CN118037198B - Event-related article management method, device, equipment and medium - Google Patents
Event-related article management method, device, equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, and provides an event-related article management method, device, equipment and medium, which can acquire related article data of historical events as a clustering sample, perform clustering operation on the clustering sample to obtain each article class, realize accurate classification of articles, further configure a warehousing strategy of each article class so as to perform targeted warehousing processing on different types of articles, respond to a warehousing request for a target article, determine the article class to which the target article belongs as the target class, acquire the warehousing strategy corresponding to the target class as the target strategy, and utilize the target strategy to perform warehousing processing on the target article, thereby realizing effective management on the event-related article.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for managing an event-related item.
Background
For safety events such as unlicensed driving and drunk driving, various articles are involved in the event processing process, so how to effectively manage the articles becomes a problem to be solved.
However, in the prior art, management of articles still mainly depends on subjective consciousness of users, so that article management is disordered, recorded information is easy to redundant or miss, and error rate is high.
And, because of information confusion, the follow-up is also more inconvenient when invoking the article.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, device and medium for event-related item management, which aims to solve the problem of confusion of event-related item management.
An event-related item management method, the event-related item management method comprising:
Acquiring associated article data of historical events as a clustering sample, and performing clustering operation on the clustering sample to obtain each article;
configuring a warehousing strategy of each object;
responding to a warehouse-in request of a target object, and determining an object class to which the target object belongs as a target class;
and acquiring a warehousing strategy corresponding to the target class as a target strategy, and warehousing the target object by utilizing the target strategy.
According to a preferred embodiment of the present invention, the performing a clustering operation on the clustered samples to obtain each article class includes:
Acquiring sample characteristics of each clustered sample;
preprocessing sample characteristics of each clustered sample to obtain target characteristics of each clustered sample;
randomly selecting a first number of cluster samples from each cluster sample as a first cluster center;
constructing a distance formula by using an exponential function;
calculating the distance from each clustering sample to each first clustering center based on the distance formula and the target feature of each clustering sample;
Distributing each cluster sample to a cluster corresponding to a first cluster center with the shortest distance;
Traversing and calculating the distances from all the cluster samples in each cluster to other clusters according to the distance formula;
Merging the two clusters with the shortest distance to update the clusters;
Updating the first cluster center according to the updated cluster center of each cluster;
Continuously updating the first clustering centers until convergence and the number of the first clustering centers is a second number, stopping updating the first clustering centers, and determining each first clustering center obtained currently as each object type;
Wherein the first number is greater than the second number.
According to a preferred embodiment of the present invention, the preprocessing the sample characteristics of each clustered sample to obtain target characteristics of each clustered sample includes:
Acquiring numerical value characteristics, character type characteristics and non-numerical value character characteristics in sample characteristics of each cluster sample;
Calculating the average value of each numerical characteristic to obtain each first characteristic;
Acquiring the mode of each character type feature as each second feature;
encoding each non-numerical character feature to obtain each third feature;
And combining the first feature, the second feature and the third feature corresponding to each cluster sample to obtain the target feature of each cluster sample.
According to a preferred embodiment of the present invention, the constructing a distance formula using an exponential function includes:
Constructing weight factors by using the exponential function;
Calculating the Hamming distance and the Euclidean distance between every two clustering samples;
Calculating the product between the weight factor and the hamming distance as a first distance;
and calculating the sum of the first distance and the Euclidean distance to obtain the distance formula.
According to a preferred embodiment of the present invention, the performing the warehousing processing on the target object by using the target policy includes:
determining warehouse-in information to be uploaded and warehouse-in positions according to the target strategy;
Displaying an uploading prompt for the warehouse entry information to be uploaded on a configuration interface;
When a warehouse-in information confirmation instruction of the target object is received, carrying out integrity check on the received warehouse-in information to obtain a check result;
collecting an image of the warehousing position as an image to be identified, and carrying out image identification on the image to be identified to obtain an identification result;
when the verification result shows that the received warehousing information is complete and the identification result shows that the target object is placed at the warehousing position, generating confirmation feedback for the warehousing information; or alternatively
When the verification result shows that the received warehouse-in information is incomplete, sending an uploading prompt for the missing information; and/or generating prompt information for placing the target object at the warehousing position when the identification result shows that the target object is not placed at the warehousing position.
