CN114089712B - Data processing method and device - Google Patents
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Abstract
The invention discloses a data processing method and device, and relates to the technical field of computers. One embodiment of the method comprises the following steps: acquiring abnormal data reported by an automatic guiding transport vehicle in a statistical period, wherein the abnormal data comprises the reported quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; and determining abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster. According to the embodiment, the abnormal data reported by the AGV due to the fault of the self module and the abnormal data reported by the fault of the warehouse communication system can be distinguished, the actual abnormal data reporting frequency of the fault module of the AGV equipment is determined, the accuracy of judging the fault occurrence object is improved, and the production and maintenance cost is reduced.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method and apparatus.
Background
AGV (Automated Guided Vehicle) is a main bearing object of the carrying task in the unmanned warehouse, and the stability of the equipment can be seriously influenced by the abnormality of the equipment, so that the production operation in the warehouse is influenced. In order to ensure normal operation of the unmanned warehouse, operation and maintenance personnel need to feed back abnormal data reported by equipment so as to discover equipment problems in time and improve equipment stability.
However, the abnormal data reported by the equipment not only includes the abnormal data reported by the AGV due to the fault of the self module, but also includes the abnormal data reported by the fault of the warehouse communication system.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
in the existing data processing method, the abnormal data reported by the AGV due to the fault of the self module and the abnormal data reported by the fault of the warehouse communication system are not distinguished, so that the report frequency of the abnormal data of the equipment fault module cannot be accurately judged, the accuracy of judging the fault occurrence object is low, the production time in the warehouse is delayed, and the maintenance cost of equipment is increased.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a data processing method and device, which can distinguish the abnormal data reported by the AGV due to the fault of the self module from the abnormal data reported by the fault of the warehouse communication system, determine the actual abnormal data reporting frequency of the fault module of the AGV equipment, improve the accuracy of judging the fault occurrence object and reduce the production and maintenance cost.
To achieve the above object, according to a first aspect of an embodiment of the present invention, there is provided a data processing method, including:
acquiring abnormal data reported by an automatic guiding transport vehicle in a statistical period, wherein the abnormal data comprises the reported quantity corresponding to each statistical period of the automatic guiding transport vehicle;
taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value;
and determining abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster.
Further, the step of obtaining the abnormal data reported by the automatic guided vehicle in the statistical period includes: and setting an automatic guiding transport vehicle number, and acquiring abnormal data reported by each automatic guiding transport vehicle in a counting period according to the automatic guiding transport vehicle number.
Further, the step of determining clusters according to the neighborhood distance threshold and the neighborhood object number threshold comprises:
determining a core point object according to the neighborhood distance threshold and the neighborhood object number threshold;
and determining clusters according to the core point objects and the neighborhood distance threshold.
Further, the step of determining the core point object according to the neighborhood distance threshold and the neighborhood object number threshold comprises:
randomly selecting one object from the clustered objects as a core point object, and determining that the number of objects in a cluster corresponding to the core point object is greater than or equal to a neighborhood object number threshold.
Further, after the step of determining clusters according to the core point object and the neighborhood distance threshold, the data processing method further comprises:
determining a noise point object according to the clustered objects and the clusters;
and determining abnormal data reporting frequency of the warehouse communication system according to the reporting number and the statistical time period corresponding to the noise point objects.
Further, the step of determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical period corresponding to the objects in the cluster includes:
summarizing the sum of the reporting quantity corresponding to the objects in the cluster and counting the time period quantity;
and determining abnormal data reporting frequency of the automatic guided vehicle according to the sum of the reporting number and the counting time period number.
Further, the abnormal data further includes a report type corresponding to each statistical period of the automated guided vehicle, and after the step of determining the cluster according to the neighborhood distance threshold and the neighborhood object number threshold, the data processing method further includes: and determining abnormal data reporting frequency corresponding to the corresponding communication modules of the automatic guided vehicle according to the reporting number, the reporting type and the statistical time period corresponding to the objects in the cluster, wherein the reporting type and the communication modules of the automatic guided vehicle are in a corresponding relation.
Further, the data processing method further comprises: and determining a fault occurrence object according to the reporting frequency of the abnormal data.
