Anti-corrosion platform digital management system and method based on big data

Abstract

The invention discloses an anti-corrosion platform digital management system and method based on big data, and relates to the technical field of anti-corrosion data management.The method comprises the steps that historical anti-corrosion data exception records in an anti-corrosion platform are obtained, and the correlation degree between anti-corrosion indexes in historical anti-corrosion data and data exception is evaluated; obtaining historical anti-corrosion data abnormal records in the anti-corrosion platform, extracting historical text information of the feature anti-corrosion indexes from the historical anti-corrosion data abnormal records, and analyzing abnormal relevance between keywords in the historical text information and the feature anti-corrosion indexes; extracting anti-corrosion index data from the anti-corrosion data, and performing data abnormality evaluation on the anti-corrosion data in combination with the abnormal data set to obtain target abnormal anti-corrosion data; and obtaining target abnormal anti-corrosion data of the anti-corrosion platform in the current period, and performing digital management on the anti-corrosion data of the anti-corrosion platform in the current period.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-corrosion data management, and in particular to a digital management system and method for an anti-corrosion platform based on big data. Background Art

[0002] Affected by the demands of industries such as infrastructure construction, oil and gas, shipbuilding and chemical industry, the global anti-corrosion market has been growing continuously in recent years. With the continuous expansion of the anti-corrosion market, the application of big data technology in anti-corrosion platforms has become more and more common. There are generally various functional modules in the anti-corrosion platform. These functional modules mainly have the functions of anti-corrosion material sales, anti-corrosion talent recruitment, and anti-corrosion project plan communication. The establishment of the anti-corrosion platform enables users to purchase and sell anti-corrosion materials, as well as recruit and find jobs, only through the anti-corrosion platform. It not only greatly facilitates users, but also promotes the development of the anti-corrosion market to a certain extent.

[0003] At present, most of the information on various functional modules of traditional anti-corrosion platforms is edited by users of the anti-corrosion platform, who then upload the content they need to publish to the anti-corrosion platform. The anti-corrosion platform then displays the content published by users on the corresponding functional modules of the anti-corrosion platform. However, the anti-corrosion platform cannot identify the anti-corrosion-related content published by users on the platform, nor can it identify abnormal data in various functional modules of the anti-corrosion platform. This will not only greatly reduce the user experience during use and affect business development, but may also cause users to believe that the platform is unreliable, thereby reducing its use or turning to other competitors, bringing serious impact to the platform. Summary of the invention

[0004] The purpose of the present invention is to provide a digital management system and method for an anti-corrosion platform based on big data to solve the problems raised in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a digital management method of an anti-corrosion platform based on big data, the method comprising:

[0006] Step S100: Obtaining historical anti-corrosion data anomaly records in the anti-corrosion platform, extracting historical anti-corrosion data from the historical anti-corrosion data anomaly records, evaluating the degree of correlation between the anti-corrosion index in the historical anti-corrosion data and the data anomaly, and obtaining a characteristic anti-corrosion index;

[0007] Step S200: Acquire characteristic anti-corrosion indicators in the anti-corrosion platform, acquire historical anti-corrosion data abnormal records in the anti-corrosion platform, extract historical text information of characteristic anti-corrosion indicators from the historical anti-corrosion data abnormal records, and analyze the keywords in the historical text information and the abnormal correlation with the characteristic anti-corrosion indicators to obtain an abnormal data set;

[0008] Step S300: Acquire the anti-corrosion data of the anti-corrosion platform in the current cycle, extract the anti-corrosion index data from the anti-corrosion data, and evaluate the data abnormality of the anti-corrosion data in combination with the abnormal data set to obtain the target abnormal anti-corrosion data;

[0009] Step S400: Obtain target abnormal anti-corrosion data of the anti-corrosion platform in the current cycle, and digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle based on the target abnormal anti-corrosion data.

[0010] Further, step S100 includes:

[0011] Step S101: Obtaining historical anti-corrosion data abnormality records in the anti-corrosion platform, extracting historical anti-corrosion data from the historical anti-corrosion data abnormality records, the historical anti-corrosion data including the anti-corrosion function module to which the historical anti-corrosion data abnormality records belong, and data corresponding to various anti-corrosion indicators;

[0012] Step S102: Obtain the anti-corrosion indicators marked as data abnormalities in the historical period from the historical anti-corrosion data abnormality records, and record them as historical abnormal anti-corrosion indicators, and obtain data corresponding to several historical abnormal anti-corrosion indicators in each historical anti-corrosion data abnormality record in the anti-corrosion platform;

[0013] Step S103: Obtain the time point at which the historical anti-corrosion data in the historical anti-corrosion data abnormal record is released in the anti-corrosion platform, and obtain the duration between the time point and the current cycle, which is recorded as the marking duration of the historical anti-corrosion data abnormal record;

[0014] Step S104: Obtain the maximum value Tmax of the marking time length in each historical anti-corrosion data abnormal record, and evaluate the correlation between each anti-corrosion index of the historical anti-corrosion data in each anti-corrosion function module and the data abnormality. The specific process of evaluating the correlation between the b-th anti-corrosion index in the a-th anti-corrosion function module and the data abnormality is to calculate the data abnormality correlation value Lb of the b-th anti-corrosion index:

