CN113780915A - Service docking method and device - Google Patents
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- CN113780915A CN113780915A CN202011355350.1A CN202011355350A CN113780915A CN 113780915 A CN113780915 A CN 113780915A CN 202011355350 A CN202011355350 A CN 202011355350A CN 113780915 A CN113780915 A CN 113780915A
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- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
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- G06Q30/00—Commerce
- G06Q30/01—Customer relationship services
- G06Q30/012—Providing warranty services
Abstract
The invention discloses a service docking method and a service docking device, and relates to the technical field of computers. One embodiment of the method comprises: acquiring scene description information of a user; according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not; inputting the verified scene description information into a preset docking recommendation model, and determining a target docking service corresponding to the scene description information. The method and the device for realizing the service docking can automatically determine the service corresponding to the appeal of the user, reduce the service docking threshold, improve the accuracy of service docking, reduce the communication cost in the service docking process and improve the user experience.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a service docking method and apparatus.
Background
As market demands gradually change to diversification and specialization, the degree of professional division of labor and social cooperation is continuously deepened, and more merchants cooperate with professional logistics enterprises to provide related services, such as transportation, storage, inventory management, order management, information integration, added value and the like.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: when the merchants cooperate with the logistics enterprises, the staff of different departments of the logistics enterprises are responsible for docking the respective merchants. For the business which is not docked with the logistics enterprise before, the business description and docking can be performed by only approximately finding the staff of a certain department, which depends on the professional ability of the staff. If the staff does not have full knowledge of the business, some adverse consequences can be caused easily. Moreover, the docking threshold is greatly improved depending on the professional knowledge ability of the workers.
Disclosure of Invention
In view of this, embodiments of the present invention provide a service docking method and apparatus, which can automatically determine a service corresponding to a user appeal, reduce a service docking threshold, improve accuracy of service docking, reduce communication cost in a service docking process, and improve user experience.
In order to achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a service docking method, including:
acquiring scene description information of a user;
according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not;
inputting the verified scene description information into a preset docking recommendation model, and determining a target docking service corresponding to the scene description information.
Optionally, the obtaining the scene description information of the user includes: and responding to the scene label selection operation of the user on a preset visual interface to generate the scene description information.
Optionally, the scene description information includes one or more of the following information: time efficiency, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode.
Optionally, before the scene description information is input into a preset docking recommendation model, the method further includes: and acquiring registration information of the user, and complementing the scene description information according to the registration information.
Optionally, completing the scene description information according to the registration information includes:
determining the identity of the user according to the registration information;
and determining the importance degree of the user according to the identity of the user.
Optionally, the method further includes obtaining the preset docking recommendation model according to the following process:
acquiring attribute data of each service, and taking the attribute data as a training data set, wherein the attribute data comprises one or more of the following information: the method comprises the following steps of (1) aging, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode;
and training the training data set by using a random forest algorithm to obtain the preset docking recommendation model.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a service interfacing apparatus including:
the acquisition module is used for acquiring scene description information of a user;
the verification module is used for verifying the scene description information according to a preset verification rule so as to determine whether the scene description information is reasonable or not;
and the docking module is used for inputting the verified scene description information into a preset docking recommendation model and determining a target docking service corresponding to the scene description information.
Optionally, the obtaining module is further configured to: and responding to the scene label selection operation of the user on a preset visual interface to generate the scene description information.
Optionally, the scene description information includes one or more of the following information: time efficiency, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode.
Optionally, the obtaining module is further configured to: and acquiring registration information of the user, and complementing the scene description information according to the registration information.
Optionally, the obtaining module is further configured to: determining the identity of the user according to the registration information; and determining the importance degree of the user according to the identity of the user.
Optionally, the apparatus further includes a training module, configured to obtain attribute data of each service, where the attribute data is used as a training data set, and the attribute data includes one or more of the following information: the method comprises the following steps of (1) aging, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode; and training the training data set by using a random forest algorithm to obtain the preset docking recommendation model.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the service docking method of the embodiment of the invention.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a computer-readable medium on which a computer program is stored, the program implementing the service interfacing method of the embodiments of the present invention when executed by a processor.
