Service area commercial operation big data analysis system based on Internet of Things

Abstract

The invention discloses a service area commercial operation big data analysis system based on the Internet of Things, and belongs to the technical field of data analysis. The method and the device are used for solving the technical problems of poor active supervision processing effect and poor multi-dimensional optimization prompting effect of different source data operated in a service area in the existing scheme. Monitoring and processing different subsystems of business operation in the service area in a complete aspect and an accurate aspect, combining monitoring and processing data of different subsystems corresponding to different aspects, and performing data processing and analysis on different subsystems of business operation in the service area in different levels by using the obtained monitoring and processing set. And according to an analysis result, targeted index early warning prompt and supervision coverage optimization prompt are adaptively performed on different subsystems, so that diversified supervision and processing integration of data of different sources of different subsystems of business operation in a service area are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a service area commercial operation big data analysis system based on Internet of Things. BACKGROUND

[0002] Service area commercial operation data refers to the relevant data collected and analyzed in a highway service area or other traffic service area for the purpose of managing and optimizing commercial activities. These data help managers understand customer behavior, evaluate business performance, optimize resource allocation, and develop marketing strategies, etc.

[0003] Since service area operation involves data from multiple systems and platforms, including sales systems, inventory management systems, customer relationship management systems, etc., the existing service area commercial operation big data analysis scheme cannot diversify the supervision and processing integration of data from different sources when implemented, resulting in poor active supervision and processing effect of data from different sources and poor multi-dimensional optimization prompt effect. SUMMARY

[0004] The purpose of the present application is to provide a service area commercial operation big data analysis system based on Internet of Things, which solves the technical problems of poor active supervision and processing effect of data from different sources and poor multi-dimensional optimization prompt effect in the existing scheme.

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] A service area commercial operation big data analysis system based on Internet of Things, comprising:

[0007] A commercial operation system multi-dimensional supervision and processing module for supervising and processing different subsystems of service area commercial operation in terms of completeness and accuracy, and combining the supervision and processing data of different subsystems corresponding to different aspects to obtain a supervision and processing set corresponding to different subsystems;

[0008] A commercial operation system multi-dimensional data analysis module for processing and analyzing data of different subsystems of service area commercial operation in different layers using the supervision and processing set, and adaptively providing targeted index early warning prompts and supervision coverage optimization prompts for different subsystems according to the analysis results;

[0009] Among them, the different subsystems are sequentially processed and analyzed in terms of supervision defects, the supervision coverage states of all existing complete indexes and accurate indexes corresponding to different subsystems are determined, the selected subsystems are marked and subjected to active optimization analysis of supervision coverage, and the first supervision coverage optimization prompt or the second supervision coverage optimization prompt is dynamically implemented for the selected subsystems according to the analysis results.

[0010] Preferably, when the different subsystems of the service area business operation are monitored and processed in the integrity aspect, the monitoring data of the several integrity indexes corresponding to the different subsystems are sequentially obtained and sorted and combined respectively to obtain the integrity index monitoring sequence corresponding to the different subsystems;

[0011] The data analysis is performed on the integrity index monitoring sequence, and according to the analysis result, the corresponding subsystem is marked as a first subsystem, or the abnormal integrity index is marked as an abnormal integrity index, and the belonging subsystem is marked as a second subsystem;

[0012] The marked all first subsystems, second subsystems and all abnormal integrity indexes are sorted and combined to obtain first supervision and processing data.

[0013] Preferably, when the different subsystems of the service area business operation are monitored and processed in the accuracy aspect, the monitoring data of the several accuracy indexes corresponding to the different subsystems are sequentially obtained and sorted and combined respectively to obtain the accuracy index monitoring sequence corresponding to the different subsystems;

[0014] The data analysis is performed on the accuracy index monitoring sequence, and according to the analysis result, the belonging subsystem is marked as a third subsystem, or the abnormal accuracy index is marked as an abnormal accuracy index, and the belonging subsystem is marked as a third subsystem;

[0015] The marked all third subsystems, fourth subsystems and all abnormal accuracy indexes are sorted and combined to obtain second supervision and processing data;

[0016] The first supervision and processing data and the second supervision and processing data are sorted and combined to obtain the supervision and processing set corresponding to the different subsystems.

[0017] Preferably, when the different subsystems of the service area business operation are analyzed and actively prompted in the overall aspect by using the supervision and processing set, the index early warning prompt of all the abnormal integrity indexes and the abnormal accuracy indexes of the different subsystems is sequentially performed according to the first supervision and processing data and the second supervision and processing data in the supervision and processing set.