[0015]

[0016] Wherein, ti represents the marking duration of the i-th abnormal record of historical anti-corrosion data of the b-th anti-corrosion index, the historical abnormal anti-corrosion index; j represents the total number of abnormal records of historical anti-corrosion data of the b-th anti-corrosion index, the historical abnormal anti-corrosion index;

[0017] Step S105: Obtain the minimum value Lmin and the maximum value Lmax of the data anomaly correlation values ​​of each anti-corrosion indicator in the a-th anti-corrosion functional module, and calculate the data anomaly score Pb of the b-th anti-corrosion indicator = (La-Lmin) / (Lmax-Lmin);

[0018] Step S106: When the data anomaly score Pb is greater than a preset data anomaly threshold, it is determined that the b-th anti-corrosion indicator in the a-th anti-corrosion functional module is associated with the data anomaly, and the b-th anti-corrosion indicator is marked as a characteristic anti-corrosion indicator.

[0019] Further, step S200 includes:

[0020] Step S201: Acquire several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, acquire the anti-corrosion indicators as historical anti-corrosion data abnormality records of characteristic anti-corrosion indicators, and record the historical anti-corrosion data abnormality records as characteristic historical anti-corrosion data abnormality records of characteristic anti-corrosion indicators;

[0021] Step S202: extracting historical text information of characteristic anti-corrosion indicators from the historical anti-corrosion data of the characteristic historical anti-corrosion data anomaly record, removing noise from the historical text information, dividing the historical text information into a number of keywords, and obtaining a historical anti-corrosion keyword group of the characteristic anti-corrosion indicators in the characteristic historical anti-corrosion data anomaly record;

[0022] Step S203: Analyze the abnormal correlation between several keywords in the historical anti-corrosion keyword group and the characteristic anti-corrosion index, wherein the specific process of analyzing the abnormal correlation between the c-th keyword in the historical anti-corrosion keyword group and the characteristic anti-corrosion index is to obtain the total number of occurrences of the c-th keyword in the historical text information of the characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion index, and record it as the mark value of the c-th keyword in the characteristic historical anti-corrosion data abnormal record;

[0023] Step S204: Calculate the abnormal correlation value Fc between the cth keyword and the characteristic anti-corrosion index:

[0024]

[0025] Wherein, Nc,sum represents the total number of each keyword in the historical anti-corrosion keyword group of the characteristic anti-corrosion indicator in each characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator; Hz,c represents the tag value of the cth keyword in the historical anti-corrosion keyword group of the zth characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator; n represents the total number of each characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator;

[0026] Step S205: Obtain each keyword of the historical text information of the characteristic anti-corrosion index from each characteristic historical anti-corrosion data abnormal record, and aggregate the keyword with the abnormal key value of the characteristic anti-corrosion index to obtain the abnormal data set of the characteristic anti-corrosion index.

[0027] Further, step S300 includes:

[0028] Step S301: For the anti-corrosion platform, the anti-corrosion data of each anti-corrosion functional module in the current cycle are acquired, and the anti-corrosion index data are extracted from the anti-corrosion data, wherein the anti-corrosion index data includes text information of several anti-corrosion indexes in the anti-corrosion data, and the text information is de-noised, and the text information after the de-noising is divided into keywords, and the keywords are aggregated to obtain anti-corrosion keyword groups of several anti-corrosion indexes of the anti-corrosion data;

[0029] Step S302: Obtain abnormal data sets of several characteristic anti-corrosion indicators of various anti-corrosion functional modules in the anti-corrosion platform, and evaluate the data abnormality of each anti-corrosion data in each anti-corrosion functional module. The specific process of evaluating several characteristic anti-corrosion indicators of the e-th anti-corrosion data of the d-th anti-corrosion functional module is as follows:

[0030] Obtain the anti-corrosion keyword groups of several characteristic anti-corrosion indicators of the e-th anti-corrosion data, and calculate the characteristic abnormal correlation values ​​of several keywords in the anti-corrosion keyword groups of several characteristic anti-corrosion indicators, among which the characteristic abnormal correlation value Q of the β-th keyword in the anti-corrosion keyword group of the α-th characteristic anti-corrosion indicator is β :

[0031]

[0032] Where r is the total number of abnormal data sets of the αth feature anti-corrosion index; F α,β is the abnormal correlation value between the βth keyword and the αth characteristic anti-corrosion index; F α , s is the abnormal correlation value of the sth keyword in the anti-corrosion keyword group of the αth feature anti-corrosion index;

[0033] Step S303: Calculate the characteristic index abnormal value U of the αth characteristic anti-corrosion index in the eth anti-corrosion data α :

[0034]

[0035] Wherein, Qx is the characteristic abnormal correlation value of the xth keyword of the anti-corrosion keyword group of the αth characteristic anti-corrosion index in the eth anti-corrosion data; m is the total number of keywords in the anti-corrosion keyword group of the αth characteristic anti-corrosion index;

[0036] Step S304: When the characteristic index abnormal value U α If the value is greater than a preset characteristic index threshold, it is determined that the data of the αth characteristic anti-corrosion index in the eth anti-corrosion data is abnormal, and the eth anti-corrosion data is recorded as the target abnormal anti-corrosion data.