One embodiment of the above invention has the following advantages or benefits: obtaining scene description information of a user; according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not; the verified scene description information is input into a preset docking recommendation model, the target docking service corresponding to the scene description information is determined, the service corresponding to the appeal of the user can be automatically determined, the service docking threshold is reduced, the accuracy of service docking is improved, the communication cost in the service docking process is reduced, and the user experience is improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of a service docking method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a visualization interface of a business docking method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the main modules of a service docking apparatus according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The service docking method provided by the embodiment of the invention can be used for docking specific logistics services by merchants, such as determining whether the logistics services docked with the merchants are cold chain services or personal express services. The service docking method describes the carrying capacity of the logistics service by using scene labels, and constructs a preset docking recommendation model by learning the scene labels of the logistics services, wherein the input of the preset docking recommendation model is a plurality of scene labels, and the output of the preset docking recommendation model is the corresponding logistics service. And then, determining a logistics carrying scheme meeting the user appeal, namely determining the logistics service docked with the user, by using the docking recommendation model and the appeal (namely scene description information) of the user.
Specifically, as shown in fig. 1, the service docking method includes:
step S101: acquiring scene description information of a user;
step S102: according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not;
step S103: inputting the verified scene description information into a preset docking recommendation model, and determining a target docking service corresponding to the scene description information.
For step S101, the scene description information is used to describe the appeal of the user to the docking service (such as cost and timeliness) and the basic appeal of the user to the service of the user.
By way of example, the scenario description information may be a data identification of the logistics industry, including but not limited to one or more of the following: time efficiency, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode.
Wherein, aging may include, but is not limited to, one or more of the following: 211. 311, 4 hours up, 1 hour up, next morning up, and the day up, etc. 211 means 11 orders, good for the day, 23 orders, and 15 good for the next day. 311 means that the order is placed at 11-12 o' clock of the day and the day is properly paid. The next morning arrival is the 12 o' clock before the next day of the order.
The types of pieces include, but are not limited to, one or more of the following: super large pieces, medium and small pieces, super small pieces and the like.
Industries include, but are not limited to, the following types: industrial products, furniture, automobile products, mother and infant products, books, entertainment, small household appliances and the like.
The importance level is used for measuring the importance of the user, for example, whether the user is a strategic client or a high-potential client.
Network types include, but are not limited to, the following: large piece net, express delivery net, cold chain net, etc.
The bin mix production model includes, but is not limited to, the following types: warehouse, delivery and warehouse matching and the like.
Modes of transport include, but are not limited to, the following types: highways, waterways, railways, aviation, and the like.
In an optional embodiment, the scene description information may further include a settlement manner, a pickup manner, a value-added service, and the like.
Among the above-mentioned scene description information, some scene description information is input by the user, and some scene information is determined based on the registration information of the user, for example, the degree of importance is determined based on the registration information of the user. Specifically, the identity of the user may be determined according to the registration information of the user, and then the importance degree of the user may be determined according to the identity of the user. The registration information may include, but is not limited to, the user's account password and company information (e.g., company name, company nature, company address, license number, etc.). The user identities comprise merchant users and casual users, and if the user identities are merchant users, the importance degree of the user can be determined according to company information registered by the user. If the user identity is a casual user, the importance level of the user can be set to a preset low level.
In an alternative embodiment, the scene description information of the user may be obtained according to the following process: and responding to the scene label selection operation of the user on a preset visual interface to generate the scene description information. The visual interface is used for providing a user interaction page and displaying the scene tags, so that the user can conveniently select the scene tags. The user only needs to click on the needed scene label on the visual interface. By way of example, the visualization interface is shown in FIG. 2.