[0018] Preferably, when the different subsystems of the service area business operation are analyzed and actively prompted in the local aspect by using the supervision and processing set, the resource occupation data of the running of all the indexes in the integrity aspect and the accuracy aspect corresponding to the different subsystems is obtained, and all the abnormal data not monitored in the integrity aspect and the accuracy aspect corresponding to the different subsystems is obtained;

[0019] When the different subsystems are sequentially analyzed and processed in the supervision defect aspect, the total number of abnormal findings of all the integrity indexes and the accuracy indexes of the subsystems is counted, and the total number of abnormal findings not found of all the indexes of the subsystems is counted, and the supervision coverage value of the corresponding subsystem is calculated and obtained according to the total number of abnormal findings and the total number of abnormal findings not found.

[0020] Preferably, data analysis is performed on the regulatory coverage value to determine the regulatory coverage status corresponding to all complete indicators and accurate indicators of the corresponding subsystem;

[0021] If the regulatory coverage value is less than or equal to 0, it is prompted that the regulatory coverage status of the subsystem is normal.

[0022] On the contrary, it is prompted that the regulatory coverage status of the subsystem is abnormal, and the subsystem is marked as a selected subsystem.

[0023] Preferably, when the selected subsystem is subjected to active optimization analysis of regulatory coverage, the maximum occupied resource amount when all complete indicators and accurate indicators in the selected subsystem are running simultaneously is obtained, and the regulatory processing value of the selected subsystem is obtained by calculation according to the maximum occupied resource amount.

[0024] Preferably, data analysis is performed on the regulatory processing value obtained by calculation when the selected subsystem is subjected to active optimization prompt of regulatory coverage.

[0025] If the regulatory processing value is greater than 0, it is determined that the regulatory coverage of the selected subsystem is expandable, and a first regulatory coverage optimization prompt is implemented on the selected subsystem.

[0026] On the contrary, it is determined that the regulatory coverage of the selected subsystem is not expandable, and a second regulatory coverage optimization prompt is implemented on the selected subsystem.

[0027] Preferably, the regulatory coverage value JF of the corresponding subsystem is calculated by the formula ; wherein YF and WF are the total number of abnormal findings and the total number of abnormal findings not found in the subsystem, respectively; and a is the regulatory coverage specification value.

[0028] Preferably, the regulatory processing value JC of the selected subsystem is calculated by the formula JC=ZZ-ZZ'-ZZ"; wherein ZZ is the maximum occupied resource amount when all complete indicators and accurate indicators in the selected subsystem are running simultaneously; ZZ' is the standard occupied resource amount corresponding to the selected subsystem; and ZZ" is the standard local occupied resource amount corresponding to the selected subsystem.

[0029] Compared with the prior art, the present application has the following advantages:

[0030] The present application realizes the supervision and processing of different subsystems of the service area business operation in the complete aspect and the accurate aspect, and combines the supervision and processing data of different aspects corresponding to different subsystems, which can realize the supervision and processing of different subsystems from different aspects, and can provide diversified local supervision data support for subsequent mining and analysis of different subsystems at different levels, thereby improving the diversity of supervision and processing of existing different aspect indicator data of different subsystems.

[0031] The present application utilizes the supervision processing set to perform data processing analysis on different levels of different subsystems of the service area commercial operation, and according to the analysis results, adaptively performs targeted index early warning prompt and supervision coverage optimization prompt on different subsystems, realizes diversified supervision and processing integration of data of different sources of different subsystems of the service area commercial operation, and can effectively improve the active supervision processing effect and multi-dimensional optimization prompt effect of different source data. BRIEF DESCRIPTION OF DRAWINGS

[0032] The present application will be further described below in conjunction with the accompanying drawings.

[0033] Figure 1 A flowchart of the operation of the service area commercial operation big data analysis system based on the Internet of Things according to the present application.

[0034] Figure 2 A flowchart of the data analysis of the supervision coverage value in the present application.

[0035] Figure 3 A module block diagram of the service area commercial operation big data analysis system based on the Internet of Things according to the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0037] As shown in Figure 1 and Figure 3 The present application is a service area commercial operation big data analysis system based on the Internet of Things, which comprises a commercial operation system multi-dimensional supervision processing module and a commercial operation system multi-dimensional data analysis module.