[0037] Further, step S400 includes:

[0038] Step S401: Acquire target abnormal anti-corrosion data in various anti-corrosion function modules in the current cycle, acquire and collect publishers and publishing times of the target abnormal anti-corrosion data, and obtain data publishing information of the target abnormal anti-corrosion data;

[0039] Step S402: Based on the data publishing information, an abnormal evaluation is performed on the target abnormal anti-corrosion data. The specific evaluation process is that when the total number of target abnormal anti-corrosion data published by the publisher is greater than the preset feature quantity threshold, the publisher is recorded as an abnormal publisher, the abnormal publisher account is processed, the target abnormal anti-corrosion data in each anti-corrosion functional module is detected, and the anti-corrosion data of the anti-corrosion platform in the current cycle is digitally managed;

[0040] In the above steps, the publishers and publishing time of the target abnormal anti-corrosion data are obtained because it is these publishers who publish the abnormal anti-corrosion data on the anti-corrosion platform. If only the published abnormal anti-corrosion data is processed but the publisher is not evaluated for abnormalities, the problem of data abnormalities cannot be fundamentally solved. Therefore, the publisher is analyzed for abnormalities and processed, thereby fundamentally solving the problem of data abnormalities and ensuring that the data in the anti-corrosion platform is normal.

[0041] In order to better implement the above method, a digital management system for anti-corrosion platform based on big data is also proposed. The system includes a characteristic anti-corrosion index module, an abnormal data set module, a target abnormal anti-corrosion data module, and a digital management module;

[0042] The characteristic anti-corrosion index module is used to obtain the abnormal records of historical anti-corrosion data in the anti-corrosion platform, extract the historical anti-corrosion data from the abnormal records of historical anti-corrosion data, evaluate the correlation between the anti-corrosion index in the historical anti-corrosion data and the data abnormality, and obtain the characteristic anti-corrosion index;

[0043] The abnormal data set module is used to obtain the characteristic anti-corrosion indicators in the anti-corrosion platform, obtain the historical anti-corrosion data abnormal records in the anti-corrosion platform, extract the historical text information of the characteristic anti-corrosion indicators from the historical anti-corrosion data abnormal records, analyze the keywords in the historical text information, and the abnormal correlation with the characteristic anti-corrosion indicators to obtain the abnormal data set;

[0044] Target abnormal anti-corrosion data module, used to evaluate the data abnormality of anti-corrosion data and obtain target abnormal anti-corrosion data;

[0045] The digital management module is used to acquire the target abnormal anti-corrosion data of the anti-corrosion platform in the current cycle, and to digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle.

[0046] Furthermore, the characteristic anti-corrosion index module includes a data anomaly scoring unit and a characteristic anti-corrosion index unit;

[0047] The data anomaly scoring unit is used to obtain the historical anti-corrosion data anomaly records in the anti-corrosion platform and calculate the data anomaly scores of various anti-corrosion indicators in various anti-corrosion functional modules;

[0048] The characteristic anti-corrosion index unit is used to determine the data anomaly correlation of various anti-corrosion indicators in various anti-corrosion functional modules according to the data anomaly scores of various anti-corrosion indicators, and obtain characteristic anti-corrosion indicators.

[0049] Furthermore, the abnormal data set module includes an abnormal correlation value unit and an abnormal data set unit;

[0050] The abnormal correlation value unit is used to obtain several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, calculate the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion indicators, and the abnormal correlation value of the characteristic anti-corrosion indicators;

[0051] The abnormal data set unit is used to obtain the abnormal correlation value of the characteristic anti-corrosion index with the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion index, so as to obtain the abnormal data set of the characteristic anti-corrosion index.

[0052] Furthermore, the target abnormal anti-corrosion data module includes a characteristic index abnormal value unit and a target abnormal anti-corrosion data unit;

[0053] A characteristic index abnormal value unit is used to calculate characteristic index abnormal values ​​of several characteristic anti-corrosion indexes of the anti-corrosion data of each anti-corrosion functional module;

[0054] The target abnormal anti-corrosion data unit is used to determine the data abnormality of the anti-corrosion data of each anti-corrosion functional module according to the abnormal value of the characteristic index, and obtain the target abnormal anti-corrosion data.

[0055] Further, the digital management module includes a digital management unit;

[0056] The digital management unit is used to acquire the target abnormal anti-corrosion data in each anti-corrosion functional module in the current cycle, process the abnormal publisher account of the anti-corrosion platform in the current cycle, detect the target abnormal anti-corrosion data in each anti-corrosion functional module, and digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle.

[0057] Compared with the prior art, the beneficial effects of the present invention are: the present invention realizes the intelligent evaluation and management of data anomalies of anti-corrosion data in the anti-corrosion platform, and finds characteristic anti-corrosion indicators with higher frequency of data anomalies through historical anti-corrosion data anomaly records. These characteristic anti-corrosion indicators have a great correlation with data anomalies. By evaluating the anomalies of each anti-corrosion data of the anti-corrosion platform in the current period through historical data of characteristic anti-corrosion indicators in historical anti-corrosion data with anomalies in history, it not only quickly finds the abnormal anti-corrosion data in the anti-corrosion platform, but also is more helpful for digital management of the anti-corrosion platform, thereby improving the user's trust in the platform, thereby promoting the utilization rate of the platform from another aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] FIG1 is a method flow chart of a digital management system and method for an anti-corrosion platform based on big data according to the present invention;

[0059] FIG2 is a module diagram of a digital management system and method for an anti-corrosion platform based on big data according to the present invention. DETAILED DESCRIPTION

[0060] 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.

