Intelligent management system for publication retail information processing based on artificial intelligence

Abstract

The application discloses a publication retail information processing intelligent management system based on artificial intelligence and belongs to the technical field of publication management; through data processing and mining analysis on the publication retail information uploaded to the publication retail management platform in real time, publication state digital data corresponding to different publication data items of a press can be acquired; through multidimensional data processing calculation and data analysis on the local supervision result acquired through the publication retail information processing, different local publication abnormal states and overall publication abnormal states corresponding to an abnormal press are obtained, and targeted optimization management is implemented on the subsequent publication of the abnormal press according to the analysis result; and the application is used for solving the technical problems that the self-mining analysis effect of the publication retail information in the prior art is poor in local publication and overall publication and the targeted optimization management effect is poor.

Description

Technical Field

[0001] This invention relates to the field of publication management technology, and more specifically to an intelligent management system for processing publication retail information based on artificial intelligence. Background Technology

[0002] Retail information processing of publications is the process of collecting, organizing, analyzing, and utilizing information related to the sale of publications. This process aims to improve retail efficiency and service quality, and better meet consumer needs.

[0003] Existing publication retail information processing and management solutions, when implemented, cannot process and analyze publication retail information from different dimensions to obtain different regulatory results for different publishers, and cannot conduct targeted management of subsequent publications by different publishers based on the regulatory results. This results in poor self-mining and analysis of publication retail information in both local and overall publications, as well as poor targeted optimization management. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent management system for processing publication retail information based on artificial intelligence, which solves the technical problems of poor autonomous mining and analysis of publication retail information in partial and overall publications and poor targeted optimization management in existing solutions.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] The intelligent management system for processing publication retail information based on artificial intelligence includes a publication retail management platform, as well as a publication retail information mining and processing module and a publication retail information utilization and management module that communicate with the publication retail management platform.

[0007] The publication retail information mining and processing module is used to process and analyze the publication retail information uploaded to the publication retail management platform in real time, obtain the corresponding local supervision results of the publication retail information, and associate and store them. The local supervision results include the processed return and exchange analysis sequence and the filled publication anomaly identifier.

[0008] The publication retail information management module is used to perform multi-dimensional data processing and calculation on the local supervision results obtained from the processing of uploaded publication retail information. The multi-dimensional data processing and calculation results are analyzed to determine the different local publication anomalies and the overall publication anomalies of the respective publishers, and to implement targeted optimization management for the subsequent publications of the respective publishers.

[0009] Among them, the local supervision results corresponding to the publisher are obtained and the value of the last element is analyzed to obtain the normal publisher or the abnormal publisher.

[0010] Identify different abnormal publishing data items corresponding to abnormal publishers, calculate and analyze the local abnormal publishing status corresponding to different abnormal publishing data items, as well as the overall abnormal publishing status of abnormal publishers, and implement targeted optimization management.

[0011] Preferably, the information type corresponding to the publication retail information uploaded to the publication retail management platform in real time is obtained. If the information type is the sales type, the publisher, publication name, author, and category in the uploaded publication retail information are obtained, and the total sales of the corresponding publication data item is incremented by one.

[0012] If the information type is a return / exchange, then retrieve the publisher, publication name, author, and category from the uploaded publication retail information, and increment the total number of returns / exchanges for the corresponding publication data items by one.

[0013] Preferably, the return rate corresponding to different publication data items of the publishing house is obtained according to the return type, and the return rates corresponding to different publication data items are sorted and combined to obtain the return supervision sequence corresponding to the publishing house.

[0014] When mining and analyzing the obtained return and replacement supervision sequence, the different elements in the return and replacement supervision sequence are sequentially analyzed by the publication anomaly identification function and the corresponding publication identification identifier BSk is output; k is a different publication data item, k = 1, 2, 3;

[0015] The publication identification identifier contains a value of 0 or 1, indicating whether the publication status of the corresponding publication data item is normal or abnormal;

[0016] The different publication identifiers output are sorted and combined to obtain the return analysis sequence corresponding to the respective publisher.