According to a second aspect of an embodiment of the present invention, there is provided a data processing apparatus including:
the abnormal data acquisition module is used for acquiring abnormal data reported by the automatic guiding transport vehicle in the statistical period, wherein the abnormal data comprises the reporting number corresponding to each statistical period of the automatic guiding transport vehicle;
the cluster determining module is used for determining clusters according to the neighborhood distance threshold and the neighborhood object number threshold by taking the reporting quantity corresponding to each statistical period as a clustering object;
and the reporting frequency determining module is used for determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster.
According to a third aspect of an embodiment of the present invention, there is provided an electronic apparatus including:
one or more processors;
storage means 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 are caused to implement any of the data processing methods described above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer readable medium having stored thereon a computer program which when executed by a processor implements a data processing method as any one of the above.
One embodiment of the above invention has the following advantages or benefits: because the abnormal data reported by the automatic guiding transport vehicle in the statistical period is acquired, the abnormal data comprises the reporting quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; according to the technical means of determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster, the technical effects that the abnormal data reporting frequency of the equipment fault module cannot be accurately judged due to the fact that the AGV is not distinguished from the abnormal data reported by the fault of the self module and the abnormal data reported by the fault of the warehouse communication system in the existing data processing method, the accuracy of judging the fault occurrence object is low, the production time in the warehouse is delayed, the maintenance cost of the equipment is increased, the real abnormal data reporting frequency of the AGV equipment fault module is accurately determined, the accuracy of judging the fault occurrence object is improved, and the production and maintenance cost is reduced are overcome.
Further effects of the above-described non-conventional alternatives are 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 the main flow of a data processing method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of the main flow of a data processing method according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of the main modules of a data processing apparatus provided according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
fig. 5 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered 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 the main flow of a data processing method according to a first embodiment of the present invention; as shown in fig. 1, the data processing method provided by the embodiment of the present invention mainly includes:
step S101, obtaining abnormal data reported by the automatic guided vehicle in a statistical period, wherein the abnormal data comprise the reporting number corresponding to each statistical period of the automatic guided vehicle.
The abnormal data is data about abnormal communication of the recording device, which is reported by the AGV, and comprises network heartbeat abnormality, battery communication faults (which means that a battery cannot communicate and cannot acquire electric quantity information), operation control communication faults and the like. The above-mentioned statistical period consists of a statistical period.
Specifically, according to an embodiment of the present invention, the step of obtaining the abnormal data reported by the automated guided vehicle in the statistical period includes: and setting an automatic guiding transport vehicle number, and acquiring abnormal data reported by each automatic guiding transport vehicle in a counting period according to the automatic guiding transport vehicle number.
Through the arrangement, the abnormal data reported by each AGV device in the unmanned warehouse are respectively obtained according to the AGV numbers, so that the abnormal data reporting frequency of each AGV device is determined later, the fault occurrence object (which AGV device is accurate) is determined accurately, and the maintenance efficiency is improved.
Step S102, taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold and a neighborhood object number threshold.
Because the warehouse communication system breaks down or when the network signal is unstable, all AGV equipment can report abnormal data together in a short time for abnormal data is explosive growth in a short time, if the partial abnormal data is not rejected, the true abnormal data reporting frequency of the AGV equipment can be misjudged, and then the AGV equipment which is not abnormal actually is maintained, the production time in the warehouse is delayed, and the maintenance cost is increased.
Specifically, according to an embodiment of the present invention, the step of determining the cluster according to the neighborhood distance threshold and the neighborhood object number threshold includes:
determining a core point object according to the neighborhood distance threshold and the neighborhood object number threshold;
and determining clusters according to the core point objects and the neighborhood distance threshold.
Through the arrangement, the abnormal data are subjected to density clustering, abnormal data reported due to faults of a warehouse communication system are removed, and the actual abnormal data reporting frequency of each AGV device is determined.
The neighborhood object number threshold is determined according to the number of statistical time periods in a statistical period, and because part of objects are removed after density clustering (namely, the statistical time periods of reporting abnormal data due to the faults of a warehouse communication system are removed), the number of the objects in the cluster is smaller than the total number of clustered objects, and in order to avoid that the removed objects are less, the reporting frequency of the actual abnormal data of the finally determined AGV equipment is inaccurate, according to a specific implementation of the embodiment of the invention, the neighborhood object number threshold is set to be greater than or equal to half of the total number of the statistical time periods.