According to a preferred embodiment of the present invention, after the target policy is used to perform warehousing processing on the target object, the method further includes:
When a remote evidence indication instruction for the target object is received, acquiring each associated object of the target object, and generating a selection prompt box according to each associated object;
Detecting a selection instruction of the selection prompt box, and acquiring a current article from each associated article according to the selection instruction;
generating an item list according to the target item and the current item;
article information of each article in the article list is called;
and sending the article information of each article to a trigger of the remote evidence instruction.
According to a preferred embodiment of the present invention, after the target policy is used to perform warehousing processing on the target object, the method further includes:
when receiving a delivery instruction for the target object, analyzing the delivery instruction to obtain a delivery reason;
and selecting article information from the article information of the target article according to the ex-warehouse reasons for displaying.
An event-related item management apparatus, the event-related item management apparatus comprising:
the clustering unit is used for acquiring the associated article data of the historical event as a clustering sample, and carrying out clustering operation on the clustering sample to obtain each article;
The configuration unit is used for configuring the warehousing strategy of each object;
A determining unit, configured to determine, as a target class, an item class to which a target item belongs in response to a warehouse-in request for the target item;
And the warehousing unit is used for acquiring a warehousing strategy corresponding to the target class as a target strategy and warehousing the target object by utilizing the target strategy.
A computer device, the computer device comprising:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the event-associated article management method.
A computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the event-related item management method.
According to the technical scheme, the related article data of the historical event can be obtained to serve as a clustering sample, clustering operation is carried out on the clustering sample to obtain each article class, accurate classification of the articles is achieved, the warehousing strategy of each article class is further configured so that targeted warehousing processing is carried out on different types of articles, the article class to which the target article belongs is determined to serve as the target class in response to the warehousing request of the target article, the warehousing strategy corresponding to the target class is obtained to serve as the target strategy, and the target article is subjected to warehousing processing by utilizing the target strategy, so that effective management of the event related articles is achieved.
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FIG. 1 is a flow chart of a preferred embodiment of the event related item management method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the event-related item management device of the present invention.
FIG. 3 is a schematic diagram of a computer device implementing a preferred embodiment of the event-related item management method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a preferred embodiment of the event related item management method of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The event-related article management method is applied to one or more computer devices, wherein the computer device is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the computer device comprises, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device and the like.
The computer device may be any electronic product that can interact with a user in a human-computer manner, such as a Personal computer, a tablet computer, a smart phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.
The computer device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.
The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.
S10, acquiring associated article data of the historical event as a clustering sample, and performing clustering operation on the clustering sample to obtain each article.
In this embodiment, the history event may include, but is not limited to: traffic events such as drunk driving and driving without license, and security events such as frame beating.
In this embodiment, the associated item data may include data of a name, an image, a number, and the like of the associated item of each event.
In this embodiment, performing a clustering operation on the clustered samples to obtain each article class includes:
Acquiring sample characteristics of each clustered sample;
preprocessing sample characteristics of each clustered sample to obtain target characteristics of each clustered sample;
randomly selecting a first number of cluster samples from each cluster sample as a first cluster center;
constructing a distance formula by using an exponential function;
calculating the distance from each clustering sample to each first clustering center based on the distance formula and the target feature of each clustering sample;
Distributing each cluster sample to a cluster corresponding to a first cluster center with the shortest distance;
Traversing and calculating the distances from all the cluster samples in each cluster to other clusters according to the distance formula;
Merging the two clusters with the shortest distance to update the clusters;
Updating the first cluster center according to the updated cluster center of each cluster;
Continuously updating the first clustering centers until convergence and the number of the first clustering centers is a second number, stopping updating the first clustering centers, and determining each first clustering center obtained currently as each object type;
Wherein the first number is greater than the second number.
