AI business management system based on enterprise large model data

Abstract

The invention relates to the technical field of artificial intelligence management systems, in particular to an AI business management system based on enterprise large model data, which comprises an information acquisition module for receiving enterprise information and determining businesses in an enterprise, a classification module for carrying out category division processing on the businesses, and a processing module for processing the businesses, the detection module is used for recording time nodes when the terminals start to process the services and time nodes when the services are processed, and detecting loads and occupancy rates in the running process of the terminals; the analysis module is used for judging whether the processing of the terminal aiming at the service meets the standard or not, analyzing the reason for not meeting the standard and generating a scheduling instruction; and the scheduling module is used for performing scheduling processing on the service processed by the corresponding terminal based on the received scheduling instruction. According to the invention, the real-time monitoring and intelligent scheduling of the terminal for the business processing are effectively realized, and the management efficiency of the AI business management system for the enterprise large model data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence management systems, and in particular to an AI business management system based on enterprise big model data. Background Art

[0002] The AI ​​business management system for enterprise big model data refers to a system that uses artificial intelligence technology to intelligently manage and optimize enterprise operations. It helps enterprises achieve intelligent management of production, sales, human resources, etc. through functions such as data collection, analysis, prediction, and automated decision-making. The role and value of the AI ​​business management system are reflected in data collection and processing, data analysis and mining, intelligent decision-making and execution, and intelligent monitoring and feedback. The AI ​​business management system based on enterprise big model data provides enterprises with intelligent decision-making support, optimizes business processes, and improves operational efficiency through deep learning and data analysis. Its development prospects are very broad, especially in enterprise management and business operations. Its application prospects and challenges coexist. Chinese Patent Publication No.: CN114782009A, discloses an enterprise business management system based on artificial intelligence, including: a human-computer interaction module, which is used to obtain interactive instructions input by a user; an instruction analysis module, which is connected to the human-computer interaction module, and the instruction analysis module is used to analyze the interactive instructions input by the user; a form storage module, which is used to store forms of data corresponding to the enterprise business; a comparison module, which is respectively connected to the instruction analysis module and the form storage module; a permission management module, which is connected to the comparison module, and the permission management module is used for operation permission management and resource permission management; a data acquisition module, which is connected to the comparison module to obtain cloud resource data; a data storage module, which is respectively connected to the comparison module and the human-computer interaction module, and the data storage module is used to store data related to the enterprise business.

[0003] It can be seen that the above scheme improves the work efficiency of user operations by identifying the interactive instructions input by the user as text data or voice data through the instruction analysis module and extracting keywords in a corresponding manner. However, the above scheme cannot achieve real-time monitoring and intelligent scheduling of the terminal's processing of business, and thus cannot guarantee the management efficiency of the AI ​​business management system for the enterprise's large model data. Summary of the invention

[0004] To this end, the present invention provides an AI business management system based on enterprise big model data to overcome the problem in the prior art that the real-time monitoring and intelligent scheduling of terminal processing for business cannot be achieved, resulting in low management efficiency of the AI ​​business management system for enterprise big model data.

[0005] To achieve the above objectives, the present invention provides an AI business management system based on enterprise big model data, comprising: An information acquisition module, used to receive enterprise information and determine the business in the enterprise; A classification module, which is connected to the information acquisition module and is used to classify the services; A plurality of terminals, each of which is connected to the classification module, for processing the corresponding services after the classification; A detection module, which is connected to each of the terminals, and is used to record the time nodes when each terminal starts to process the service and when the service processing is completed, and to detect the load and occupancy rate of each terminal during operation; an analysis module connected to the detection module, for determining whether the processing of the service by the terminal meets the standard based on the occupancy rate, and for analyzing the reason for not meeting the standard based on the time node and the load; the analysis module is also used to generate a corresponding scheduling instruction according to the determined reason for not meeting the standard; A scheduling module is connected to each of the terminals and the analysis module, and is used to schedule the services processed by the corresponding terminals based on the received scheduling instructions.