[0061] Example: Figure 1-Figure 2As shown, the present invention provides a technical solution, a digital management method of an anti-corrosion platform based on big data, the method comprising:

[0062] Step S100: Obtaining historical anti-corrosion data anomaly records in the anti-corrosion platform, extracting historical anti-corrosion data from the historical anti-corrosion data anomaly records, evaluating the degree of correlation between the anti-corrosion index in the historical anti-corrosion data and the data anomaly, and obtaining a characteristic anti-corrosion index;

[0063] Wherein, step S100 includes:

[0064] Step S101: Obtaining historical anti-corrosion data abnormality records in the anti-corrosion platform, extracting historical anti-corrosion data from the historical anti-corrosion data abnormality records, the historical anti-corrosion data including the anti-corrosion function module to which the historical anti-corrosion data abnormality records belong, and data corresponding to various anti-corrosion indicators;

[0065] For example, the anti-corrosion function modules include the anti-corrosion talent market module, the anti-corrosion solution exchange module, etc.

[0066] For example, various anti-corrosion indicators include anti-corrosion job posting indicators, anti-corrosion material indicators, etc.

[0067] Step S102: Obtain the anti-corrosion indicators marked as data abnormalities in the historical period from the historical anti-corrosion data abnormality records, and record them as historical abnormal anti-corrosion indicators, and obtain data corresponding to several historical abnormal anti-corrosion indicators in each historical anti-corrosion data abnormality record in the anti-corrosion platform;

[0068] Step S103: Obtain the time point at which the historical anti-corrosion data in the historical anti-corrosion data abnormal record is released in the anti-corrosion platform, and obtain the duration between the time point and the current cycle, which is recorded as the marking duration of the historical anti-corrosion data abnormal record;

[0069] Step S104: Obtain the maximum value Tmax of the marking time length in each historical anti-corrosion data abnormal record, and evaluate the correlation between each anti-corrosion index of the historical anti-corrosion data in each anti-corrosion function module and the data abnormality. The specific process of evaluating the correlation between the b-th anti-corrosion index in the a-th anti-corrosion function module and the data abnormality is to calculate the data abnormality correlation value Lb of the b-th anti-corrosion index:

[0070]

[0071] Wherein, ti represents the marking duration of the i-th abnormal record of historical anti-corrosion data of the b-th anti-corrosion index, the historical abnormal anti-corrosion index; j represents the total number of abnormal records of historical anti-corrosion data of the b-th anti-corrosion index, the historical abnormal anti-corrosion index;

[0072] For example, the first anti-corrosion indicator is that the total number j of historical anti-corrosion data abnormal records of historical abnormal anti-corrosion indicators is 3; the first anti-corrosion indicator is that the marking time t1 of the first historical anti-corrosion data abnormal record of the historical abnormal anti-corrosion indicator is 1h; the second anti-corrosion indicator is that the marking time t2 of the second historical anti-corrosion data abnormal record of the historical abnormal anti-corrosion indicator is 3h; the third anti-corrosion indicator is that the marking time t3 of the third historical anti-corrosion data abnormal record of the historical abnormal anti-corrosion indicator is 4h; the maximum value Tmax of the marking time in each historical anti-corrosion data abnormal record is 10h;

[0073] Calculate the data anomaly correlation value L1 of the first anti-corrosion index:

[0074]

[0075] Step S105: Obtain the minimum value Lmin and the maximum value Lmax of the data anomaly correlation values ​​of each anti-corrosion indicator in the a-th anti-corrosion functional module, and calculate the data anomaly score Pb of the b-th anti-corrosion indicator = (La-Lmin) / (Lmax-Lmin);

[0076] Step S106: When the data anomaly score Pb is greater than a preset data anomaly threshold, it is determined that the b-th anti-corrosion indicator in the a-th anti-corrosion functional module is associated with the data anomaly, and the b-th anti-corrosion indicator is marked as a characteristic anti-corrosion indicator;

[0077] Step S200: Acquire characteristic anti-corrosion indicators in the anti-corrosion platform, acquire historical anti-corrosion data abnormal records in the anti-corrosion platform, extract historical text information of characteristic anti-corrosion indicators from the historical anti-corrosion data abnormal records, and analyze the keywords in the historical text information and the abnormal correlation with the characteristic anti-corrosion indicators to obtain an abnormal data set;

[0078] Wherein, step S200 includes:

[0079] Step S201: Acquire several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, acquire the anti-corrosion indicators as historical anti-corrosion data abnormality records of characteristic anti-corrosion indicators, and record the historical anti-corrosion data abnormality records as characteristic historical anti-corrosion data abnormality records of characteristic anti-corrosion indicators;

