A big data-based anticorrosion platform digital management system and method

Abstract

The application discloses a kind of based on big data's anticorrosion platform digital management system and method, it is related to anticorrosion data management technical field, including obtaining the historical anticorrosion data anomaly record in anticorrosion platform, assesses the correlation degree between anticorrosion index and data anomaly in historical anticorrosion data;Obtain the historical anticorrosion data anomaly record in anticorrosion platform, extract the historical text information of characteristic anticorrosion index from historical anticorrosion data anomaly record, and analyze the keyword in historical text information, with the abnormal correlation of characteristic anticorrosion index;Characteristic anticorrosion index data is extracted from anticorrosion data, and in combination with abnormal data set, anticorrosion data is carried out data anomaly evaluation, and target abnormal anticorrosion data is obtained;Obtain the target abnormal anticorrosion data of anticorrosion platform in current cycle, and the anticorrosion data of anticorrosion platform in current cycle is carried out digital management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of corrosion prevention data management, and particularly relates to a big data-based corrosion prevention platform digital management system and method. BACKGROUND

[0002] Due to the influence of the demand of infrastructure construction, oil and gas, shipbuilding and chemical industry and other industries, the global corrosion prevention market has been growing continuously in recent years. With the continuous expansion of the corrosion prevention market, big data technology is increasingly applied to corrosion prevention platforms. Generally, various functional modules exist in the corrosion prevention platform. These functional modules mainly have the functions of anticorrosion material sales, anticorrosion talent recruitment, and anticorrosion project scheme exchange. The establishment of the corrosion prevention platform enables users to purchase and sell anticorrosion materials and recruit and seek jobs through the corrosion prevention platform. This not only greatly facilitates users, but also promotes the development of the anticorrosion market to some extent.

[0003] At present, most of the information on the various functional modules of the traditional corrosion prevention platform is edited by the users of the corrosion prevention platform and then uploaded to the corrosion prevention platform. The corrosion prevention platform displays the content published by the users on the corresponding functional modules of the corrosion prevention platform. However, the corrosion prevention platform cannot identify the anticorrosion-related content published by the users on the platform, and thus cannot identify the abnormal data of the various functional modules in the corrosion prevention platform. This not only greatly reduces the user experience in the use process and affects the development of the business, but also may lead the users to believe that the platform is unreliable, thereby reducing the use or switching to other competitors, which seriously affects the platform. SUMMARY

[0004] The present application aims to provide a big data-based corrosion prevention platform digital management system and method to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: a big data-based corrosion prevention platform digital management method, the method comprising:

[0006] Step S100: obtaining historical anticorrosion data abnormal records in the corrosion prevention platform, extracting historical anticorrosion data from the historical anticorrosion data abnormal records, evaluating the correlation between anticorrosion indexes and data abnormalities in the historical anticorrosion data, and obtaining characteristic anticorrosion indexes;

[0007] Step S200: Obtain the characteristic anti-corrosion index in the anti-corrosion platform, obtain the historical anti-corrosion data anomaly record in the anti-corrosion platform, extract the historical text information of the characteristic anti-corrosion index from the historical anti-corrosion data anomaly record, and analyze the keywords in the historical text information and the abnormal association of the characteristic anti-corrosion index to obtain an abnormal data set;

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

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

[0010] Further, step S100 includes:

[0011] Step S101: Obtain the historical anti-corrosion data anomaly record in the anti-corrosion platform, and extract the historical anti-corrosion data from the historical anti-corrosion data anomaly record, wherein the historical anti-corrosion data includes the anti-corrosion function module to which the historical anti-corrosion data anomaly record belongs, and the data corresponding to each anti-corrosion index;

[0012] Step S102: From the historical anti-corrosion data anomaly record, obtain the anti-corrosion index marked as data anomaly in the historical period, and record it as a historical abnormal anti-corrosion index, and obtain the data corresponding to a plurality of historical abnormal anti-corrosion indexes in each historical anti-corrosion data anomaly record in the anti-corrosion platform;

[0013] Step S103: Obtain the historical anti-corrosion data in the historical anti-corrosion data anomaly record, the time point published in the anti-corrosion platform, and the time length of the distance between the time point and the current period, recorded as the marking time length of the historical anti-corrosion data anomaly record;

