CN117726028A - Bulk cargo port business process optimization method, device, equipment and storage medium - Google Patents
Bulk cargo port business process optimization method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a bulk cargo port business process optimization method, a bulk cargo port business process optimization device, bulk cargo port business process optimization equipment and a storage medium, wherein the bulk cargo port business process optimization method comprises the following steps: receiving sensor data of each item in the working vehicle, correlating service data in a service system, and forming a statistical data set based on the sensor data and the service data; extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period; analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result; and generating alarm information according to the analysis result and the grade rule, and feeding back the alarm information to post personnel so that the post personnel can manage and control the operation track of the operation vehicle according to the alarm information. Compared with the prior art, the intelligent unmanned cargo management system has the advantages that the unmanned intelligent cargo management mode is used for replacing the manual cargo management mode, so that post personnel are reduced, and meanwhile, healthy and efficient operation of a bulk cargo port is promoted.
Description
Technical Field
The present invention relates to the field of business process management and control technologies, and in particular, to a bulk cargo port business process optimization method, apparatus, device, and storage medium.
Background
At present, along with the higher transportation situation of port bulk cargo transportation capacity, the existing facility resources of the port are utilized, a modern logistics management mode is adopted, the information construction and application level of the port is enhanced, the throughput pressure of the port is relieved, and the improvement of the loading and unloading operation efficiency and the service capacity becomes the key of further development of the port.
In recent years, the development and application of the front edge logistics informatization technology have also been advanced obviously. In the aspect of cloud computing, the mature and deep application of the IaaS, paaS, saaS and other technologies accelerates the implementation and popularization of a logistics system, and the technology of the Internet of things can connect an operation vehicle, intelligent equipment and a network in a harbor area to realize intelligent sensing and management and control of production operation; in the aspect of Beidou and GPS positioning, high-precision positioning can be realized by utilizing differential and mobile internet technologies; the manual tally mode before the wharf has the problems of unsafe man-machine interaction, low operation efficiency, lag of data generation, high error rate and high labor cost, and is based on the existing informatization technology base, and the aims of improving the quality of the port through technological innovation, reducing cost and improving quality and realizing continuous energization of port development and construction are fulfilled.
Therefore, there is a need for a bulk cargo port business process optimization method, which can help post personnel accurately manage cargoes in the actual operation process, prevent the bulk cargo port from stealing and leaking cargoes, and further assist the healthy and efficient operation of the bulk cargo port.
Disclosure of Invention
The invention mainly aims to provide a bulk cargo port business process optimization method, device, equipment and storage medium, aiming at reducing post personnel and realizing healthy and efficient operation of a bulk cargo port.
In order to achieve the above purpose, the present invention provides a bulk port business process optimization method, which comprises the following steps:
receiving sensor data of each item in the working vehicle, correlating service data in a service system, and forming a statistical data set based on the sensor data and the service data;
extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period;
analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result;
and generating alarm information according to the analysis result and the grade rule, and feeding back the alarm information to post personnel so that the post personnel can manage and control the operation track of the operation vehicle according to the alarm information.
Optionally, before the step of generating the alarm information according to the analysis result and the level rule, the method further includes:
mapping a harbor stockpiling overall view of a bulk cargo harbor by a GPS technology and a GIS technology to generate a stockpiling plan;
and detecting the position information of the working vehicle in the stacking plan in real time through an on-board GPS in the working vehicle, and displaying the position information in the stacking plan.
Optionally, the step of receiving each item of sensor data in the working vehicle and associating service data in a service system, and forming a statistical data set based on the sensor data and the service data includes:
in the vehicle operation process, acquiring various sensor data in each working vehicle according to license plate numbers of the working vehicles;
searching service data corresponding to the license plate number in a service system according to the license plate number, and correlating the sensor data corresponding to the license plate number with the service data to obtain correlation data;
and forming a statistical data set based on each associated data according to the time sequence.
