CN112559838A - Unmanned cabin operation condition analysis method and device - Google Patents
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Abstract
The invention discloses a method and a device for analyzing the operation condition of an unmanned storehouse, and relates to the field of storage. One embodiment of the method comprises: when an observation instruction is received, acquiring event data of transportation equipment in a warehouse, which corresponds to the observation instruction; aggregating the event data based on the position points in the warehouse to obtain an event aggregation result; and drawing a warehouse thermodynamic diagram according to the event aggregation result, and analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram. According to the embodiment, various warehousing data can be processed quickly and efficiently, the warehouse thermodynamic diagrams are rendered and drawn quickly according to the processed warehousing data, the warehouse thermodynamic diagrams in multiple dimensions are presented, and the warehousing production related operation is analyzed and improved based on the drawn warehouse thermodynamic diagrams.
Description
Technical Field
The invention relates to the field of warehousing, in particular to a method and a device for analyzing the operation condition of an unmanned warehouse.
Background
In the working environment of unmanned storehouse, various problems often appear in the warehouse production operation.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
for the existing storage production operation problems, specific relevant data needs to be searched for analysis according to the existing problems in the prior art, various conditions in the storage production operation cannot be intuitively and comprehensively known, and the operation conditions of each area and module in a warehouse cannot be intuitively and comprehensively known.
Disclosure of Invention
In view of this, embodiments of the present invention provide an unmanned warehouse operation condition analysis method and apparatus, which can process various warehouse data quickly and efficiently, perform quick rendering and drawing of a warehouse thermodynamic diagram according to the processed warehouse data, present the warehouse thermodynamic diagram in various dimensions, and analyze and improve warehouse production related operations based on the drawn warehouse thermodynamic diagram.
In order to achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an unmanned storehouse operation condition analysis method, including:
when an observation instruction is received, acquiring event data of transportation equipment in a warehouse, which corresponds to the observation instruction;
aggregating the event data based on the position points in the warehouse to obtain an event aggregation result;
and drawing a warehouse thermodynamic diagram according to the event aggregation result, and analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
Optionally, the observation instruction comprises: the method comprises the steps of obtaining data type information of an event to be observed, observation time period information and dimension information to be observed; wherein the dimension to be observed is a dimension under the event data to be observed;
the event data type of the transportation device comprises at least one of the following: abnormal data, ex-warehouse data and network data;
the abnormal data is abnormal information transmitted by the transportation equipment under the abnormal condition;
the ex-warehouse data is ex-warehouse information transmitted by the transportation equipment under the condition of having ex-warehouse tasks;
the network data is network information transmitted by real-time heartbeat when the transportation equipment runs;
wherein the event data of the transportation device may be stored in an Elasticsearch database.
Optionally, before aggregating the event data by location points in the warehouse, the method further includes:
and according to a preset time magnitude, performing data compression on the event data to obtain a new query index.
Optionally, the location points comprise at least one of: code points and coordinate grids;
aggregating the event data based on the location points in the warehouse to obtain an event aggregation result, including:
according to the observation instruction, counting the total value of the event data to be observed, the dimension value to be observed and the event list information of each position point in the observation time period as the aggregation result;
wherein, for an exception data type, the event dimension includes at least one of: abnormal times and abnormal equipment number; the event list includes at least: an exception information list;
wherein, for an ex-warehouse data type, the event dimension comprises at least one of: the number of times is taken as the number of the warehouse-out starting points and the number of times is taken as the number of the warehouse-out terminal points; the event list includes at least: a list of articles out of the warehouse;
wherein, for the network data type, the event dimension comprises at least one of: the number of data, the number of network transmission pause times and the accumulated ping delay time; the event list includes at least: a list of wireless network access points.
Optionally, drawing a warehouse thermodynamic diagram according to the aggregation result, including:
determining data to be drawn corresponding to each position point in a preset map according to the aggregation result; the data to be drawn includes: the total value of the event data to be observed, the dimension value of the dimension to be observed and the event list information of the type of the event data to be observed;
displaying the total value of the event data to be observed in a dynamic rendering mode according to the data to be drawn, drawing the presentation style of the dimension to be observed on each position point according to a preset magnitude presentation style, and drawing the event list information of the event data type to be observed in a list mode;
specifically, a dynamic rendering mode combining jquery and svg can be adopted for drawing.
Optionally, when a new observation instruction is received, clearing the existing drawing result on the preset map;
and drawing a warehouse thermodynamic diagram corresponding to the new observation instruction based on the new observation instruction.
Optionally, when a screening instruction about the event list is received, determining a screened sub-event list according to the screening instruction;
selecting a sub-aggregation result corresponding to the sub-event list from the event aggregation results of the current event type according to the sub-event list;
and drawing a warehouse thermodynamic diagram corresponding to the screening instruction according to the sub-aggregation result.
Optionally, when a click position point instruction is received, displaying a point event data quantity value, a point event dimension value and point event list information of the clicked position point based on the event type and the dimension to be observed corresponding to the currently drawn warehouse thermodynamic diagram.