[0080] Step S202: extracting historical text information of characteristic anti-corrosion indicators from the historical anti-corrosion data of the characteristic historical anti-corrosion data anomaly record, removing noise from the historical text information, dividing the historical text information into a number of keywords, and obtaining a historical anti-corrosion keyword group of the characteristic anti-corrosion indicators in the characteristic historical anti-corrosion data anomaly record;

[0081] Step S203: Analyze the abnormal correlation between several keywords in the historical anti-corrosion keyword group and the characteristic anti-corrosion index, wherein the specific process of analyzing the abnormal correlation between the c-th keyword in the historical anti-corrosion keyword group and the characteristic anti-corrosion index is to obtain the total number of occurrences of the c-th keyword in the historical text information of the characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion index, and record it as the mark value of the c-th keyword in the characteristic historical anti-corrosion data abnormal record;

[0082] Step S204: Calculate the abnormal correlation value Fc between the cth keyword and the characteristic anti-corrosion index:

[0083]

[0084] Wherein, Nc,sum represents the total number of each keyword in the historical anti-corrosion keyword group of the characteristic anti-corrosion indicator in each characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator; Hz,c represents the tag value of the cth keyword in the historical anti-corrosion keyword group of the zth characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator; n represents the total number of each characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator;

[0085] Step S205: Obtain each keyword of the historical text information of the characteristic anti-corrosion index from each characteristic historical anti-corrosion data abnormal record, and aggregate the keyword with the abnormal key value of the characteristic anti-corrosion index to obtain the abnormal data set of the characteristic anti-corrosion index;

[0086] Step S300: Acquire the anti-corrosion data of the anti-corrosion platform in the current cycle, extract the anti-corrosion index data from the anti-corrosion data, and evaluate the data abnormality of the anti-corrosion data in combination with the abnormal data set to obtain the target abnormal anti-corrosion data;

[0087] Wherein, step S300 includes:

[0088] Step S301: For the anti-corrosion platform, the anti-corrosion data of each anti-corrosion functional module in the current cycle are acquired, and the anti-corrosion index data are extracted from the anti-corrosion data, wherein the anti-corrosion index data includes text information of several anti-corrosion indexes in the anti-corrosion data, and the text information is de-noised, and the text information after the de-noising is divided into keywords, and the keywords are aggregated to obtain anti-corrosion keyword groups of several anti-corrosion indexes of the anti-corrosion data;

[0089] Step S302: Obtain abnormal data sets of several characteristic anti-corrosion indicators of various anti-corrosion functional modules in the anti-corrosion platform, and evaluate the data abnormality of each anti-corrosion data in each anti-corrosion functional module. The specific process of evaluating several characteristic anti-corrosion indicators of the e-th anti-corrosion data of the d-th anti-corrosion functional module is as follows:

[0090] Obtain the anti-corrosion keyword groups of several characteristic anti-corrosion indicators of the e-th anti-corrosion data, and calculate the characteristic abnormal correlation values ​​of several keywords in the anti-corrosion keyword groups of several characteristic anti-corrosion indicators, among which the characteristic abnormal correlation value Q of the β-th keyword in the anti-corrosion keyword group of the α-th characteristic anti-corrosion indicator is β :

[0091]

[0092] Where r is the total number of abnormal data sets of the αth feature anti-corrosion index; F α,β is the abnormal correlation value between the βth keyword and the αth characteristic anti-corrosion index; F α , s is the abnormal correlation value of the sth keyword in the anti-corrosion keyword group of the αth feature anti-corrosion index;

[0093] Step S303: Calculate the characteristic index abnormal value U of the αth characteristic anti-corrosion index in the eth anti-corrosion data α :

[0094]

[0095] Wherein, Qx is the characteristic abnormal correlation value of the xth keyword of the anti-corrosion keyword group of the αth characteristic anti-corrosion index in the eth anti-corrosion data; m is the total number of keywords in the anti-corrosion keyword group of the αth characteristic anti-corrosion index;

[0096] Step S304: When the characteristic index abnormal value U α is greater than a preset characteristic index threshold, determining that the data of the αth characteristic anti-corrosion index in the eth anti-corrosion data is abnormal, and recording the eth anti-corrosion data as the target abnormal anti-corrosion data;

[0097] Step S400: Acquire target abnormal anti-corrosion data of the anti-corrosion platform in the current cycle, and digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle based on the target abnormal anti-corrosion data;

[0098] Wherein, step S400 includes:

[0099] Step S401: Acquire target abnormal anti-corrosion data in various anti-corrosion function modules in the current cycle, acquire and collect publishers and publishing times of the target abnormal anti-corrosion data, and obtain data publishing information of the target abnormal anti-corrosion data;

[0100] Step S402: Based on the data publishing information, an abnormal evaluation is performed on the target abnormal anti-corrosion data. The specific evaluation process is that when the total number of target abnormal anti-corrosion data published by the publisher is greater than the preset feature quantity threshold, the publisher is recorded as an abnormal publisher, the abnormal publisher account is processed, the target abnormal anti-corrosion data in each anti-corrosion functional module is detected, and the anti-corrosion data of the anti-corrosion platform in the current cycle is digitally managed;