Optionally, after the step of forming the statistical data set based on each of the associated data according to the chronological order, the method further includes:
intercepting the statistical data set according to preset time windows to obtain a statistical data subset corresponding to each preset time window, wherein the statistical data subset comprises associated data corresponding to a plurality of time points;
smoothing each associated data in each statistical data subset through a data smoothing algorithm to obtain a smoothed statistical data subset;
and combining the smoothed statistical data subsets to obtain a new statistical data set.
Optionally, the step of extracting target sensor data of a preset time period from the statistical data set and acquiring job data of the working vehicle within the preset time period includes:
continuously extracting target sensor data of a preset time period from the statistical data set according to a preset time interval;
determining the operation vehicle in the preset time period according to the license plate number corresponding to the target sensor data;
acquiring service data of each working vehicle in the preset time period, and generating the working data of the working vehicles in the preset time period according to the time sequence.
Optionally, the step of analyzing the target sensor data and the job data based on a preset data statistical analysis algorithm to obtain an analysis result includes:
extracting attribute information from the target sensor data and the job data;
counting the attribute values corresponding to the attribute information to obtain a characteristic value set;
comparing the attribute value corresponding to each attribute information in the characteristic value set with a preset attribute threshold value through a data judgment comparison model to obtain a comparison result;
and analyzing whether abnormal activities exist in the target sensor data and the operation data based on the comparison result to obtain an analysis result.
Optionally, the step of generating alarm information according to the analysis result and the level rule, and feeding back the alarm information to the post personnel includes:
if the analysis result shows that abnormal activity exists, determining the type corresponding to the abnormal activity;
taking a preset abnormal activity type-alarm level mapping table as a level rule, and determining an alarm level corresponding to the type according to the level rule;
and generating alarm information according to the alarm level, and feeding the alarm information back to post personnel.
In addition, in order to achieve the above purpose, the present invention also provides a bulk cargo port business process optimizing device, which comprises:
the data receiving module is used for receiving the sensor data of each item in the working vehicle, correlating the service data in the service system and forming a statistical data set based on the sensor data and the service data;
the data extraction module is used for extracting target sensor data of a preset time period from the statistical data set and acquiring the operation data of the operation vehicle in the preset time period;
the data analysis module is used for analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result;
and the data feedback module is used for generating alarm information according to the analysis result and the grade rule, and feeding the alarm information back to post personnel so that the post personnel can control the operation track of the operation vehicle according to the alarm information.
In addition, in order to achieve the above object, the present invention also provides a bulk port business process optimizing device, which includes: the system comprises a memory, a processor and a bulk port business process optimization program stored on the memory and operable on the processor, wherein the bulk port business process optimization program is configured to implement the steps of the bulk port business process optimization method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium, on which a bulk port business process optimization program is stored, which when executed by a processor implements the steps of the bulk port business process optimization method as described above.
The method comprises the steps of receiving sensor data of each item in a working vehicle, correlating service data in a service system, and forming a statistical data set based on the sensor data and the service data; extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period; analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result; and generating alarm information according to the analysis result and the grade rule, and feeding back the alarm information to post personnel so that the post personnel can manage and control the operation track of the operation vehicle according to the alarm information. Because the invention analyzes the target sensor data and the operation data through the preset data statistics analysis algorithm, generates the alarm information according to the analysis result and the grade rule, and feeds the alarm information back to the post personnel.
Drawings
Fig. 1 is a schematic structural diagram of bulk port business process optimization equipment in a hardware operation environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a bulk port business process optimization method according to a first embodiment of the invention;
FIG. 3 is a flow chart of a second embodiment of the bulk port business process optimization method of the present invention;
FIG. 4 is a flow chart of a third embodiment of the bulk port business process optimization method of the present invention;
fig. 5 is a block diagram of a bulk port business process optimizing apparatus according to a first embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a bulk port business process optimization device in a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 1, the bulk port business process optimization device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the bulk port business process optimization device, and may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include an operating system, a network communication module, a user interface module, and a bulk port business process optimization program.
