CN116167596B - Distribution path analysis method and system based on big data - Google Patents
Distribution path analysis method and system based on big data Download PDFInfo
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Abstract
The invention discloses a distribution path analysis method and a distribution path analysis system based on big data, which are characterized in that the transportation plan analysis is carried out according to the 3D scanning data of goods and the transportation load data of a current logistics transfer station by acquiring the 3D scanning data of logistics goods and the transportation load data of the current logistics transfer station, so as to obtain transportation plan data, and the transportation plan data is visually displayed based on a logistics map model; carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions based on the goods 3D scanning data, marking the corresponding goods as the abnormal goods if the abnormal conditions exist, carrying out tracing distribution path inquiry according to the abnormal goods 3D scanning data to obtain a distribution path, analyzing the corresponding goods logistics data from the logistics big data based on the distribution path, and carrying out information correction on the abnormal goods according to the goods logistics data.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a distribution path analysis method and system based on big data.
Background
The logistics industry is a composite or polymeric industry formed by industrialization of logistics resources. The logistics resources comprise transportation, storage, loading and unloading, carrying, packaging, circulation processing, distribution, information platforms and the like. The method is influenced by the rapid development of electronic commerce and the efficient development of informationized logistics, provides stronger development power for modern logistics industry, and continuously increases logistics scale, so that logistics cargo carrying capacity is also increased in an explosive manner, logistics data is also increased continuously, accordingly, in logistics transportation, abnormal logistics cargo information, such as information loss of bar codes, labels and the like, is inevitably generated, and on the premise of huge logistics transportation capacity, the method is an important problem for ensuring orderly and stable logistics in the current logistics industry.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a distribution path analysis method and system based on big data.
The first aspect of the present invention provides a distribution path analysis method based on big data, comprising:
acquiring 3D scanning data of logistics goods;
acquiring transportation load data of a current logistics transfer station, and carrying out cargo transportation plan analysis according to the cargo 3D scanning data and the transportation load data to obtain transportation plan data;
Based on a logistics map model, visually displaying the transportation plan data;
carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions or not based on the goods 3D scanning data, and marking the corresponding goods as abnormal goods if the abnormal conditions exist;
tracing distribution path inquiry is carried out according to the 3D scanning data of the abnormal goods, a distribution path is obtained, and corresponding goods logistics data are analyzed from logistics big data based on the distribution path;
and carrying out information correction on the abnormal goods according to the goods logistics data.
In this scheme, acquire the transportation load capacity data of current commodity circulation transfer station, carry out cargo transportation plan analysis according to cargo 3D scanning data and transportation load capacity data, obtain transportation plan data, specifically do:
acquiring transportation resource information and manpower resource information in a current logistics transfer station;
carrying out transportation capability analysis based on transportation resource information and human resource information to obtain input load data;
carrying out cargo specification analysis according to the cargo 3D scanning data of the current transfer station to obtain cargo volume and appearance size information;
Acquiring cargo weight information, and combining the cargo weight information and the volume appearance size information to obtain cargo specification information;
and carrying out transportation resource allocation and manpower resource allocation calculation analysis on the goods according to the transportation resource information and the manpower resource information and combining the goods specification information to obtain the goods transportation plan data and the manpower allocation plan data.
In this scheme, based on commodity circulation map model, will transport plan data carries out visual show, specifically is:
acquiring a road model of a logistics area through the Internet;
acquiring position information of all logistics transfer stations in a logistics area;
constructing a logistics map model, and importing the road model and the logistics transfer station position information into the logistics map model;
importing the transportation plan data into a logistics map model to simulate the transportation of goods, and recording transportation simulation data;
and visually displaying the logistics map model through preset terminal equipment, and dynamically displaying the transportation plan data based on the transportation simulation data.
In this scheme, carry out logistics transmission according to transportation plan data to the goods information of every commodity circulation transfer station of real-time detection, based on goods 3D scanning data, whether check commodity circulation goods have information abnormal conditions, if there is the abnormal conditions will correspond the goods sign as abnormal goods, specifically do:
Acquiring corresponding cargo image data based on cargo 3D scanning data;
carrying out cargo label area identification on the cargo image data, and obtaining a corresponding cargo label area image;
carrying out cargo information identification and information integrity analysis on the cargo label area image to obtain cargo information integrity;
and if the cargo information integrity is lower than the preset value, marking the cargo as abnormal cargo.