[0101] In order to better implement the above method, a digital management system for anti-corrosion platform based on big data is also proposed. The system includes a characteristic anti-corrosion index module, an abnormal data set module, a target abnormal anti-corrosion data module, and a digital management module;

[0102] The characteristic anti-corrosion index module is used to obtain the abnormal records of historical anti-corrosion data in the anti-corrosion platform, extract the historical anti-corrosion data from the abnormal records of historical anti-corrosion data, evaluate the correlation between the anti-corrosion index in the historical anti-corrosion data and the data abnormality, and obtain the characteristic anti-corrosion index;

[0103] The abnormal data set module is used to obtain the characteristic anti-corrosion indicators in the anti-corrosion platform, obtain the historical anti-corrosion data abnormal records in the anti-corrosion platform, extract the historical text information of the characteristic anti-corrosion indicators from the historical anti-corrosion data abnormal records, analyze the keywords in the historical text information, and the abnormal correlation with the characteristic anti-corrosion indicators to obtain the abnormal data set;

[0104] Target abnormal anti-corrosion data module, used to evaluate the data abnormality of anti-corrosion data and obtain target abnormal anti-corrosion data;

[0105] A digital management module is used to obtain the target abnormal anti-corrosion data of the anti-corrosion platform in the current cycle, and to digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle;

[0106] Among them, the characteristic anti-corrosion index module includes a data anomaly scoring unit and a characteristic anti-corrosion index unit;

[0107] The data anomaly scoring unit is used to obtain the historical anti-corrosion data anomaly records in the anti-corrosion platform and calculate the data anomaly scores of various anti-corrosion indicators in various anti-corrosion functional modules;

[0108] The characteristic anti-corrosion index unit is used to judge the data anomaly correlation of various anti-corrosion indicators in various anti-corrosion function modules according to the data anomaly scores of various anti-corrosion indicators, and obtain the characteristic anti-corrosion indicators;

[0109] Among them, the abnormal data set module includes an abnormal correlation value unit and an abnormal data set unit;

[0110] The abnormal correlation value unit is used to obtain several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, calculate the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion indicators, and the abnormal correlation value of the characteristic anti-corrosion indicators;

[0111] An abnormal data set unit is used to obtain the abnormal correlation value of the characteristic anti-corrosion index with the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion index, so as to obtain the abnormal data set of the characteristic anti-corrosion index;

[0112] Among them, the target abnormal anti-corrosion data module includes a characteristic index abnormal value unit and a target abnormal anti-corrosion data unit;

[0113] A characteristic index abnormal value unit is used to calculate characteristic index abnormal values ​​of several characteristic anti-corrosion indexes of the anti-corrosion data of each anti-corrosion functional module;

[0114] The target abnormal anti-corrosion data unit is used to determine the data abnormality of the anti-corrosion data of each anti-corrosion functional module according to the abnormal value of the characteristic index, and obtain the target abnormal anti-corrosion data;

[0115] Wherein, the digital management module includes a digital management unit;

[0116] The digital management unit is used to acquire the target abnormal anti-corrosion data in each anti-corrosion functional module in the current cycle, process the abnormal publisher account of the anti-corrosion platform in the current cycle, detect the target abnormal anti-corrosion data in each anti-corrosion functional module, and digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle.

[0117] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A digital management method for anti-corrosion platform based on big data, characterized in that: The method comprises: Step S100: Obtaining historical anti-corrosion data anomaly records in the anti-corrosion platform, extracting historical anti-corrosion data from the historical anti-corrosion data anomaly records, evaluating the degree of correlation between the anti-corrosion index in the historical anti-corrosion data and the data anomaly, and obtaining a characteristic anti-corrosion index; Step S200: Acquire characteristic anti-corrosion indicators in the anti-corrosion platform, acquire abnormal records of historical anti-corrosion data in the anti-corrosion platform, extract historical text information of the characteristic anti-corrosion indicators from the abnormal records of historical anti-corrosion data, and analyze the keywords in the historical text information and their abnormal correlation with the characteristic anti-corrosion indicators to obtain an abnormal data set; Step S300: acquiring the anti-corrosion data of the anti-corrosion platform in the current cycle, extracting the anti-corrosion index data from the anti-corrosion data, and performing data abnormality evaluation on the anti-corrosion data in combination with the abnormal data set to obtain target abnormal anti-corrosion data; Step S400: Acquire target abnormal anti-corrosion data of the anti-corrosion platform in the current cycle, and digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle based on the target abnormal anti-corrosion data.