[0006] Further, the analysis module is used to determine whether the processing of the service by the terminal meets the standard based on the occupancy rate, and, if it is determined that the processing of the service by the terminal does not meet the standard, determine whether the operation of the terminal meets the standard based on the historical service processing duration, or determine the reason why the processing of the service by the terminal does not meet the standard based on the load; The historical service processing time is the average processing time of each historical service by the terminal, and the processing time of a single historical service is the time interval between the time point when the terminal starts processing the service and the time point when the service processing is completed.

[0007] Furthermore, the analysis module is also used to determine whether the operation of the terminal meets the standards based on the historical business processing time, and, based on the determination result and based on the type label of the historical business processed by the terminal, record the terminal as a terminal to be scheduled for the corresponding type of business, or, based on the time difference, increase the data compression ratio during data transmission within the system; wherein the time difference is the difference between the historical business processing time and a pre-stored preset historical business processing time.

[0008] Furthermore, the analysis module is also used to increase the data compression ratio during data transmission in the system based on the time difference, and the increase in the data compression ratio is proportional to the time difference.

[0009] Furthermore, the analysis module is also used to determine the reason why the terminal's processing of the service does not meet the standards based on the historical load variance, and to generate corresponding scheduling instructions based on the determined reasons, including: issuing a hardware maintenance notice and performing scheduling processing when it is determined that the hardware configuration does not meet the standards, determining the reason why the terminal's processing of the service does not meet the standards based on the average slope, or selecting the terminal to be scheduled for the corresponding type of service for collaborative processing when the workload of the service allocated to the terminal is higher than the processing capacity; wherein the historical load variance is the variance of the historical load, and the average slope is the average slope of the established time-historical load curve.

[0010] Furthermore, the analysis module is also used to determine the reason why the terminal's processing of the service does not meet the standards based on the average slope, and to issue a hardware maintenance notification and perform scheduling processing based on the determination result, or to determine whether it is necessary to correct the priority of the subsequent allocation of corresponding types of services to the terminal based on the average processing time; wherein the average processing time is the average processing time of the terminal for various types of services.

[0011] Furthermore, the analysis module is also used to determine whether it is necessary to correct the priority of the corresponding type of service subsequently allocated to the terminal based on the average processing time, and to correct the priority of the corresponding type of service subsequently allocated to the terminal based on the average processing time according to the determination result, or to issue a hardware maintenance notification and perform scheduling processing.

[0012] Furthermore, the analysis module is also used to select the terminals to be scheduled of corresponding types of services for scheduling processing, and to determine whether to perform service changes based on the change range of the load of the terminals to be scheduled during the scheduling processing.

[0013] Furthermore, the analysis module is also used to select the terminals to be scheduled with corresponding types of services for collaborative processing, and increase the service allocation ratio of the terminals and the terminals to be scheduled based on the change amplitude of the load of the terminals; wherein the service allocation ratio is the ratio of the service allocation amount of the terminals to be scheduled to that of the terminals.

[0014] Furthermore, the analysis module is also used to detect the change range of the occupancy rate of the terminals to be scheduled in real time, and to increase the number of terminals to be scheduled for collaborative processing based on the occupancy rate change rate; wherein the occupancy rate change rate is the ratio of the occupancy rate change range to time.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that, by setting an analysis module and a scheduling module, the analysis module is used to determine whether the terminal's processing of the service meets the standard based on the occupancy rate, and can quickly and accurately complete the determination of whether the terminal's processing of the service meets the standard, effectively realizing real-time monitoring of the terminal's processing of the service. At the same time, when the analysis module determines that the processing of the service does not meet the standard, it generates a corresponding scheduling instruction according to the determined reason, and the scheduling module schedules the services processed by the corresponding terminals based on the scheduling instructions, effectively realizing intelligent scheduling of the terminal's processing of the service, so that the terminal's processing of the service meets the standard, and effectively improving the management efficiency of the AI ​​business management system for enterprise big model data.

[0016] Furthermore, the analysis module provided in the present invention is also used to determine whether the operation of the terminal meets the standards based on the historical business processing time, so as to more accurately determine whether the operation of the terminal meets the standards and avoid misjudgment. While further realizing real-time monitoring of the terminal's processing of business, it further improves the management efficiency of the AI ​​business management system for the enterprise's large model data.