Further, according to an embodiment of the present invention, the step of determining the core point object according to the neighborhood distance threshold and the neighborhood object number threshold includes:
randomly selecting one object from the clustered objects as a core point object, and determining that the number of objects in a cluster corresponding to the core point object is greater than or equal to a neighborhood object number threshold.
Firstly, randomly selecting one candidate core point object from the clustered objects, determining whether the candidate core point object meets the requirement according to a neighborhood object number threshold, and if so, determining that the candidate core point object is the core point object in the density clustering process.
Specifically, according to an embodiment of the present invention, after the step of determining the cluster according to the core point object and the neighborhood distance threshold, the data processing method further includes:
determining a noise point object according to the clustered objects and the clusters;
and determining abnormal data reporting frequency of the warehouse communication system according to the reporting number and the statistical time period corresponding to the noise point objects.
Through the arrangement, the abnormal data reporting frequency of the warehouse communication system is obtained, and therefore the warehouse communication system with faults can be maintained in a targeted mode.
Step S103, determining abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster.
Specifically, according to an embodiment of the present invention, the step of determining the abnormal data reporting frequency of the automated guided vehicle according to the reporting number and the statistical period corresponding to the objects in the cluster includes:
summarizing the sum of the reporting quantity corresponding to the objects in the cluster and counting the time period quantity;
and determining abnormal data reporting frequency of the automatic guided vehicle according to the sum of the reporting number and the counting time period number.
Preferably, according to an embodiment of the present invention, the abnormal data further includes a report type corresponding to each statistical period of the automated guided vehicle, and after the step of determining the cluster according to the neighborhood distance threshold and the neighborhood object number threshold, the data processing method further includes: and determining abnormal data reporting frequency corresponding to the corresponding communication modules of the automatic guided vehicle according to the reporting number, the reporting type and the statistical time period corresponding to the objects in the cluster, wherein the reporting type and the communication modules of the automatic guided vehicle are in a corresponding relation.
Through the arrangement, the abnormal data reporting frequency corresponding to the corresponding communication modules of the automatic guided vehicle is determined according to the reporting number, the reporting type and the statistical time period corresponding to the objects in the cluster, the failed AGV communication module is accurately determined, and the maintenance efficiency is improved.
Further, according to an embodiment of the present invention, the data processing method further includes: and determining a fault occurrence object according to the reporting frequency of the abnormal data.
According to the abnormal data reporting frequency and the abnormal frequency threshold value corresponding to each AGV device, each communication module or the warehouse communication system, the fault occurrence object is accurately locked, and convenience is provided for the fixed-point maintenance of the fault occurrence object.
According to the technical scheme of the embodiment of the invention, because the abnormal data reported by the automatic guiding transport vehicle in the statistical period is acquired, the abnormal data comprises the reporting quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; according to the technical means of determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster, the technical effects that the abnormal data reporting frequency of the equipment fault module cannot be accurately judged due to the fact that the AGV is not distinguished from the abnormal data reported by the fault of the self module and the abnormal data reported by the fault of the warehouse communication system in the existing data processing method, the accuracy of judging the fault occurrence object is low, the production time in the warehouse is delayed, the maintenance cost of the equipment is increased, the real abnormal data reporting frequency of the AGV equipment fault module is accurately determined, the accuracy of judging the fault occurrence object is improved, and the production and maintenance cost is reduced are overcome.
FIG. 2 is a schematic diagram of the main flow of a data processing method according to a second embodiment of the present invention; as shown in fig. 2, the data processing method provided by the embodiment of the present invention mainly includes:
step S201, setting an automatic guiding transport vehicle number, and acquiring abnormal data reported by each automatic guiding transport vehicle in a statistical period according to the automatic guiding transport vehicle number.
Through the arrangement, the abnormal data reported by each AGV device in the unmanned warehouse are respectively obtained according to the AGV numbers, so that the abnormal data reporting frequency of each AGV device is determined later, the fault occurrence object (which AGV device is accurate) is determined accurately, and the maintenance efficiency is improved.
Step S202, taking the reporting quantity corresponding to each statistical period as a clustering object, and randomly selecting one object from the clustering objects as an alternative core point object.