In this scheme, carry out the inquiry of tracing to source delivery route according to unusual goods 3D scanning data, obtain the delivery route, analyze out corresponding goods commodity circulation data from commodity circulation big data based on the delivery route, specifically do:
acquiring 3D scanning data of abnormal goods;
splitting the abnormal cargo 3D scanning data into cargo image data with multiple angles;
and carrying out feature analysis on the cargo image data based on different angles to obtain abnormal cargo image features corresponding to the different angles.
In this scheme, carry out the inquiry of tracing to source delivery route according to unusual goods 3D scanning data, obtain the delivery route, analyze out corresponding goods commodity circulation data from commodity circulation big data based on the delivery route, specifically do:
taking the current logistics transfer station as an original transfer station;
Acquiring a logistics transfer station of a last transportation link in the original logistics transfer stations, and marking the logistics transfer station as a route logistics station;
the route logistics station comprises at least one;
acquiring cargo 3D scanning data of a route logistics station;
performing feature matching on the 3D scanning data of the goods according to the abnormal goods image features;
if the matching is successful, corresponding matching cargo information and route logistics stations are recorded in a logistics route table;
and judging whether the current route logistics station is an initial station or not, if not, circularly acquiring a logistics transfer station of the last transportation link in the current route logistics station, marking the logistics transfer station as the route logistics station, and performing feature matching and data recording of a logistics route table on the cargo 3D scanning data of the route logistics station again until the current route logistics station is judged to be the initial station.
In this scheme, carry out the inquiry of tracing to source delivery route according to unusual goods 3D scanning data, obtain the delivery route, still include:
when the current route logistics station is judged to be the starting station, acquiring a logistics route list;
according to the logistics route table, all route logistics stations from the original transfer station to the starting station are obtained and marked as intermediate stations;
the intermediate station comprises at least one;
And tracing routes of all intermediate stations based on the time sequence of feature matching, and obtaining a distribution path.
In this scheme, carry out information correction to unusual goods according to goods logistics data, specifically do:
generating a path retrieval tag based on the delivery path;
taking the goods delivery time information of the delivery path as a time retrieval condition;
according to the route retrieval tag and the time retrieval condition, route matching retrieval and logistics data extraction are carried out from logistics big data, and corresponding goods logistics data are obtained;
based on the goods logistics data, goods label information is extracted, and information correction is carried out on abnormal goods according to the goods label information.
The second aspect of the present invention also provides a big data based distribution path analysis system, which is characterized in that the system comprises: the system comprises a memory and a processor, wherein the memory comprises a distribution path analysis program based on big data, and the distribution path analysis program based on big data realizes the following steps when being executed by the processor:
acquiring 3D scanning data of logistics goods;
acquiring transportation load data of a current logistics transfer station, and carrying out cargo transportation plan analysis according to the cargo 3D scanning data and the transportation load data to obtain transportation plan data;
Based on a logistics map model, visually displaying the transportation plan data;
carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions or not based on the goods 3D scanning data, and marking the corresponding goods as abnormal goods if the abnormal conditions exist;
tracing distribution path inquiry is carried out according to the 3D scanning data of the abnormal goods, a distribution path is obtained, and corresponding goods logistics data are analyzed from logistics big data based on the distribution path;
and carrying out information correction on the abnormal goods according to the goods logistics data.
In this scheme, acquire the transportation load capacity data of current commodity circulation transfer station, carry out cargo transportation plan analysis according to cargo 3D scanning data and transportation load capacity data, obtain transportation plan data, specifically do:
acquiring transportation resource information and manpower resource information in a current logistics transfer station;
carrying out transportation capability analysis based on transportation resource information and human resource information to obtain input load data;
carrying out cargo specification analysis according to the cargo 3D scanning data of the current transfer station to obtain cargo volume and appearance size information;
And acquiring cargo weight information, and combining the cargo weight information and the volume appearance size information to obtain cargo specification information.
The invention discloses a distribution path analysis method and a distribution path analysis system based on big data, which are characterized in that the transportation plan analysis is carried out according to the 3D scanning data of goods and the transportation load data of a current logistics transfer station by acquiring the 3D scanning data of logistics goods and the transportation load data of the current logistics transfer station, so as to obtain transportation plan data, and the transportation plan data is visually displayed based on a logistics map model; carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions based on the goods 3D scanning data, marking the corresponding goods as the abnormal goods if the abnormal conditions exist, carrying out tracing distribution path inquiry according to the abnormal goods 3D scanning data to obtain a distribution path, analyzing the corresponding goods logistics data from the logistics big data based on the distribution path, and carrying out information correction on the abnormal goods according to the goods logistics data.
