CN113282385A - Service processing method and system based on online office equipment - Google Patents
Service processing method and system based on online office equipment Download PDFInfo
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- CN113282385A CN113282385A CN202011194398.9A CN202011194398A CN113282385A CN 113282385 A CN113282385 A CN 113282385A CN 202011194398 A CN202011194398 A CN 202011194398A CN 113282385 A CN113282385 A CN 113282385A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
The invention provides a business processing method and a system based on online office equipment, which are used for acquiring online cloud office business data uploaded by first online office equipment; when online cloud office business data are analyzed, whether the emergency degree of event description information in a preset business event set reaches a set emergency degree or not is detected in real time; if yes, calculating the current time-consuming weight; and when the current time consumption weight is greater than the set time consumption weight, identifying the corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data, and removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information to adjust the processing duration of each first target event description information. Therefore, the problem that the service processing consumes too long time in the operation process of the online office equipment can be solved, and the online office efficiency is improved.
Description
Technical Field
The invention relates to the technical field of office equipment, in particular to a business processing method and system based on online office equipment.
Background
With the development of online office, the office mode is realized by online data information interaction. However, in the operation process of the online office equipment, the problem that the service processing takes too long time may occur, thereby resulting in the reduction of the online office efficiency.
Disclosure of Invention
In order to solve the problems, the invention provides a business processing method and system based on online office equipment.
A business processing method based on online office equipment is applied to an office dispatching server, the office dispatching server is in communication connection with first online office equipment, the office dispatching server is also in communication connection with second online office equipment, and the method comprises the following steps:
acquiring online cloud office service data uploaded by the first online office equipment through a preset data transmission channel; the first online office equipment comprises a service log database and equipment interaction records, and the online cloud office service data comprises first online cloud office service data corresponding to the service log database and second online cloud office service data corresponding to the equipment interaction records;
when the online cloud office business data is analyzed, detecting whether the emergency degree of the event description information in a preset business event set reaches a set emergency degree or not in real time;
if the emergency degree reaches the set emergency degree, calculating the current time-consuming weight corresponding to the online cloud office business data; the current time-consuming weight is used for representing the time required for loading the online cloud office business data into the event description information;
judging whether the current time-consuming weight is larger than a set time-consuming weight or not;
when the current time consumption weight is larger than the set time consumption weight, identifying corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; the first target event description information is event description information with the duration in a target time period;
performing reservation request elimination on at least part of target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of target service event reservation requests to adjust the processing duration of each first target event description information;
the method for identifying the target online cloud office service data corresponding to each piece of first target event description information to obtain the service identification queue corresponding to the target online cloud office service data includes the following steps:
acquiring service event distribution data and each service label attribute data of target online cloud office service data;
under the condition that the target online cloud office business data contains a first label business classification according to the business event distribution data, determining attribute similarity between each business label attribute data of the target online cloud office business data under a second label business classification and each business label attribute data of the target online cloud office business data under the first label business classification according to business label attribute data of the target online cloud office business data under the first label business classification and attribute dynamic factors of the business label attribute data of the target online cloud office business data under the second label business classification, and classifying the business label attribute data of which the attribute similarity between the business label attribute data of the target online cloud office business data under the second label business classification and under the first label business classification is higher than a set similarity into the first label business classification Under the service classification;
under the condition that a plurality of service label attribute data are contained in a second label service classification corresponding to the target online cloud office service data, determining attribute similarity between each service label attribute data of the target online cloud office service data in the second label service classification according to the service label attribute data of the target online cloud office service data in the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data in the first label service classification, and marking each service label attribute data in the second label service classification according to the attribute similarity between each service label attribute data;
setting attribute identification degrees for target service label attribute data obtained by the marks according to the service label attribute data of the target online cloud office service data under the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data under the first label service classification, and classifying at least part of the target service label attribute data under the first label service classification according to the attribute identification degrees;
and generating a business identification queue corresponding to the target online cloud office business data according to the business label attribute data under the first label business classification.