[0017] Furthermore, the analysis module provided in the present invention is also used to increase the data compression ratio in the data transmission process within the system based on the time difference, which can avoid the situation where the terminal operation does not meet the standard due to the data compression ratio not meeting the standard during the transmission process. While further realizing the intelligent scheduling of the terminal for business processing, it further improves the management efficiency of the AI ​​business management system for the enterprise large model data. Furthermore, the analysis module provided in the present invention is also used to determine the reason why the terminal's processing of the business does not meet the standards based on the historical load variance, and quickly and accurately complete the determination of the reason why the terminal's processing of the business does not meet the standards, thereby generating corresponding scheduling instructions according to the corresponding reasons, while further realizing real-time monitoring of the terminal's processing of the business, and further improving the management efficiency of the AI ​​business management system for the enterprise's large model data.

[0018] Furthermore, the analysis module provided in the present invention is also used to determine the reason why the terminal's processing of the service does not meet the standards based on the average slope, which can avoid the occurrence of misjudgment between whether the hardware configuration does not meet the standards and whether the priority of the subsequent allocation of the corresponding type of service to the terminal needs to be corrected. While further realizing real-time monitoring of the terminal's processing of the service, it further improves the management efficiency of the AI ​​business management system for the enterprise's large model data.

[0019] Furthermore, the analysis module provided in the present invention is also used to determine whether it is necessary to correct the priority of the subsequent allocation of corresponding types of services to the terminal based on the average processing time, and to make the correction if it is determined that correction is necessary, thereby avoiding the occurrence of misjudgment between the need to correct the priority of the subsequent allocation of corresponding types of services to the terminal and the hardware configuration not meeting the standards. While further realizing real-time monitoring of the terminal's processing of the business, it further improves the management efficiency of the AI ​​business management system for the enterprise's large model data.

[0020] Furthermore, the analysis module provided in the present invention is also used to select terminals to be scheduled for corresponding types of services for scheduling processing, further realizing intelligent scheduling of terminal processing for services, and determining whether to make service changes based on the magnitude of change in the load of the terminal to be scheduled during the scheduling process, thereby effectively realizing real-time monitoring of the load of the terminal to be scheduled, avoiding the situation where the terminal's processing of services does not meet the standards due to the load of the terminal to be scheduled not meeting the standards, and further improving the management efficiency of the AI ​​business management system for enterprise big model data.

[0021] Furthermore, the analysis module provided in the present invention is also used to select terminals to be scheduled for corresponding types of services for collaborative processing, effectively avoiding the situation where the workload of the service allocated to the terminal is higher than the processing capacity, and further realizing the intelligent scheduling of the terminal's processing of the service. The analysis module also increases the service allocation ratio of the terminal and the terminal to be scheduled based on the change amplitude of the terminal's load, effectively realizing real-time monitoring of the change amplitude of the terminal's load, avoiding the situation where the terminal's processing of the service does not meet the standard due to the change amplitude of the terminal's load not meeting the standard, and further improving the management efficiency of the AI ​​business management system for enterprise large model data.

[0022] Furthermore, the analysis module provided in the present invention is also used to detect in real time the change range of the occupancy rate of the terminal to be scheduled, effectively realizing the real-time monitoring of the change range of the occupancy rate of the terminal to be scheduled. The analysis module also increases the number of terminals to be scheduled for collaborative processing based on the occupancy rate change rate, further realizing the intelligent scheduling of terminal processing for business, and further improving the management efficiency of the AI ​​business management system for enterprise big model data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a structural block diagram of the AI ​​business management system based on enterprise big model data of the present invention; Figure 2 This is a workflow diagram of the AI ​​business management system based on enterprise big model data of the present invention; Figure 3 A flowchart of the present invention for determining whether the processing of a service by a terminal complies with a standard and determining whether the operation of the terminal complies with a standard; Figure 4 This is a flow chart of analyzing that the processing of services by a terminal does not meet the standards according to the present invention. DETAILED DESCRIPTION