For step S102, the step checks the scene tag selected by the user in real time by using a preset check rule based on the selected tag, determines the scene tags that conflict with each other or the scene tags having a binding relationship, and then removes one of the scene tags that conflict with each other or the scene tags having a binding relationship, so that the scene description information input by the user is reasonable. In an alternative embodiment, when determining the conflicting scene tags or the scene tags with binding relationships, the conflicting scene tags or the scene tags with binding relationships may be output to be displayed so that the user can modify one of the conflicting scene tags or the scene tags with binding relationships. The preset check rule can be flexibly set according to an application scenario, and the present invention is not limited herein. In the embodiment, the label association relation of some specific scenes is realized by maintaining the check rule, and some rules with forced conflict are mainly maintained, so that the reasonability of the scene label selected by the user is mainly ensured. As an example, the verification rule may be a limit specified in clear text in the express industry, such as that the transportation mode is selected to be aviation, and the regulation of related laws and administrative regulations on the transportation of goods should be complied with. In an alternative embodiment, the check rules may be implemented by a Drools rules engine. The Drools rules engine is an open source business rules engine that is easy to access enterprise policies, easy to adjust, and easy to manage.
For step S103, the preset docking recommendation model may be obtained according to the following procedure:
acquiring attribute data of each service, and taking the attribute data as a training data set, wherein the attribute data comprises one or more of the following information: the method comprises the following steps of (1) aging, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode;
and training the training data set by using a random forest algorithm to obtain the preset docking recommendation model.
The attribute data of each service refers to the service data of each logistics service line, and the service data is used as a training data set for model training. In practical application, data of each logistics business can be periodically extracted through a big data technology or an existing platform, and the data is used as a training data set of the docking recommendation model.
And determining scene labels of the logistics services based on the training data set, and taking the scene labels as feature data. For example, the characteristic data is [ age, network type, piece type, industry type, importance … … ]. And after the characteristic attribute is obtained, training the characteristic attribute by using a random forest algorithm to obtain a preset docking recommendation model. The specific algorithm of the random forest is realized as follows:
step 1: assuming there are N samples, there is a back-out of randomly selecting N samples (one sample at a time and then back to continue selection). The selected N samples are used to train a decision tree as the samples at the root node of the decision tree. Wherein N is an integer greater than 1.
Step 2: when each sample has M attributes, when each node of the decision tree needs to be split, M attributes are randomly selected from the M attributes, and the condition M < < M is met. Then, a certain policy (for example, information gain) is applied to select 1 attribute from the m attributes as the splitting attribute of the node. Wherein M is an integer greater than or equal to 1, M is greater than or equal to 0 and less than or equal to M, and M is an integer.
And step 3: each node in the decision tree formation process is split according to step 2 (it is easy to understand that if the attribute selected by the node next time is the attribute used when the parent node is split, the node has already reached the leaf node, and there is no need to continue splitting). Until no further splitting is possible. Note that pruning is not performed throughout the decision tree formation process.
And 4, step 4: and (4) establishing a large number of decision trees according to the steps 1-3, thus forming a random forest.
In practical applications, the model can be trained using scimit-learn, which is a free software machine learning library for Python programming language.
After a preset docking recommendation model is obtained, scene description information of a user is input into the preset docking recommendation model, and a target docking service corresponding to the scene description information is determined.
The service docking method of the embodiment of the invention obtains the scene description information of the user; according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not; the verified scene description information is input into a preset docking recommendation model, and the target docking service corresponding to the scene description information is determined, so that the service corresponding to the appeal of the user is automatically determined, the service docking threshold is reduced, the accuracy of service docking is improved, the communication cost in the service docking process is reduced, and the user experience is improved.
Fig. 3 is a schematic diagram of main modules of a service docking device 300 according to an embodiment of the present invention, and as shown in fig. 3, the service docking device 300 includes:
an obtaining module 301, configured to obtain scene description information of a user;
a checking module 302, configured to check the scene description information according to a preset checking rule, so as to determine whether the scene description information is reasonable;
the docking module 303 is configured to input the verified scene description information into a preset docking recommendation model, and determine a target docking service corresponding to the scene description information.
Optionally, the obtaining module 301 is further configured to: and responding to the scene label selection operation of the user on a preset visual interface to generate the scene description information.