[0017] Preferably, the expression for the publication anomaly detection function is: In the formula, HLk is the return rate corresponding to different publication data items; HL0k is the standard return rate corresponding to different publication data items.

[0018] Preferably, the return analysis sequence is traversed and the total number of abnormal elements with a value of 1 is counted, and the value of the total number of abnormal elements is set as the publication anomaly identifier;

[0019] The abnormal publication identifier is filled into the element position after the last element in the return and replacement analysis sequence to obtain the local supervision result corresponding to the publishing house.

[0020] Preferably, the local monitoring results corresponding to the publisher are obtained and the value of the last element is analyzed. If the value of the last element is 0, a single retail overall normal instruction is generated and the publisher is marked as a normal publisher. The existing publishing plan is maintained for the publisher.

[0021] If the value of the last element is not 0, a single retail overall abnormal instruction is generated and the publisher is marked as an abnormal publisher. The publication data item with a value of 1 in the return and exchange analysis sequence corresponding to the abnormal publisher is marked as an abnormal publication data item. The local abnormal publication coefficient JYk′ corresponding to different abnormal publication data items of the abnormal publisher is obtained by calculating the formula JYk′=QSk′×(HLk′-HL0k′); where HLk′ is the return and exchange rate corresponding to the abnormal publication data item; HL0k′ is the standard return and exchange rate corresponding to the abnormal publication data item; and QSk′ is the publication weight corresponding to the abnormal publication data item.

[0022] Preferably, when determining the local publishing anomaly status of the corresponding abnormal publishing data item of the abnormal publisher through data analysis of the local abnormal publishing coefficient, the formula is used. Calculate and analyze the local abnormal publication status identifier BJk′ corresponding to different abnormal publication data items of abnormal publishers; where JY0k′ is the local abnormal publication standard value of the corresponding abnormal publication data item; [*] is the rounding function;

[0023] If the local abnormal publication status flag is 0, a local minor abnormal publication status will be generated and a prompt will be displayed.

[0024] If the local abnormal publication status flag is greater than 0, a local severe abnormal publication status will be generated and a prompt will be displayed.

[0025] Preferably, by formula Calculate the overall abnormal publication coefficient ZY for all abnormal publication data items corresponding to the abnormal publisher; where n is the total number of abnormal publication data items corresponding to the abnormal publisher.

[0026] The data is analyzed using the first overall abnormal publication identification function, and the overall abnormal publication status identifier BZ corresponding to the abnormal publisher is output.

[0027] The expression for the first overall abnormal publication identification function is: In the formula, BJ0 is the overall abnormal publication standard value.

[0028] Preferably, the data is analyzed using the second overall abnormal publication identification function, and the overall abnormal publication status identifier BZ corresponding to the abnormal publisher is output.

[0029] The expression for the second overall abnormal publication identification function is as follows: In the formula, N1 and N2 are the total number of locally mildly abnormal publishing states and the total number of locally severe abnormal publishing states for different abnormal publishing data items corresponding to abnormal publishers, respectively.

[0030] Preferably, when analyzing the overall abnormal publishing status identifiers obtained through different methods to determine the overall abnormal publishing status of abnormal publishers and implementing targeted optimization management;

[0031] If the overall abnormal publication status is marked as -1, an overall slightly abnormal publication status will be generated and a prompt will be made to implement the first publication optimization management plan.

[0032] If the overall abnormal publication status is marked as -2, an overall severe abnormal publication status will be generated and a second publication optimization management plan will be prompted.

[0033] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0034] This invention processes and analyzes the retail information of publications uploaded in real time to the publication retail management platform. It can obtain digital data on the publishing status corresponding to different publishing data items of the publisher, as well as digital data on the overall status of a single retail transaction corresponding to the real-time uploaded retail information of the publications. This can provide diverse and reliable single retail supervision support for subsequent multi-dimensional data processing and analysis of different publishers, and improve the diversity of the initial publication retail data processing.

