Publication retail information processing intelligent management system based on artificial intelligence

Abstract

The invention discloses a publication retail information processing intelligent management system based on artificial intelligence, and belongs to the technical field of publication management. Data processing and mining analysis are carried out on publication retail information uploaded to a publication retail management platform in real time, and publishing state digital data corresponding to different publishing data items of a publishing house can be obtained; multi-dimensional data processing calculation and data analysis are carried out on a local supervision result obtained by processing uploaded publication retail information, different local publishing abnormal states and overall publishing abnormal states corresponding to abnormal publications are obtained, and targeted optimization management is carried out on subsequent publishing of the abnormal publications according to an analysis result. The method is used for solving the technical problems that in an existing scheme, the autonomous mining analysis effect of the publication retail information in local publishing and overall publishing is poor, and the targeted optimization management effect is poor.

Description

Technical Field

[0001] The present invention relates to the technical field of publication management, and in particular to an intelligent management system for publication retail information processing based on artificial intelligence. Background Art

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

[0003] When implemented, the existing publication retail information processing and management solutions cannot process, mine and analyze publication retail information from different dimensions, obtain regulatory results corresponding to different aspects of different publishing houses, and conduct targeted management of subsequent publications of different publishing houses based on the regulatory results. This results in poor autonomous mining and analysis of publication retail information in local and overall publishing, as well as poor targeted optimization management. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent management system for publication retail information processing based on artificial intelligence, which is used to solve the technical problems in existing solutions of poor autonomous mining and analysis of publication retail information in local publishing and overall publishing, as well as poor targeted optimization management effect.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An artificial intelligence-based publication retail information processing intelligent management system, 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 connected to the publication retail management platform in communication;

[0007] The publication retail information mining and processing module is used to process and mine the publication retail information uploaded to the publication retail management platform in real time, obtain the local supervision results corresponding to the publication retail information and associate and store them with it. The local supervision results include the return analysis sequence obtained by processing and the filled publication anomaly identification;

[0008] The publication retail information utilization management module is used to perform multi-dimensional data processing and calculation on the local supervision results obtained by processing the uploaded publication retail information, analyze the multi-dimensional data processing and calculation results to determine the different local publishing abnormal states and overall publishing abnormal states corresponding to the affiliated publishing house, and implement targeted optimization management for the subsequent publishing of the affiliated publishing house;

[0009] Among them, the local supervision result corresponding to the publishing house is obtained and the value of the last element is traversed and analyzed to obtain a normal publishing house or an abnormal publishing house;

[0010] Identify the different abnormal publishing data items corresponding to abnormal publishing houses, calculate and analyze the local publishing abnormality status corresponding to different abnormal publishing data items, as well as the overall publishing abnormality status of abnormal publishing houses, 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 a sales type, the publisher, publication name, author, and category in the uploaded publication retail information are obtained, and the total sales amount of the corresponding publication data item is increased by one;

[0012] If the information type is a return type, the publisher, publication name, author, and category in the uploaded publication retail information are obtained, and the total return amount of the corresponding publication data item is increased by one.

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

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

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

[0016] The output different publishing identification identifiers are sorted and combined to obtain the return analysis sequence corresponding to the publishing house.

[0017] Preferably, the expression of the publication anomaly identification function is: Wherein, 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 abnormality flag;

[0019] Fill the publishing anomaly mark to the element position after the last element in the return analysis sequence to obtain the local supervision result corresponding to the publishing house.

[0020] Preferably, the local supervision result corresponding to the publishing house is obtained and the value of the last element is traversed and analyzed. If the value of the last element is 0, a single retail overall normal instruction is generated and the publishing house is marked as a normal publishing house, and the management of the existing publishing plan of the publishing house is maintained;

[0021] If the value of the last element is not 0, a single retail overall abnormal instruction is generated and the publishing house to which it belongs is marked as an abnormal publishing house, and the publishing data item with a value of 1 in the return analysis sequence corresponding to the abnormal publishing house is marked as an abnormal publishing data item, and the local abnormal publishing coefficient JYk′ corresponding to different abnormal publishing data items of the abnormal publishing house is calculated by the formula JYk′=QSk′×(HLk′-HL0k′); wherein 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; QSk′ is the publishing weight corresponding to the abnormal publishing data item.