In the above embodiment, unlike the number of initial cluster centers randomly selected in the existing clustering algorithm, which is the same as the number of clusters finally obtained, the number of cluster centers initially selected in the embodiment is much larger than the number of clusters finally obtained, so that more sample features can be covered in the clustering process, and the clustering result is more reasonable.
The preprocessing the sample characteristics of each clustered sample to obtain target characteristics of each clustered sample includes:
Acquiring numerical value characteristics, character type characteristics and non-numerical value character characteristics in sample characteristics of each cluster sample;
Calculating the average value of each numerical characteristic to obtain each first characteristic;
Acquiring the mode of each character type feature as each second feature;
encoding each non-numerical character feature to obtain each third feature;
And combining the first feature, the second feature and the third feature corresponding to each cluster sample to obtain the target feature of each cluster sample.
For example: the numerical type features may include item search heat, item number, etc., the character type features may include item authenticity, etc., and the non-numerical character features may include item description, item image, etc.
By preprocessing the sample characteristics, the formats of the sample characteristics can be unified so as to facilitate subsequent calculation.
In addition, in the conventional clustering algorithm, only the common algorithms such as hamming distance and euclidean distance are adopted to calculate the distance between samples, and the difference between samples is usually ignored. Therefore, in this embodiment, the calculation mode of optimizing the distance between samples by using the exponential function is adopted, and since the exponential function increases with the increase of the variable, the difference between samples can be effectively increased, and further, the accuracy of clustering can be improved, so that samples at the edge can be accurately classified into corresponding clusters.
Specifically, the constructing a distance formula using an exponential function includes:
Constructing weight factors by using the exponential function;
Calculating the Hamming distance and the Euclidean distance between every two clustering samples;
Calculating the product between the weight factor and the hamming distance as a first distance;
and calculating the sum of the first distance and the Euclidean distance to obtain the distance formula.
S11, configuring a warehousing strategy of each object.
In this embodiment, by configuring a warehousing policy for each object, warehousing processing can be performed for each object in a targeted manner.
S12, responding to a warehouse-in request of a target object, and determining the object class to which the target object belongs as a target class.
Wherein, the warehouse entry request can be triggered by a processor corresponding to the event.
The object class to which the target object belongs can be uploaded by the processing personnel, and an image of the target object can be acquired through an artificial intelligent model and detected.
S13, acquiring a warehousing strategy corresponding to the target class as a target strategy, and warehousing the target object by using the target strategy.
In this embodiment, the performing the warehousing processing on the target object by using the target policy includes:
determining warehouse-in information to be uploaded and warehouse-in positions according to the target strategy;
Displaying an uploading prompt for the warehouse entry information to be uploaded on a configuration interface;
When a warehouse-in information confirmation instruction of the target object is received, carrying out integrity check on the received warehouse-in information to obtain a check result;
collecting an image of the warehousing position as an image to be identified, and carrying out image identification on the image to be identified to obtain an identification result;
when the verification result shows that the received warehousing information is complete and the identification result shows that the target object is placed at the warehousing position, generating confirmation feedback for the warehousing information; or alternatively
When the verification result shows that the received warehouse-in information is incomplete, sending an uploading prompt for the missing information; and/or generating prompt information for placing the target object at the warehousing position when the identification result shows that the target object is not placed at the warehousing position.
For example: when the target object is a vehicle, the information to be uploaded into the garage can comprise license plate numbers, owner information, vehicle insurance information and the like, and the warehouse-in position can be a specified garage.
Through the embodiment, the missing information can be timely prompted when the warehouse-in information is incomplete, so that the article warehouse-in efficiency is improved, and the integrity of the warehouse-in information is ensured; meanwhile, whether the articles are put in storage is automatically detected by an artificial intelligence means, so that the omission of the articles is avoided.
In this embodiment, after the target policy is used to perform the warehousing processing on the target object, the method further includes:
When a remote evidence indication instruction for the target object is received, acquiring each associated object of the target object, and generating a selection prompt box according to each associated object;
Detecting a selection instruction of the selection prompt box, and acquiring a current article from each associated article according to the selection instruction;
generating an item list according to the target item and the current item;
article information of each article in the article list is called;
and sending the article information of each article to a trigger of the remote evidence instruction.