Drawings
FIG. 1 is a flow chart of a big data based delivery path analysis method of the present invention;
FIG. 2 illustrates a flow chart of the transportation plan data acquisition of the present invention;
FIG. 3 shows a flow chart of the present invention for constructing a logistics map model;
FIG. 4 shows a block diagram of a big data based delivery path analysis system of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 is a flow chart of a big data based delivery path analysis method of the present invention.
As shown in fig. 1, a first aspect of the present invention provides a big data based distribution path analysis method, including:
s102, acquiring 3D scanning data of logistics goods;
s104, acquiring transportation load data of a current logistics transfer station, and carrying out cargo transportation plan analysis according to the cargo 3D scanning data and the transportation load data to obtain transportation plan data;
S106, based on a logistics map model, visually displaying the transportation plan data;
s108, carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions or not based on the goods 3D scanning data, and marking the corresponding goods as abnormal goods if the abnormal conditions exist;
s110, tracing a distribution path according to the 3D scanning data of the abnormal goods to obtain a distribution path, and analyzing corresponding goods logistics data from logistics big data based on the distribution path;
s112, carrying out information correction on the abnormal goods according to the goods logistics data.
It should be noted that, the logistics cargo 3D scan data is specifically obtained by performing multi-angle scanning through a high-definition scanning camera, and cargo appearance information can be obtained through the cargo 3D scan data, and the appearance information includes information such as cargo shape, cargo volume, cargo size and the like.
Fig. 2 shows a flow chart of the transportation plan data acquisition of the present invention.
According to the embodiment of the invention, the transport load data of the current logistics transfer station is obtained, and the cargo transport plan analysis is performed according to the cargo 3D scanning data and the transport load data to obtain transport plan data, specifically:
S202, acquiring transportation resource information and human resource information in a current logistics transfer station;
s204, carrying out transportation capability analysis based on transportation resource information and human resource information to obtain input load data;
s206, carrying out cargo specification analysis according to the cargo 3D scanning data of the current transfer station to obtain cargo volume and appearance size information;
s208, acquiring cargo weight information, and combining the cargo weight information and the volume appearance size information to obtain cargo specification information;
and S210, carrying out transportation resource allocation and manpower resource allocation calculation analysis on the goods according to the transportation resource information and the manpower resource information and combining the goods specification information to obtain the goods transportation plan data and the manpower allocation plan data.
The transportation resource information includes the type and quantity of transportation vehicles in the current logistics transfer station, and the human resource information includes transportation personnel, management personnel and the like of the transportation resource.
FIG. 3 shows a flow chart of the present invention for constructing a logistics map model.
According to the embodiment of the invention, the transportation plan data is visually displayed based on the logistics map model, specifically:
S302, acquiring a road model of a logistics area through the Internet;
s304, acquiring position information of all logistics transfer stations in a logistics area;
s306, constructing a logistics map model, and importing the road model and the logistics transfer station position information into the logistics map model;
s308, importing the transportation plan data into a logistics map model to perform cargo transportation simulation, and recording transportation simulation data;
s310, visually displaying the logistics map model through a preset terminal device, and dynamically displaying the transportation plan data based on the transportation simulation data.
According to the embodiment of the invention, the logistics transmission is performed according to the transportation plan data, the cargo information of each logistics transfer station is detected in real time, whether the logistics cargo has an information abnormal condition or not is checked based on the cargo 3D scanning data, and if the abnormal condition exists, the corresponding cargo is marked as the abnormal cargo, specifically:
acquiring corresponding cargo image data based on cargo 3D scanning data;
carrying out cargo label area identification on the cargo image data, and obtaining a corresponding cargo label area image;
carrying out cargo information identification and information integrity analysis on the cargo label area image to obtain cargo information integrity;
And if the cargo information integrity is lower than the preset value, marking the cargo as abnormal cargo.
It should be noted that the cargo 3D scan data includes cargo multi-angle image information. Due to factors such as nonstandard operation or abrasion in a transportation diagram, the label information of the goods package may be incomplete, and at this time, if screening is performed manually, time and effort are wasted. According to the invention, the labels on the surface of the goods are identified and analyzed based on the multi-angle image data, if the conditions such as lack of information or information simulation occur, the labels are automatically marked as abnormal goods, and the abnormal goods cannot obtain corresponding specific logistics information, so that the goods are further subjected to source tracing analysis through a transfer station path based on the 3D scanning data of the goods, so that the corresponding transportation path is obtained, and the specific goods information is analyzed.