Further, the method further comprises:
after the processing time length of each first target event description information is adjusted, calculating target time-consuming weights corresponding to all the adjusted processing time lengths;
judging whether the target time-consuming weight reaches the current time-consuming weight or not;
and splitting the service event set to add at least one piece of second target event description information if the target time-consuming weight does not reach the current time-consuming weight.
Further, the method further comprises:
determining an event correlation curve of the business event set when the second target event description information is added;
extracting the association description data of the event association curve, and judging whether the service event set is in an adjustable state according to the association description data;
and returning to the step of removing the reservation request of at least part of the reservation requests of the target business events corresponding to the description information of each first target event according to the business identification queue when the business event set is in the adjustable state.
Further, the method further comprises:
and before returning to the step of removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, modifying preset log text data to update an artificial intelligence model for removing the reservation request.
Further, performing reservation request elimination on at least part of the target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests, including:
analyzing a service data field of the target online cloud office service data in each first target event description information according to the label level distribution of the queue update data and the service label attribute data of the service identification queue to obtain scene description data in service scene information included in the service data field of the target online cloud office service data in each first target event description information;
and according to the scene description data, removing reservation requests of at least part of the target service event reservation requests corresponding to each first target event description information, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests.
A business processing system based on online office equipment comprises an office scheduling server, and first online office equipment which are communicated with the office scheduling server; wherein the office dispatch server is configured to:
acquiring online cloud office service data uploaded by the first online office equipment through a preset data transmission channel; the first online office equipment comprises a service log database and equipment interaction records, and the online cloud office service data comprises first online cloud office service data corresponding to the service log database and second online cloud office service data corresponding to the equipment interaction records;
when the online cloud office business data is analyzed, detecting whether the emergency degree of the event description information in a preset business event set reaches a set emergency degree or not in real time;
if the emergency degree reaches the set emergency degree, calculating the current time-consuming weight corresponding to the online cloud office business data; the current time-consuming weight is used for representing the time required for loading the online cloud office business data into the event description information;
judging whether the current time-consuming weight is larger than a set time-consuming weight or not;
when the current time consumption weight is larger than the set time consumption weight, identifying corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; the first target event description information is event description information with the duration in a target time period;
performing reservation request elimination on at least part of target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of target service event reservation requests to adjust the processing duration of each first target event description information;
the method for identifying the target online cloud office service data corresponding to each piece of first target event description information to obtain the service identification queue corresponding to the target online cloud office service data includes the following steps:
acquiring service event distribution data and each service label attribute data of target online cloud office service data;
under the condition that the target online cloud office business data contains a first label business classification according to the business event distribution data, determining attribute similarity between each business label attribute data of the target online cloud office business data under a second label business classification and each business label attribute data of the target online cloud office business data under the first label business classification according to business label attribute data of the target online cloud office business data under the first label business classification and attribute dynamic factors of the business label attribute data of the target online cloud office business data under the second label business classification, and classifying the business label attribute data of which the attribute similarity between the business label attribute data of the target online cloud office business data under the second label business classification and under the first label business classification is higher than a set similarity into the first label business classification Under the service classification;
under the condition that a plurality of service label attribute data are contained in a second label service classification corresponding to the target online cloud office service data, determining attribute similarity between each service label attribute data of the target online cloud office service data in the second label service classification according to the service label attribute data of the target online cloud office service data in the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data in the first label service classification, and marking each service label attribute data in the second label service classification according to the attribute similarity between each service label attribute data;
setting attribute identification degrees for target service label attribute data obtained by the marks according to the service label attribute data of the target online cloud office service data under the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data under the first label service classification, and classifying at least part of the target service label attribute data under the first label service classification according to the attribute identification degrees;
and generating a business identification queue corresponding to the target online cloud office business data according to the business label attribute data under the first label business classification.
Further, the office dispatch server is further configured to:
after the processing time length of each first target event description information is adjusted, calculating target time-consuming weights corresponding to all the adjusted processing time lengths;
judging whether the target time-consuming weight reaches the current time-consuming weight or not;
and splitting the service event set to add at least one piece of second target event description information if the target time-consuming weight does not reach the current time-consuming weight.