In the bulk port business process optimization device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the bulk cargo port business process optimizing device of the present invention may be disposed in the bulk cargo port business process optimizing device, where the bulk cargo port business process optimizing device invokes the bulk cargo port business process optimizing program stored in the memory 1005 through the processor 1001, and executes the bulk cargo port business process optimizing method provided by the embodiment of the present invention.
The embodiment of the invention provides a bulk cargo port business process optimization method, and referring to fig. 2, fig. 2 is a flow diagram of a first embodiment of the bulk cargo port business process optimization method.
In this embodiment, the bulk port business process optimization method includes the following steps:
step S10: and receiving various sensor data in the working vehicle, correlating service data in a service system, and forming a statistical data set based on the sensor data and the service data.
It should be noted that, the execution body of the embodiment may be a computer server device with functions of data processing, network communication and program running, for example, a server, a tablet computer, a personal computer, an ipad, or an electronic device, a bulk port business process optimizing device, or the like, which can implement the above functions. The following exemplifies this embodiment and the following embodiments by taking bulk port business process optimization equipment as an example.
It will be appreciated that the work vehicle may be a vehicle that is loaded and unloaded in a bulk port, and that the business system may be a system that counts business data for details of the loading and unloading of the work vehicle.
It should be explained that step S10 includes:
step S101: and in the vehicle operation process, acquiring various sensor data in each working vehicle according to the license plate number of each working vehicle.
Step S102: and searching service data corresponding to the license plate number in the service system according to the license plate number, and correlating the sensor data corresponding to the license plate number with the service data to obtain correlation data.
Step S103: and forming a statistical data set based on each associated data according to the time sequence.
Step S20: and extracting target sensor data of a preset time period from the statistical data set, and acquiring the operation data of the operation vehicle in the preset time period.
It should be appreciated that the target sensor data described above may be sensor data in which the statistical data is concentrated over a preset period of time.
It should be explained that the job data of a single work vehicle at a single time node may be defined as: in the form of a= < k11, k12, & gt, k1n >, the data can comprise attribute information data such as event ID, number of vehicles, license plate number, lifting state, tonnage of load, vehicle state, job ticket number, longitude and latitude of GPS and the like.
It should be noted that the job data information of all the work vehicles received on a single job date may be represented by the data set a and the number in the following form: < L > = [ < A1> n, < A2> n, < An > n ], the data set of all work vehicles on the day of work is a (< L >) =a1U A U a 1..uam, i.e., the union of the work data of all work vehicles on the day of work, and further the activity data of each vehicle in the vehicle data set is not repeated.
It can be appreciated that, in order to implement effective management and control of the bulk port business process, step S20 includes:
step S201: continuously extracting target sensor data of a preset time period from the statistical data set according to a preset time interval.
It should be noted that the above-mentioned preset time interval may be a post personnel custom setting, for example, 1 minute, 10 minutes, etc., and the present embodiment and the following embodiments are not limited thereto. The predetermined time period may be a time period customized by a post person, for example, 10 seconds, 20 seconds, etc., which is not limited in this embodiment and the following embodiments.
Step S202: and determining the working vehicle in the preset time period according to the license plate number corresponding to the target sensor data.
Step S203: acquiring service data of each working vehicle in the preset time period, and generating the working data of the working vehicles in the preset time period according to the time sequence.
It should be explained that, in order to support the subsequent post personnel to play back the historical track, the operation data of the operation vehicle in the preset time period may be represented in the following form: < L > = [ < A1> n, < A2> n, < An > n ] form a series of track information.
Step S30: and analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result.
It should be explained that the preset data statistics analysis algorithm includes a vehicle operation data analysis processing technology based on quantity statistics, a vehicle operation data analysis processing technology based on data change trend statistics, and a vehicle operation data filtering technology based on feature attribute statistics.