According to the embodiment of the invention, the tracing distribution path is inquired according to the 3D scanning data of the abnormal goods to obtain the distribution path, and the corresponding goods logistics data is analyzed from the logistics big data based on the distribution path, specifically:
acquiring 3D scanning data of abnormal goods;
Splitting the abnormal cargo 3D scanning data into cargo image data with multiple angles;
and carrying out feature analysis on the cargo image data based on different angles to obtain abnormal cargo image features corresponding to the different angles.
It should be noted that the abnormal cargo image features are specifically high-resolution image features, and may be used for rapid feature matching.
According to the embodiment of the invention, the tracing distribution path is inquired according to the 3D scanning data of the abnormal goods to obtain the distribution path, and the corresponding goods logistics data is analyzed from the logistics big data based on the distribution path, specifically:
taking the current logistics transfer station as an original transfer station;
acquiring a logistics transfer station of a last transportation link in the original logistics transfer stations, and marking the logistics transfer station as a route logistics station;
the route logistics station comprises at least one;
acquiring cargo 3D scanning data of a route logistics station;
performing feature matching on the 3D scanning data of the goods according to the abnormal goods image features;
if the matching is successful, corresponding matching cargo information and route logistics stations are recorded in a logistics route table;
and judging whether the current route logistics station is an initial station or not, if not, circularly acquiring a logistics transfer station of the last transportation link in the current route logistics station, marking the logistics transfer station as the route logistics station, and performing feature matching and data recording of a logistics route table on the cargo 3D scanning data of the route logistics station again until the current route logistics station is judged to be the initial station.
If the matching is successful, the corresponding matching cargo information and the route logistics station are recorded in the logistics route table, the characteristic matching is successful, and the abnormal cargo is represented as the cargo with the consistent specification characteristic in the logistics transfer station of the last transportation link. In addition, when the current route logistics station is judged not to be the starting station, the system continuously matches the route logistics station meeting the condition to the upper layer transportation link circularly and records the route logistics station, and the tracing path from the original transfer station to the starting station can be finally found by continuously circularly searching the route logistics station.
According to the embodiment of the invention, the information correction of the abnormal goods according to the goods logistics data is specifically as follows:
generating a path retrieval tag based on the delivery path;
taking the goods delivery time information of the delivery path as a time retrieval condition;
according to the route retrieval tag and the time retrieval condition, route matching retrieval and logistics data extraction are carried out from logistics big data, and corresponding goods logistics data are obtained;
Based on the goods logistics data, goods label information is extracted, and information correction is carried out on abnormal goods according to the goods label information.
According to an embodiment of the present invention, further comprising:
acquiring initial cold chain logistics transportation plan data;
the initial cold chain logistics transportation plan data is imported into a logistics map model to carry out transportation simulation, and a simulated transportation route and simulated transportation time are obtained;
acquiring road condition information in a simulated transportation route based on the Internet;
calculating the abnormal probability of cold chain transportation according to the length of the simulated transportation route, the simulated transportation time and the road condition information;
if the abnormal probability is greater than the preset probability, dividing the simulated transportation route into N sections of sub-routes and ensuring that the corresponding simulated transportation time of each section of sub-route is consistent;
analyzing the abnormal transportation probability in each section of sub-route according to the road condition information, and marking the sub-route with the probability larger than the second preset probability to obtain a second sub-route;
acquiring cold chain logistics point information and importing the cold chain logistics point information into a logistics map model;
carrying out logistics emergency route planning on the second sub-route based on the cold chain logistics point;
the planning is specifically that a second sub-route is taken as a starting point, a cold chain logistics point is taken as an end point, a distance is taken as a first condition, and road conditions are taken as a second condition to carry out route planning, so that emergency route information corresponding to the second sub-route is obtained;
Based on the Internet of things, acquiring environment monitoring data of goods in cold-chain logistics transportation in real time;
the environment monitoring data comprises temperature, humidity and oxygen information;
if the environment monitoring data do not accord with the preset environment conditions, acquiring a sub-route corresponding to the current transportation position;
judging whether the sub-route is a second sub-route, if so, sending emergency route information corresponding to the second sub-route to preset terminal equipment for display.
The abnormal probability of the cold chain transportation is the abnormal probability of the overall transportation route, and the abnormality is specifically an abnormal condition that the temperature, the humidity and the oxygen in the cargo environment in the cold chain do not meet preset conditions. When the cold chain logistics is abnormal, the large probability is in the second sub-section, and at the moment, the logistics goods need to enter nearby cold chain logistics points in time to carry out goods inspection and transportation equipment reforming.