According to still another aspect of an embodiment of the present invention, there is provided an unmanned aerial vehicle cabin operation condition analysis apparatus including:
the acquisition module is used for acquiring event data, corresponding to the observation instruction, of the transportation equipment in the warehouse when the observation instruction is received;
the aggregation module is used for aggregating the event data based on the position points in the warehouse to obtain an event aggregation result;
the drawing module is used for drawing a warehouse thermodynamic diagram according to the event aggregation result;
and the analysis module is used for analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
Optionally, the observation instruction comprises: the method comprises the steps of obtaining data type information of an event to be observed, observation time period information and dimension information to be observed; wherein the dimension to be observed is a dimension under the event data to be observed;
the event data type of the transportation device comprises at least one of the following: abnormal data, ex-warehouse data and network data;
the abnormal data is abnormal information transmitted by the transportation equipment under the abnormal condition;
the ex-warehouse data is ex-warehouse information transmitted by the transportation equipment under the condition of having ex-warehouse tasks;
the network data is network information transmitted by real-time heartbeat when the transportation equipment runs.
Optionally, before aggregating the event data as location points in a warehouse, the aggregation module further includes:
and according to a preset time magnitude, performing data compression on the event data to obtain a new query index.
Optionally, the location points comprise at least one of: code points and coordinate grids;
aggregating the event data based on the location points in the warehouse to obtain an event aggregation result, including:
according to the observation instruction, counting the total value of the event data to be observed, the dimension value to be observed and the event list information of each position point in the observation time period as the aggregation result;
wherein, for an exception data type, the event dimension includes at least one of: abnormal times and abnormal equipment number; the event list includes at least: an exception information list;
wherein, for an ex-warehouse data type, the event dimension comprises at least one of: the number of times is taken as the number of the warehouse-out starting points and the number of times is taken as the number of the warehouse-out terminal points; the event list includes at least: a list of articles out of the warehouse;
wherein, for the network data type, the event dimension comprises at least one of: the number of data, the number of network transmission pause times and the accumulated ping delay time; the event list includes at least: a list of wireless network access points.
Optionally, the drawing module draws a warehouse thermodynamic diagram according to the aggregation result, and includes:
determining data to be drawn corresponding to each position point in a preset map according to the aggregation result; the data to be drawn includes: the total value of the event data to be observed, the dimension value of the dimension to be observed and the event list information of the type of the event data to be observed;
and displaying the total value of the event data to be observed in a dynamic rendering mode according to the data to be drawn, drawing the presentation style of the dimension to be observed on each position point according to a preset magnitude presentation style, and drawing the event list information of the event data type to be observed in a list mode.
Optionally, the rendering module is further configured to:
when a new observation instruction is received, clearing the existing drawing result on the preset map;
and drawing a warehouse thermodynamic diagram corresponding to the new observation instruction based on the new observation instruction.
Optionally, the rendering module is further configured to:
when a screening instruction about the event list is received, determining a screened sub-event list according to the screening instruction;
selecting a sub-aggregation result corresponding to the sub-event list from the event aggregation results of the current event type according to the sub-event list;
and drawing a warehouse thermodynamic diagram corresponding to the screening instruction according to the sub-aggregation result.
Optionally, the rendering module is further configured to:
and when a click position point instruction is received, displaying a point event data value, a point event dimension value and point event list information of the clicked position point based on the event type and the dimension to be observed corresponding to the currently drawn warehouse thermodynamic diagram.
According to another aspect of the embodiments of the present invention, there is provided an unmanned cabin operation condition analysis electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the unmanned bin operation condition analysis method provided by the invention.
According to a further aspect of the embodiments of the present invention, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the unmanned cabin operation condition analysis method provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: because the technical means of compressing and aggregating based on the position points according to the event data of the transportation equipment acquired from the Elasticissearch database and drawing the warehouse thermodynamic diagram by combining jquery with svg according to the aggregation result is adopted, the technical problem that various conditions in the warehousing production operation cannot be intuitively and comprehensively known in the prior art is solved, and the technical effects of quickly drawing the warehouse thermodynamic diagram and analyzing and improving the warehousing production related operation based on the drawn warehouse thermodynamic diagram are achieved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of an unmanned cabin operation condition analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an overall architecture of a warehouse thermodynamic diagram drawing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the effect of a rendering style at a location point when a bin thermodynamic diagram is drawn according to an embodiment of the present invention;
fig. 4(a) is a schematic diagram showing an effect of an abnormal thermodynamic diagram in a warehouse thermodynamic diagram according to an embodiment of the invention;
fig. 4(b) is a schematic diagram showing an effect of the ex-warehouse thermodynamic diagram in the warehouse thermodynamic diagram according to the embodiment of the invention;
fig. 4(c) is a schematic diagram showing an effect of a network thermodynamic diagram in a warehouse thermodynamic diagram according to an embodiment of the invention;
FIG. 5 is a schematic diagram of the major modules of an unmanned bin operational condition analysis in accordance with an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of an unmanned cabin operation condition analysis method according to an embodiment of the present invention.