Optionally, the scene description information includes one or more of the following information: time efficiency, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode.
Optionally, the obtaining module 301 is further configured to: and acquiring registration information of the user, and complementing the scene description information according to the registration information.
Optionally, the obtaining module 301 is further configured to: determining the identity of the user according to the registration information; and determining the importance degree of the user according to the identity of the user.
Optionally, the apparatus further includes a training module, configured to obtain attribute data of each service, where the attribute data is used as a training data set, and the attribute data includes one or more of the following information: the method comprises the following steps of (1) aging, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode; and training the training data set by using a random forest algorithm to obtain the preset docking recommendation model.
The service docking device of the embodiment of the invention acquires the scene description information of the user; according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not; the verified scene description information is input into a preset docking recommendation model, and the target docking service corresponding to the scene description information is determined, so that the service corresponding to the appeal of the user is automatically determined, the service docking threshold is reduced, the accuracy of service docking is improved, the communication cost in the service docking process is reduced, and the user experience is improved.
The device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
Fig. 4 shows an exemplary system architecture 400 of a service docking method or service docking apparatus to which an embodiment of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have various communication client applications installed thereon, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like.
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 401, 402, and 403. The background management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (e.g., target push information and product information) to the terminal device.
It should be noted that the service interfacing method provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the service interfacing apparatus is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a sending module, an obtaining module, a determining module, and a first processing module. The names of these modules do not in some cases constitute a limitation on the unit itself, and for example, the sending module may also be described as a "module that sends a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
acquiring scene description information of a user;
according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not;
inputting the verified scene description information into a preset docking recommendation model, and determining a target docking service corresponding to the scene description information.
According to the technical scheme of the embodiment of the invention, scene description information of a user is obtained; according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not; the verified scene description information is input into a preset docking recommendation model, and the target docking service corresponding to the scene description information is determined, so that the service corresponding to the appeal of the user is automatically determined, the service docking threshold is reduced, the accuracy of service docking is improved, the communication cost in the service docking process is reduced, and the user experience is improved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A service docking method, comprising:
acquiring scene description information of a user;
according to a preset check rule, checking the scene description information to determine whether the scene description information is reasonable or not;
inputting the verified scene description information into a preset docking recommendation model, and determining a target docking service corresponding to the scene description information.
2. The method of claim 1, wherein obtaining the scene description information of the user comprises:
and responding to the scene label selection operation of the user on a preset visual interface to generate the scene description information.
3. The method of claim 1, wherein the scene description information comprises one or more of the following: time efficiency, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode.
4. The method of claim 3, wherein obtaining the scene description information of the user comprises:
and acquiring registration information of the user, and complementing the scene description information according to the registration information.
5. The method of claim 4, wherein complementing the scene description information according to the registration information comprises:
determining the identity of the user according to the registration information;
and determining the importance degree of the user according to the identity of the user.
6. The method according to any one of claims 1-5, further comprising obtaining the preset docking recommendation model according to the following procedure:
acquiring attribute data of each service, and taking the attribute data as a training data set, wherein the attribute data comprises one or more of the following information: the method comprises the following steps of (1) aging, part type, industry type, importance degree, network type, warehouse allocation production mode and transportation mode;
and training the training data set by using a random forest algorithm to obtain the preset docking recommendation model.
7. A service interfacing apparatus, comprising:
the acquisition module is used for acquiring scene description information of a user;
the verification module is used for verifying the scene description information according to a preset verification rule so as to determine whether the scene description information is reasonable or not;
and the docking module is used for inputting the verified scene description information into a preset docking recommendation model and determining a target docking service corresponding to the scene description information.
8. The apparatus of claim 7, wherein the obtaining module is further configured to:
and responding to the scene label selection operation of the user on a preset visual interface to generate the scene description information.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116680507A (en) * | 2023-05-10 | 2023-09-01 | 湖北航天化学技术研究所 | Method, system and device for automatically determining combustion time of solid rocket engine |
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