Further, the office dispatch server is further configured to:
determining an event correlation curve of the business event set when the second target event description information is added;
extracting the association description data of the event association curve, and judging whether the service event set is in an adjustable state according to the association description data;
and returning to the step of removing the reservation request of at least part of the reservation requests of the target business events corresponding to the description information of each first target event according to the business identification queue when the business event set is in the adjustable state.
Further, the office dispatch server is further configured to:
and before returning to the step of removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, modifying preset log text data to update an artificial intelligence model for removing the reservation request.
Further, the office scheduling server performs reservation request removal on at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, and reserves the target reservation requests corresponding to the at least part of the target business event reservation requests, including:
analyzing a service data field of the target online cloud office service data in each first target event description information according to the label level distribution of the queue update data and the service label attribute data of the service identification queue to obtain scene description data in service scene information included in the service data field of the target online cloud office service data in each first target event description information;
and according to the scene description data, removing reservation requests of at least part of the target service event reservation requests corresponding to each first target event description information, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests.
Acquiring online cloud office business data uploaded by first online office equipment by executing the online office equipment-based business processing method and system; when online cloud office business data are analyzed, whether the emergency degree of event description information in a preset business event set reaches a set emergency degree or not is detected in real time; if the emergency degree of the event reaches the set emergency degree of the event, calculating the current time-consuming weight corresponding to the online cloud office business data; when the current time consumption weight is larger than the set time consumption weight, identifying the corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; and performing reservation request elimination on at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, and reserving the target reservation requests corresponding to at least part of the target business event reservation requests so as to adjust the processing duration of each first target event description information. Therefore, the problem that the service processing time is too long in the operation process of the online office equipment can be solved, and the online office efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a service processing method based on an online office device according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a hardware structure of an office scheduling server according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, a flowchart of a business processing method based on online office equipment is shown, the method is applied to an office dispatch server, the office dispatch server is in communication connection with a first block link point, the office dispatch server is also in communication connection with a second block link point, and the method includes the contents described in the following steps S110 to S160;
step S110, acquiring online cloud office business data uploaded by the first online office equipment through a preset data transmission channel; the first online office equipment comprises a service log database and equipment interaction records, and the online cloud office service data comprises first online cloud office service data corresponding to the service log database and second online cloud office service data corresponding to the equipment interaction records;
step S120, when the online cloud office business data is analyzed, detecting whether the emergency degree of the event description information in a preset business event set reaches a set emergency degree or not in real time;
step S130, if the emergency degree reaches the set emergency degree, calculating the current time-consuming weight corresponding to the online cloud office business data; the current time-consuming weight is used for representing the time required for loading the online cloud office business data into the event description information;
step S140, judging whether the current time-consuming weight is greater than a set time-consuming weight;
step S150, when the current time consumption weight is larger than the set time consumption weight, identifying corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; the first target event description information is event description information with the duration in a target time period;
step S160, performing reservation request elimination on at least part of the target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests to adjust the processing duration of each first target event description information.
Thus, based on the steps S110 to S160, acquiring online cloud office service data uploaded by the first online office device; when online cloud office business data are analyzed, whether the emergency degree of event description information in a preset business event set reaches a set emergency degree or not is detected in real time; if yes, calculating the current time-consuming weight; and when the current time consumption weight is greater than the set time consumption weight, identifying the corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data, and removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information to adjust the processing duration of each first target event description information. Therefore, the problem that the service processing consumes too long time in the operation process of the online office equipment can be solved, and the online office efficiency is improved.