[0022] Preferably, when the data analysis of the local abnormal publication coefficient is performed to determine the local publication abnormal state of the abnormal publication data item corresponding to the abnormal publication publisher, the formula Calculate and obtain the local abnormal publishing status identifier BJk′ corresponding to different abnormal publishing data items of abnormal publishing publishers and analyze them; where JY0k′ is the local abnormal publishing standard value corresponding to the abnormal publishing data item; [*] is the rounding function;

[0023] If the local abnormal publication status indicator is 0, a local mild abnormal publication status is generated and prompted;

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

[0025] Preferably, by the formula Calculate and obtain the overall abnormal publication coefficient ZY corresponding to all abnormal publication data items of abnormal publishing houses; where n is the total number of all abnormal publication data items corresponding to abnormal publishing houses;

[0026] And perform data analysis through the first overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publishing house;

[0027] Among them, the expression of the first overall abnormal publication identification function is: Where BJ0 is the overall abnormal publication standard value.

[0028] Preferably, the data is analyzed by a second overall abnormal publication identification function and an overall abnormal publication status identifier BZ corresponding to the abnormal publishing house is output;

[0029] Among them, the expression of the second overall abnormal publication identification function is: Where N1 and N2 are the total number of local mild abnormal publishing states and the total number of local severe abnormal publishing states corresponding to different abnormal publishing data items of abnormal publishers, respectively.

[0030] Preferably, when data analysis is performed on the overall abnormal publishing status identifiers obtained in different ways to determine the overall abnormal publishing status of the abnormal publishing house and implement targeted optimization management;

[0031] If the overall abnormal publication status indicator is -1, an overall mildly abnormal publication status is generated and a prompt is given to perform the first publication optimization management plan;

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

[0033] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0034] The present invention processes and mines the publication retail information uploaded to the publication retail management platform in real time, thereby obtaining both the digitalized data on the publication status corresponding to different publication data items of the publishing house and the digitalized data on the overall status of a single retail sale corresponding to the published retail information uploaded in real time. This can provide diverse and reliable single retail supervision support for subsequent multidimensional data processing and analysis of different publishing houses, thereby improving the diversity of early publication retail data processing.

[0035] The present invention performs multi-dimensional data processing calculations and data analysis on the local supervision results obtained by processing the uploaded publication retail information, obtains different local publishing abnormal states and overall publishing abnormal states corresponding to abnormal publishing houses, and implements targeted optimization management for the subsequent publications of the abnormal publishing houses based on the analysis results, thereby improving the autonomous mining and analysis effect of publication retail information in local publishing and overall publishing and the targeted optimization management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Figure 1 The present invention is a module block diagram of the publication retail information processing intelligent management system based on artificial intelligence.

[0038] Figure 2 The present invention is a flowchart of the operation of the publication retail information processing intelligent management system based on artificial intelligence. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] like Figure 1 to Figure 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 mine the publication retail information uploaded to the publication retail management platform in real time, obtain the local supervision results corresponding to the publication retail information, and associate and store them; including:

[0042] Obtain 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, obtain the publisher, publication name, author, and category in the uploaded publication retail information, and add one to the total sales amount of the corresponding publication data item;

[0043] If the information type is a return type, the publisher, publication name, author, and category in the uploaded publication retail information are obtained, and the total number of returns of the corresponding publication data item is increased by one;

[0044] It should be noted that the information types corresponding to the publication retail information include sales types or return types. By counting and analyzing the information types corresponding to the publication retail information, targeted data processing can be performed on the publication retail information uploaded in real time.