As shown in fig. 1, a method for analyzing an unmanned cabin operation condition according to an embodiment of the present invention includes:
s101, when an observation instruction is received, acquiring event data, corresponding to the observation instruction, of transportation equipment in a warehouse;
step S102, aggregating the event data based on the position points in the warehouse to obtain an event aggregation result;
and S103, drawing a warehouse thermodynamic diagram according to the event aggregation result, and analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
The observation instruction may be an instruction initiated by a user to begin observing specified event data; the command can be sent by inputting and/or selecting some specified requirements by the user, or can be a timing automatic observation command set in advance by the user.
The transport device may be a device in a warehouse for supporting transported goods, such as a conveyor agv (Automated Guided Vehicle).
The event data may be various data generated by the transportation device at runtime; the location points may be fixed points or areas in a warehousing environment.
According to the technical means of aggregating based on the position points according to the acquired event data of the transportation equipment and drawing the warehouse thermodynamic diagram according to the aggregation result, the method can process various warehouse data, perform rendering drawing of the warehouse thermodynamic diagram according to the processed warehouse data, present the technical effect of the warehouse thermodynamic diagram under various dimensions, and analyze and improve related operations of warehouse production based on the drawn warehouse thermodynamic diagram.
In some embodiments, the observation instructions include: the method comprises the steps of obtaining data type information of an event to be observed, observation time period information and dimension information to be observed; wherein the dimension to be observed is a dimension under the event data to be observed;
the event data of the transportation device may be stored in an Elasticsearch database; the event data type of the transportation device comprises at least one of the following: abnormal data, ex-warehouse data and network data;
the abnormal data is abnormal information transmitted by the transportation equipment under the abnormal condition;
the ex-warehouse data is ex-warehouse information transmitted by the transportation equipment under the condition of having ex-warehouse tasks;
the network data is network information transmitted by real-time heartbeat when the transportation equipment runs.
The ElasticSearch database is a Lucene-based search server that provides a full-text search index for distributed multi-user capabilities. The Elasticsearch was developed in Java and published as open source under the Apache licensing terms, and is currently a popular enterprise-level search engine. The system is designed and used in cloud computing, can stably, reliably and quickly search in real time, and is convenient to install and use. The method has the advantages that the Elasticisearch database is used for data storage, large data volume scenes can be met, the performance is outstanding, and when aggregation statistics is carried out according to position points, the aggregation statistics of the Elasticisearch can reach the second level, and the speed is very high.
The event data of the transportation device is transmitted to an ElasticSearch database when the transportation device runs, specifically:
the abnormal data can be generated by the transportation equipment when encountering an abnormality and is synchronized to the ElasticSearch database through the console application and the database under the console application;
the ex-warehouse data can be generated by the transportation equipment when the ex-warehouse task is executed, and is synchronized to the ElasticSearch database through the console application and the database under the console application;
the network data can be generated all the time when the transportation equipment runs, and the heartbeat is reported to the data acquisition application, then is processed to a certain extent, and then is transferred to the ElasticSearch database.
In some embodiments, before aggregating the event data as location points in a warehouse, further comprising:
and according to a preset time magnitude, performing data compression on the event data to obtain a new query index.
After the event data is transmitted to the elastic search database by the transportation equipment, because the original data volume is large (especially the network data can be reported according to second heartbeat, and the data volume can reach hundred million levels), the second-level data needs to be compressed into a preset time level (such as one hour, six hours and the like) through data compression, and when the data is used, a new compressed index can be used to achieve a faster data access speed;
taking the preset time level as one hour as an example: pieces of data within one hour may be compressed into one piece; accordingly, the observation period selected by the user when viewing the thermodynamic diagram is a minimum of one hour, the selected observation period also needs to be a multiple of one hour; therefore, the data counting speed can be improved, and the problems of memory overflow, overlong page response time and the like caused by overlarge data volume are prevented.
In some embodiments, the location points include at least one of: code points and coordinate grids;
aggregating the event data based on the location points in the warehouse to obtain an event aggregation result, including:
according to the observation instruction, counting the total value of the event data to be observed, the dimension value to be observed and the event list information of each position point in the observation time period as the aggregation result;
wherein, for an exception data type, the event dimension includes at least one of: abnormal times and abnormal equipment number; the event list includes at least: an exception information list;
wherein, for an ex-warehouse data type, the event dimension comprises at least one of: the number of times is taken as the number of the warehouse-out starting points and the number of times is taken as the number of the warehouse-out terminal points; the event list includes at least: a list of articles out of the warehouse;
wherein, for the network data type, the event dimension comprises at least one of: the number of data, the number of network transmission pause times and the accumulated ping delay time; the event list includes at least: a list of wireless network access points.