The method for identifying the target online cloud office service data corresponding to each piece of first target event description information to obtain the service identification queue corresponding to the target online cloud office service data includes the following steps:
acquiring service event distribution data and each service label attribute data of target online cloud office service data;
under the condition that the target online cloud office business data contains a first label business classification according to the business event distribution data, determining attribute similarity between each business label attribute data of the target online cloud office business data under a second label business classification and each business label attribute data of the target online cloud office business data under the first label business classification according to business label attribute data of the target online cloud office business data under the first label business classification and attribute dynamic factors of the business label attribute data of the target online cloud office business data under the second label business classification, and classifying the business label attribute data of which the attribute similarity between the business label attribute data of the target online cloud office business data under the second label business classification and under the first label business classification is higher than a set similarity into the first label business classification Under the service classification;
under the condition that a plurality of service label attribute data are contained in a second label service classification corresponding to the target online cloud office service data, determining attribute similarity between each service label attribute data of the target online cloud office service data in the second label service classification according to the service label attribute data of the target online cloud office service data in the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data in the first label service classification, and marking each service label attribute data in the second label service classification according to the attribute similarity between each service label attribute data;
setting attribute identification degrees for target service label attribute data obtained by the marks according to the service label attribute data of the target online cloud office service data under the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data under the first label service classification, and classifying at least part of the target service label attribute data under the first label service classification according to the attribute identification degrees;
and generating a business identification queue corresponding to the target online cloud office business data according to the business label attribute data under the first label business classification.
Further, the method further comprises:
after the processing time length of each first target event description information is adjusted, calculating target time-consuming weights corresponding to all the adjusted processing time lengths;
judging whether the target time-consuming weight reaches the current time-consuming weight or not;
and splitting the service event set to add at least one piece of second target event description information if the target time-consuming weight does not reach the current time-consuming weight.
Further, the method further comprises:
determining an event correlation curve of the business event set when the second target event description information is added;
extracting the association description data of the event association curve, and judging whether the service event set is in an adjustable state according to the association description data;
and returning to the step of removing the reservation request of at least part of the reservation requests of the target business events corresponding to the description information of each first target event according to the business identification queue when the business event set is in the adjustable state.
Further, the method further comprises:
and before returning to the step of removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, modifying preset log text data to update an artificial intelligence model for removing the reservation request.
Further, performing reservation request elimination on at least part of the target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests, including:
analyzing a service data field of the target online cloud office service data in each first target event description information according to the label level distribution of the queue update data and the service label attribute data of the service identification queue to obtain scene description data in service scene information included in the service data field of the target online cloud office service data in each first target event description information;
and according to the scene description data, removing reservation requests of at least part of the target service event reservation requests corresponding to each first target event description information, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests.
Based on the same inventive concept, the business processing system based on the online office equipment comprises an office dispatching server, and first online office equipment which are communicated with the office dispatching server; wherein the office dispatch server is configured to:
acquiring online cloud office service data uploaded by the first online office equipment through a preset data transmission channel; the first online office equipment comprises a service log database and equipment interaction records, and the online cloud office service data comprises first online cloud office service data corresponding to the service log database and second online cloud office service data corresponding to the equipment interaction records;
when the online cloud office business data is analyzed, detecting whether the emergency degree of the event description information in a preset business event set reaches a set emergency degree or not in real time;
if the emergency degree reaches the set emergency degree, calculating the current time-consuming weight corresponding to the online cloud office business data; the current time-consuming weight is used for representing the time required for loading the online cloud office business data into the event description information;
judging whether the current time-consuming weight is larger than a set time-consuming weight or not;
when the current time consumption weight is larger than the set time consumption weight, identifying corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; the first target event description information is event description information with the duration in a target time period;
performing reservation request elimination on at least part of target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of target service event reservation requests to adjust the processing duration of each first target event description information;
the method for identifying the target online cloud office service data corresponding to each piece of first target event description information to obtain the service identification queue corresponding to the target online cloud office service data includes the following steps:
acquiring service event distribution data and each service label attribute data of target online cloud office service data;
under the condition that the target online cloud office business data contains a first label business classification according to the business event distribution data, determining attribute similarity between each business label attribute data of the target online cloud office business data under a second label business classification and each business label attribute data of the target online cloud office business data under the first label business classification according to business label attribute data of the target online cloud office business data under the first label business classification and attribute dynamic factors of the business label attribute data of the target online cloud office business data under the second label business classification, and classifying the business label attribute data of which the attribute similarity between the business label attribute data of the target online cloud office business data under the second label business classification and under the first label business classification is higher than a set similarity into the first label business classification Under the service classification;
under the condition that a plurality of service label attribute data are contained in a second label service classification corresponding to the target online cloud office service data, determining attribute similarity between each service label attribute data of the target online cloud office service data in the second label service classification according to the service label attribute data of the target online cloud office service data in the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data in the first label service classification, and marking each service label attribute data in the second label service classification according to the attribute similarity between each service label attribute data;
setting attribute identification degrees for target service label attribute data obtained by the marks according to the service label attribute data of the target online cloud office service data under the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data under the first label service classification, and classifying at least part of the target service label attribute data under the first label service classification according to the attribute identification degrees;
and generating a business identification queue corresponding to the target online cloud office business data according to the business label attribute data under the first label business classification.