[0014] Step S104: Obtain the maximum value Tmax of the marking time length in each historical anti-corrosion data anomaly record, and evaluate the correlation degree between each anti-corrosion index of the historical anti-corrosion data in each anti-corrosion function module and data anomaly, wherein the specific process of evaluating the correlation degree between the bth anti-corrosion index in the ath anti-corrosion function module and data anomaly is to calculate the data anomaly association value Lb of the bth anti-corrosion index:

[0015]

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

[0017] Step S105: Obtain the minimum value Lmin and the maximum value Lmax of the data anomaly correlation value of each anticorrosion index in the a-th anticorrosion function module, and calculate the data anomaly score Pb of the b-th anticorrosion index as Pb=(La-Lmin) / (Lmax-Lmin).

[0018] Step S106: When the data anomaly score Pb is greater than the preset data anomaly threshold value, it is determined that the b-th anticorrosion index in the a-th anticorrosion function module has correlation with data anomaly, and the b-th anticorrosion index is marked as a characteristic anticorrosion index.

[0019] Further, step S200 includes:

[0020] Step S201: Obtain a plurality of characteristic anticorrosion indexes in each anticorrosion function module of the anticorrosion platform, obtain the historical anticorrosion data anomaly record of the anticorrosion index that is the characteristic anticorrosion index, and record the historical anticorrosion data anomaly record as the characteristic historical anticorrosion data anomaly record of the characteristic anticorrosion index.

[0021] Step S202: Extract the historical text information of the characteristic anticorrosion index from the historical anticorrosion data of the characteristic historical anticorrosion data anomaly record, remove noise from the historical text information, and cut the historical text information into a plurality of keywords to obtain the historical anticorrosion keyword group of the characteristic anticorrosion index in the characteristic historical anticorrosion data anomaly record.

[0022] Step S203: Analyze the abnormal correlation between a plurality of keywords in the historical anticorrosion keyword group and the characteristic anticorrosion index, wherein the specific process of analyzing the abnormal correlation between the c-th keyword in the historical anticorrosion keyword group and the characteristic anticorrosion index is to obtain the total number of occurrences of the c-th keyword in the historical text information in the characteristic historical anticorrosion data anomaly record of the characteristic anticorrosion index, and record the value as the marking value of the c-th keyword in the characteristic historical anticorrosion data anomaly record.

[0023] Step S204: Calculate the abnormal correlation value Fc between the c-th keyword and the characteristic anticorrosion index as Fc=|j-t| / (j+t).

[0024]

[0025] Wherein, Nc,sum represents the total number of each keyword in the historical anticorrosion keyword group of the characteristic anticorrosion index in each characteristic historical anticorrosion data anomaly record of the characteristic anticorrosion index; Hz,c represents the marked value of the cth keyword in the historical anticorrosion keyword group of the zth characteristic historical anticorrosion data anomaly record of the characteristic anticorrosion index; n represents the total number of each characteristic historical anticorrosion data anomaly record of the characteristic anticorrosion index;

[0026] Step S205: From each characteristic historical anticorrosion data anomaly record, obtain each keyword of the historical text information of the characteristic anticorrosion index, and collect the keywords and the abnormal key value of the characteristic anticorrosion index, to obtain an abnormal data set of the characteristic anticorrosion index.

[0027] Further, step S300 includes:

[0028] Step S301: For the anticorrosion platform, anticorrosion data of each anticorrosion function module in the current period is obtained, anticorrosion index data is extracted from the anticorrosion data, the anticorrosion index data includes text information of several anticorrosion indexes in the anticorrosion data, the text information is de-noised, the de-noised text information is cut into keywords, and is collected to obtain anticorrosion keyword groups of several anticorrosion indexes of the anticorrosion data;

[0029] Step S302: Obtain an abnormal data set of several characteristic anticorrosion indexes of each anticorrosion function module in the anticorrosion platform, and evaluate the data anomaly of each anticorrosion data in each anticorrosion function module, wherein the specific process of evaluating several characteristic anticorrosion indexes of the e th anticorrosion data of the d th anticorrosion function module is as follows:

[0030] Obtain the anticorrosion keyword groups of several characteristic anticorrosion indexes of the e th anticorrosion data, and calculate the characteristic abnormal correlation value of several keywords in the anticorrosion keyword groups of several characteristic anticorrosion indexes, wherein the characteristic abnormal correlation value Q of the β th keyword in the anticorrosion keyword group of the α th characteristic anticorrosion index is calculated as follows: β