[0038] The commercial operation system multi-dimensional supervision processing module is used for supervising and processing different subsystems of the service area commercial operation in a complete aspect and an accurate aspect, and combining the supervision and processing data of different aspects corresponding to different subsystems to obtain a supervision processing set corresponding to different subsystems; and comprises:

[0039] When supervising and processing different subsystems of the service area commercial operation in a complete aspect, the monitoring data of a plurality of complete indexes corresponding to different subsystems are sequentially obtained and sorted and combined to obtain a complete index monitoring sequence corresponding to different subsystems;

[0040] Among them, the different subsystems include but are not limited to a sales system, a stock management system, and a customer relationship management system.

[0041] The number and content of the complete indicators are not limited, for example, the complete indicators are the missing field ratio and the partition coverage rate.

[0042] The missing field ratio is the ratio of empty or unfilled fields (for example, the ratio of missing customer ID in POS transaction records)

[0043] The partition coverage rate is the coverage range of data according to the time / region dimension (for example, whether the inventory data of all service areas is collected)

[0044] It should be noted that the identification and monitoring statistics of different complete indicators can be realized based on existing technical means, and the specific implementation steps are not described here.

[0045] The data analysis of the complete indicator monitoring sequence is performed, and if the elements in the complete indicator monitoring sequence belong to the corresponding standard range, it is determined that the data completeness of the subsystem is normal, and the subsystem is marked as the first subsystem.

[0046] It should be explained that different complete indicators are pre-set with a corresponding standard range, and the specific value of the standard range is not limited, which can be determined according to the initial design data of the subsystem developed in advance, or can be expanded or reduced according to the application requirements of the actual application scene.

[0047] If the elements in the complete indicator monitoring sequence do not belong to the corresponding standard range, it is determined that the data completeness of the subsystem is abnormal, and the corresponding complete indicator is marked as an abnormal complete indicator, and the subsystem is marked as the second subsystem.

[0048] All the first subsystems, the second subsystems and all the abnormal complete indicators are sorted and combined to obtain the first monitoring processing data.

[0049] In the embodiment of the application, the different subsystems of the service area commercial operation are monitored and processed from the complete aspect, and the first monitoring processing data obtained by processing the different subsystems from the complete aspect is obtained, which can provide reliable local monitoring data support for subsequent data mining analysis of different subsystems at different levels.

[0050] When the different subsystems of the service area commercial operation are accurately monitored and processed, the monitoring data of a plurality of accurate indicators corresponding to different subsystems are sequentially obtained and sorted and combined to obtain the accurate indicator monitoring sequence corresponding to different subsystems.

[0051] The number and content of the accurate indicators are also not limited, for example, the accurate indicators are the primary key repetition rate, the field mapping difference rate and the distributed transaction rollback rate.

[0052] Primary key duplication rate: check for unique identification conflicts (e.g. CRM customer ID duplication after merging);

[0053] Field mapping difference rate: count the number of mapping failures (e.g. conversion failure rate of CRM "membership level" to "VIP level" in the points system);

[0054] Distributed transaction rollback rate: frequency of compensation triggering in Saga mode (e.g. collaborative failure rate of order creation and inventory deduction);

[0055] Similarly, the identification and monitoring statistics of different accuracy indicators can be realized based on existing technical means, and the specific implementation steps are not described here.

[0056] Data analysis is performed on the accuracy indicator monitoring sequence, and if the elements in the accuracy indicator monitoring sequence belong to the corresponding standard range, it is determined that the data accuracy of the subsystem is normal, and it is marked as a third subsystem.

[0057] Different accuracy indicators are pre-set with a corresponding standard range, and the specific value of the standard range is not limited, and the specific acquisition method is the same as that of the standard range corresponding to the complete indicator, which is not described here.

[0058] If the elements in the accuracy indicator monitoring sequence do not belong to the corresponding standard range, it is determined that the data accuracy of the subsystem is abnormal, and the corresponding accuracy indicator is marked as an abnormal accuracy indicator, and the subsystem is marked as a third subsystem.

[0059] All third subsystems, fourth subsystems and all abnormal accuracy indicators are sorted and combined to obtain second supervision processing data.

[0060] The first supervision processing data and the second supervision processing data are sorted and combined to obtain a supervision processing set corresponding to different subsystems.

[0061] In the embodiments of the present application, by monitoring and processing different subsystems of the service area business operation in terms of completeness and accuracy, and combining the supervision and processing data of different subsystems corresponding to different aspects, different subsystems can be monitored and processed from different aspects, and diversified local supervision data support can be provided for subsequent mining and analysis of different subsystems at different levels, improving the diversity of existing different aspect indicator data supervision and processing of different subsystems.