FIG. 4 shows a block diagram of a big data based delivery path analysis system of the present invention.
The second aspect of the present invention also provides a big data based delivery path analysis system 4, the system comprising: a memory 41, a processor 42, wherein the memory includes a big data based distribution path analysis program, and the big data based distribution path analysis program when executed by the processor realizes the following steps:
acquiring 3D scanning data of logistics goods;
acquiring transportation load data of a current logistics transfer station, and carrying out cargo transportation plan analysis according to the cargo 3D scanning data and the transportation load data to obtain transportation plan data;
based on a logistics map model, visually displaying the transportation plan data;
carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions or not based on the goods 3D scanning data, and marking the corresponding goods as abnormal goods if the abnormal conditions exist;
tracing distribution path inquiry is carried out according to the 3D scanning data of the abnormal goods, a distribution path is obtained, and corresponding goods logistics data are analyzed from logistics big data based on the distribution path;
and carrying out information correction on the abnormal goods according to the goods logistics data.
It should be noted that, the logistics cargo 3D scan data is specifically obtained by performing multi-angle scanning through a high-definition scanning camera, and cargo appearance information can be obtained through the cargo 3D scan data, and the appearance information includes information such as cargo shape, cargo volume, cargo size and the like.
According to the embodiment of the invention, the transport load data of the current logistics transfer station is obtained, and the cargo transport plan analysis is performed according to the cargo 3D scanning data and the transport load data to obtain transport plan data, specifically:
acquiring transportation resource information and manpower resource information in a current logistics transfer station;
carrying out transportation capability analysis based on transportation resource information and human resource information to obtain input load data;
carrying out cargo specification analysis according to the cargo 3D scanning data of the current transfer station to obtain cargo volume and appearance size information;
acquiring cargo weight information, and combining the cargo weight information and the volume appearance size information to obtain cargo specification information;
and carrying out transportation resource allocation and manpower resource allocation calculation analysis on the goods according to the transportation resource information and the manpower resource information and combining the goods specification information to obtain the goods transportation plan data and the manpower allocation plan data.
The transportation resource information includes the type and quantity of transportation vehicles in the current logistics transfer station, and the human resource information includes transportation personnel, management personnel and the like of the transportation resource.
According to the embodiment of the invention, the transportation plan data is visually displayed based on the logistics map model, specifically:
acquiring a road model of a logistics area through the Internet;
acquiring position information of all logistics transfer stations in a logistics area;
constructing a logistics map model, and importing the road model and the logistics transfer station position information into the logistics map model;
importing the transportation plan data into a logistics map model to simulate the transportation of goods, and recording transportation simulation data;
and visually displaying the logistics map model through preset terminal equipment, and dynamically displaying the transportation plan data based on the transportation simulation data.
According to the embodiment of the invention, the logistics transmission is performed according to the transportation plan data, the cargo information of each logistics transfer station is detected in real time, whether the logistics cargo has an information abnormal condition or not is checked based on the cargo 3D scanning data, and if the abnormal condition exists, the corresponding cargo is marked as the abnormal cargo, specifically:
Acquiring corresponding cargo image data based on cargo 3D scanning data;
carrying out cargo label area identification on the cargo image data, and obtaining a corresponding cargo label area image;
carrying out cargo information identification and information integrity analysis on the cargo label area image to obtain cargo information integrity;
and if the cargo information integrity is lower than the preset value, marking the cargo as abnormal cargo.
It should be noted that the cargo 3D scan data includes cargo multi-angle image information. Due to factors such as nonstandard operation or abrasion in a transportation diagram, the label information of the goods package may be incomplete, and at this time, if screening is performed manually, time and effort are wasted. According to the invention, the labels on the surface of the goods are identified and analyzed based on the multi-angle image data, if the conditions such as lack of information or information simulation occur, the labels are automatically marked as abnormal goods, and the abnormal goods cannot obtain corresponding specific logistics information, so that the goods are further subjected to source tracing analysis through a transfer station path based on the 3D scanning data of the goods, so that the corresponding transportation path is obtained, and the specific goods information is analyzed.
According to the embodiment of the invention, the tracing distribution path is inquired according to the 3D scanning data of the abnormal goods to obtain the distribution path, and the corresponding goods logistics data is analyzed from the logistics big data based on the distribution path, specifically:
acquiring 3D scanning data of abnormal goods;
splitting the abnormal cargo 3D scanning data into cargo image data with multiple angles;
and carrying out feature analysis on the cargo image data based on different angles to obtain abnormal cargo image features corresponding to the different angles.