[0031]

[0032] Wherein, r is the total number of the abnormal data set of the α th characteristic anticorrosion index; F α,β is the abnormal correlation value of the β th keyword and the α th characteristic anticorrosion index; F α , s is the abnormal correlation value of the s th keyword in the anticorrosion keyword group of the α th characteristic anticorrosion index;

[0033] ​Step S303: calculating the feature index abnormal value U of the αth feature corrosion index in the e th anticorrosion data α :

[0034]

[0035] Wherein, Qx is the feature abnormal correlation value of the xth keyword of the anticorrosion keyword group of the αth feature corrosion index in the e th anticorrosion data; m is the total number of several keywords in the anticorrosion keyword group of the αth feature corrosion index;

[0036] Step S304: when the feature index abnormal value U α is greater than the preset feature index threshold, it is determined that the αth feature corrosion index in the e th anticorrosion data is abnormal, and the e th anticorrosion data is recorded as the target abnormal anticorrosion data.

[0037] Further, step S400 includes:

[0038] Step S401: obtaining the target abnormal anticorrosion data in each anticorrosion function module in the current period, obtaining and collecting the publisher and publishing time of the target abnormal anticorrosion data to obtain the data publishing information of the target abnormal anticorrosion data;

[0039] Step S402: based on the data publishing information, the target abnormal anticorrosion data is evaluated, and the specific evaluation process is that when the total number of target abnormal anticorrosion data published by the publisher is greater than the preset feature number threshold, the publisher is recorded as an abnormal publisher, the abnormal publisher account is processed, the target abnormal anticorrosion data in each anticorrosion function module is detected, and the anticorrosion data of the anticorrosion platform in the current period is digitally managed;

[0040] In the above steps, the publisher and publishing time of the target abnormal anticorrosion data are obtained because these publishers publish abnormal anticorrosion data in the platform in the anticorrosion platform. If only the published abnormal anticorrosion data is processed, but the publisher is not evaluated, the data abnormality problem cannot be fundamentally solved, so the publisher is analyzed and processed, so that the data abnormality problem is fundamentally solved, and the data in the anticorrosion platform is normal.

[0041] In order to better realize the above method, a digital management system of anticorrosion platform based on big data is also proposed, which includes a feature corrosion index module, an abnormal data set module, a target abnormal anticorrosion data module, and a digital management module.

[0042] The characteristic anti-corrosion index module is configured to acquire historical anti-corrosion data anomaly records in the anti-corrosion platform, extract historical anti-corrosion data from the historical anti-corrosion data anomaly records, evaluate the correlation between anti-corrosion indexes and data anomalies in the historical anti-corrosion data, and obtain characteristic anti-corrosion indexes.

[0043] The abnormal data set module is configured to acquire the characteristic anti-corrosion indexes in the anti-corrosion platform, acquire historical text information of the characteristic anti-corrosion indexes from the historical anti-corrosion data anomaly records, analyze keywords in the historical text information, and obtain abnormal data sets of the characteristic anti-corrosion indexes.

[0044] The target abnormal anti-corrosion data module is configured to evaluate data anomalies of the anti-corrosion data and obtain target abnormal anti-corrosion data.

[0045] The digital management module is configured to acquire the target abnormal anti-corrosion data of the anti-corrosion platform in a current period and perform digital management on the anti-corrosion data of the anti-corrosion platform in the current period.

[0046] Further, 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 configured to acquire historical anti-corrosion data anomaly records in the anti-corrosion platform and calculate data anomaly scores of anti-corrosion indexes in each anti-corrosion function module.

[0048] The characteristic anti-corrosion index unit is configured to determine data anomaly correlations of the anti-corrosion indexes in each anti-corrosion function module according to the data anomaly scores of the anti-corrosion indexes and obtain characteristic anti-corrosion indexes.

[0049] Further, the abnormal data set module includes an abnormal correlation value unit and an abnormal data set unit.

[0050] The abnormal correlation value unit is configured to acquire a plurality of characteristic anti-corrosion indexes in each anti-corrosion function module of the anti-corrosion platform, calculate keywords in a historical anti-corrosion keyword group of the characteristic anti-corrosion indexes, and obtain abnormal correlation values of the characteristic anti-corrosion indexes.