[0024] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0026] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0027] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0028] See also Figure 1 As shown, it is a structural block diagram of the AI ​​business management system based on enterprise big model data of the present invention. The AI ​​business management system based on enterprise big model data of the embodiment of the present invention includes an information acquisition module, a classification module, several terminals, a detection module, an analysis module and a scheduling module; wherein: The information acquisition module is used to receive enterprise information and determine the business in the enterprise; the classification module is connected to the information acquisition module to classify the business; each terminal is connected to the classification module to process the corresponding business after classification; the detection module is connected to each terminal respectively to record the time nodes when each terminal starts to process the business and the time nodes when the business processing is completed, and to detect the load and occupancy rate of each terminal during operation; the analysis module is connected to the detection module to determine whether the terminal's processing of the business meets the standard based on the occupancy rate and to analyze the reasons for not meeting the standard based on the time nodes and the load; the analysis module is also used to generate corresponding scheduling instructions according to the determined reasons for not meeting the standards; the scheduling module is connected to each terminal and the analysis module to schedule the business processed by the corresponding terminal based on the received scheduling instructions.

[0029] See also Figure 2 As shown, it is a workflow diagram of the AI ​​business management system based on enterprise big model data of the present invention. When the AI ​​business management system based on enterprise big model data of the embodiment of the present invention is running, The information acquisition module receives enterprise information and determines the business in the enterprise. The business is classified and processed by the classification module. The corresponding business after classification is processed by the corresponding terminal. The business categories after classification include customer relationship management, supply chain management, production and manufacturing management, financial management, human resources management and marketing management. The detection module records the time nodes when each terminal starts to process the business and the time nodes when the business processing is completed, and detects the load and occupancy rate of each terminal during operation. The analysis module determines whether the terminal's processing of the business meets the standards based on the occupancy rate and analyzes the reasons for not meeting the standards based on the time nodes and load. The analysis module is also used to generate corresponding scheduling instructions according to the determined reasons for not meeting the standards. The scheduling module schedules the businesses processed by the corresponding terminals based on the received scheduling instructions.

[0030] See also Figure 3 As shown, it is a flowchart of the present invention for determining whether the processing of the terminal for the service meets the standard and determining whether the operation of the terminal meets the standard. The analysis module of the embodiment of the present invention is used to determine whether the processing of the terminal for the service meets the standard based on the occupancy rate: When the occupancy rate is greater than a second preset occupancy rate R2 set in the analysis module, the analysis module determines that the processing of the service by the terminal does not meet the standard, and determines the reason why the processing of the service by the terminal does not meet the standard based on the load, wherein the second preset occupancy rate R2 in this embodiment=72%; When the occupancy rate is less than or equal to the second preset occupancy rate R2 and greater than the first preset occupancy rate R1 set in the analysis module, the analysis module determines that the processing of the service by the terminal meets the standard, wherein the first preset occupancy rate R1 in this embodiment is 30%; When the occupancy rate is less than or equal to the first preset occupancy rate R1, the analysis module determines that the terminal's processing of the service does not meet the standards, and determines whether the terminal's operation meets the standards based on the historical service processing duration, wherein the historical service processing duration is the average processing duration of each historical service by the terminal, and the processing duration for a single historical service is the time interval between the time node when the terminal starts processing the service and the time node when the service processing is completed.

[0031] Please continue reading Figure 3 As shown, the analysis module in the embodiment of the present invention is also used to determine whether the operation of the terminal meets the standard based on the historical service processing time: When the historical service processing time is less than or equal to the preset historical service processing time T set in the analysis module, the analysis module determines that the operation of the terminal meets the standard, and records the terminal as a terminal to be scheduled for the corresponding type of service based on the type label of the historical service processed by the terminal, wherein the preset historical service processing time T=0.35S in this embodiment; When the historical business processing time is greater than the preset historical business processing time T, the analysis module determines that the operation of the terminal does not meet the standards, and increases the data compression ratio during data transmission within the system based on the time difference, wherein the time difference is the difference between the historical business processing time and the preset historical business processing time.