[0062] The business operation system multi-dimensional data analysis module is used for performing data processing and analysis of different subsystems of the service area business operation using the supervision processing set, and adaptively providing targeted indicator early warning and supervision coverage optimization prompts for different subsystems according to the analysis results; comprising:

[0063] When the regulatory processing set is used to perform overall-level data analysis and active prompting on different subsystems of the service area business operation, the first regulatory processing data and the second regulatory processing data in the regulatory processing set are used to sequentially perform index early warning prompting of all abnormal complete indexes and abnormal accurate indexes on different subsystems;

[0064] Specifically, the synchronous pushing can be performed on the operation and maintenance personnel of the corresponding subsystems, so that the targeted abnormal operation and maintenance of the subsystems can be performed in a timely and efficient manner.

[0065] In addition, when the regulatory processing set is used to perform local-level data analysis and active prompting on different subsystems of the service area business operation, resource occupation data of all indexes of the corresponding complete aspect and accurate aspect of different subsystems is obtained, and all abnormal data of the corresponding complete aspect and accurate aspect of different subsystems that is not monitored is obtained.

[0066] The resource occupation data includes the corresponding occupation resource amount of single, multiple and all index data processing in the subsystem. The all abnormal data can be obtained according to the operation and maintenance data of different subsystems and the feedback data of the staff. It can be understood that the existing complete regulatory scheme and accurate regulatory scheme of the subsystem has a loophole and does not achieve comprehensive regulatory coverage.

[0067] When the regulatory defect aspect of different subsystems is sequentially analyzed, the total number of abnormal findings of all complete indexes and accurate indexes of the subsystems is counted, and the total number of abnormal findings of all indexes of the subsystems that is not monitored is counted, and the regulatory coverage value JF of the corresponding subsystem is calculated by the formula ; in the formula, YF and WF are the total number of abnormal findings and the total number of abnormal findings of the subsystems that is not monitored, respectively; and a is a regulatory coverage specification value, and the specific value is not limited, which can be determined according to the initial design parameters of the subsystem in the early stage, or can be customized according to the application requirements of the actual application scene.

[0068] It should be noted that the regulatory coverage value is used to simultaneously calculate the abnormal data of different aspects of the subsystem, and digitally represents the regulatory coverage state of all indexes of the subsystem. The smaller the regulatory coverage value is, the more normal the regulatory coverage state of the corresponding subsystem is.

[0069] In addition, the formula calculation involved in the embodiment of the application is standardized before data calculation, and the standardization includes but is not limited to unitization and dimensionality of the calculated data.

[0070] Meanwhile, the data calculation of the regulatory coverage value in this invention can also be achieved through existing technical means, such as using a trained regulatory coverage model to perform data analysis on the total number of anomalies discovered and the total number of anomalies not discovered in the subsystem, and setting the value output by the regulatory coverage model as the regulatory coverage value.

[0071] Among them, the regulatory coverage model can be built based on the artificial intelligence model. The artificial intelligence model can be trained and constructed through sample training data, and after training is completed, it is marked as the regulatory coverage model. The artificial intelligence model includes, but is not limited to, the BP neural network model and the RBF neural network model.

[0072] The sample training data includes standard input data with attributes consistent with the total number of anomalies found and the total number of anomalies not found, as well as standard output data representing the status of regulatory coverage.

[0073] The sample training data is obtained through historical operation and maintenance data. The training of the artificial intelligence model is carried out using existing conventional technical methods. The specific implementation steps will not be elaborated here.

[0074] like Figure 2 As shown, data analysis is performed on the regulatory coverage value to determine the regulatory coverage status corresponding to all existing complete and accurate indicators of the corresponding subsystem.

[0075] If the regulatory coverage value is less than or equal to 0, it indicates that the regulatory coverage status of the subsystem is normal.

[0076] Conversely, it will indicate that the monitoring coverage status of the subsystem is abnormal and mark the subsystem as a selected subsystem;

[0077] It needs to be explained that the existing subsystem operation monitoring mostly still relies on a few preset monitoring indicators for fixed monitoring and early warning, and the monitoring indicators are dynamically adjusted by staff in this field based on their work experience. This cannot achieve automated identification and analysis of the subsystem operation monitoring status.

[0078] In this embodiment of the invention, by performing joint calculations on abnormal data from different aspects of the subsystem, the regulatory coverage status of all existing indicators of the subsystem is digitally represented, which can provide reliable regulatory data support for proactive optimization prompts of regulatory coverage of different subsystems in the future.