After data compression is carried out, rapid and efficient aggregation processing can be carried out on the data with the new index according to the position points, so that each piece of information required by the thermodynamic diagram can be obtained; for different types of events, observed dimensions are different, and the type of the event and the observed dimensions under the type can be selected when a user sends an observation instruction;
specifically, for an exception data type, the exception number dimension may be used to represent the number of times an exception occurs at a certain location point; the abnormal equipment number dimension can be used for representing the number of equipment with abnormality at a certain position point; the exception information list may be used to represent a set of exception information that has occurred at a certain location point;
for the ex-warehouse data type, the dimension of the times as the ex-warehouse starting point can be used for representing the times of a certain position point as the ex-warehouse starting point; the number dimension as the ex-warehouse terminal point can be used for representing the number of times that a certain position point is taken as the ex-warehouse terminal point; the ex-warehouse article list can be used for representing the collection of articles ex-warehouse at a certain position point, and on the basis of the ex-warehouse article list, a shelf list can be generated for representing the collection of shelves from which the articles ex-warehouse at a certain position point come;
for network data types, the data number dimension can be used to represent the number of network data transmitted at a certain location point; the network transmission pause number dimension can be used for representing the number of times of network transmission pauses occurring at a certain position point; the accumulated ping delay duration dimension can be used for representing the sum of network ping delay durations occurring at a certain position point; the list of wireless network access points may be used to represent a set of network access points that have been accessed at a certain location point.
In some embodiments, drawing a warehouse thermodynamic diagram from the aggregated results comprises:
determining data to be drawn corresponding to each position point in a preset map according to the aggregation result; the data to be drawn includes: the total value of the event data to be observed, the dimension value of the dimension to be observed and the event list information of the type of the event data to be observed;
according to the data to be drawn, a jquery and svg combined dynamic rendering mode can be adopted to display the total value of the event data to be observed, the presentation style of the dimension to be observed on each position point is drawn according to a preset magnitude presentation style, and the event list information of the event data type to be observed is drawn in a list mode.
The preset magnitude rendering pattern may be expressed as a color on a location point on the thermodynamic diagram, such as: the number of preset levels (such as 5, 7 and the like) can be set, the color can be from light to dark, and the darker the color is, the larger the representative numerical value is; the preset magnitude presentation style can be expressed as filling shapes on position points on the thermodynamic diagram, and different filling shapes correspond to different magnitudes;
the numerical range of each style level can be equally divided according to the maximum value set by the user, and can also be equally divided according to the maximum value of the current data, so that each numerical value can find the corresponding style for drawing.
By adopting a dynamic rendering mode combining jquery and svg, other unnecessary functions can be prevented from being introduced when the warehouse thermodynamic diagram is drawn, and resource waste is avoided; and svg is used as a vector image, the image quality of the vector image is not lost under the condition of changing the size, and the user experience is good.
FIG. 2 is a schematic diagram of an overall architecture of a warehouse thermodynamic diagram drawing method according to an embodiment of the present invention; as shown in fig. 2, includes:
step S201: the transportation equipment transmits event data generated in the running process to an Elasticissearch database;
step S201: in an Elasticissearch database, performing data compression on original event data to increase the data volume corresponding to each index and obtain a new index for subsequent use;
step S203: performing data aggregation on the compressed event data, and transmitting an aggregation result to Web application for subsequent drawing;
step S204: rendering and drawing according to the aggregation result in the Web application, wherein the display content comprises: counting the total data amount, observing event dimensions and displaying an event list; wherein, the statistics for the total amount of data can be represented in numbers; for event dimension observation, the presentation can be performed on each position point according to a preset magnitude presentation style; for event list presentation, it may be listed in the form of a window.
FIG. 3 is a schematic diagram illustrating the effect of a rendering style at a location point when a bin thermodynamic diagram is drawn according to an embodiment of the present invention; as shown in fig. 3:
in the embodiment, 5 levels of patterns are set, and the color is from light to dark, and the numerical value represented by the darker color can be set to be larger. The numerical range of the pattern level of each position point can be set in two modes of maximum value equipartition set by a user or current maximum value equipartition, so that each numerical value can find a corresponding pattern to be drawn.
In some embodiments, when a new observation instruction is received, clearing the existing drawing result on the preset map;
and drawing a warehouse thermodynamic diagram corresponding to the new observation instruction based on the new observation instruction.
The new observation instruction can be used for observing other dimensions under the current event type, and can also be used for observing dimensions under other events; such as: and when displaying the dimension of the abnormal times of the abnormal thermodynamic diagram, receiving an observation instruction for observing the dimension of the abnormal equipment number of the abnormal thermodynamic diagram, clearing the drawn effect of the dimension of the abnormal times on a preset map, acquiring the dimension value of the abnormal equipment number corresponding to the new observation instruction, and drawing and displaying.
In some embodiments, when a filtering instruction about the event list is received, determining a filtered sub-event list according to the filtering instruction;
selecting a sub-aggregation result corresponding to the sub-event list from the event aggregation results of the current event type according to the sub-event list;
and drawing a warehouse thermodynamic diagram corresponding to the screening instruction according to the sub-aggregation result.
