistics characterization data to acquire intelligent logistics characterization data based on the Internet of things, finding out intelligent logistics standard data corresponding to the intelligent logistics characterization data, performing contrast analysis on the intelligent logistics characterization data based on the intelligent logistics standard data, and determining an intelligent logistics contrast analysis report based on the Internet of things;
s3: based on the data mining technology, carrying out association analysis on the intelligent logistics comparison analysis report, determining the intelligent logistics fault reason, and based on the intelligent logistics fault reason, preparing a corresponding intelligent logistics monitoring and controlling strategy, and carrying out intelligent monitoring and controlling on the intelligent logistics according to the intelligent logistics monitoring and controlling strategy.
3. The internet of things-based intelligent logistics monitoring method of claim 2, wherein the method comprises the steps of: in the step S1, the intelligent logistics characteristic information acquired in real time is preprocessed, and the following operations are executed:
acquiring intelligent logistics characteristic information based on the Internet of things, which is acquired in real time;
converting the acquired intelligent logistics characteristic information into a form which can be received by an intelligent logistics monitoring system;
searching the intelligent logistics characteristic information based on a sequential searching method;
Filtering out intelligent logistics characteristic information which is valuable for intelligent logistics monitoring, and determining the intelligent logistics characteristic information which is valuable for intelligent logistics monitoring;
based on the mutual exclusivity principle, the determined intelligent logistics characteristic information valuable for intelligent logistics monitoring is grouped, and intelligent logistics characteristic information with distribution characteristics is determined;
and calculating intelligent logistics characteristic information with distribution characteristics through arithmetic and logical operation, and determining intelligent logistics characterization data based on the Internet of things.
4. The internet of things-based intelligent logistics monitoring method of claim 3, wherein the method comprises the steps of: in the step S2, the intelligent logistics characterization data are subjected to comparative analysis, and the following operations are executed:
acquiring intelligent logistics characterization data based on the Internet of things;
searching engine from stored intelligent logistics standard data of different categories to obtain intelligent logistics standard data corresponding to intelligent logistics characterization data, and determining and searching the intelligent logistics standard data;
based on the intelligent logistics standard data, performing comparative analysis on the intelligent logistics characterization data to determine an intelligent logistics comparative analysis report based on the Internet of things;
aiming at the condition that the intelligent logistics characterization data is in the intelligent logistics standard data range, determining an intelligent logistics comparison analysis report based on the Internet of things as intelligent logistics monitoring is normal;
Aiming at the condition that the intelligent logistics characterization data are not in the intelligent logistics standard data range, determining an intelligent logistics comparison analysis report based on the Internet of things as an intelligent logistics monitoring abnormality;
to the unusual condition of wisdom commodity circulation control, then based on the data mining technique, carry out the association analysis to wisdom commodity circulation contrast analysis report, confirm wisdom commodity circulation fault cause, carry out multidimensional analysis to wisdom commodity circulation fault cause, formulate corresponding wisdom commodity circulation monitoring management and control tactics, carry out intelligent monitoring management and control to wisdom commodity circulation according to wisdom commodity circulation monitoring management and control tactics.
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