[0035] This invention performs multi-dimensional data processing and analysis on the local regulatory results obtained from the processing of uploaded publication retail information to determine the different local and overall abnormal publishing states of abnormal publishers. Based on the analysis results, targeted optimization management is implemented for the subsequent publications of abnormal publishers, thereby improving the effectiveness of autonomous mining and analysis of publication retail information in both local and overall publishing, as well as the effectiveness of targeted optimization management. Attached Figure Description

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1 This is a block diagram of the intelligent management system for publishing retail information processing based on artificial intelligence, as described in this invention.

[0038] Figure 2 This is a flowchart illustrating the operation of the AI-based intelligent management system for processing retail information on publications, as described in this invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] like Figures 1 to 2 As shown, the present invention is an intelligent management system for processing publication retail information based on artificial intelligence, including a publication retail management platform, and a publication retail information mining and processing module and a publication retail information utilization and management module that are communicatively connected to the publication retail management platform.

[0041] The publication retail information mining and processing module is used to process and analyze publication retail information uploaded to the publication retail management platform in real time, obtain local regulatory results corresponding to the publication retail information, and associate and store them; including:

[0042] Get the information type corresponding to the publication retail information uploaded to the publication retail management platform in real time. If the information type is the sales type, get the publisher, publication name, author and category in the uploaded publication retail information, and add one to the total sales of the corresponding publication data item.

[0043] If the information type is a return / exchange, then retrieve the publisher, publication name, author, and category from the uploaded publication retail information, and increment the total number of returns / exchanges for the corresponding publication data items by one;

[0044] It should be noted that the information types corresponding to the publication retail information include sales type or return / exchange type. By statistically analyzing the information types corresponding to the publication retail information, targeted data processing can be carried out on the real-time uploaded publication retail information.

[0045] Furthermore, the return rate corresponding to different publication data items of the publishing house can be obtained according to the return type. This can be calculated based on all sales data and all returns corresponding to different publication data items. The return rates corresponding to different publication data items are then sorted and combined to obtain the return supervision sequence corresponding to the publishing house. Different publication data items include publication name, author, and category.

[0046] The category refers to the classification to which the publication belongs, such as literature, science fiction, education, etc.

[0047] When mining and analyzing the obtained return and replacement supervision sequence, the different elements in the return and replacement supervision sequence are sequentially analyzed by the publication anomaly identification function and the corresponding publication identification identifier BSk is output; k is a different publication data item, k = 1, 2, 3;

[0048] The expression for the publication anomaly detection function is: In the formula, HLk is the return rate corresponding to different publication data items; HL0k is the standard return rate corresponding to different publication data items. Different standard return rates can be determined based on existing industry publication requirements data, or based on the median of all return rates corresponding to the same publication data items in history.

[0049] It should be noted that the publishing identification mark is used to digitally represent the publishing status corresponding to different publishing data items in the publisher's retail business;

[0050] The publication identification identifier contains a value of 0 or 1, indicating whether the publication status of the corresponding publication data item is normal or abnormal;

[0051] The different publication identifiers output are sorted and combined to obtain the return analysis sequence corresponding to the respective publisher;

[0052] In this embodiment of the invention, by processing and digitally representing different publishing data items of the publisher obtained in the early stage, it is possible to obtain the publishing status corresponding to different publishing data items of the publisher, and to provide reliable local regulatory data support for the subsequent multi-dimensional data processing and analysis of the publisher.

[0053] Traverse the return analysis sequence and count the total number of abnormal elements with a value of 1, and set the total number of abnormal elements as the publication anomaly flag;

[0054] The abnormal publication identifier is filled into the element position after the last element in the return and replacement analysis sequence to obtain the local supervision result corresponding to the publishing house.

[0055] Among them, the publishing anomaly identifier serves to digitally represent the overall status of a single retail transaction corresponding to the real-time uploaded publication retail information, and can provide reliable regulatory data support for publishers to carry out targeted multi-dimensional data processing and analysis for subsequent single retail anomalies.