It should be noted that, the vehicle operation data analysis processing technique based on quantity statistics may be to count several specific attributes in operation data of the operation vehicle, for example, m= (M1, M2,..once, mn), sort and count the operation data according to the data quantity F by time, automatically identify and count the operation data according to the attributes, and the collection of the statistics is, for example, aa=a F ∪A S ∪A R ∪A F 。
The vehicle operation data analysis processing technology based on the data change trend statistics can be to statistically analyze the weight ton attribute value in the operation data of the operation vehicle through the duration S, extract the data of a certain time window S1 AS a data set m= (M1, M2, the..mn) AS data smoothing processing, extract the data set s2= (S1, S2, the..st) of a certain time window S2, and automatically identify and fit into an as=ks+b linear curve according to the duration sorting and statistical analysis data.
The vehicle operation data filtering technology based on feature attribute statistics may be a set of operation data m= (M1, M2, mn.) of the operation vehicle extracted for a certain period S, sorting and statistical analysis are performed according to the number of data F according to time, for example, automatic identification related to lifting times, vehicle movement distance statistics, vehicle communication state statistics, whether a cargo handling area is or not, etc., and statistics are performed according to attributes, and the statistics form a feature value set, for example aa=a F ∪A S ∪A R ∪A F 。
And comparing the threshold values with the characteristic value set through a data judging and comparing model so as to determine whether alarm information is formed or not or generate corresponding tally service data.
In a specific implementation, attribute information may be extracted from the target sensor data and the job data; counting the attribute values corresponding to the attribute information to obtain a characteristic value set; comparing the attribute value corresponding to each attribute information in the characteristic value set with a preset attribute threshold value through a data judgment comparison model to obtain a comparison result; and analyzing whether abnormal activities exist in the target sensor data and the operation data based on the comparison result to obtain an analysis result.
Step S40: and generating alarm information according to the analysis result and the grade rule, and feeding back the alarm information to post personnel so that the post personnel can manage and control the operation track of the operation vehicle according to the alarm information.
It should be noted that, if the analysis result is that there is abnormal activity, determining a type corresponding to the abnormal activity; taking a preset abnormal activity type-alarm level mapping table as a level rule, and determining an alarm level corresponding to the type according to the level rule; and generating alarm information according to the alarm level, and feeding the alarm information back to post personnel.
It should be explained that after the alarm information is generated according to the alarm level, the operator can be notified by using the voice terminal, the operator is prompted to process the alarm by using voice, and the operator can extract the historical data based on any time period T to replay the operation track for further judgment and processing, so as to achieve the purpose of flow control.
It should be understood that if the analysis result indicates that no abnormal activity exists, corresponding loading tally data is generated according to normal loading and corresponding unloading tally data is generated according to normal unloading, and finally operation tally data is generated according to the loading tally data and the unloading tally data of each operation vehicle, so that the purpose of intelligent tally is achieved.
It will be appreciated that the operator may be the driver of the work vehicle and the post personnel may be the manager of the bulk port.
The method comprises the steps of receiving sensor data of each item in a working vehicle, associating service data in a service system, and forming a statistical data set based on the sensor data and the service data; extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period; analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result; if the analysis result shows that abnormal activity exists, determining the type corresponding to the abnormal activity, taking a preset abnormal activity type-alarm level mapping table as a level rule, determining an alarm level corresponding to the type according to the level rule, generating alarm information according to the alarm level, and feeding back the alarm information to post personnel; and if the analysis result shows that no abnormal activity exists, generating corresponding loading tally data according to normal loading and corresponding unloading tally data according to normal unloading, and finally generating operation tally data according to the loading tally data and the unloading tally data of each operation vehicle so as to realize the purpose of intelligent tally. Because the target sensor data and the operation data are analyzed through the preset data statistics analysis algorithm, the alarm information is generated according to the analysis result and the grade rule, and the alarm information is fed back to the post personnel.
Referring to fig. 3, fig. 3 is a flow chart of a second embodiment of the bulk port service flow optimization method according to the present invention.
Based on the first embodiment, in this embodiment, before step S40, the method further includes:
step S311: and mapping the harbor stockpiling overall view of the bulk cargo harbor by using a GPS technology and a GIS technology to generate a stockpiling plan.