Through the embodiment, remote evidence showing of the articles can be supported, and related articles are automatically called for selection, so that the evidence showing efficiency is improved, and the problem that the articles cannot be shown due to regional differences is solved.
In this embodiment, after the target policy is used to perform the warehousing processing on the target object, the method further includes:
when receiving a delivery instruction for the target object, analyzing the delivery instruction to obtain a delivery reason;
and selecting article information from the article information of the target article according to the ex-warehouse reasons for displaying.
Through the embodiment, the article information can be displayed according to different ex-warehouse reasons, so that the problem that the user consults the article information due to the fact that the information is too much is avoided, the user who does not have relevant authority can be effectively avoided from consulting the article information, and the safety of the article information is improved.
In this embodiment, the modification authority of the article information may be configured for different types of users, so that the user may perform operations such as adding, deleting, modifying, and checking the article information according to actual situations, so as to perfect the article information.
In this embodiment, after the articles are put in storage, information such as the storage mode of the articles, the storage person, the contact mode of the storage person and the like can be recorded, so that the corresponding storage person can be found out quickly.
In this embodiment, for a damaged article, the damaged portion image may be recorded at the same time when the article is put in storage, for subsequent viewing.
According to the technical scheme, the related article data of the historical event can be obtained to serve as a clustering sample, clustering operation is carried out on the clustering sample to obtain each article class, accurate classification of the articles is achieved, the warehousing strategy of each article class is further configured so that targeted warehousing processing is carried out on different types of articles, the article class to which the target article belongs is determined to serve as the target class in response to the warehousing request of the target article, the warehousing strategy corresponding to the target class is obtained to serve as the target strategy, and the target article is subjected to warehousing processing by utilizing the target strategy, so that effective management of the event related articles is achieved.
FIG. 2 is a functional block diagram of a preferred embodiment of the event related item management device of the present invention. The event-related item management device 11 includes a clustering unit 110, a configuration unit 111, a determination unit 112, and a warehouse-in unit 113. The module/unit referred to in the present invention refers to a series of computer program segments, which are stored in a memory, capable of being executed by a processor and of performing a fixed function. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
The clustering unit 110 is configured to obtain related item data of a historical event as a clustering sample, and perform a clustering operation on the clustering sample to obtain each item;
the configuration unit 111 is configured to configure a warehousing policy of each object class;
the determining unit 112 is configured to determine, as a target class, an item class to which the target item belongs in response to a warehouse-in request for the target item;
the warehousing unit 113 is configured to obtain a warehousing policy corresponding to the target class as a target policy, and perform warehousing processing on the target object by using the target policy.
According to the technical scheme, the related article data of the historical event can be obtained to serve as a clustering sample, clustering operation is carried out on the clustering sample to obtain each article class, accurate classification of the articles is achieved, the warehousing strategy of each article class is further configured so that targeted warehousing processing is carried out on different types of articles, the article class to which the target article belongs is determined to serve as the target class in response to the warehousing request of the target article, the warehousing strategy corresponding to the target class is obtained to serve as the target strategy, and the target article is subjected to warehousing processing by utilizing the target strategy, so that effective management of the event related articles is achieved.
FIG. 3 is a schematic diagram of a computer device for implementing a preferred embodiment of the event-related item management method of the present invention.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as an event-related item management program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation of the computer device 1, the computer device 1 may be a bus type structure, a star type structure, the computer device 1 may further comprise more or less other hardware or software than illustrated, or a different arrangement of components, for example, the computer device 1 may further comprise an input-output device, a network access device, etc.
It should be noted that the computer device 1 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, such as a removable hard disk of the computer device 1. The memory 12 may also be an external storage device of the computer device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 may be used not only for storing application software installed in the computer device 1 and various types of data, such as codes of event-related item management programs, but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the computer device 1, connects the respective components of the entire computer device 1 using various interfaces and lines, executes or executes programs or modules (for example, executes event-related item management programs or the like) stored in the memory 12, and invokes data stored in the memory 12 to perform various functions of the computer device 1 and process data.