Because the warehouse communication system breaks down or when the network signal is unstable, all AGV equipment can report abnormal data together in a short time for abnormal data is explosive growth in a short time, therefore, through the setting, the report quantity that each statistics period corresponds is the cluster object and is favorable to rejecting the abnormal data that leads to AGV equipment to report because of the warehouse communication system breaks down.
According to a specific implementation manner of the embodiment of the present invention, abnormal data in 30 days (i.e. a statistical period, it should be noted that, the setting of the period is only an example, and is not limiting to the present invention, it can be understood that the numerical value of the statistical period can be adjusted according to the occurrence frequency or maintenance degree of the failure of the AGV device in each warehouse); and then daily as a statistical period (the specific values are set as above).
Step S203, determining an alternative cluster according to the alternative core point object and the neighborhood distance threshold.
According to a specific implementation of the embodiment of the present invention, the setting of the neighborhood distance threshold may also be adjusted according to the frequency of occurrence of the failure or the degree of maintenance (i.e., the maintenance efficiency of the failed module is desired) of the AGV devices in each warehouse.
According to the embodiment of the invention, the neighborhood distance threshold can be determined according to the quartile range corresponding to the number of the abnormal data reported in all the statistical time periods, and according to a specific implementation of the embodiment of the invention, 1.5 times of the quartile range can be adopted as the neighborhood distance threshold (only an example and can be adjusted according to actual conditions).
Step S204, judging whether the number of objects in the candidate cluster is larger than or equal to a neighborhood object number threshold. If yes, that is, the number of objects in the candidate cluster is greater than or equal to the threshold value of the number of neighbor objects, step S205 is executed; if not, that is, the number of objects in the candidate cluster is smaller than the neighborhood object number threshold, the process goes to step S202.
Step S205, determining the candidate core point object as a core point object, and determining the candidate cluster as a cluster corresponding to the core point object.
Specifically, according to the embodiment of the present invention, the threshold value of the number of the neighborhood objects is determined according to the number of the statistical periods in the statistical period, and since part of the objects are removed after density clustering (i.e. the statistical period of reporting abnormal data due to the failure of the warehouse communication system is removed), the number of the objects in the cluster is smaller than the total number of the clustered objects, and in order to avoid that the removed objects are less and the finally determined reporting frequency of the actual abnormal data of the AGV device is inaccurate, according to a specific implementation of the embodiment of the present invention, the threshold value of the number of the neighborhood objects is set to be greater than or equal to half of the total number of the statistical period.
Through the arrangement, the problem that the reporting frequency of the real abnormal data of the AGV equipment determined later is inaccurate due to the fact that the number of the removed objects is large is avoided.
Step S206, determining noise point objects according to the clustered objects and the clusters, and determining abnormal data reporting frequency of the warehouse communication system according to the reporting quantity and the statistical time period corresponding to the noise point objects.
And removing objects included in the clusters in the clustered objects to obtain noise point objects, wherein the noise point objects are caused by the faults of the warehouse communication system. Through the arrangement, the warehouse communication system can be maintained in a targeted mode, and the production and operation efficiency in the warehouse is improved.
Step S207, summarizing the total sum of the reported numbers corresponding to the objects in the cluster and counting the number of time periods; and determining abnormal data reporting frequency of the automatic guided vehicle according to the sum of the reporting number and the counting time period number.
Through the arrangement, the real abnormal data reporting frequency corresponding to each AGV device can be obtained, and therefore the failed AGV device can be accurately determined, the maintenance efficiency is improved, and the maintenance cost is reduced.
Step S208, determining abnormal data reporting frequency corresponding to corresponding communication modules of the automatic guided vehicles according to the reporting number, the reporting types and the statistical time periods corresponding to the objects in the clusters, wherein the abnormal data further comprises the reporting types corresponding to the statistical time periods of the automatic guided vehicles, and the reporting types and the communication modules of the automatic guided vehicles are in corresponding relation.
Through the arrangement, the reporting frequency corresponding to each communication module of the AGV equipment is determined according to the reporting types corresponding to each different data, and then the module with faults of the AGV equipment can be determined, so that the maintenance efficiency is further improved, and the production cost in a bin is reduced.
Step S209, determining a fault occurrence object according to the reporting frequency of the abnormal data.
According to the abnormal data reporting frequency and the abnormal frequency threshold value corresponding to each AGV device, each communication module or the warehouse communication system, the fault occurrence object is accurately locked, and convenience is provided for the fixed-point maintenance of the fault occurrence object.