Further, the office dispatch server is further configured to:
after the processing time length of each first target event description information is adjusted, calculating target time-consuming weights corresponding to all the adjusted processing time lengths;
judging whether the target time-consuming weight reaches the current time-consuming weight or not;
and splitting the service event set to add at least one piece of second target event description information if the target time-consuming weight does not reach the current time-consuming weight.
Further, the office dispatch server is further configured to:
determining an event correlation curve of the business event set when the second target event description information is added;
extracting the association description data of the event association curve, and judging whether the service event set is in an adjustable state according to the association description data;
and returning to the step of removing the reservation request of at least part of the reservation requests of the target business events corresponding to the description information of each first target event according to the business identification queue when the business event set is in the adjustable state.
Further, the office dispatch server is further configured to:
and before returning to the step of removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, modifying preset log text data to update an artificial intelligence model for removing the reservation request.
Further, the office scheduling server performs reservation request removal on at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, and reserves the target reservation requests corresponding to the at least part of the target business event reservation requests, including:
analyzing a service data field of the target online cloud office service data in each first target event description information according to the label level distribution of the queue update data and the service label attribute data of the service identification queue to obtain scene description data in service scene information included in the service data field of the target online cloud office service data in each first target event description information;
and according to the scene description data, removing reservation requests of at least part of the target service event reservation requests corresponding to each first target event description information, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests.
Referring to fig. 2, a hardware structure diagram of the office dispatch server 110 is provided.
Fig. 2 is a block diagram illustrating an office dispatch server 110 according to an embodiment of the present invention. An office dispatch server 110 in the embodiment of the present invention may be a server with data storage, transmission, and processing functions, as shown in fig. 2, the office dispatch server 110 includes: memory 111, processor 112, network module 113 and online office equipment based business processing apparatus 200.
The memory 111, the processor 112, and the network module 113 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 111 stores the online office equipment-based service processing device 200, the online office equipment-based service processing device 200 includes at least one software function module which can be stored in the memory 111 in the form of software or firmware (firmware), and the processor 112 executes various function applications and data processing by running software programs and modules stored in the memory 111, such as the online office equipment-based service processing device 200 in the embodiment of the present invention, so as to implement the method in the embodiment of the present invention.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is used for storing a program, and the processor 112 executes the program after receiving the execution instruction.