[0032] Please continue reading Figure 3 As shown, the analysis module in the embodiment of the present invention is also used to increase the data compression ratio during data transmission in the system based on the time difference: When the time difference is greater than a second preset time difference △D2 set in the analysis module, the analysis module determines to increase the data compression ratio to 1.96 times the initial data compression ratio, wherein the second preset time difference △D2 in this embodiment is 1.25S; When the time difference is less than or equal to the second preset time difference △D2 and greater than the first preset time difference △D1 set in the analysis module, the analysis module determines to increase the data compression ratio to 1.65 times the initial data compression ratio, wherein the first preset time difference △D1 in this embodiment is 0.55S; When the time difference is less than or equal to the first preset time difference ΔD1, the analysis module determines to increase the data compression ratio to 1.39 times the initial data compression ratio.

[0033] See also Figure 4 As shown, it is a flow chart of the present invention for determining that the processing of the service by the terminal does not meet the standard. The analysis module of the embodiment of the present invention is also used to determine the reason why the processing of the service by the terminal does not meet the standard based on the historical load variance, and to generate a corresponding scheduling instruction based on the determined reason, wherein the historical load variance is the variance of the historical load: When the historical load variance is less than or equal to the first preset historical load variance C1 set in the analysis module, the analysis module determines that the hardware configuration does not meet the standard, issues a hardware maintenance notice and performs scheduling processing, wherein the first preset historical load variance C1 in this embodiment is 0.15; When the historical load variance is greater than the first preset historical load variance C1 and less than or equal to the second preset historical load variance C2 set in the analysis module, the analysis module determines the reason why the terminal does not meet the standard for processing the service based on the average slope, wherein the second preset historical load variance C2 of this embodiment is 0.22, and the average slope is the average slope of the established time-historical load curve; When the historical load variance is greater than the second preset historical load variance C2, the analysis module determines that the workload of the service allocated to the terminal is higher than the processing capacity, and selects the to-be-scheduled terminal of the corresponding type of service for collaborative processing.

[0034] Please continue reading Figure 4 As shown, the analysis module in the embodiment of the present invention is further used to determine the reason why the processing of the service by the terminal does not meet the standard based on the average slope: When the average slope is less than or equal to the preset average slope E set in the analysis module, the analysis module determines that the hardware configuration does not meet the standard, issues a hardware maintenance notice and performs scheduling processing, wherein the preset average slope E=1.1 in this embodiment; When the average slope is greater than the preset average slope E, the analysis module determines that the processing capability of the terminal does not meet the standard, and determines whether it is necessary to correct the priority of the subsequent allocation of corresponding types of services to the terminal based on the average processing time, wherein the average processing time is the average processing time of the terminal for various types of services.

[0035] Please continue reading Figure 4 As shown, the analysis module in the embodiment of the present invention is also used to determine whether it is necessary to correct the priority of the corresponding type of service subsequently allocated to the terminal based on the average processing time: When the average processing time is less than or equal to the preset average processing time H set in the analysis module, the analysis module determines that it is necessary to correct the priority of the corresponding type of service subsequently allocated to the terminal, and corrects the priority of the corresponding type of service subsequently allocated to the terminal based on the average processing time, wherein the preset average processing time H=1S in this embodiment; When the average processing time is greater than the preset average processing time H, the analysis module determines that there is no need to correct the priority of the corresponding type of service subsequently allocated to the terminal, the hardware configuration does not meet the standards, and a hardware maintenance notice is issued and scheduling is performed.

[0036] Please continue reading Figure 4 As shown, the analysis module in the embodiment of the present invention is further used to select the terminals to be scheduled of the corresponding types of services for scheduling, and to determine whether to change the services based on the load variation of the terminals to be scheduled during the scheduling process: When the change amplitude of the terminal to be scheduled is less than or equal to the change amplitude A1 of the first preset terminal to be scheduled set in the analysis module, the analysis module determines that the scheduling process is to be continued, wherein the change amplitude A1 of the first preset terminal to be scheduled in this embodiment is 60%; When the change amplitude of the terminal to be scheduled is greater than the change amplitude A1 of the first preset terminal to be scheduled and is less than or equal to the change amplitude A2 of the second preset terminal to be scheduled set in the analysis module, the analysis module determines that the scheduling process needs to be coordinated with other terminals to be scheduled, wherein the change amplitude A2 of the second preset terminal to be scheduled in this embodiment is 80%; When the change range of the terminal to be scheduled is greater than the change range A2 of the second preset terminal to be scheduled, the analysis module determines that the terminal to be scheduled in the scheduling process does not meet the standard and needs to be replaced for scheduling.