According to the technical scheme of the embodiment of the invention, because the abnormal data reported by the automatic guiding transport vehicle in the statistical period is acquired, the abnormal data comprises the reporting quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; according to the technical means of determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster, the technical effects that the abnormal data reporting frequency of the equipment fault module cannot be accurately judged due to the fact that the AGV is not distinguished from the abnormal data reported by the fault of the self module and the abnormal data reported by the fault of the warehouse communication system in the existing data processing method, the accuracy of judging the fault occurrence object is low, the production time in the warehouse is delayed, the maintenance cost of the equipment is increased, the real abnormal data reporting frequency of the AGV equipment fault module is accurately determined, the accuracy of judging the fault occurrence object is improved, and the production and maintenance cost is reduced are overcome.
FIG. 3 is a schematic diagram of the main modules of a data processing apparatus provided according to an embodiment of the present invention; as shown in fig. 3, a data processing apparatus 300 according to an embodiment of the present invention mainly includes:
the abnormal data obtaining module 301 is configured to obtain abnormal data reported by the automatic guided vehicle in a statistics period, where the abnormal data includes reporting numbers corresponding to each statistics period of the automatic guided vehicle.
The abnormal data refer to data about recording equipment abnormality reported by the AGV, and include network heartbeat abnormality, battery communication faults (which means that a battery cannot communicate and cannot acquire electric quantity information), operation control communication faults and the like.
Specifically, according to an embodiment of the present invention, the abnormal data acquisition module 301 is further configured to: and setting an automatic guiding transport vehicle number, and acquiring abnormal data reported by each automatic guiding transport vehicle in a counting period according to the automatic guiding transport vehicle number.
Through the arrangement, the abnormal data reported by each AGV device in the unmanned warehouse are respectively obtained according to the AGV numbers, so that the abnormal data reporting frequency of each AGV device is determined later, the fault occurrence object (which AGV device is accurate) is determined accurately, and the maintenance efficiency is improved.
The cluster determining module 302 is configured to determine a cluster according to a neighborhood distance threshold and a neighborhood object number threshold by using the reporting number corresponding to each statistical period as a clustering object.
Because the warehouse communication system breaks down or when the network signal is unstable, all AGV equipment can report abnormal data together in a short time for abnormal data is explosive growth in a short time, if the partial abnormal data is not rejected, the true abnormal data reporting frequency of the AGV equipment can be misjudged, and then the AGV equipment which is not abnormal actually is maintained, the production time in the warehouse is delayed, and the maintenance cost is increased.
Specifically, according to an embodiment of the present invention, the cluster determining module 302 is further configured to:
determining a core point object according to the neighborhood distance threshold and the neighborhood object number threshold;
and determining clusters according to the core point objects and the neighborhood distance threshold.
Through the arrangement, the abnormal data are subjected to density clustering, abnormal data reported due to faults of a warehouse communication system are removed, and the actual abnormal data reporting frequency of each AGV device is determined.
The neighborhood object number threshold is determined according to the number of statistical time periods in a statistical period, and because part of objects are removed after density clustering (namely, the statistical time periods of reporting abnormal data due to the faults of a warehouse communication system are removed), the number of the objects in the cluster is smaller than the total number of clustered objects, and in order to avoid that the removed objects are less, the reporting frequency of the actual abnormal data of the finally determined AGV equipment is inaccurate, according to a specific implementation of the embodiment of the invention, the neighborhood object number threshold is set to be greater than or equal to half of the total number of the statistical time periods.
Further, according to an embodiment of the present invention, the cluster determining module 302 is further configured to:
randomly selecting one object from the clustered objects as a core point object, and determining that the number of objects in a cluster corresponding to the core point object is greater than or equal to a neighborhood object number threshold.
Firstly, randomly selecting one candidate core point object from the clustered objects, determining whether the candidate core point object meets the requirement according to a neighborhood object number threshold, and if so, determining that the candidate core point object is the core point object in the density clustering process.
Specifically, according to an embodiment of the present invention, the data processing apparatus 300 further includes an abnormal data reporting frequency determining module of the warehouse communication system, where after the step of determining the cluster according to the core point object and the neighborhood distance threshold, the abnormal data reporting frequency determining module is configured to:
determining a noise point object according to the clustered objects and the clusters;
and determining abnormal data reporting frequency of the warehouse communication system according to the reporting number and the statistical time period corresponding to the noise point objects.