The processor 13 executes the operating system of the computer device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps of the various event-related item management method embodiments described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a clustering unit 110, a configuration unit 111, a determination unit 112, a binning unit 113.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to execute portions of the event-related article management method according to the embodiments of the present invention.
The modules/units integrated in the computer device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one straight line is shown in fig. 3, but not only one bus or one type of bus. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further comprise a power source (such as a battery) for powering the various components, preferably the power source may be logically connected to the at least one processor 13 via a power management means, whereby the functions of charge management, discharge management, and power consumption management are achieved by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
Further, the computer device 1 may also comprise a network interface, optionally comprising a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the computer device 1 and other computer devices.
The computer device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the computer device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Fig. 3 shows only a computer device 1 with components 12-13, it being understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the computer device 1 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In connection with fig. 1, the memory 12 in the computer device 1 stores a plurality of instructions to implement an event-related item management method, the processor 13 being executable to implement:
Acquiring associated article data of historical events as a clustering sample, and performing clustering operation on the clustering sample to obtain each article;
configuring a warehousing strategy of each object;
responding to a warehouse-in request of a target object, and determining an object class to which the target object belongs as a target class;
and acquiring a warehousing strategy corresponding to the target class as a target strategy, and warehousing the target object by utilizing the target strategy.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
The data in this case were obtained legally.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The invention is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. An event-related item management method, characterized in that the event-related item management method comprises:
Acquiring associated article data of historical events as a clustering sample, and performing clustering operation on the clustering sample to obtain each article; wherein the history event comprises a traffic event and a security event; the associated article data comprises the name, the image and the quantity of the associated article of each event;
configuring a warehousing strategy of each object;
responding to a warehouse-in request of a target object, and determining an object class to which the target object belongs as a target class;
the warehousing strategy corresponding to the target class is obtained as a target strategy, and the target strategy is utilized to carry out warehousing treatment on the target object;
Wherein, the clustering operation is performed on the clustering samples, and obtaining each article class comprises: acquiring sample characteristics of each clustered sample; preprocessing sample characteristics of each clustered sample to obtain target characteristics of each clustered sample; randomly selecting a first number of cluster samples from each cluster sample as a first cluster center; constructing a distance formula by using an exponential function; calculating the distance from each clustering sample to each first clustering center based on the distance formula and the target feature of each clustering sample; distributing each cluster sample to a cluster corresponding to a first cluster center with the shortest distance; traversing and calculating the distances from all the cluster samples in each cluster to other clusters according to the distance formula; merging the two clusters with the shortest distance to update the clusters; updating the first cluster center according to the updated cluster center of each cluster; continuously updating the first clustering centers until convergence and the number of the first clustering centers is a second number, stopping updating the first clustering centers, and determining each first clustering center obtained currently as each object type; wherein the first number is greater than the second number;
The preprocessing the sample characteristics of each clustered sample to obtain target characteristics of each clustered sample includes: acquiring numerical value characteristics, character type characteristics and non-numerical value character characteristics in sample characteristics of each cluster sample; calculating the average value of each numerical characteristic to obtain each first characteristic; acquiring the mode of each character type feature as each second feature; encoding each non-numerical character feature to obtain each third feature; combining the first feature, the second feature and the third feature corresponding to each cluster sample to obtain the target feature of each cluster sample;
Wherein the constructing a distance formula using an exponential function comprises: constructing weight factors by using the exponential function; calculating the Hamming distance and the Euclidean distance between every two clustering samples; calculating the product between the weight factor and the hamming distance as a first distance; and calculating the sum of the first distance and the Euclidean distance to obtain the distance formula.