[0056] In this embodiment of the invention, by processing and mining the publication retail information uploaded to the publication retail management platform in real time, it is possible to obtain both the digital data of the publication status corresponding to different publication data items of the publisher and the digital data of the overall status of a single retail transaction corresponding to the real-time uploaded publication retail information. This can provide diverse and reliable single retail supervision support for subsequent multi-dimensional data processing and analysis of different publishers, and improve the diversity of the early publication retail data processing.

[0057] The publication retail information management module is used to perform multi-dimensional data processing and calculation on the local regulatory results obtained from the uploaded publication retail information. It analyzes the results to determine the specific local and overall publishing anomalies of the respective publishers, and implements targeted optimization management for their subsequent publications. This includes:

[0058] Obtain the local monitoring results corresponding to the publisher and iterate through and analyze the value of the last element. If the value of the last element is 0, generate a single retail overall normal instruction and mark the publisher as a normal publisher, and maintain the existing publishing plan management for the publisher.

[0059] If the value of the last element is not 0, a single retail overall abnormal instruction is generated, and the publisher to which it belongs is marked as an abnormal publisher. The publishing data items with a value of 1 in the return analysis sequence corresponding to the abnormal publisher are also marked as abnormal publishing data items. The local abnormal publishing coefficient JYk′ corresponding to different abnormal publishing data items of the abnormal publisher is calculated using the formula JYk′=QSk′×(HLk′-HL0k′); where HLk′ is the return rate corresponding to the abnormal publishing data item; HL0k′ is the standard return rate corresponding to the abnormal publishing data item, which can be determined based on existing industry publishing requirements data or based on the median of all return rates corresponding to the same abnormal publishing data item in history; and QSk′ is the publishing weight corresponding to the abnormal publishing data item.

[0060] Among them, the publication weight is used to digitally represent the degree of publication influence corresponding to the publication data item. The specific value can be determined by professionals in the field based on experience, or it can be determined based on the historical return rate values ​​corresponding to different publication data items.

[0061] It should be noted that the local abnormal publication coefficient is used to calculate and digitally represent the local abnormal publication status corresponding to different abnormal publication data items of abnormal publishers.

[0062] When analyzing local abnormal publication coefficients to determine the local abnormal publication status of corresponding abnormal publication data items of abnormal publishers, the formula is used. Calculate and analyze the local abnormal publishing status identifier BJk′ corresponding to different abnormal publishing data items of abnormal publishers; where JY0k′ is the local abnormal publishing standard value of the corresponding abnormal publishing data item, which can be determined based on existing industry publishing requirement data, or based on the median of all local abnormal publishing coefficients corresponding to the same abnormal publishing data item in history; [*] is the floor function;

[0063] If the local abnormal publication status flag is 0, a local minor abnormal publication status will be generated and a prompt will be displayed.

[0064] If the local abnormal publication status flag is greater than 0, a local severe abnormal publication status will be generated and a prompt will be displayed.

[0065] In this embodiment of the invention, by performing data calculation and analysis on different abnormal publishing data items corresponding to different abnormal publishers obtained in the previous processing, the local abnormal publishing status corresponding to different abnormal publishing data items is obtained. At the same time, it can also provide reliable multi-dimensional analysis data support for the subsequent processing and analysis of the overall abnormal publishing status of the corresponding abnormal publishers.

[0066] And, through the formula Calculate the overall abnormal publication coefficient ZY for all abnormal publication data items corresponding to the abnormal publisher; where n is the total number of abnormal publication data items corresponding to the abnormal publisher; n≤k;

[0067] It should be noted that the overall abnormal publication coefficient is used to calculate and digitally represent the regulatory analysis data corresponding to all abnormal publication data items of abnormal publishers;

[0068] Option 1: Analyze the data using the first overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publisher;

[0069] The expression for the first overall abnormal publication identification function is: In the formula, BJ0 is the overall abnormal publication standard value, which can be determined based on existing industry publication requirement data;

[0070] Option 2: Analyze the data using the second overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publisher;

[0071] The expression for the second overall abnormal publication identification function is as follows: In the formula, N1 and N2 are the total number of locally mildly abnormal publishing states and the total number of locally severe abnormal publishing states for different abnormal publishing data items corresponding to abnormal publishers, respectively.