2. According to the big data-based digital management method for anti-corrosion platforms of claim 1, it is characterized in that: The step S100 includes: Step S101: Acquire the historical anti-corrosion data abnormality record in the anti-corrosion platform, and extract the historical anti-corrosion data from the historical anti-corrosion data abnormality record, wherein the historical anti-corrosion data includes the anti-corrosion function module to which the historical anti-corrosion data abnormality record belongs, and data corresponding to various anti-corrosion indicators; Step S102: obtaining the anti-corrosion indicators marked as data abnormalities in the historical period from the historical anti-corrosion data abnormality records, and recording them as historical abnormal anti-corrosion indicators, and obtaining data corresponding to several historical abnormal anti-corrosion indicators in each historical anti-corrosion data abnormality record in the anti-corrosion platform; Step S103: obtaining the time point at which the historical anti-corrosion data in the historical anti-corrosion data abnormal record is released in the anti-corrosion platform, and obtaining the duration of the distance between the time point and the current cycle, which is recorded as the marking duration of the historical anti-corrosion data abnormal record; Step S104: Obtain the maximum value T of the marking time length in each of the historical anti-corrosion data abnormal records max , evaluate the correlation between each anti-corrosion index of the historical anti-corrosion data in each anti-corrosion function module and the data anomaly, wherein the specific process of evaluating the correlation between the b-th anti-corrosion index in the a-th anti-corrosion function module and the data anomaly is to calculate the data anomaly correlation value L of the b-th anti-corrosion index b : Among them, t i The b-th anti-corrosion index is represented by the marking duration of the i-th historical anti-corrosion data abnormal record of the historical abnormal anti-corrosion index; j is represented by the b-th anti-corrosion index is the total number of historical anti-corrosion data abnormal records of the historical abnormal anti-corrosion index; Step S105: Obtain the minimum value L of the data abnormality correlation values ​​of each anti-corrosion indicator in the a-th anti-corrosion function module min and the maximum value L max , calculate the data abnormality score P of the anti-corrosion index of item b b =(L a -L min ) / (L max -L min ); Step S106: When the data abnormality score P b If the value is greater than a preset data anomaly threshold, it is determined that the b-th anti-corrosion indicator in the a-th anti-corrosion functional module is associated with the data anomaly, and the b-th anti-corrosion indicator is marked as a characteristic anti-corrosion indicator.

3. The digital management method of the anti-corrosion platform based on big data according to claim 2 is characterized in that: The step S200 includes: Step S201: acquiring several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, acquiring the anti-corrosion indicators as historical anti-corrosion data abnormality records of characteristic anti-corrosion indicators, and recording the historical anti-corrosion data abnormality records as characteristic historical anti-corrosion data abnormality records of the characteristic anti-corrosion indicators; Step S202: extracting historical text information of the characteristic anti-corrosion index from the historical anti-corrosion data of the characteristic historical anti-corrosion data abnormal record, removing noise from the historical text information, dividing the historical text information into a plurality of keywords, and obtaining a historical anti-corrosion keyword group of the characteristic anti-corrosion index in the characteristic historical anti-corrosion data abnormal record; Step S203: analyzing the abnormal correlation between the several keywords in the historical anti-corrosion keyword group and the characteristic anti-corrosion index, wherein the specific process of analyzing the abnormal correlation between the c-th keyword in the historical anti-corrosion keyword group and the characteristic anti-corrosion index is to obtain the total number of occurrences of the historical text information of the c-th keyword in the characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion index, and record it as the mark value of the c-th keyword in the characteristic historical anti-corrosion data abnormal record; Step S204: Calculate the abnormal correlation value F between the cth keyword and the characteristic anti-corrosion index c : Among them, N c,sum H is the total number of keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion indicator in each characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator; z,c The tag value of the cth keyword in the historical anti-corrosion keyword group of the zth characteristic historical anti-corrosion data abnormal record of the characteristic anti-corrosion indicator is represented; n is represented as the total number of the characteristic historical anti-corrosion data abnormal records of the characteristic anti-corrosion indicator; Step S205: Obtain each keyword of the historical text information of the characteristic anti-corrosion index from the abnormal records of each characteristic historical anti-corrosion data, and aggregate the keywords with the abnormal key values ​​of the characteristic anti-corrosion index to obtain the abnormal data set of the characteristic anti-corrosion index.

4. The digital management method of the anti-corrosion platform based on big data according to claim 3 is characterized in that: The step S300 includes: Step S301: acquiring the anti-corrosion data of each anti-corrosion functional module in the current cycle of the anti-corrosion platform, extracting anti-corrosion index data from the anti-corrosion data, wherein the anti-corrosion index data includes text information of several anti-corrosion indexes in the anti-corrosion data, removing noise from the text information, dividing the text information after the noise removal into keywords, and aggregating the keywords to obtain anti-corrosion keyword groups of the several anti-corrosion indexes of the anti-corrosion data; Step S302: Obtain abnormal data sets of several characteristic anti-corrosion indicators of various anti-corrosion functional modules in the anti-corrosion platform, and evaluate the data abnormality of each anti-corrosion data in the various anti-corrosion functional modules. The specific process of evaluating several characteristic anti-corrosion indicators of the e-th anti-corrosion data of the d-th anti-corrosion functional module is as follows: Obtain the anti-corrosion keyword groups of the several characteristic anti-corrosion indicators of the e-th anti-corrosion data, and calculate the characteristic abnormality association values ​​of several keywords in the anti-corrosion keyword groups of the several characteristic anti-corrosion indicators, wherein the characteristic abnormality association value Q of the β-th keyword in the anti-corrosion keyword group of the α-th characteristic anti-corrosion indicator is β : Where r is the total number of abnormal data sets of the αth characteristic anti-corrosion index; F α,β is the abnormal correlation value between the βth keyword and the αth characteristic anti-corrosion index; F α，s is the abnormal correlation value of the sth keyword in the anti-corrosion keyword group of the αth characteristic anti-corrosion indicator; Step S303: Calculate the characteristic index abnormal value U of the αth characteristic anti-corrosion index in the eth anti-corrosion data α : Among them, Q x is the characteristic abnormality association value of the xth keyword of the anti-corrosion keyword group of the αth characteristic anti-corrosion index in the eth anti-corrosion data; m is the total number of keywords in the anti-corrosion keyword group of the αth characteristic anti-corrosion index; Step S304: When the characteristic index abnormal value U α If the value is greater than a preset characteristic indicator threshold, it is determined that the data of the αth characteristic anti-corrosion indicator in the eth anti-corrosion data is abnormal, and the eth anti-corrosion data is recorded as the target abnormal anti-corrosion data.