The processor 112 may be an integrated circuit chip having data processing capabilities. The Processor 112 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 113 is used for establishing a communication connection between the office scheduling server 110 and other communication terminal devices through a network, so as to implement transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 2 is merely illustrative and that an office dispatch server 110 may include more or fewer components than shown in fig. 2 or have a different configuration than shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present invention also provides a computer-readable storage medium, which includes a computer program. The computer program controls the readable storage medium to execute the following method in an office dispatch server 110.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a scheduling server (which may be a personal computer, an electronic device 10, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. 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 apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A business processing method based on online office equipment is characterized in that the business processing method is applied to an office dispatching server, the office dispatching server is in communication connection with first online office equipment, the office dispatching server is also in communication connection with second online office equipment, and the method comprises the following steps:
acquiring online cloud office service data uploaded by the first online office equipment through a preset data transmission channel; the first online office equipment comprises a service log database and equipment interaction records, and the online cloud office service data comprises first online cloud office service data corresponding to the service log database and second online cloud office service data corresponding to the equipment interaction records;
when the online cloud office business data is analyzed, detecting whether the emergency degree of the event description information in a preset business event set reaches a set emergency degree or not in real time;
if the emergency degree reaches the set emergency degree, calculating the current time-consuming weight corresponding to the online cloud office business data; the current time-consuming weight is used for representing the time required for loading the online cloud office business data into the event description information;
judging whether the current time-consuming weight is larger than a set time-consuming weight or not;
when the current time consumption weight is larger than the set time consumption weight, identifying corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; the first target event description information is event description information with the duration in a target time period;
performing reservation request elimination on at least part of target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of target service event reservation requests to adjust the processing duration of each first target event description information;
the method for identifying the target online cloud office service data corresponding to each piece of first target event description information to obtain the service identification queue corresponding to the target online cloud office service data includes the following steps:
acquiring service event distribution data and each service label attribute data of target online cloud office service data;
under the condition that the target online cloud office business data contains a first label business classification according to the business event distribution data, determining attribute similarity between each business label attribute data of the target online cloud office business data under a second label business classification and each business label attribute data of the target online cloud office business data under the first label business classification according to business label attribute data of the target online cloud office business data under the first label business classification and attribute dynamic factors of the business label attribute data of the target online cloud office business data under the second label business classification, and classifying the business label attribute data of which the attribute similarity between the business label attribute data of the target online cloud office business data under the second label business classification and under the first label business classification is higher than a set similarity into the first label business classification Under the service classification;
under the condition that a plurality of service label attribute data are contained in a second label service classification corresponding to the target online cloud office service data, determining attribute similarity between each service label attribute data of the target online cloud office service data in the second label service classification according to the service label attribute data of the target online cloud office service data in the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data in the first label service classification, and marking each service label attribute data in the second label service classification according to the attribute similarity between each service label attribute data;
setting attribute identification degrees for target service label attribute data obtained by the marks according to the service label attribute data of the target online cloud office service data under the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data under the first label service classification, and classifying at least part of the target service label attribute data under the first label service classification according to the attribute identification degrees;
and generating a business identification queue corresponding to the target online cloud office business data according to the business label attribute data under the first label business classification.
2. The method of claim 1, further comprising:
after the processing time length of each first target event description information is adjusted, calculating target time-consuming weights corresponding to all the adjusted processing time lengths;
judging whether the target time-consuming weight reaches the current time-consuming weight or not;
and splitting the service event set to add at least one piece of second target event description information if the target time-consuming weight does not reach the current time-consuming weight.
3. The method of claim 2, further comprising:
determining an event correlation curve of the business event set when the second target event description information is added;
extracting the association description data of the event association curve, and judging whether the service event set is in an adjustable state according to the association description data;
and returning to the step of removing the reservation request of at least part of the reservation requests of the target business events corresponding to the description information of each first target event according to the business identification queue when the business event set is in the adjustable state.
4. The method of claim 3, further comprising:
and before returning to the step of removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, modifying preset log text data to update an artificial intelligence model for removing the reservation request.
5. The method of claim 1, wherein performing reservation request elimination on at least a part of the target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least a part of the target service event reservation requests comprises:
analyzing a service data field of the target online cloud office service data in each first target event description information according to the label level distribution of the queue update data and the service label attribute data of the service identification queue to obtain scene description data in service scene information included in the service data field of the target online cloud office service data in each first target event description information;
and according to the scene description data, removing reservation requests of at least part of the target service event reservation requests corresponding to each first target event description information, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests.