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

[0046] Category is the classification to which the publication belongs, such as literature, science fiction, education, etc.;

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

[0048] Among them, the expression of the publication anomaly identification function is: Wherein, 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 requirement data or based on the median of all return rates corresponding to the same publication data item in history;

[0049] It should be noted that the publication identification mark is used to digitally represent the publication status corresponding to different publication data items published and sold by the publishing house;

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

[0051] Sort and combine the output different publishing identification marks to obtain the return analysis sequence corresponding to the publishing house;

[0052] In an embodiment of the present invention, by performing data processing and digital representation on different publishing data items of a publishing house obtained through preliminary processing, it is possible to obtain the publishing status corresponding to different publishing data items of the publishing house, and to provide reliable local supervision data support for subsequent multidimensional data processing and analysis of the publishing house.

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

[0054] Fill the publishing anomaly mark to the element position after the last element in the return analysis sequence to obtain the local supervision result corresponding to the publishing house;

[0055] Among them, the publishing anomaly identification plays a role in digitally representing the overall status of a single retail sale corresponding to the real-time uploaded publication retail information, and can provide reliable regulatory data support for the subsequent targeted multi-dimensional data processing and analysis of the overall abnormal status of single retail sales by publishing houses.

[0056] In the embodiment of the present invention, by performing data processing and mining analysis on 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 publishing data items of the publishing house and the digital data of the overall status of the single retail corresponding to the real-time uploaded publication retail information. This can provide diverse and reliable single retail supervision support for subsequent multidimensional data processing and analysis of different publishing houses, thereby improving the diversity of early publication retail data processing.

[0057] The publication retail information utilization management module is used to perform multi-dimensional data processing and calculation on the local supervision results obtained by processing the uploaded publication retail information, analyze the multi-dimensional data processing and calculation results to determine the different local publishing abnormal states and overall publishing abnormal states corresponding to the affiliated publishing house, and implement targeted optimization management for the subsequent publishing of the affiliated publishing house; including:

[0058] Obtain the local supervision result corresponding to the publishing house and traverse 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 publishing house as a normal publishing house, and maintain the management of the existing publishing plan for the publishing house;

[0059] 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, and the publication data item with a value of 1 in the return analysis sequence corresponding to the abnormal publisher is marked as an abnormal publication data item, and the local abnormal publication coefficient JYk′ corresponding to different abnormal publication data items of the abnormal publisher is calculated by the formula JYk′=QSk′×(HLk′-HL0k′); wherein 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, which can be determined based on the existing industry publication requirement data, or based on the median of all return rates corresponding to the same historical abnormal publication data item; QSk′ is the publication weight corresponding to the abnormal publication data item;

[0060] The publication weight is used to digitally represent the degree of publication influence corresponding to the corresponding publication data item. The specific value can be determined by professionals in this field based on experience, or 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 perform data calculation and digital representation of the local abnormal publication status corresponding to different abnormal publication data items appearing in abnormal publishing houses;

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

[0063] If the local abnormal publication status indicator is 0, a local mild abnormal publication status is generated and prompted;

[0064] If the local abnormal publication status flag is greater than 0, a local severe abnormal publication status is generated and prompted;

[0065] In the embodiment of the present invention, by performing data calculation and analysis on different abnormal publishing data items corresponding to different abnormal publishing houses obtained in the previous processing, the local abnormal publishing status corresponding to the different abnormal publishing data items is obtained, and reliable multi-dimensional analysis data support can also be provided for the subsequent overall abnormal publishing status processing and analysis corresponding to the abnormal publishing houses;

[0066] And, through the formula Calculate and obtain the overall abnormal publication coefficient ZY corresponding to all abnormal publication data items of abnormal publishers; where n is the total number of all abnormal publication data items corresponding to abnormal publishers; 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] Solution 1: Perform data analysis through the first overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publishing house;

[0069] Among them, the expression of the first overall abnormal publication identification function is: Where BJ0 is the overall abnormal publication standard value, which can be determined based on the existing industry publication requirement data;