[0037] Please continue reading Figure 4 As shown, the analysis module in the embodiment of the present invention is further used to select the terminals to be scheduled of the corresponding types of services for collaborative processing, and increase the service allocation ratio of the terminals to be scheduled based on the change amplitude of the load of the terminals, wherein the service allocation ratio is the ratio of the service allocation amount of the terminals to be scheduled to the terminals: When the change amplitude of the load of the terminal is less than or equal to the change amplitude M1 of the load of the first preset terminal set in the analysis module, the analysis module determines to increase the service allocation ratio to 1.87 times of the initial service allocation ratio, wherein the change amplitude M1 of the load of the first preset terminal in this embodiment is 20%; When the change amplitude of the load of the terminal is greater than the change amplitude M1 of the load of the first preset terminal and is less than or equal to the change amplitude M2 ​​of the load of the second preset terminal set in the analysis module, the analysis module determines to increase the service allocation ratio to 1.53 times the initial service allocation ratio, wherein the change amplitude M2 ​​of the load of the second preset terminal in this embodiment is 39%; When the change amplitude of the load of the terminal is greater than the change amplitude M2 ​​of the load of the second preset terminal, the analysis module determines to increase the service allocation ratio to 1.28 times of the initial service allocation ratio; Specifically, the collaborative processing described in the embodiments of the present invention is performed when the business can be split for processing. For example, the terminal used for customer relationship management can screen customers who meet the standards and interact with them such as following up on sales opportunities, and the terminal used for marketing management can screen users who meet the standards for precision marketing. The terminal used for customer relationship management and the terminal used for marketing management are within the scope of collaborative processing. Please continue reading Figure 4 As shown, the analysis module in the embodiment of the present invention is also used to detect the change range of the occupancy rate of the terminal to be scheduled in real time, and to increase the number of terminals to be scheduled for collaborative processing based on the occupancy rate change rate, wherein the occupancy rate change rate is the ratio of the occupancy rate change range to time: When the change range of the occupancy rate of the terminal to be scheduled is greater than the change range P of the preset occupancy rate of the terminal to be scheduled set in the analysis module, the analysis module determines to increase the number of terminals to be scheduled for collaborative processing based on the occupancy rate change rate, wherein the change range P of the occupancy rate of the terminal to be scheduled is preset to be 52% in this embodiment; When the occupancy rate change rate is greater than the second preset occupancy rate change rate △N2 set in the analysis module, the analysis module determines to increase the number of the terminals to be scheduled for collaborative processing to 1.75 times the number of the terminals to be scheduled for initial collaborative processing, wherein the second preset occupancy rate change rate △N2 in this embodiment is 18% / S; When the occupancy rate change rate is less than or equal to the second preset occupancy rate change rate △N2 and greater than the first preset occupancy rate change rate △N1 set in the analysis module, the analysis module determines to increase the number of the terminals to be scheduled for collaborative processing to 1.52 times the number of the terminals to be scheduled for initial collaborative processing, wherein the first preset occupancy rate change rate △N1 in this embodiment is 10% / S; When the occupancy rate change rate is less than or equal to the first preset occupancy rate change rate ΔN1, the analysis module determines to increase the number of the terminals to be scheduled for collaborative processing to 1.27 times the number of the terminals to be scheduled for initial collaborative processing.

[0038] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An AI business management system based on enterprise big model data, characterized in that: include: An information acquisition module, used to receive enterprise information and determine the business in the enterprise; A classification module, which is connected to the information acquisition module and is used to classify the services; A plurality of terminals, each of which is connected to the classification module, for processing the corresponding services after the classification; A detection module, which is connected to each of the terminals, and is used to record the time nodes when each terminal starts to process the service and when the service processing is completed, and to detect the load and occupancy rate of each terminal during operation; an analysis module connected to the detection module, for determining whether the processing of the service by the terminal meets the standard based on the occupancy rate, and for analyzing the reason for not meeting the standard based on the time node and the load; the analysis module is also used to generate a corresponding scheduling instruction according to the determined reason for not meeting the standard; A scheduling module is connected to each of the terminals and the analysis module, and is used to schedule the services processed by the corresponding terminals based on the received scheduling instructions.