It should be noted that GPS technology, collectively known as the global positioning system, provides accurate geographic location, vehicle speed, and accurate time information anywhere in the world as well as in near-earth space.
GIS technology is known as geographic information system, that is, geographic information science. In the embodiment, based on the visual management map realized by GIS, the full view of the harbor stockpiling is visually displayed, the stockpiling management and the loading and unloading operation are closely related, and the stockpiling plan is generated in real time according to the change of the basic data, so that the stockpiling and supervision are facilitated; visual management of harbor inventory shipment, harbor departure, stack shifting and the like is realized; the electronic fence can be realized, a warehouse is defined on the map, and the map supervision is realized; the trailer path is intelligently planned, and the operation track of the trailer in the field is visually presented.
Step S312: and detecting the position information of the working vehicle in the stacking plan in real time through an on-board GPS in the working vehicle, and displaying the position information in the stacking plan.
It can be understood that based on technical means such as GPS technology and GIS technology to combine together with on-vehicle GPS and demonstrate pier storehouse operation execution process in real time, based on piling plan view with the business operation of pier, resource management, safety management, inside and outside trade supervision etc. multiple business forms realize graphical operation and intelligent management, let the business operation of traditional bulk cargo harbour more high-efficient, intelligent, convenient, use manpower sparingly and input.
The method comprises the steps of receiving sensor data of each item in a working vehicle, associating service data in a service system, and forming a statistical data set based on the sensor data and the service data; extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period; analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result; mapping a harbor stockpiling overall view of a bulk cargo harbor by a GPS technology and a GIS technology to generate a stockpiling plan; detecting the position information of the working vehicle in the stacking plan in real time through a vehicle-mounted GPS in the working vehicle, and displaying the position information in the stacking plan; and generating alarm information according to the analysis result and the grade rule, and feeding back the alarm information to post personnel so that the post personnel can manage and control the operation track of the operation vehicle according to the alarm information. Because the embodiment is based on the GPS technology and the GIS technology, and the operation execution process of the wharf yard is displayed in real time by combining with the vehicle-mounted GPS, the graphical operation and intelligent management are realized on the basis of a stacking plan view in various business forms such as business operation, resource management, safety management, internal and external trade supervision and the like of the wharf, so that the business operation of the traditional bulk port is more efficient, intelligent and convenient, and labor and investment are saved.
Referring to fig. 4, fig. 4 is a flow chart of a third embodiment of the bulk port service flow optimization method according to the present invention.
Based on the above embodiments, in this embodiment, after step S103, the method further includes:
step S1031: intercepting the statistical data set according to preset time windows to obtain a statistical data subset corresponding to each preset time window, wherein the statistical data subset comprises associated data corresponding to a plurality of time points.
Step S1032: and smoothing each associated data in each statistical data subset through a data smoothing algorithm to obtain a smoothed statistical data subset.
Step S1033 combines the smoothed statistical data subsets to obtain a new statistical data set.
The method comprises the steps of receiving sensor data of each item in a working vehicle, associating service data in a service system, and forming a statistical data set based on the sensor data and the service data; intercepting the statistical data set according to preset time windows to obtain a statistical data subset corresponding to each preset time window, wherein the statistical data subset comprises associated data corresponding to a plurality of time points; smoothing each associated data in each statistical data subset to obtain a smoothed statistical data subset; and combining the smoothed statistical data subsets to obtain a new statistical data set. Compared with the prior art, the data reliability is guaranteed by using the smoothing algorithm to smooth the data so as to correct the burr data, and the accuracy of analyzing the target sensor data and the operation data is improved.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a bulk port business process optimization program, and the bulk port business process optimization program realizes the steps of the bulk port business process optimization method when being executed by a processor.
Referring to fig. 5, fig. 5 is a block diagram illustrating a first embodiment of a bulk port transaction process optimization device according to the present invention.
As shown in fig. 5, the bulk port business process optimizing device provided by the embodiment of the invention includes: a data receiving module 501, a data extracting module 502, a data analyzing module 503 and a data feedback module 504.