[0070] Solution 2: Perform data analysis through the second overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publishing house;

[0071] Among them, the expression of the second overall abnormal publication identification function is: Where N1 and N2 are the total number of local mild abnormal publishing states and the total number of local severe abnormal publishing states corresponding to different abnormal publishing data items of 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 corresponding to the abnormal publishing house;

[0073] In addition, the overall abnormal publishing status identification corresponding to abnormal publishers is calculated through different calculation methods, which improves the diversity and reliability of the processing and analysis of the overall abnormal publishing status corresponding to abnormal publishers;

[0074] When conducting data analysis on overall abnormal publishing status identifiers obtained in different ways to determine the overall abnormal publishing status of abnormal publishers and implement targeted optimization management;

[0075] If the overall abnormal publication status indicator is -1, an overall mildly abnormal publication status is generated and a prompt is given to perform the first publication optimization management plan;

[0076] If the overall abnormal publication status is marked as -2, an overall severe abnormal publication status is generated and a second publication optimization management plan is 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, which can enable abnormal publishing houses to publish normally and make equipment or process rectification for all their abnormal publishing data items;

[0079] The second publishing optimization management plan is specific in that it can suspend publishing for abnormal publishing houses and make equipment or process rectifications for all their abnormal publishing data items.

[0080] In the embodiment of the present invention, multi-dimensional data processing calculations and data analysis are performed on the local supervision results obtained by processing the uploaded publication retail information, so as to obtain different local publishing abnormal states and overall publishing abnormal states corresponding to the abnormal publishing houses, and targeted optimization management is implemented for the subsequent publications of the abnormal publishing houses according to the analysis results, thereby improving the autonomous mining and analysis effect of publication retail information in local publishing and overall publishing and the targeted optimization management effect.

[0081] In addition, the formulas involved in the above are all dimensionless and numerical calculations, and are the formulas closest to the actual situation obtained by collecting a large amount of data and performing software simulation.

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

[0083] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0085] It is obvious to a person 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 solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligent management system for publication retail information processing based on artificial intelligence, characterized by: It includes a publication retail management platform, and a publication retail information mining and processing module and a publication retail information utilization and management module which are connected to the publication retail management platform in communication; The publication retail information mining and processing module is used to process and mine the publication retail information uploaded to the publication retail management platform in real time, obtain the local supervision results corresponding to the publication retail information and associate and store them with it. The local supervision results include the return analysis sequence obtained by processing and the filled publication anomaly identification; The publication retail information utilization management module is used to perform multi-dimensional data processing and calculation on the local supervision results obtained by processing the uploaded publication retail information, analyze the multi-dimensional data processing and calculation results to determine the different local publishing abnormal states and overall publishing abnormal states corresponding to the affiliated publishing house, and implement targeted optimization management for the subsequent publishing of the affiliated publishing house; Among them, the local supervision result corresponding to the publishing house is obtained and the value of the last element is traversed and analyzed to obtain a normal publishing house or an abnormal publishing house; Identify the different abnormal publishing data items corresponding to abnormal publishing houses, calculate and analyze the local publishing abnormality status corresponding to different abnormal publishing data items, as well as the overall publishing abnormality status of abnormal publishing houses, and implement targeted optimization management.

2. The publication retail information processing intelligent management system based on artificial intelligence according to claim 1 is characterized in that: Obtain 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, obtain the publisher, publication name, author, and category in the uploaded publication retail information, and add one to the total sales amount of the corresponding publication data item; If the information type is a return type, the publisher, publication name, author, and category in the uploaded publication retail information are obtained, and the total return amount of the corresponding publication data item is increased by one.