[0072] It should be noted that the overall abnormal publishing status identifier is used to perform data analysis and digital representation of the overall abnormal publishing status of the abnormal publishers.

[0073] In addition, by calculating the overall abnormal publishing status identifier corresponding to the abnormal publishers through different calculation methods, the diversity and reliability of the analysis of the overall abnormal publishing status of the abnormal publishers are improved.

[0074] When analyzing data on overall abnormal publishing status identifiers obtained through different methods to determine the overall abnormal publishing status of abnormal publishers and implementing targeted optimization management;

[0075] If the overall abnormal publication status is marked as -1, an overall slightly abnormal publication status will be generated and a prompt will be made to implement the first publication optimization management plan.

[0076] If the overall abnormal publication status is marked as -2, an overall severe abnormal publication status will be generated and a second publication optimization management plan will be prompted.

[0077] Among them, the rectification intensity of the second publishing optimization management plan is greater than that of the first publishing optimization management plan;

[0078] The first publishing optimization management plan is specific, and can enable abnormal publishers to publish normally and make equipment or process improvements for all abnormal publishing data items.

[0079] The second publishing optimization management plan can specifically involve suspending publication for publishers with abnormal issues and rectifying their equipment or processes for all abnormal publishing data items.

[0080] In this embodiment of the invention, by performing multi-dimensional data processing calculations and data analysis on the local regulatory results obtained from the processing of uploaded publication retail information, different local publishing anomaly states and overall publishing anomaly states corresponding to abnormal publishers are obtained. Based on the analysis results, targeted optimization management is implemented for the subsequent publications of abnormal publishers, thereby improving the autonomous mining and analysis effect of publication retail information in local and overall publishing, as well as the targeted optimization management effect.

[0081] Furthermore, the formulas mentioned above are all numerical calculations obtained by removing dimensions and using software simulation based on a large amount of data, and are the closest to the real situation.

[0082] In the several embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0083] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0084] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0085] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent management system for processing and managing retail information of publications based on artificial intelligence, characterized in that: It includes a publication retail management platform, as well as a publication retail information mining and processing module and a publication retail information utilization and management module that are connected to the publication retail management platform. The publication retail information mining and processing module is used to process and analyze the publication retail information uploaded to the publication retail management platform in real time, obtain the corresponding local supervision results of the publication retail information, and associate and store them. The local supervision results include the processed return and exchange analysis sequence and the filled publication anomaly identifier. Specifically, the system retrieves the information type corresponding to the publication retail information uploaded to the publication retail management platform in real time. If the information type is a sales type, it retrieves the publisher, publication name, author, and category from the uploaded publication retail information and increments the total sales count of the corresponding publication data item by one. If the information type is a return / exchange, then retrieve the publisher, publication name, author, and category from the uploaded publication retail information, and increment the total number of returns / exchanges for the corresponding publication data items by one; Based on the return / exchange type, obtain the return / exchange rate corresponding to different publication data items of the publishing house, and sort and combine the return / exchange rates corresponding to different publication data items to obtain the return / exchange supervision sequence corresponding to the publishing house. When mining and analyzing the obtained return and replacement supervision sequence, the different elements in the return and replacement supervision sequence are sequentially analyzed by the publication anomaly identification function and the corresponding publication identification identifier BSk is output; k is a different publication data item, k=1, 2, 3; The expression for the publication anomaly detection function is: In the formula, HLk represents the return rate for different publication data items; HL0k represents the standard return rate for different publication data items. The publication identification identifier contains a value of 0 or 1, indicating whether the publication status of the corresponding publication data item is normal or abnormal; The different publication identifiers output are sorted and combined to obtain the return analysis sequence corresponding to the respective publisher; Traverse the return analysis sequence and count the total number of abnormal elements with a value of 1, and set the total number of abnormal elements as the publication anomaly flag; The abnormal publication identifier is filled into the element position after the last element in the return and replacement analysis sequence to obtain the local supervision result corresponding to the publishing house. The publication retail information management module is used to perform multi-dimensional data processing and calculation on the local supervision results obtained from the processing of uploaded publication retail information. The multi-dimensional data processing and calculation results are analyzed to determine the different local publication anomalies and the overall publication anomalies of the respective publishers, and to implement targeted optimization management for the subsequent publications of the respective publishers. Among them, the local supervision results corresponding to the publisher are obtained and the value of the last element is analyzed. If the value of the last element is 0, a single retail overall normal instruction is generated and the publisher is marked as a normal publisher, and the existing publishing plan is maintained for the publisher. If the value of the last element is not 0, a single retail overall anomaly instruction is generated, and the publisher to which it belongs is marked as an anomaly publisher. The publication data item with a value of 1 in the return analysis sequence corresponding to the anomaly publisher is also marked as an anomaly publication data item, and this is done using the formula... Calculate the local abnormal publication coefficient JYk´ for different abnormal publication data items corresponding to abnormal publishers; where HLk´ is the return rate corresponding to the abnormal publication data item; HL0k´ is the standard return rate corresponding to the abnormal publication data item; and QSk´ is the publication weight corresponding to the abnormal publication data item. Identify different abnormal publishing data items corresponding to abnormal publishers, calculate and analyze the local abnormal publishing status corresponding to different abnormal publishing data items, as well as the overall abnormal publishing status of abnormal publishers, and implement targeted optimization management.