The data receiving module 501 is configured to receive sensor data of each item in the working vehicle, and associate service data in a service system, and form a statistical data set based on the sensor data and the service data.
The data extraction module 502 is configured to extract target sensor data of a preset time period from the statistical data set, and obtain operation data of the operation vehicle in the preset time period.
The data analysis module 503 is configured to analyze the target sensor data and the job data based on a preset data statistical analysis algorithm, so as to obtain an analysis result.
The data feedback module 504 is configured to generate alarm information according to the analysis result and the level rule, and feed back the alarm information to a post personnel, so that the post personnel manages and controls the operation track of the operation vehicle according to the alarm information.
The data feedback module 504 is further configured to determine a type corresponding to the abnormal activity if the analysis result indicates that the abnormal activity exists; taking a preset abnormal activity type-alarm level mapping table as a level rule, and determining an alarm level corresponding to the type according to the level rule; and generating alarm information according to the alarm level, and feeding the alarm information back to post personnel.
The data feedback module 504 is further configured to generate corresponding loading tally data according to normal loading and corresponding unloading tally data according to normal unloading if the analysis result indicates that no abnormal activity exists, and finally generate operation tally data according to the loading tally data and the unloading tally data of each operation vehicle, so as to achieve the purpose of intelligent tally.
The method comprises the steps of receiving sensor data of each item in a working vehicle, associating service data in a service system, and forming a statistical data set based on the sensor data and the service data; extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period; analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result; if the analysis result shows that abnormal activity exists, determining the type corresponding to the abnormal activity, taking a preset abnormal activity type-alarm level mapping table as a level rule, determining an alarm level corresponding to the type according to the level rule, generating alarm information according to the alarm level, and feeding back the alarm information to post personnel; and if the analysis result shows that no abnormal activity exists, generating corresponding loading tally data according to normal loading and corresponding unloading tally data according to normal unloading, and finally generating operation tally data according to the loading tally data and the unloading tally data of each operation vehicle so as to realize the purpose of intelligent tally. Because the target sensor data and the operation data are analyzed through the preset data statistics analysis algorithm, the alarm information is generated according to the analysis result and the grade rule, and the alarm information is fed back to the post personnel.
Based on the first embodiment of the bulk cargo port business process optimizing device of the present invention, a second embodiment of the bulk cargo port business process optimizing device of the present invention is provided.
In this embodiment, the data feedback module 504 is further configured to map a harbor stockpiling overview of the bulk cargo harbor by using a GPS technology and a GIS technology, so as to generate a stockpiling plan; and detecting the position information of the working vehicle in the stacking plan in real time through an on-board GPS in the working vehicle, and displaying the position information in the stacking plan.
Other embodiments or specific implementation manners of the bulk cargo port business process optimizing device of the present invention may refer to the above method embodiments, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The bulk cargo port business process optimization method is characterized by comprising the following steps of:
receiving sensor data of each item in the working vehicle, correlating service data in a service system, and forming a statistical data set based on the sensor data and the service data;
extracting target sensor data of a preset time period from the statistical data set, and acquiring operation data of the operation vehicle in the preset time period;
analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result;
and generating alarm information according to the analysis result and the grade rule, and feeding back the alarm information to post personnel so that the post personnel can manage and control the operation track of the operation vehicle according to the alarm information.
2. The method for optimizing the business process of the bulk port of claim 1, wherein before the step of generating the alarm information according to the level rule based on the analysis result, the method further comprises:
mapping a harbor stockpiling overall view of a bulk cargo harbor by a GPS technology and a GIS technology to generate a stockpiling plan;
and detecting the position information of the working vehicle in the stacking plan in real time through an on-board GPS in the working vehicle, and displaying the position information in the stacking plan.