It should be noted that the abnormal cargo image features are specifically high-resolution image features, and may be used for rapid feature matching.
According to the embodiment of the invention, the tracing distribution path is inquired according to the 3D scanning data of the abnormal goods to obtain the distribution path, and the corresponding goods logistics data is analyzed from the logistics big data based on the distribution path, specifically:
taking the current logistics transfer station as an original transfer station;
acquiring a logistics transfer station of a last transportation link in the original logistics transfer stations, and marking the logistics transfer station as a route logistics station;
the route logistics station comprises at least one;
acquiring cargo 3D scanning data of a route logistics station;
performing feature matching on the 3D scanning data of the goods according to the abnormal goods image features;
If the matching is successful, corresponding matching cargo information and route logistics stations are recorded in a logistics route table;
and judging whether the current route logistics station is an initial station or not, if not, circularly acquiring a logistics transfer station of the last transportation link in the current route logistics station, marking the logistics transfer station as the route logistics station, and performing feature matching and data recording of a logistics route table on the cargo 3D scanning data of the route logistics station again until the current route logistics station is judged to be the initial station.
If the matching is successful, the corresponding matching cargo information and the route logistics station are recorded in the logistics route table, the characteristic matching is successful, and the abnormal cargo is represented as the cargo with the consistent specification characteristic in the logistics transfer station of the last transportation link. In addition, when the current route logistics station is judged not to be the starting station, the system continuously matches the route logistics station meeting the condition to the upper layer transportation link circularly and records the route logistics station, and the tracing path from the original transfer station to the starting station can be finally found by continuously circularly searching the route logistics station.
According to the embodiment of the invention, the information correction of the abnormal goods according to the goods logistics data is specifically as follows:
generating a path retrieval tag based on the delivery path;
taking the goods delivery time information of the delivery path as a time retrieval condition;
according to the route retrieval tag and the time retrieval condition, route matching retrieval and logistics data extraction are carried out from logistics big data, and corresponding goods logistics data are obtained;
based on the goods logistics data, goods label information is extracted, and information correction is carried out on abnormal goods according to the goods label information.
According to an embodiment of the present invention, further comprising:
acquiring initial cold chain logistics transportation plan data;
the initial cold chain logistics transportation plan data is imported into a logistics map model to carry out transportation simulation, and a simulated transportation route and simulated transportation time are obtained;
acquiring road condition information in a simulated transportation route based on the Internet;
calculating the abnormal probability of cold chain transportation according to the length of the simulated transportation route, the simulated transportation time and the road condition information;
if the abnormal probability is greater than the preset probability, dividing the simulated transportation route into N sections of sub-routes and ensuring that the corresponding simulated transportation time of each section of sub-route is consistent;
Analyzing the abnormal transportation probability in each section of sub-route according to the road condition information, and marking the sub-route with the probability larger than the second preset probability to obtain a second sub-route;
acquiring cold chain logistics point information and importing the cold chain logistics point information into a logistics map model;
carrying out logistics emergency route planning on the second sub-route based on the cold chain logistics point;
the planning is specifically that a second sub-route is taken as a starting point, a cold chain logistics point is taken as an end point, a distance is taken as a first condition, and road conditions are taken as a second condition to carry out route planning, so that emergency route information corresponding to the second sub-route is obtained;
based on the Internet of things, acquiring environment monitoring data of goods in cold-chain logistics transportation in real time;
the environment monitoring data comprises temperature, humidity and oxygen information;
if the environment monitoring data do not accord with the preset environment conditions, acquiring a sub-route corresponding to the current transportation position;
judging whether the sub-route is a second sub-route, if so, sending emergency route information corresponding to the second sub-route to preset terminal equipment for display.
The abnormal probability of the cold chain transportation is the abnormal probability of the overall transportation route, and the abnormality is specifically an abnormal condition that the temperature, the humidity and the oxygen in the cargo environment in the cold chain do not meet preset conditions. When the cold chain logistics is abnormal, the large probability is in the second sub-section, and at the moment, the logistics goods need to enter nearby cold chain logistics points in time to carry out goods inspection and transportation equipment reforming.