[0051] The abnormal data set unit is configured to acquire the keywords in the historical anti-corrosion keyword group of the characteristic anti-corrosion indexes and the abnormal correlation values of the characteristic anti-corrosion indexes and obtain abnormal data sets of the characteristic anti-corrosion indexes.

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

[0053] characteristic index abnormal value unit for characteristic index abnormal values of a plurality of characteristic anticorrosion indexes of the anticorrosion data of each anticorrosion function module;

[0054] target abnormal anticorrosion data unit for performing data abnormality determination on the anticorrosion data of each anticorrosion function module according to the characteristic index abnormal values to obtain target abnormal anticorrosion data.

[0055] Further, the digital management module comprises a digital management unit.

[0056] The digital management unit is configured to acquire the target abnormal anticorrosion data in each anticorrosion function module in the current period, process the abnormal publisher account of the anticorrosion platform in the current period, detect the target abnormal anticorrosion data in each anticorrosion function module, and perform digital management on the anticorrosion data of the anticorrosion platform in the current period.

[0057] Compared with the prior art, the present application has the beneficial effects that the present application realizes intelligent evaluation and management of data abnormality of anticorrosion data in the anticorrosion platform, finds characteristic anticorrosion indexes with high frequency of data abnormality through historical anticorrosion data abnormality records, these characteristic anticorrosion indexes have great correlation with data abnormality, and the characteristic anticorrosion indexes in the historical anticorrosion data with abnormality in the history are used to evaluate the data abnormality of each anticorrosion data of the anticorrosion platform in the current period, which not only quickly finds the anticorrosion data with abnormality in the anticorrosion platform, but also is more helpful for digital management of the anticorrosion platform, improves the trust degree of users to the platform, and thus promotes the use rate of the platform from another aspect. BRIEF DESCRIPTION OF DRAWINGS

[0058] Fig. 1 is a method flowchart of the anticorrosion platform digital management system and method based on big data according to the present application;

[0059] Fig. 2 is a module schematic diagram of the anticorrosion platform digital management system and method based on big data according to the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0061] Embodiment: As Figures 1-2As shown, the present application 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 correlation degree between the anti-corrosion indexes and data anomalies in the historical anti-corrosion data, and obtaining characteristic anti-corrosion indexes;

[0063] Among them, step S100 includes:

[0064] Step S101: 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, the historical anti-corrosion data including the anti-corrosion function modules to which the historical anti-corrosion data anomaly records belong, and the data corresponding to each anti-corrosion index;

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

[0066] For example, each anti-corrosion index includes an anti-corrosion position release index, an anti-corrosion material index, etc.

[0067] Step S102: obtaining anti-corrosion indexes marked as data anomalies in a historical period from the historical anti-corrosion data anomaly records, and recording them as historical abnormal anti-corrosion indexes, and obtaining the data corresponding to a plurality of historical abnormal anti-corrosion indexes in each historical anti-corrosion data anomaly record in the anti-corrosion platform;

[0068] Step S103: obtaining the historical anti-corrosion data in the historical anti-corrosion data anomaly records, the time point of being published in the anti-corrosion platform, and the time length of the distance between the time point and the current period, recorded as the marking time length of the historical anti-corrosion data anomaly records;

[0069] Step S104: obtaining the maximum value Tmax of the marking time length in each historical anti-corrosion data anomaly record, and evaluating the correlation degree between each anti-corrosion index of the historical anti-corrosion data in each anti-corrosion function module and data anomalies, wherein the specific process of evaluating the correlation degree between the bth anti-corrosion index in the ath anti-corrosion function module and data anomalies is to calculate the data anomaly correlation value Lb of the bth anti-corrosion index:

[0070]

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

[0072] For example, the total number of historical anticorrosion data anomaly records of the b-th anticorrosion index j is 3; the marking duration of the 1-th historical anticorrosion data anomaly record of the b-th anticorrosion index ti is 1h; the marking duration of the 2-th historical anticorrosion data anomaly record of the b-th anticorrosion index t2 is 3h; the marking duration of the 3-th historical anticorrosion data anomaly record of the b-th anticorrosion index t3 is 4h; and the maximum value Tmax of the marking duration in each historical anticorrosion data anomaly record is 10h;

[0073] The data anomaly correlation value L1 of the 1-th anticorrosion index is calculated as follows:

[0074]

[0075] Step S105: The minimum value Lmin and the maximum value Lmax of the data anomaly correlation values of the anticorrosion indexes in the a-th anticorrosion function module are obtained, and the data anomaly score Pb of the b-th anticorrosion index is calculated as Pb=(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 anticorrosion index in the a-th anticorrosion function module is correlated with data anomaly, and the b-th anticorrosion index is marked as a characteristic anticorrosion index.