3. The bulk port transaction process optimization method according to claim 1, wherein the step of receiving each item of sensor data in the operation vehicle and correlating the transaction data in the transaction system, and forming a statistical data set based on the sensor data and the transaction data, includes:
in the vehicle operation process, acquiring various sensor data in each working vehicle according to license plate numbers of the working vehicles;
searching service data corresponding to the license plate number in a service system according to the license plate number, and correlating the sensor data corresponding to the license plate number with the service data to obtain correlation data;
and forming a statistical data set based on each associated data according to the time sequence.
4. The bulk port liner process optimization method of claim 3, further comprising, after the step of forming a statistical dataset based on each of the associated data in chronological order:
intercepting the statistical data set according to preset time windows to obtain a statistical data subset corresponding to each preset time window, wherein the statistical data subset comprises associated data corresponding to a plurality of time points;
smoothing each associated data in each statistical data subset through a data smoothing algorithm to obtain a smoothed statistical data subset;
and combining the smoothed statistical data subsets to obtain a new statistical data set.
5. The bulk port business process optimization method of claim 3, wherein the step of extracting target sensor data for a preset time period from the statistical data set and acquiring the operation data of the operation vehicle for the preset time period comprises the steps of:
continuously extracting target sensor data of a preset time period from the statistical data set according to a preset time interval;
determining the operation vehicle in the preset time period according to the license plate number corresponding to the target sensor data;
acquiring service data of each working vehicle in the preset time period, and generating the working data of the working vehicles in the preset time period according to the time sequence.
6. The bulk port business process optimization method according to claim 1, wherein the step of analyzing the target sensor data and the job data based on a preset data statistical analysis algorithm to obtain an analysis result comprises:
extracting attribute information from the target sensor data and the job data;
counting the attribute values corresponding to the attribute information to obtain a characteristic value set;
comparing the attribute value corresponding to each attribute information in the characteristic value set with a preset attribute threshold value through a data judgment comparison model to obtain a comparison result;
and analyzing whether abnormal activities exist in the target sensor data and the operation data based on the comparison result to obtain an analysis result.
7. The bulk port business process optimization method of claim 6, wherein the step of generating alarm information according to the analysis result and the level rule, and feeding back the alarm information to the post personnel comprises the steps of:
if the analysis result shows that abnormal activity exists, determining the type corresponding to the abnormal activity;
taking a preset abnormal activity type-alarm level mapping table as a level rule, and determining an alarm level corresponding to the type according to the level rule;
and generating alarm information according to the alarm level, and feeding the alarm information back to post personnel.
8. A bulk port business process optimization device, the device comprising:
the data receiving module is used for receiving the sensor data of each item in the working vehicle, correlating the service data in the service system and forming a statistical data set based on the sensor data and the service data;
the data extraction module is used for extracting target sensor data of a preset time period from the statistical data set and acquiring the operation data of the operation vehicle in the preset time period;
the data analysis module is used for analyzing the target sensor data and the operation data based on a preset data statistical analysis algorithm to obtain an analysis result;
and the data feedback module is used for generating alarm information according to the analysis result and the grade rule, and feeding the alarm information back to post personnel so that the post personnel can control the operation track of the operation vehicle according to the alarm information.
9. Bulk port business process optimization equipment, characterized in that it comprises: memory, a processor and a bulk port business process optimization program stored on the memory and operable on the processor, the bulk port business process optimization program being configured to implement the steps of the bulk port business process optimization method of any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a bulk port service flow optimization program, which when executed by a processor, implements the steps of the bulk port service flow optimization method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311682919.9A CN117726028A (en) | 2023-12-08 | 2023-12-08 | Bulk cargo port business process optimization method, device, equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311682919.9A CN117726028A (en) | 2023-12-08 | 2023-12-08 | Bulk cargo port business process optimization method, device, equipment and storage medium |
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| CN117726028A true CN117726028A (en) | 2024-03-19 |
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| CN202311682919.9A Pending CN117726028A (en) | 2023-12-08 | 2023-12-08 | Bulk cargo port business process optimization method, device, equipment and storage medium |
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| Country | Link |
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| CN (1) | CN117726028A (en) |
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- 2023-12-08 CN CN202311682919.9A patent/CN117726028A/en active Pending
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