[0079] In the active optimization analysis of the supervision coverage of the selected subsystem, the maximum occupied resource amount of all complete indexes and accurate indexes in the selected subsystem is obtained, and the supervision processing value JC of the selected subsystem is calculated according to the maximum occupied resource amount through the formula JC=ZZ-ZZ'-ZZ"; in the formula, ZZ is the maximum occupied resource amount of all complete indexes and accurate indexes in the selected subsystem when running simultaneously; ZZ' is the standard occupied resource amount corresponding to the selected subsystem, and the specific value is not limited, which can be determined according to the initial design parameters of the corresponding subsystem in the early development, or can be customized according to the application requirements of the actual application scene; ZZ" is the standard local occupied resource amount corresponding to the selected subsystem, and the specific value is the local occupied resource amount with the largest value when a single index runs and processes, or can be the median value of all local occupied resource amounts when different indexes run and process;

[0080] It should be noted that the supervision processing value is used to process and calculate different resource occupation data of the selected subsystem, so as to digitally represent the corresponding supervision coverage expansion state;

[0081] In addition, the data calculation of the supervision processing value in the present application can also be realized by existing technical means, for example, the supervision coverage value is realized by the existing technical means;

[0082] According to the supervision processing value, the active optimization prompt of the supervision coverage of the selected subsystem is performed, and the data analysis of the calculated supervision processing value is performed;

[0083] If the supervision processing value is greater than 0, it is determined that the supervision coverage of the selected subsystem is expandable, and the first supervision coverage optimization prompt of the selected subsystem is implemented;

[0084] On the contrary, it is determined that the supervision coverage of the selected subsystem is not expandable, and the second supervision coverage optimization prompt of the selected subsystem is implemented;

[0085] The first supervision coverage optimization, specifically, can add a plurality of monitoring indexes to the selected subsystem, so as to improve the supervision coverage capability of the selected subsystem while not affecting the overall operation effect of the selected subsystem;

[0086] The second supervision coverage optimization, specifically, can replace a plurality of monitoring indexes in the selected subsystem to improve the supervision coverage capability of the selected subsystem, so as to avoid affecting the efficiency and stability of the overall operation of the selected subsystem by the operation of the plurality of newly added monitoring indexes.

[0087] In the embodiment of the present application, the supervision processing set is used to perform data processing analysis at different levels on different subsystems of the service area commercial operation, and according to the analysis results, the different subsystems are adaptively prompted with index early warning and supervision coverage optimization, so that the data of different sources of different subsystems of the service area commercial operation are diversifiedly supervised and processed, and the active supervision and processing effect and the multi-dimensional optimization prompting effect of the data of different sources are effectively improved.

[0088] In several embodiments provided by the present application, it should be understood that the disclosed system can be implemented in other manners. For example, the embodiments of the application described above are merely schematic; for example, the division of the modules is only a logical function division; and there can be another division manner in actual implementation.

[0089] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, and can be located in one place or distributed on a plurality of network modules. According to actual needs, some or all of the modules can be selected to achieve the purpose of the embodiments of the present application.

[0090] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of hardware plus software functional module.