2. The event-related item management method of claim 1, wherein said utilizing said target policy to perform a warehousing process on said target item comprises:
determining warehouse-in information to be uploaded and warehouse-in positions according to the target strategy;
Displaying an uploading prompt for the warehouse entry information to be uploaded on a configuration interface;
When a warehouse-in information confirmation instruction of the target object is received, carrying out integrity check on the received warehouse-in information to obtain a check result;
collecting an image of the warehousing position as an image to be identified, and carrying out image identification on the image to be identified to obtain an identification result;
when the verification result shows that the received warehousing information is complete and the identification result shows that the target object is placed at the warehousing position, generating confirmation feedback for the warehousing information; or alternatively
When the verification result shows that the received warehouse-in information is incomplete, sending an uploading prompt for the missing information; and/or generating prompt information for placing the target object at the warehousing position when the identification result shows that the target object is not placed at the warehousing position.
3. The event-related item management method according to claim 1, wherein after the target item is subjected to the warehouse entry processing using the target policy, the method further comprises:
When a remote evidence indication instruction for the target object is received, acquiring each associated object of the target object, and generating a selection prompt box according to each associated object;
Detecting a selection instruction of the selection prompt box, and acquiring a current article from each associated article according to the selection instruction;
generating an item list according to the target item and the current item;
article information of each article in the article list is called;
and sending the article information of each article to a trigger of the remote evidence instruction.
4. The event-related item management method according to claim 1, wherein after the target item is subjected to the warehouse entry processing using the target policy, the method further comprises:
when receiving a delivery instruction for the target object, analyzing the delivery instruction to obtain a delivery reason;
and selecting article information from the article information of the target article according to the ex-warehouse reasons for displaying.
5. An event-related article management apparatus, characterized by comprising:
The clustering unit is used for acquiring the associated article data of the historical event as a clustering sample, and carrying out clustering operation on the clustering sample to obtain each article; wherein the history event comprises a traffic event and a security event; the associated article data comprises the name, the image and the quantity of the associated article of each event;
The configuration unit is used for configuring the warehousing strategy of each object;
A determining unit, configured to determine, as a target class, an item class to which a target item belongs in response to a warehouse-in request for the target item;
The warehousing unit is used for acquiring a warehousing strategy corresponding to the target class as a target strategy and warehousing the target object by utilizing the target strategy;
Wherein, the clustering operation is performed on the clustering samples, and obtaining each article class comprises: acquiring sample characteristics of each clustered sample; preprocessing sample characteristics of each clustered sample to obtain target characteristics of each clustered sample; randomly selecting a first number of cluster samples from each cluster sample as a first cluster center; constructing a distance formula by using an exponential function; calculating the distance from each clustering sample to each first clustering center based on the distance formula and the target feature of each clustering sample; distributing each cluster sample to a cluster corresponding to a first cluster center with the shortest distance; traversing and calculating the distances from all the cluster samples in each cluster to other clusters according to the distance formula; merging the two clusters with the shortest distance to update the clusters; updating the first cluster center according to the updated cluster center of each cluster; continuously updating the first clustering centers until convergence and the number of the first clustering centers is a second number, stopping updating the first clustering centers, and determining each first clustering center obtained currently as each object type; wherein the first number is greater than the second number;
The preprocessing the sample characteristics of each clustered sample to obtain target characteristics of each clustered sample includes: acquiring numerical value characteristics, character type characteristics and non-numerical value character characteristics in sample characteristics of each cluster sample; calculating the average value of each numerical characteristic to obtain each first characteristic; acquiring the mode of each character type feature as each second feature; encoding each non-numerical character feature to obtain each third feature; combining the first feature, the second feature and the third feature corresponding to each cluster sample to obtain the target feature of each cluster sample;
Wherein the constructing a distance formula using an exponential function comprises: constructing weight factors by using the exponential function; calculating the Hamming distance and the Euclidean distance between every two clustering samples; calculating the product between the weight factor and the hamming distance as a first distance; and calculating the sum of the first distance and the Euclidean distance to obtain the distance formula.
6. A computer device, the computer device comprising:
a memory storing at least one instruction; and
A processor executing instructions stored in the memory to implement the event-related item management method of any of claims 1 to 4.
7. A computer-readable storage medium, characterized by: the computer-readable storage medium has stored therein at least one instruction that is executed by a processor in a computer device to implement the event-related item management method of any of claims 1 to 4.
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