The filtering instruction may be a filtering instruction initiated by the user for the current event list based on the dimension being observed, and specifically, the filtering instruction may be a filtering instruction initiated by the user for selecting a specified event from the current event list to perform data filtering and observation;
for example: when the dimension of the network transmission pause times of the network thermodynamic diagram is displayed, a user initiates a screening instruction by checking one or a plurality of specified wireless network access points in a current wireless network access point list, then the drawn dimension effect of the network transmission pause times on a preset map is eliminated, the network transmission pause times corresponding to the wireless network access points selected by the user on each position point are obtained again based on the wireless network access points selected by the user, and the drawing display is carried out.
In some embodiments, when a click location point instruction is received, a point event data quantity value, a point event dimension value and point event list information of a clicked location point are displayed based on an event type and a dimension to be observed corresponding to a currently drawn warehouse thermodynamic diagram.
The click position point instruction can be an instruction initiated by a user aiming at a certain position point and used for observing data on the position point; the method can be realized by binding a user click event to the position point, and when a user clicks a certain position point, the statistical data display related to the position point can be separately requested from the server.
Fig. 4(a) is a schematic diagram showing an effect of an abnormal thermodynamic diagram in a warehouse thermodynamic diagram according to an embodiment of the invention; the window at the upper right corner displays the abnormal times, the abnormal equipment number and the total abnormal data value in a period of time; the window in the lower right corner shows an exception list; for the position point outlined by highlight in the figure, the window at the lower left corner shows some specific data on the position point, such as an abnormal code, an abnormal name, abnormal times and abnormal equipment number.
Fig. 4(b) is a schematic diagram showing an effect of the ex-warehouse thermodynamic diagram in the warehouse thermodynamic diagram according to the embodiment of the invention; the window at the upper right corner displays the total ex-warehouse data quantity value, the quantity of commodity SKUs and the commodity value within a period of time; the window at the lower right corner displays the ex-warehouse commodity list; for the location point outlined in highlight in the figure, the window in the lower left corner shows the number of times the location point is taken as the ex-warehouse starting point.
Fig. 4(c) is a schematic diagram showing an effect of a network thermodynamic diagram in a warehouse thermodynamic diagram according to an embodiment of the invention; wherein, the window at the upper right corner shows the total value of the network data in a period of time; the window in the lower right corner shows a list of wireless network access points; for the location points outlined by highlight in the figure, the window at the lower left corner shows some specific data on the location points, such as the number of data counted on the location points, the number of times of network transmission suspension, the accumulated ping delay time, and the number of involved network access points.
Through the drawn thermodynamic diagram, various conditions in the warehousing production operation can be more intuitively and comprehensively known, the running conditions of various areas and modules in the warehouse can be more intuitively and comprehensively known, and the warehousing production related operation can be analyzed and improved based on the drawn thermodynamic diagram of the warehouse; specifically, the method comprises the following steps:
(1) through the drawn abnormal thermodynamic diagram, the abnormal conditions of the transportation equipment in the warehouse within a period of time can be counted, such as: the number of times of abnormality of the trolleys in the warehouse is totally generated in the last week, and the number of the trolleys is abnormal. In addition, the advantages of the thermodynamic diagram are mainly embodied in that the positions of which areas are abnormal more can be seen clearly at a glance, and the positions where the abnormal more occurs (namely the positions with darker colors) mainly occur are processed aiming at the positions with the abnormal more occurrence (possibly the ground of the area is uneven; or when the position point information is in a code point form, a two-dimensional code pasted on the ground is not clear and needs to be changed with a new code);
(2) the drawn warehouse-out thermodynamic diagrams can be used for counting the warehouse-out task times of shelves in a warehouse within a period of time, further analyzing which shelves are frequently subjected to warehouse tasks, analyzing whether the distribution of the target points of warehoused goods is reasonable or not, and unreasonably optimizing according to the display conditions of the thermodynamic diagrams;
(3) through the drawn network thermodynamic diagrams, the network connection conditions of the trolleys in the warehouse in each area within a period of time can be counted, and further, the network conditions of the routers can be analyzed to be poor, so that updating and maintenance can be carried out.
Fig. 5 is a schematic diagram of the main modules of an unmanned bin operation condition analysis according to an embodiment of the invention.
As shown in fig. 5, an unmanned cabin operation condition analysis apparatus 500 according to an embodiment of the present invention includes:
an obtaining module 501, configured to obtain event data, corresponding to an observation instruction, of transportation equipment in a warehouse when the observation instruction is received;
an aggregation module 502, configured to aggregate the event data based on a location point in the warehouse to obtain an event aggregation result;
a drawing module 503, configured to draw a warehouse thermodynamic diagram according to the event aggregation result;
and the analysis module 504 is used for analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
The observation instruction may be an instruction initiated by a user to begin observing specified event data; the command can be sent by inputting and/or selecting some specified requirements by the user, or can be a timing automatic observation command set in advance by the user.
The transport device may be a device in a warehouse for supporting transported goods, such as a conveyor agv (Automated Guided Vehicle).