The invention discloses a distribution path analysis method and a distribution path analysis system based on big data, which are characterized in that the transportation plan analysis is carried out according to the 3D scanning data of goods and the transportation load data of a current logistics transfer station by acquiring the 3D scanning data of logistics goods and the transportation load data of the current logistics transfer station, so as to obtain transportation plan data, and the transportation plan data is visually displayed based on a logistics map model; carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions based on the goods 3D scanning data, marking the corresponding goods as the abnormal goods if the abnormal conditions exist, carrying out tracing distribution path inquiry according to the abnormal goods 3D scanning data to obtain a distribution path, analyzing the corresponding goods logistics data from the logistics big data based on the distribution path, and carrying out information correction on the abnormal goods according to the goods logistics data.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A big data based distribution path analysis method, comprising:
acquiring 3D scanning data of logistics goods;
acquiring transportation load data of a current logistics transfer station, and carrying out cargo transportation plan analysis according to the cargo 3D scanning data and the transportation load data to obtain transportation plan data;
based on a logistics map model, visually displaying the transportation plan data;
carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions based on the goods 3D scanning data, wherein the information abnormal conditions comprise goods label area image abnormal, and marking the corresponding goods as abnormal goods if the abnormal conditions exist;
the method comprises the steps that tracing distribution path inquiry is carried out according to 3D scanning data of abnormal goods, a distribution path is obtained, and corresponding goods logistics data are analyzed from logistics big data based on the distribution path;
carrying out information correction on abnormal goods according to the goods logistics data;
the method comprises the steps of carrying out tracing distribution path inquiry according to 3D scanning data of abnormal goods to obtain a distribution path, analyzing corresponding goods logistics data from logistics big data based on the distribution path, and specifically comprises the following steps:
Acquiring 3D scanning data of abnormal goods;
splitting the abnormal cargo 3D scanning data into cargo image data with multiple angles;
based on different angles, carrying out feature analysis on the cargo image data to obtain abnormal cargo image features corresponding to the different angles;
taking the current logistics transfer station as an original transfer station;
acquiring a logistics transfer station of a last transportation link in the original logistics transfer stations, and marking the logistics transfer station as a route logistics station;
the route logistics station comprises at least one;
acquiring cargo 3D scanning data of a route logistics station;
performing feature matching on the 3D scanning data of the goods according to the abnormal goods image features;
if the matching is successful, corresponding matching cargo information and route logistics stations are recorded in a logistics route table;
and judging whether the current route logistics station is an initial station or not, if not, circularly acquiring a logistics transfer station of the last transportation link in the current route logistics station, marking the logistics transfer station as the route logistics station, and performing feature matching and data recording of a logistics route table on the cargo 3D scanning data of the route logistics station again until the current route logistics station is judged to be the initial station.
2. The big data-based distribution route analysis method according to claim 1, wherein the acquiring transportation load data of the current logistics transfer station, and performing cargo transportation plan analysis according to the cargo 3D scan data and the transportation load data, obtains transportation plan data, specifically:
Acquiring transportation resource information and manpower resource information in a current logistics transfer station;
carrying out transportation capability analysis based on transportation resource information and human resource information to obtain input load data;
carrying out cargo specification analysis according to the cargo 3D scanning data of the current transfer station to obtain cargo volume and appearance size information;
acquiring cargo weight information, and combining the cargo weight information and the volume appearance size information to obtain cargo specification information;
and carrying out transportation resource allocation and manpower resource allocation calculation analysis on the goods according to the transportation resource information and the manpower resource information and combining the goods specification information to obtain the goods transportation plan data and the manpower allocation plan data.
3. The big data-based distribution path analysis method according to claim 1, wherein the logistics map model is used for visually displaying the transportation plan data, specifically:
acquiring a road model of a logistics area through the Internet;
acquiring position information of all logistics transfer stations in a logistics area;
constructing a logistics map model, and importing the road model and the logistics transfer station position information into the logistics map model;
importing the transportation plan data into a logistics map model to simulate the transportation of goods, and recording transportation simulation data;
And visually displaying the logistics map model through preset terminal equipment, and dynamically displaying the transportation plan data based on the transportation simulation data.
4. The big data based distribution path analysis method according to claim 1, wherein the logistics transmission is performed according to the transportation plan data, and the cargo information of each logistics transfer station is detected in real time, based on the cargo 3D scan data, whether the logistics cargo has an information abnormal condition is checked, and if the abnormal condition exists, the corresponding cargo is marked as an abnormal cargo, specifically:
acquiring corresponding cargo image data based on cargo 3D scanning data;
carrying out cargo label area identification on the cargo image data, and obtaining a corresponding cargo label area image;
carrying out cargo information identification and information integrity analysis on the cargo label area image to obtain cargo information integrity;
and if the cargo information integrity is lower than the preset value, marking the cargo as abnormal cargo.