2. The AI ​​business management system based on enterprise big model data according to claim 1 is characterized in that: The analysis module is used to determine whether the processing of the service by the terminal meets the standard based on the occupancy rate, and, if it is determined that the processing of the service by the terminal does not meet the standard, determine whether the operation of the terminal meets the standard based on the historical service processing duration, or determine the reason why the processing of the service by the terminal does not meet the standard based on the load; The historical service processing time is the average processing time of each historical service by the terminal, and the processing time of a single historical service is the time interval between the time point when the terminal starts processing the service and the time point when the service processing is completed.

3. The AI ​​business management system based on enterprise big model data according to claim 2 is characterized in that: The analysis module is also used to determine whether the operation of the terminal meets the standards based on the historical business processing time, and, based on the determination result and based on the type label of the historical business processed by the terminal, record the terminal as a terminal to be scheduled for the corresponding type of business, or, based on the time difference, increase the data compression ratio during data transmission within the system; wherein the time difference is the difference between the historical business processing time and the pre-stored preset historical business processing time.

4. The AI ​​business management system based on enterprise big model data according to claim 3 is characterized in that: The analysis module is further configured to increase the data compression ratio during data transmission within the system based on the time difference, and the increase in the data compression ratio is proportional to the time difference.

5. The AI ​​business management system based on enterprise big model data according to claim 4 is characterized in that: The analysis module is also used to determine the reason why the terminal's processing of the service does not meet the standards based on the historical load variance, and to generate corresponding scheduling instructions based on the determined reasons, including: issuing a hardware maintenance notice and performing scheduling processing when it is determined that the hardware configuration does not meet the standards, determining the reason why the terminal's processing of the service does not meet the standards based on the average slope, or selecting the terminal to be scheduled for the corresponding type of service for collaborative processing when the workload of the service allocated to the terminal is higher than the processing capacity; wherein the historical load variance is the variance of the historical load, and the average slope is the average slope of the established time-historical load curve.

6. The AI ​​business management system based on enterprise big model data according to claim 5 is characterized in that: The analysis module is also used to determine the reason why the terminal's processing of the service does not meet the standards based on the average slope, and issue a hardware maintenance notice and perform scheduling processing according to the determination result, or, based on the average processing time, determine whether it is necessary to correct the priority of the corresponding type of service subsequently allocated to the terminal; wherein the average processing time is the average processing time of the terminal for various types of services.

7. The AI ​​business management system based on enterprise big model data according to claim 6 is characterized in that: The analysis module is also used to determine whether it is necessary to correct the priority of the corresponding type of service subsequently allocated to the terminal based on the average processing time, and to correct the priority of the corresponding type of service subsequently allocated to the terminal based on the average processing time according to the determination result, or to issue a hardware maintenance notification and perform scheduling processing.

8. The AI ​​business management system based on enterprise big model data according to claim 7 is characterized in that: The analysis module is further used to select the terminals to be scheduled of the corresponding types of services for scheduling, and to determine whether to change the services based on the change range of the loads of the terminals to be scheduled during the scheduling process.

9. The AI ​​business management system based on enterprise big model data according to claim 5 is characterized in that: The analysis module is also used to select the terminals to be scheduled with corresponding types of services for collaborative processing, and increase the service allocation ratio of the terminals and the terminals to be scheduled based on the change amplitude of the load of the terminals; wherein the service allocation ratio is the ratio of the service allocation amount of the terminals to be scheduled to that of the terminals.

10. The AI ​​business management system based on enterprise big model data according to claim 9 is characterized in that: The analysis module is also used to detect the change range of the occupancy rate of the terminals to be scheduled in real time, and to increase the number of terminals to be scheduled for collaborative processing based on the occupancy rate change rate; wherein the occupancy rate change rate is the ratio of the occupancy rate change range to time.

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