The event data may be various data generated by the transportation device at runtime; the location points may be fixed points or areas in a warehousing environment.
According to the technical means of aggregating based on the position points according to the acquired event data of the transportation equipment and drawing the warehouse thermodynamic diagram according to the aggregation result, the method can process various warehouse data, perform rendering drawing of the warehouse thermodynamic diagram according to the processed warehouse data, present the technical effect of the warehouse thermodynamic diagram under various dimensions, and analyze and improve related operations of warehouse production based on the drawn warehouse thermodynamic diagram.
In some embodiments, the observation instructions include: the method comprises the steps of obtaining data type information of an event to be observed, observation time period information and dimension information to be observed; wherein the dimension to be observed is a dimension under the event data to be observed;
the event data of the transportation device may be stored in an Elasticsearch database; the event data type of the transportation device comprises at least one of the following: abnormal data, ex-warehouse data and network data;
the abnormal data is abnormal information transmitted by the transportation equipment under the abnormal condition;
the ex-warehouse data is ex-warehouse information transmitted by the transportation equipment under the condition of having ex-warehouse tasks;
the network data is network information transmitted by real-time heartbeat when the transportation equipment runs.
The ElasticSearch database is a Lucene-based search server that provides a full-text search index for distributed multi-user capabilities. The Elasticsearch was developed in Java and published as open source under the Apache licensing terms, and is currently a popular enterprise-level search engine. The system is designed and used in cloud computing, can stably, reliably and quickly search in real time, and is convenient to install and use. The method has the advantages that the Elasticisearch database is used for data storage, large data volume scenes can be met, the performance is outstanding, and when aggregation statistics is carried out according to position points, the aggregation statistics of the Elasticisearch can reach the second level, and the speed is very high.
The event data of the transportation device is transmitted to an ElasticSearch database when the transportation device runs, specifically:
the abnormal data can be generated by the transportation equipment when encountering an abnormality and is synchronized to the ElasticSearch database through the console application and the database under the console application;
the ex-warehouse data can be generated by the transportation equipment when the ex-warehouse task is executed, and is synchronized to the ElasticSearch database through the console application and the database under the console application;
the network data can be generated all the time when the transportation equipment runs, and the heartbeat is reported to the data acquisition application, then is processed to a certain extent, and then is transferred to the ElasticSearch database.
In some embodiments, the aggregation module 502, before aggregating the event data as location points in a warehouse, further comprises:
and according to a preset time magnitude, performing data compression on the event data to obtain a new query index.
After the event data is transmitted to the elastic search database by the transportation equipment, because the original data volume is large (especially the network data can be reported according to second heartbeat, and the data volume can reach hundred million levels), the second-level data needs to be compressed into a preset time level (such as one hour, six hours and the like) through data compression, and when the data is used, a new compressed index can be used to achieve a faster data access speed;
taking the preset time level as one hour as an example: pieces of data within one hour may be compressed into one piece; accordingly, the observation period selected by the user when viewing the thermodynamic diagram is a minimum of one hour, the selected observation period also needs to be a multiple of one hour; therefore, the data counting speed can be improved, and the problems of memory overflow, overlong page response time and the like caused by overlarge data volume are prevented.
In some embodiments, the location points include at least one of: code points and coordinate grids;
aggregating the event data based on the location points in the warehouse to obtain an event aggregation result, including:
according to the observation instruction, counting the total value of the event data to be observed, the dimension value to be observed and the event list information of each position point in the observation time period as the aggregation result;
wherein, for an exception data type, the event dimension includes at least one of: abnormal times and abnormal equipment number; the event list includes at least: an exception information list;
wherein, for an ex-warehouse data type, the event dimension comprises at least one of: the number of times is taken as the number of the warehouse-out starting points and the number of times is taken as the number of the warehouse-out terminal points; the event list includes at least: a list of articles out of the warehouse;
wherein, for the network data type, the event dimension comprises at least one of: the number of data, the number of network transmission pause times and the accumulated ping delay time; the event list includes at least: a list of wireless network access points.
After data compression is carried out, rapid and efficient aggregation processing can be carried out on the data with the new index according to the position points, so that each piece of information required by the thermodynamic diagram can be obtained; for different types of events, observed dimensions are different, and the type of the event and the observed dimensions under the type can be selected when a user sends an observation instruction;
specifically, for an exception data type, the exception number dimension may be used to represent the number of times an exception occurs at a certain location point; the abnormal equipment number dimension can be used for representing the number of equipment with abnormality at a certain position point; the exception information list may be used to represent a set of exception information that has occurred at a certain location point;
for the ex-warehouse data type, the dimension of the times as the ex-warehouse starting point can be used for representing the times of a certain position point as the ex-warehouse starting point; the number dimension as the ex-warehouse terminal point can be used for representing the number of times that a certain position point is taken as the ex-warehouse terminal point; the ex-warehouse article list can be used for representing the collection of articles ex-warehouse at a certain position point, and on the basis of the ex-warehouse article list, a shelf list can be generated for representing the collection of shelves from which the articles ex-warehouse at a certain position point come;
for network data types, the data number dimension can be used to represent the number of network data transmitted at a certain location point; the network transmission pause number dimension can be used for representing the number of times of network transmission pauses occurring at a certain position point; the accumulated ping delay duration dimension can be used for representing the sum of network ping delay durations occurring at a certain position point; the list of wireless network access points may be used to represent a set of network access points that have been accessed at a certain location point.