5. The big data based distribution path analysis method according to claim 1, wherein the tracing distribution path query is performed according to the abnormal cargo 3D scan data to obtain a distribution path, and further comprising:
When the current route logistics station is judged to be the starting station, acquiring a logistics route list;
according to the logistics route table, all route logistics stations from the original transfer station to the starting station are obtained and marked as intermediate stations;
the intermediate station comprises at least one;
and tracing routes of all intermediate stations based on the time sequence of feature matching, and obtaining a distribution path.
6. The big data based distribution path analysis method according to claim 5, wherein the information correction is performed on the abnormal goods according to the goods logistics data, specifically:
generating a path retrieval tag based on the delivery path;
taking the goods delivery time information of the delivery path as a time retrieval condition;
according to the route retrieval tag and the time retrieval condition, route matching retrieval and logistics data extraction are carried out from logistics big data, and corresponding goods logistics data are obtained;
based on the goods logistics data, goods label information is extracted, and information correction is carried out on abnormal goods according to the goods label information.
7. A big data based delivery path analysis system, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a distribution path analysis program based on big data, and the distribution path analysis program based on big data realizes the following steps when being executed by the processor:
Acquiring 3D scanning data of logistics goods;
acquiring transportation load data of a current logistics transfer station, and carrying out cargo transportation plan analysis according to the cargo 3D scanning data and the transportation load data to obtain transportation plan data;
based on a logistics map model, visually displaying the transportation plan data;
carrying out logistics transmission according to the transportation plan data, detecting the goods information of each logistics transfer station in real time, checking whether the logistics goods have information abnormal conditions based on the goods 3D scanning data, wherein the information abnormal conditions comprise goods label area image abnormal, and marking the corresponding goods as abnormal goods if the abnormal conditions exist;
the method comprises the steps that tracing distribution path inquiry is carried out according to 3D scanning data of abnormal goods, a distribution path is obtained, and corresponding goods logistics data are analyzed from logistics big data based on the distribution path;
carrying out information correction on abnormal goods according to the goods logistics data;
the method comprises the steps of carrying out tracing distribution path inquiry according to 3D scanning data of abnormal goods to obtain a distribution path, analyzing corresponding goods logistics data from logistics big data based on the distribution path, and specifically comprises the following steps:
acquiring 3D scanning data of abnormal goods;
Splitting the abnormal cargo 3D scanning data into cargo image data with multiple angles;
based on different angles, carrying out feature analysis on the cargo image data to obtain abnormal cargo image features corresponding to the different angles;
taking the current logistics transfer station as an original transfer station;
acquiring a logistics transfer station of a last transportation link in the original logistics transfer stations, and marking the logistics transfer station as a route logistics station;
the route logistics station comprises at least one;
acquiring cargo 3D scanning data of a route logistics station;
performing feature matching on the 3D scanning data of the goods according to the abnormal goods image features;
if the matching is successful, corresponding matching cargo information and route logistics stations are recorded in a logistics route table;
and judging whether the current route logistics station is an initial station or not, if not, circularly acquiring a logistics transfer station of the last transportation link in the current route logistics station, marking the logistics transfer station as the route logistics station, and performing feature matching and data recording of a logistics route table on the cargo 3D scanning data of the route logistics station again until the current route logistics station is judged to be the initial station.
8. The big data based distribution route analysis system according to claim 7, wherein the acquiring transportation load data of the current logistics transfer station, and performing a cargo transportation plan analysis according to the cargo 3D scan data and the transportation load data, obtains transportation plan data, specifically:
Acquiring transportation resource information and manpower resource information in a current logistics transfer station;
carrying out transportation capability analysis based on transportation resource information and human resource information to obtain input load data;
carrying out cargo specification analysis according to the cargo 3D scanning data of the current transfer station to obtain cargo volume and appearance size information;
and acquiring cargo weight information, and combining the cargo weight information and the volume appearance size information to obtain cargo specification information.
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| CN116911470B (en) * | 2023-09-12 | 2024-01-02 | 深圳市宏大供应链服务有限公司 | Data analysis method, system and storage medium based on deep learning |
| CN117744907B (en) * | 2024-02-19 | 2024-04-26 | 天津小铁马科技有限公司 | Goods source route planning method and device, electronic equipment and storage medium |
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