6. A business processing system based on online office equipment is characterized by comprising an office dispatching server, first online office equipment and first online office equipment, wherein the first online office equipment and the first online office equipment are communicated with the office dispatching server; wherein the office dispatch server is configured to:
acquiring online cloud office service data uploaded by the first online office equipment through a preset data transmission channel; the first online office equipment comprises a service log database and equipment interaction records, and the online cloud office service data comprises first online cloud office service data corresponding to the service log database and second online cloud office service data corresponding to the equipment interaction records;
when the online cloud office business data is analyzed, detecting whether the emergency degree of the event description information in a preset business event set reaches a set emergency degree or not in real time;
if the emergency degree reaches the set emergency degree, calculating the current time-consuming weight corresponding to the online cloud office business data; the current time-consuming weight is used for representing the time required for loading the online cloud office business data into the event description information;
judging whether the current time-consuming weight is larger than a set time-consuming weight or not;
when the current time consumption weight is larger than the set time consumption weight, identifying corresponding target online cloud office business data in each first target event description information to obtain a business identification queue corresponding to the target online cloud office business data; the first target event description information is event description information with the duration in a target time period;
performing reservation request elimination on at least part of target service event reservation requests corresponding to each first target event description information according to the service identification queue, and reserving the target reservation requests corresponding to the at least part of target service event reservation requests to adjust the processing duration of each first target event description information;
the method for identifying the target online cloud office service data corresponding to each piece of first target event description information to obtain the service identification queue corresponding to the target online cloud office service data includes the following steps:
acquiring service event distribution data and each service label attribute data of target online cloud office service data;
under the condition that the target online cloud office business data contains a first label business classification according to the business event distribution data, determining attribute similarity between each business label attribute data of the target online cloud office business data under a second label business classification and each business label attribute data of the target online cloud office business data under the first label business classification according to business label attribute data of the target online cloud office business data under the first label business classification and attribute dynamic factors of the business label attribute data of the target online cloud office business data under the second label business classification, and classifying the business label attribute data of which the attribute similarity between the business label attribute data of the target online cloud office business data under the second label business classification and under the first label business classification is higher than a set similarity into the first label business classification Under the service classification;
under the condition that a plurality of service label attribute data are contained in a second label service classification corresponding to the target online cloud office service data, determining attribute similarity between each service label attribute data of the target online cloud office service data in the second label service classification according to the service label attribute data of the target online cloud office service data in the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data in the first label service classification, and marking each service label attribute data in the second label service classification according to the attribute similarity between each service label attribute data;
setting attribute identification degrees for target service label attribute data obtained by the marks according to the service label attribute data of the target online cloud office service data under the first label service classification and the attribute dynamic factor of the service label attribute data of the target online cloud office service data under the first label service classification, and classifying at least part of the target service label attribute data under the first label service classification according to the attribute identification degrees;
and generating a business identification queue corresponding to the target online cloud office business data according to the business label attribute data under the first label business classification.
7. The system of claim 6, wherein the office dispatch server is further configured to:
after the processing time length of each first target event description information is adjusted, calculating target time-consuming weights corresponding to all the adjusted processing time lengths;
judging whether the target time-consuming weight reaches the current time-consuming weight or not;
and splitting the service event set to add at least one piece of second target event description information if the target time-consuming weight does not reach the current time-consuming weight.
8. The system of claim 7, wherein the office dispatch server is further configured to:
determining an event correlation curve of the business event set when the second target event description information is added;
extracting the association description data of the event association curve, and judging whether the service event set is in an adjustable state according to the association description data;
and returning to the step of removing the reservation request of at least part of the reservation requests of the target business events corresponding to the description information of each first target event according to the business identification queue when the business event set is in the adjustable state.
9. The method of claim 8, wherein the office dispatch server is further configured to:
and before returning to the step of removing the reservation request of at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, modifying preset log text data to update an artificial intelligence model for removing the reservation request.
10. The method according to claim 6, wherein the office dispatch server performs reservation request elimination on at least part of the target business event reservation requests corresponding to each first target event description information according to the business identification queue, and reserves the target reservation requests corresponding to the at least part of the target business event reservation requests, and the method comprises:
analyzing a service data field of the target online cloud office service data in each first target event description information according to the label level distribution of the queue update data and the service label attribute data of the service identification queue to obtain scene description data in service scene information included in the service data field of the target online cloud office service data in each first target event description information;
and according to the scene description data, removing reservation requests of at least part of the target service event reservation requests corresponding to each first target event description information, and reserving the target reservation requests corresponding to the at least part of the target service event reservation requests.
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