In some embodiments, the drawing module 503 draws a warehouse thermodynamic diagram according to the aggregation result, including:
determining data to be drawn corresponding to each position point in a preset map according to the aggregation result; the data to be drawn includes: the total value of the event data to be observed, the dimension value of the dimension to be observed and the event list information of the type of the event data to be observed;
according to the data to be drawn, a jquery and svg combined dynamic rendering mode can be adopted to display the total value of the event data to be observed, the presentation style of the dimension to be observed on each position point is drawn according to a preset magnitude presentation style, and the event list information of the event data type to be observed is drawn in a list mode.
The preset magnitude rendering pattern may be expressed as a color on a location point on the thermodynamic diagram, such as: the number of preset levels (such as 5, 7 and the like) can be set, the color can be from light to dark, and the darker the color is, the larger the representative numerical value is; the preset magnitude presentation style can be expressed as filling shapes on position points on the thermodynamic diagram, and different filling shapes correspond to different magnitudes;
the numerical range of each style level can be equally divided according to the maximum value set by the user, and can also be equally divided according to the maximum value of the current data, so that each numerical value can find the corresponding style for drawing.
By adopting a dynamic rendering mode combining jquery and svg, other unnecessary functions can be prevented from being introduced when the warehouse thermodynamic diagram is drawn, and resource waste is avoided; and svg is used as a vector image, the image quality of the vector image is not lost under the condition of changing the size, and the user experience is good.
In some embodiments, the rendering module 503 is further configured to:
when a new observation instruction is received, clearing the existing drawing result on the preset map;
and drawing a warehouse thermodynamic diagram corresponding to the new observation instruction based on the new observation instruction.
The new observation instruction can be used for observing other dimensions under the current event type, and can also be used for observing dimensions under other events; such as: and when the abnormal frequency dimensionality of the abnormal thermodynamic diagram is displayed, receiving an observation instruction for observing the abnormal equipment number dimensionality of the abnormal thermodynamic diagram, clearing the drawn abnormal frequency dimensionality effect on the preset map, acquiring the abnormal equipment number dimensionality value corresponding to the new observation instruction, and drawing.
In some embodiments, the rendering module 503 is further configured to:
when a screening instruction about the event list is received, determining a screened sub-event list according to the screening instruction;
selecting a sub-aggregation result corresponding to the sub-event list from the event aggregation results of the current event type according to the sub-event list;
and drawing a warehouse thermodynamic diagram corresponding to the screening instruction according to the sub-aggregation result.
The filtering instruction may be a filtering instruction initiated by the user for the current event list based on the dimension being observed, and specifically, the filtering instruction may be a filtering instruction initiated by the user for selecting a specified event from the current event list to perform data filtering and observation;
for example: when the dimension of the network transmission pause times of the network thermodynamic diagram is displayed, a user initiates a screening instruction by checking one or a plurality of specified wireless network access points in a current wireless network access point list, then the drawn dimension effect of the network transmission pause times on a preset map is eliminated, the network transmission pause times corresponding to the wireless network access points selected by the user on each position point are obtained again based on the wireless network access points selected by the user, and the drawing display is carried out.
In some embodiments, the rendering module 503 is further configured to:
and when a click position point instruction is received, displaying a point event data value, a point event dimension value and point event list information of the clicked position point based on the event type and the dimension to be observed corresponding to the currently drawn warehouse thermodynamic diagram.
The click position point instruction can be an instruction initiated by a user aiming at a certain position point and used for observing data on the position point; the method can be realized by binding a user click event to the position point, and when a user clicks a certain position point, the statistical data display related to the position point can be separately requested from the server.
Fig. 6 illustrates an exemplary system architecture 600 of a unmanned bin operation condition analysis method or an unmanned bin operation condition analysis apparatus to which an embodiment of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. Various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 601, 602, and 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server that provides various services. The background management server can analyze and process the received data such as the product information inquiry request and feed back the processing result to the terminal equipment.