Through the arrangement, the abnormal data reporting frequency of the warehouse communication system is obtained, and therefore the warehouse communication system with faults can be maintained in a targeted mode.
The reporting frequency determining module 303 is configured to determine an abnormal data reporting frequency of the automated guided vehicle according to the reporting number and the statistical period corresponding to the objects in the cluster.
Specifically, according to an embodiment of the present invention, the reporting frequency determining module 303 is configured to:
summarizing the sum of the reporting quantity corresponding to the objects in the cluster and counting the time period quantity;
and determining abnormal data reporting frequency of the automatic guided vehicle according to the sum of the reporting number and the counting time period number.
Preferably, according to an embodiment of the present invention, the abnormal data further includes a reporting type corresponding to each statistical period of the automated guided vehicle, and the data processing apparatus 300 further includes an abnormal data reporting frequency determining module corresponding to the communication module, where after the step of determining the cluster according to the neighborhood distance threshold and the neighborhood object number threshold, the abnormal data reporting frequency determining module corresponding to the communication module is configured to:
and determining abnormal data reporting frequency corresponding to the corresponding communication modules of the automatic guided vehicle according to the reporting number, the reporting type and the statistical time period corresponding to the objects in the cluster, wherein the reporting type and the communication modules of the automatic guided vehicle are in a corresponding relation.
Through the arrangement, the abnormal data reporting frequency corresponding to the corresponding communication modules of the automatic guided vehicle is determined according to the reporting number, the reporting type and the statistical time period corresponding to the objects in the cluster, the failed AGV communication module is accurately determined, and the maintenance efficiency is improved.
Further, according to an embodiment of the present invention, the data processing apparatus 300 further includes a failure occurrence object determining module configured to: and determining a fault occurrence object according to the reporting frequency of the abnormal data.
According to the abnormal data reporting frequency and the abnormal frequency threshold value corresponding to each AGV device, each communication module or the warehouse communication system, the fault occurrence object is accurately locked, and convenience is provided for the fixed-point maintenance of the fault occurrence object.
According to the technical scheme of the embodiment of the invention, because the abnormal data reported by the automatic guiding transport vehicle in the statistical period is acquired, the abnormal data comprises the reporting quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; according to the technical means of determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster, the technical effects that the abnormal data reporting frequency of the equipment fault module cannot be accurately judged due to the fact that the AGV is not distinguished from the abnormal data reported by the fault of the self module and the abnormal data reported by the fault of the warehouse communication system in the existing data processing method, the accuracy of judging the fault occurrence object is low, the production time in the warehouse is delayed, the maintenance cost of the equipment is increased, the real abnormal data reporting frequency of the AGV equipment fault module is accurately determined, the accuracy of judging the fault occurrence object is improved, and the production and maintenance cost is reduced are overcome.
FIG. 4 illustrates an exemplary system architecture 400 in which a data processing method or data processing apparatus of an embodiment of the present invention may be applied.
As shown in fig. 4, a system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405 (this architecture is merely an example, and the components contained in a particular architecture may be tailored to the application specific case). The network 404 is used as a medium to provide communication links between the terminal devices 401, 402, 403 and the server 405. The network 404 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 405 via the network 404 using the terminal devices 401, 402, 403 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 401, 402, 403.
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (by way of example only) providing support for shopping-type websites browsed by users using the terminal devices 401, 402, 403. The background management server may analyze and process the received data such as the abnormal data, and feed back the processing result (e.g., cluster, reporting frequency of the abnormal data—only an example) to the terminal device.
It should be noted that, the data processing method provided in the embodiment of the present invention is generally executed by the server 405, and accordingly, the data processing apparatus is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, there is illustrated a schematic diagram of a computer system 500 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 501.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this document, 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, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 flowcharts 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 involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes an abnormal data acquisition module, a cluster determination module, and a reporting frequency determination module. The names of these modules do not limit the module itself in some cases, for example, the abnormal data acquisition module may also be described as "a module for acquiring abnormal data reported by an automated guided vehicle in a statistical period, where the abnormal data includes the reporting number corresponding to each statistical period of the automated guided vehicle".