2. The intelligent management system for publishing retail information processing based on artificial intelligence according to claim 1, characterized in that, When analyzing local abnormal publication coefficients to determine the local abnormal publication status of corresponding abnormal publication data items of abnormal publishers, the formula is used. Calculate and analyze the local abnormal publication status identifier BJk´ corresponding to different abnormal publication data items of abnormal publishers; where JY0k´ is the local abnormal publication standard value of the corresponding abnormal publication data item; [*] is the rounding function; If the local abnormal publication status flag is 0, a local minor abnormal publication status will be generated and a prompt will be displayed. If the local abnormal publication status flag is greater than 0, a local severe abnormal publication status will be generated and a prompt will be displayed.

3. The intelligent management system for publishing retail information processing based on artificial intelligence according to claim 2, characterized in that, Through formula Calculate the overall abnormal publication coefficient ZY corresponding to all abnormal publication data items of the abnormal publisher; where n is the total number of abnormal publication data items corresponding to the abnormal publisher. The data is analyzed using the first overall abnormal publication identification function, and the overall abnormal publication status identifier BZ corresponding to the abnormal publisher is output. The expression for the first overall abnormal publication identification function is: In the formula, BJ0 is the overall abnormal publication standard value.

4. The intelligent management system for publishing retail information processing based on artificial intelligence according to claim 3, characterized in that, The data is analyzed using the second overall abnormal publication identification function, and the overall abnormal publication status identifier BZ corresponding to the abnormal publisher is output. The expression for the second overall abnormal publication identification function is as follows: In the formula, N1 and N2 are the total number of locally mildly abnormal publishing states and the total number of locally severe abnormal publishing states for different abnormal publishing data items corresponding to abnormal publishers, respectively.

5. The intelligent management system for publishing retail information processing based on artificial intelligence according to claim 4, characterized in that, When analyzing data on overall abnormal publishing status identifiers obtained through different methods to determine the overall abnormal publishing status of abnormal publishers and implementing targeted optimization management; If the overall abnormal publication status is marked as -1, an overall slightly abnormal publication status will be generated and a prompt will be made to implement the first publication optimization management plan. If the overall abnormal publication status is marked as -2, an overall severe abnormal publication status will be generated and a second publication optimization management plan will be prompted.

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