It should be noted that the method for analyzing the operating condition of the unmanned bin provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the unmanned bin operating condition analyzing apparatus is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, an aggregation module, a rendering module, and an analysis module. The names of these modules do not in some cases constitute a limitation to the modules themselves, and for example, the acquiring module may also be described as a "unit that sends a picture acquiring request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: s101, when an observation instruction is received, acquiring event data, corresponding to the observation instruction, of transportation equipment in a warehouse; step S102, aggregating the event data based on the position points in the warehouse to obtain an event aggregation result; and S103, drawing a warehouse thermodynamic diagram according to the event aggregation result, and analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
According to the technical scheme of the embodiment of the invention, because the technical means of compressing and aggregating based on the position points according to the event data of the transportation equipment acquired from the Elasticissearch database and drawing the warehouse thermodynamic diagram in a jquery combined with svg mode according to the aggregation result is adopted, the technical problem that various conditions in the warehouse production operation cannot be intuitively and comprehensively understood in the prior art is solved, and the technical effects of quickly drawing the warehouse thermodynamic diagram, analyzing and improving the warehouse production related operation based on the drawn warehouse thermodynamic diagram are further achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. An unmanned storehouse operation condition analysis method is characterized by comprising the following steps:
when an observation instruction is received, acquiring event data of transportation equipment in a warehouse, which corresponds to the observation instruction;
aggregating the event data based on the position points in the warehouse to obtain an event aggregation result;
and drawing a warehouse thermodynamic diagram according to the event aggregation result, and analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
2. The method of claim 1, wherein the observation instructions comprise: the method comprises the steps of obtaining data type information of an event to be observed, observation time period information and dimension information to be observed; wherein the dimension to be observed is a dimension under the event data to be observed;
the event data type of the transportation device comprises at least one of the following: abnormal data, ex-warehouse data and network data;
the abnormal data is abnormal information transmitted by the transportation equipment under the abnormal condition;
the ex-warehouse data is ex-warehouse information transmitted by the transportation equipment under the condition of having ex-warehouse tasks;
the network data is network information transmitted by real-time heartbeat when the transportation equipment runs.
3. The method of claim 2, further comprising, prior to aggregating the event data as location points in a warehouse:
and according to a preset time magnitude, performing data compression on the event data to obtain a new query index.
4. The method of claim 3, wherein the location points comprise at least one of: code points and coordinate grids;
aggregating the event data based on the location points in the warehouse to obtain an event aggregation result, including:
according to the observation instruction, counting the total value of the event data to be observed, the dimension value to be observed and the event list information of each position point in the observation time period as the aggregation result;
wherein, for an exception data type, the event dimension includes at least one of: abnormal times and abnormal equipment number; the event list includes at least: an exception information list;
wherein, for an ex-warehouse data type, the event dimension comprises at least one of: the number of times is taken as the number of the warehouse-out starting points and the number of times is taken as the number of the warehouse-out terminal points; the event list includes at least: a list of articles out of the warehouse;
wherein, for the network data type, the event dimension comprises at least one of: the number of data, the number of network transmission pause times and the accumulated ping delay time; the event list includes at least: a list of wireless network access points.
5. The method of claim 4, wherein drawing a warehouse thermodynamic diagram from the aggregated results comprises:
determining data to be drawn corresponding to each position point in a preset map according to the aggregation result; the data to be drawn includes: the total value of the event data to be observed, the dimension value of the dimension to be observed and the event list information of the type of the event data to be observed;
and displaying the total value of the event data to be observed in a dynamic rendering mode according to the data to be drawn, drawing the presentation style of the dimension to be observed on each position point according to a preset magnitude presentation style, and drawing the event list information of the event data type to be observed in a list mode.
6. The method of claim 5, further comprising:
when a new observation instruction is received, clearing the existing drawing result on the preset map;
and drawing a warehouse thermodynamic diagram corresponding to the new observation instruction based on the new observation instruction.
7. The method of claim 5, further comprising:
when a screening instruction about the event list is received, determining a screened sub-event list according to the screening instruction;
selecting a sub-aggregation result corresponding to the sub-event list from the event aggregation results of the current event type according to the sub-event list;
and drawing a warehouse thermodynamic diagram corresponding to the screening instruction according to the sub-aggregation result.
8. The method of claim 5, further comprising:
and when a click position point instruction is received, displaying a point event data value, a point event dimension value and point event list information of the clicked position point based on the event type and the dimension to be observed corresponding to the currently drawn warehouse thermodynamic diagram.
9. An unmanned storehouse behavior analysis device, comprising:
the acquisition module is used for acquiring event data, corresponding to the observation instruction, of the transportation equipment in the warehouse when the observation instruction is received;
the aggregation module is used for aggregating the event data based on the position points in the warehouse to obtain an event aggregation result;
the drawing module is used for drawing a warehouse thermodynamic diagram according to the event aggregation result;
and the analysis module is used for analyzing the operation condition of the unmanned warehouse according to the warehouse thermodynamic diagram.
10. An unmanned storehouse operation condition analysis electronic equipment, characterized by comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
11. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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| CN110164516A (en) * | 2019-05-24 | 2019-08-23 | 山东大学齐鲁医院 | A kind of inspection document time distribution map method for drafting and system |
| CN114648272A (en) * | 2022-04-01 | 2022-06-21 | 上海聚货通电子商务有限公司 | Commodity layout adjustment method and system based on picking thermodynamic diagram |
| CN114648272B (en) * | 2022-04-01 | 2023-07-21 | 上海聚货通电子商务有限公司 | Commodity layout adjustment method and system based on goods picking thermodynamic diagram |
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