[0091] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the essential characteristics of the present application.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An Internet of Things-based service area commercial operation big data analysis system, characterized in that, The application relates to a multi-dimensional supervision and processing module for a commercial operation system, a multi-dimensional data analysis module for the commercial operation system, and a method for supervising and processing a commercial operation system. The multi-dimensional supervision and processing module is used for supervising and processing different subsystems of a commercial operation system in terms of completeness and accuracy, and combining supervision and processing data corresponding to different aspects of different subsystems to obtain supervision and processing sets corresponding to different subsystems. The multi-dimensional data analysis module is used for processing and analyzing data of different subsystems of a commercial operation system in different layers by using the supervision and processing sets, and adaptively giving targeted index early warning and supervision coverage optimization suggestions according to analysis results. The method comprises the following steps: supervising and processing different subsystems of a commercial operation system in terms of completeness, obtaining monitoring data of several complete indexes corresponding to different subsystems, and respectively sorting and combining the monitoring data to obtain complete index monitoring sequences corresponding to different subsystems. 2.The service area commercial operation big data analysis system based on the Internet of Things according to claim 1, wherein, The method comprises the following steps: analyzing the complete index monitoring sequences, marking a corresponding subsystem as a first subsystem according to analysis results, or marking an abnormal complete index as an abnormal complete index, and marking a corresponding subsystem as a second subsystem. The method comprises the following steps: sorting and combining all marked first subsystems, second subsystems and all abnormal complete indexes to obtain first supervision and processing data. The method comprises the following steps: supervising and processing different subsystems of a commercial operation system in terms of accuracy, obtaining monitoring data of several accurate indexes corresponding to different subsystems, and respectively sorting and combining the monitoring data to obtain accurate index monitoring sequences corresponding to different subsystems. 3.The service area business operation big data analysis system based on the Internet of Things according to claim 2, characterized in that, The method comprises the following steps: analyzing the accurate index monitoring sequences, marking a corresponding subsystem as a third subsystem according to analysis results, or marking an abnormal accurate index as an abnormal accurate index, and marking a corresponding subsystem as a fourth subsystem. The method comprises the following steps: sorting and combining all marked third subsystems, fourth subsystems and all abnormal accurate indexes to obtain second supervision and processing data. The method comprises the following steps: sorting and combining the first supervision and processing data and the second supervision and processing data to obtain supervision and processing sets corresponding to different subsystems. The method comprises the following steps: when the supervision and processing sets are used for analyzing data and giving active suggestions of different subsystems of a commercial operation system in an overall layer, index early warning of all abnormal complete indexes and abnormal accurate indexes of different subsystems is given according to the first supervision and processing data and the second supervision and processing data in the supervision and processing sets. 4.The service area business operation big data analysis system based on the Internet of Things according to claim 3, wherein, The method comprises the following steps: when the supervision and processing sets are used for analyzing data and giving active suggestions of different subsystems of a commercial operation system in a local layer, resource occupation data of all indexes running in terms of completeness and accuracy of different subsystems is obtained, and all abnormal data not monitored in terms of completeness and accuracy of different subsystems is obtained. 5.The service area business operation big data analysis system based on the Internet of Things according to claim 4, wherein, ​ When the processing analysis of the monitoring defects of different subsystems is performed in sequence, the total number of abnormal findings of the monitoring statistics of all complete indicators and accurate indicators of the subsystem is counted, and the total number of un-found abnormalities of the subsystem corresponding to all un-monitored indicators is counted, and the monitoring coverage value of the corresponding subsystem is calculated and obtained according to the total number of abnormal findings and the total number of un-found abnormalities. 6.The service area business operation big data analysis system based on the Internet of Things according to claim 5, wherein, The data analysis of the monitoring coverage value is performed to determine the monitoring coverage state of all complete indicators and accurate indicators corresponding to the corresponding subsystem; If the monitoring coverage value is less than or equal to 0, it is prompted that the monitoring coverage state of the subsystem is normal. Otherwise, it is prompted that the monitoring coverage state of the subsystem is abnormal, and the subsystem is marked as a selected subsystem. 7.The service area business operation big data analysis system based on the Internet of Things according to claim 6, wherein, When the active optimization analysis of the monitoring coverage of the selected subsystem is performed, the maximum occupied resource amount of all complete indicators and accurate indicators of the selected subsystem when running simultaneously is obtained, and the monitoring processing value of the selected subsystem is calculated and obtained according to the maximum occupied resource amount. 8.The service area business operation big data analysis system based on the Internet of Things according to claim 7, wherein, When the active optimization prompt of the monitoring coverage of the selected subsystem is performed according to the monitoring processing value, the data analysis of the calculated monitoring processing value is performed; If the monitoring processing value is greater than 0, it is determined that the monitoring coverage of the selected subsystem is expandable, and a first monitoring coverage optimization prompt is implemented for the selected subsystem. Otherwise, it is determined that the monitoring coverage of the selected subsystem is not expandable, and a second monitoring coverage optimization prompt is implemented for the selected subsystem. 9.The service area business operation big data analysis system based on the Internet of Things according to claim 5, wherein, The regulatory coverage value JF of the corresponding subsystem is calculated by the formula YF, WF, and α are the total number of abnormality findings, the total number of abnormality non-findings, and the regulatory coverage specification value of the subsystem, respectively. 10.The service area business operation big data analysis system based on the Internet of Things according to claim 7, wherein, The monitoring processing value JC of the selected subsystem is calculated by the formula JC=ZZ-ZZ'-ZZ"; in the formula, ZZ is the maximum occupied resource amount of all complete indicators and accurate indicators of the selected subsystem when running simultaneously; ZZ' is the standard occupied resource amount corresponding to the selected subsystem; and ZZ" is the standard local occupied resource amount corresponding to the selected subsystem.

Images