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 present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring abnormal data reported by an automatic guiding transport vehicle in a statistical period, wherein the abnormal data comprises the reported quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; and determining abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster.
According to the technical scheme of the embodiment of the invention, because the abnormal data reported by the automatic guiding transport vehicle in the statistical period is acquired, the abnormal data comprises the reporting quantity corresponding to each statistical period of the automatic guiding transport vehicle; taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; according to the technical means of determining the abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster, the technical effects that the abnormal data reporting frequency of the equipment fault module cannot be accurately judged due to the fact that the AGV is not distinguished from the abnormal data reported by the fault of the self module and the abnormal data reported by the fault of the warehouse communication system in the existing data processing method, the accuracy of judging the fault occurrence object is low, the production time in the warehouse is delayed, the maintenance cost of the equipment is increased, the real abnormal data reporting frequency of the AGV equipment fault module is accurately determined, the accuracy of judging the fault occurrence object is improved, and the production and maintenance cost is reduced are overcome.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method of data processing, comprising:
acquiring abnormal data reported by an automatic guiding transport vehicle in a statistical period, wherein the abnormal data comprises the reporting quantity corresponding to each statistical period of the automatic guiding transport vehicle;
taking the reporting quantity corresponding to each statistical period as a clustering object, and determining clusters according to a neighborhood distance threshold value and a neighborhood object number threshold value; the method specifically comprises the following steps: determining a core point object according to the neighborhood distance threshold and the neighborhood object number threshold; determining clusters according to the core point objects and the neighborhood distance threshold;
determining a noise point object according to the clustered objects and the clusters;
determining abnormal data reporting frequency of the warehouse communication system according to the reporting number and the statistical time period corresponding to the noise point objects;
and determining abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster.
2. The method for processing data according to claim 1, wherein the step of acquiring the abnormal data reported by the automated guided vehicle in the statistical period comprises: and setting an automatic guiding transport vehicle number, and acquiring abnormal data reported by each automatic guiding transport vehicle in a statistical period according to the automatic guiding transport vehicle number.
3. The data processing method of claim 1, wherein the step of determining the core point object based on the neighborhood distance threshold and the neighborhood object number threshold comprises:
and randomly selecting one object from the clustered objects as a core point object, and determining that the number of the objects in the cluster corresponding to the core point object is greater than or equal to a neighborhood object number threshold.
4. The data processing method according to claim 1, wherein the step of determining the abnormal data reporting frequency of the automated guided vehicle according to the reporting number and the statistical period corresponding to the objects in the cluster comprises:
summarizing the sum of the reporting numbers corresponding to the objects in the cluster and counting the number of time periods;
and determining abnormal data reporting frequency of the automatic guided vehicle according to the sum of the reporting numbers and the statistical time period number.
5. The data processing method according to claim 1, wherein the abnormal data further includes a report type corresponding to each statistical period of the automated guided vehicle, and after the step of determining the cluster according to the neighborhood distance threshold and the neighborhood object number threshold, the data processing method further includes: and determining abnormal data reporting frequency corresponding to the corresponding communication modules of the automatic guided vehicle according to the reporting number, the reporting type and the statistical time period corresponding to the objects in the cluster, wherein the reporting type and the communication modules of the automatic guided vehicle are in a corresponding relation.
6. The data processing method according to claim 1, characterized in that the data processing method further comprises: and determining a fault occurrence object according to the abnormal data reporting frequency.
7. A data processing apparatus, comprising:
the abnormal data acquisition module is used for acquiring abnormal data reported by the automatic guiding transport vehicle in a statistical period, wherein the abnormal data comprises the reporting number corresponding to each statistical period of the automatic guiding transport vehicle;
the cluster determining module is used for determining clusters according to the neighborhood distance threshold and the neighborhood object number threshold by taking the reporting quantity corresponding to each statistical period as a clustering object; the method is particularly used for: determining a core point object according to the neighborhood distance threshold and the neighborhood object number threshold; determining clusters according to the core point objects and the neighborhood distance threshold;
the reporting frequency determining module is used for determining a noise point object according to the clustered objects and the clusters; determining abnormal data reporting frequency of the warehouse communication system according to the reporting number and the statistical time period corresponding to the noise point objects; and determining abnormal data reporting frequency of the automatic guided vehicle according to the reporting number and the statistical time period corresponding to the objects in the cluster.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
9. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
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