5. The digital management method of the anti-corrosion platform based on big data according to claim 4 is characterized in that: The step S400 includes: Step S401: Acquire target abnormal anti-corrosion data in various anti-corrosion function modules in the current cycle, acquire and collect publishers and publishing times of the target abnormal anti-corrosion data, and obtain data publishing information of the target abnormal anti-corrosion data; Step S402: Based on the data publishing information, the target abnormal anti-corrosion data is evaluated for abnormality. The specific evaluation process is that when the total number of target abnormal anti-corrosion data published by the publisher is greater than the preset feature quantity threshold, the publisher is recorded as an abnormal publisher, the account of the abnormal publisher is processed, the target abnormal anti-corrosion data in each anti-corrosion functional module is detected, and the anti-corrosion data of the anti-corrosion platform in the current cycle is digitally managed.

6. A digital management system for anti-corrosion platforms based on big data, used to implement a digital management method for anti-corrosion platforms based on big data as described in any one of claims 1 to 5, characterized in that: The system includes a characteristic anti-corrosion index module, an abnormal data set module, a target abnormal anti-corrosion data module, and a digital management module; The characteristic anti-corrosion index module is used to obtain the abnormal records of historical anti-corrosion data in the anti-corrosion platform, extract the historical anti-corrosion data from the abnormal records of historical anti-corrosion data, evaluate the correlation between the anti-corrosion index in the historical anti-corrosion data and the data abnormality, and obtain the characteristic anti-corrosion index; The abnormal data set module is used to obtain the characteristic anti-corrosion index in the anti-corrosion platform, obtain the historical anti-corrosion data abnormal records in the anti-corrosion platform, extract the historical text information of the characteristic anti-corrosion index from the historical anti-corrosion data abnormal records, analyze the keywords in the historical text information, and the abnormal correlation with the characteristic anti-corrosion index to obtain the abnormal data set; The target abnormal anti-corrosion data module is used to evaluate the data abnormality of the anti-corrosion data to obtain target abnormal anti-corrosion data; The digital management module is used to acquire the target abnormal anti-corrosion data of the anti-corrosion platform in the current cycle and to digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle.

7. The digital management system for anti-corrosion platform based on big data according to claim 6 is characterized in that: The characteristic anti-corrosion index module includes a data anomaly scoring unit and a characteristic anti-corrosion index unit; The data anomaly scoring unit is used to obtain the historical anti-corrosion data anomaly records in the anti-corrosion platform and calculate the data anomaly scores of various anti-corrosion indicators in various anti-corrosion functional modules; The characteristic anti-corrosion indicator unit is used to determine the data anomaly correlation of various anti-corrosion indicators in various anti-corrosion function modules according to the data anomaly scores of the various anti-corrosion indicators, so as to obtain characteristic anti-corrosion indicators.

8. The digital management system for anti-corrosion platform based on big data according to claim 6 is characterized in that: The abnormal data set module includes an abnormal correlation value unit and an abnormal data set unit; The abnormal correlation value unit is used to obtain several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, and calculate the abnormal correlation value between the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion indicator and the characteristic anti-corrosion indicator; The abnormal data set unit is used to obtain the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion index and the abnormal correlation value of the characteristic anti-corrosion index to obtain the abnormal data set of the characteristic anti-corrosion index.

9. The digital management system for anti-corrosion platform based on big data according to claim 6 is characterized in that: The target abnormal anti-corrosion data module includes a characteristic index abnormal value unit and a target abnormal anti-corrosion data unit; The characteristic index abnormal value unit is used to calculate the characteristic index abnormal values ​​of several characteristic anti-corrosion indicators of the anti-corrosion data of each anti-corrosion functional module; The target abnormal anti-corrosion data unit is used to perform data abnormality judgment on the anti-corrosion data of each anti-corrosion functional module according to the abnormal value of the characteristic indicator to obtain the target abnormal anti-corrosion data.

10. The digital management system for anti-corrosion platform based on big data according to claim 6 is characterized in that: The digital management module includes a digital management unit; The digital management unit is used to acquire target abnormal anti-corrosion data in various anti-corrosion functional modules in the current cycle, process abnormal publisher accounts of the anti-corrosion platform in the current cycle, detect target abnormal anti-corrosion data in various anti-corrosion functional modules, and digitally manage the anti-corrosion data of the anti-corrosion platform in the current cycle.

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