3. The publication retail information processing intelligent management system based on artificial intelligence according to claim 2 is characterized in that: According to the return type, the return rates corresponding to different publishing data items of the publishing house are obtained, and the return rates corresponding to different publishing data items are sorted and combined to obtain the return supervision sequence corresponding to the publishing house; When mining and analyzing the returned supervision sequence obtained by processing, different elements in the returned supervision sequence are sequentially analyzed by the publication anomaly identification function and the corresponding publication identification mark BSk is output; k is a different publication data item, k = 1, 2, 3; The publication identification flag contains a value of 0 or 1, indicating whether the publication status of the corresponding publication data item is normal or abnormal; The output different publishing identification identifiers are sorted and combined to obtain the return analysis sequence corresponding to the publishing house.

4. The publication retail information processing intelligent management system based on artificial intelligence according to claim 3 is characterized in that: The expression of the publication anomaly identification function is Wherein, HLk is the return rate corresponding to different publication data items; HL0k is the standard return rate corresponding to different publication data items.

5. The publication retail information processing intelligent management system based on artificial intelligence according to claim 3 is characterized in that: Traverse the return analysis sequence and count the total number of abnormal elements with a value of 1, and set the value of the total number of abnormal elements as the publication abnormality flag; Fill the publishing anomaly mark to the element position after the last element in the return analysis sequence to obtain the local supervision result corresponding to the publishing house.

6. The publication retail information processing intelligent management system based on artificial intelligence according to claim 5 is characterized in that: Obtain the local supervision result corresponding to the publishing house and traverse 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 publishing house as a normal publishing house, and maintain the management of the existing publishing plan for the publishing house; If the value of the last element is not 0, a single retail overall abnormal instruction is generated and the publishing house to which it belongs is marked as an abnormal publishing house, and the publishing data item with a value of 1 in the return analysis sequence corresponding to the abnormal publishing house is marked as an abnormal publishing data item, and the local abnormal publishing coefficient JYk′ corresponding to different abnormal publishing data items of the abnormal publishing house is calculated by the formula JYk′=QSk′×(HLk′-HL0k′); wherein 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; QSk′ is the publishing weight corresponding to the abnormal publishing data item.

7. The publication retail information processing intelligent management system based on artificial intelligence according to claim 6 is characterized in that: When analyzing the local abnormal publication coefficient to determine the local abnormal publication status of the abnormal publishing house corresponding to the abnormal publication data item, the formula Calculate and obtain the local abnormal publishing status identifier BJk′ corresponding to different abnormal publishing data items of abnormal publishing publishers and analyze them; where JY0k′ is the local abnormal publishing standard value corresponding to the abnormal publishing data item; [*] is the rounding function; If the local abnormal publication status indicator is 0, a local mild abnormal publication status is generated and prompted; If the local abnormal publication status flag is greater than 0, a local severe abnormal publication status is generated and prompted.

8. The publication retail information processing intelligent management system based on artificial intelligence according to claim 7 is characterized in that: By formula Calculate and obtain the overall abnormal publication coefficient ZY corresponding to all abnormal publication data items of abnormal publishers; where n is the total number of all abnormal publication data items corresponding to abnormal publishers; And perform data analysis through the first overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publishing house; Among them, the expression of the first overall abnormal publication identification function is: Where BJ0 is the overall abnormal publication standard value.

9. The publication retail information processing intelligent management system based on artificial intelligence according to claim 8 is characterized in that: Perform data analysis through the second overall abnormal publication identification function and output the overall abnormal publication status identifier BZ corresponding to the abnormal publishing house; Among them, the expression of the second overall abnormal publication identification function is: Where N1 and N2 are the total number of local mild abnormal publishing states and the total number of local severe abnormal publishing states corresponding to different abnormal publishing data items of abnormal publishers, respectively.

10. The publication retail information processing intelligent management system based on artificial intelligence according to claim 9 is characterized in that: When conducting data analysis on overall abnormal publishing status identifiers obtained in different ways to determine the overall abnormal publishing status of abnormal publishers and implement targeted optimization management; If the overall abnormal publication status indicator is -1, an overall mildly abnormal publication status is generated and a prompt is given to perform the first publication optimization management plan; If the overall abnormal publication status is marked as -2, an overall severe abnormal publication status is generated and a second publication optimization management plan is prompted.

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