[0077] Step S200: The characteristic anticorrosion index in the anticorrosion platform is obtained, the historical anticorrosion data anomaly record in the anticorrosion platform is obtained, the historical text information of the characteristic anticorrosion index is extracted from the historical anticorrosion data anomaly record, and the keywords in the historical text information are analyzed to obtain the abnormal data set.

[0078] Wherein, step S200 comprises:

[0079] Step S201: A plurality of characteristic anticorrosion indexes in each anticorrosion function module of the anticorrosion platform are obtained, the historical anticorrosion data anomaly record of the anticorrosion index is obtained, and the historical anticorrosion data anomaly record is marked as the characteristic historical anticorrosion data anomaly record of the characteristic anticorrosion index.

[0080] Step S202: Extract the historical text information of the feature corrosion index from the historical corrosion data recorded in the feature historical corrosion data anomaly record, remove the noise from the historical text information, segment the historical text information into a plurality of keywords, and obtain the historical corrosion keywords of the feature corrosion index in the feature historical corrosion data anomaly record;

[0081] Step S203: Analyze the abnormal association between the plurality of keywords in the historical corrosion keywords and the feature corrosion index, wherein the specific process of analyzing the abnormal association between the cth keyword in the historical corrosion keywords and the feature corrosion index is to obtain the total number of occurrences of the cth keyword in the historical text information of the feature historical corrosion data anomaly record of the feature corrosion index, and record the label value of the cth keyword in the feature historical corrosion data anomaly record of the feature corrosion index as Fc,c.

[0082] Step S204: Calculate the abnormal association value Fc of the cth keyword and the feature corrosion index:

[0083]

[0084] Wherein, Nc,sum represents the total number of keywords in the historical corrosion keyword group of the feature corrosion index in each feature historical corrosion data anomaly record of the feature corrosion index; Hz,c represents the label value of the cth keyword in the historical corrosion keyword group of the zth feature historical corrosion data anomaly record of the feature corrosion index; n represents the total number of each feature historical corrosion data anomaly record of the feature corrosion index.

[0085] Step S205: Obtain each keyword of the historical text information of the feature corrosion index from each feature historical corrosion data anomaly record, and collect the keywords and the abnormal key value of the feature corrosion index to obtain an abnormal data set of the feature corrosion index.

[0086] Step S300: Obtain the corrosion data of the corrosion platform in the current period, extract the corrosion index data from the corrosion data, and combine the abnormal data set to evaluate the data abnormality of the corrosion data, and obtain target abnormal corrosion data.

[0087] Wherein, step S300 includes:

[0088] Step S301: For the anti-corrosion platform, anti-corrosion data of each anti-corrosion function module in the current period is acquired, anti-corrosion index data is extracted from the anti-corrosion data, the anti-corrosion index data includes text information of several anti-corrosion indexes in the anti-corrosion data, noise removal is performed on the text information, the text information after noise removal is cut into keywords, and the anti-corrosion keywords of the several anti-corrosion indexes of the anti-corrosion data are obtained by collection;

[0089] Step S302: The abnormal data set of several characteristic anti-corrosion indexes of each anti-corrosion function module in the anti-corrosion platform is acquired, and the data abnormality of each anti-corrosion data in each anti-corrosion function module is evaluated, wherein the specific process of evaluating the several characteristic anti-corrosion indexes of the e th anti-corrosion data of the d th anti-corrosion function module is as follows:

[0090] The anti-corrosion keyword group of the several characteristic anti-corrosion indexes of the e th anti-corrosion data is acquired, and the feature abnormal association value of the several keywords in the anti-corrosion keyword group of the several characteristic anti-corrosion indexes is calculated, wherein the feature abnormal association value Q β of the β th keyword in the anti-corrosion keyword group of the α th characteristic anti-corrosion index is calculated as follows:

[0091]

[0092] Wherein, r is the total number of the abnormal data set of the α th characteristic anti-corrosion index; F α,β is the abnormal association value of the β th keyword and the α th characteristic anti-corrosion index; F α is the abnormal association value of the s th keyword in the anti-corrosion keyword group of the α th characteristic anti-corrosion index;

[0093] Step S303: The feature index abnormal value U α of the α th characteristic anti-corrosion index in the e th anti-corrosion data is calculated as follows:

[0094]

[0095] Wherein, Qx is the feature abnormal association value of the x th keyword in the anti-corrosion keyword group of the α th characteristic anti-corrosion index in the e th anti-corrosion data; m is the total number of the several keywords in the anti-corrosion keyword group of the α th characteristic anti-corrosion index;

[0096] Step S304: When the feature index abnormal value U α is greater than a preset feature index threshold value, it is determined that the data of the α th characteristic anti-corrosion index in the e th anti-corrosion data is abnormal, and the e th anti-corrosion data is recorded as target abnormal anti-corrosion data;

[0097] Step S400: obtaining target abnormal corrosion protection data of the corrosion protection platform in the current period, and based on the target abnormal corrosion protection data, performing digital management on the corrosion protection data of the corrosion protection platform in the current period;

[0098] Step S400 includes:

[0099] Step S401: obtaining target abnormal corrosion protection data in each corrosion protection function module in the current period, obtaining and collecting the publisher and publishing time of the target abnormal corrosion protection data to obtain data publishing information of the target abnormal corrosion protection data;

[0100] Step S402: based on the data publishing information, performing abnormal evaluation on the target abnormal corrosion protection data, and the specific evaluation process is that when the total number of target abnormal corrosion protection data published by the publisher is greater than a preset characteristic number threshold, the publisher is recorded as an abnormal publisher, the abnormal publisher account is processed, the target abnormal corrosion protection data in each corrosion protection function module is detected, and the corrosion protection data of the corrosion protection platform in the current period is digitally managed;

[0101] In order to better realize the above method, a corrosion protection platform digital management system based on big data is also proposed, which includes a characteristic corrosion protection index module, an abnormal data set module, a target abnormal corrosion protection data module, and a digital management module.

[0102] The characteristic corrosion protection index module is used to obtain historical corrosion protection data abnormal records in the corrosion protection platform, extract historical corrosion protection data from the historical corrosion protection data abnormal records, evaluate the correlation degree between corrosion protection indexes and data abnormalities in the historical corrosion protection data, and obtain characteristic corrosion protection indexes.

[0103] The abnormal data set module is used to obtain the characteristic corrosion protection indexes in the corrosion protection platform, obtain the historical corrosion protection data abnormal records in the corrosion protection platform, extract historical text information of the characteristic corrosion protection indexes from the historical corrosion protection data abnormal records, analyze the keywords in the historical text information, and obtain the abnormal data set based on the abnormal correlation of the characteristic corrosion protection indexes.

[0104] The target abnormal corrosion protection data module is used to evaluate the data abnormality of the corrosion protection data to obtain target abnormal corrosion protection data.

[0105] The digital management module is used to obtain target abnormal corrosion protection data of the corrosion protection platform in the current period, and perform digital management on the corrosion protection data of the corrosion protection platform in the current period.

[0106] The characteristic corrosion protection index module includes a data abnormality scoring unit and a characteristic corrosion protection index unit.

[0107] a data anomaly scoring unit configured to acquire historical anticorrosion data anomaly records in the anticorrosion platform, and calculate data anomaly scores of each anticorrosion index in each anticorrosion function module;

[0108] a characteristic anticorrosion index unit configured to determine data anomaly correlation of each anticorrosion index in each anticorrosion function module according to the data anomaly scores of the anticorrosion indexes, and obtain characteristic anticorrosion indexes;

[0109] The anomaly data set module includes an anomaly correlation value unit and an anomaly data set unit.

[0110] The anomaly correlation value unit is configured to acquire a plurality of characteristic anticorrosion indexes in each anticorrosion function module of the anticorrosion platform, calculate a keyword in a historical anticorrosion keyword group of the characteristic anticorrosion index, and the anomaly correlation value of the characteristic anticorrosion index.

[0111] The anomaly data set unit is configured to acquire the keyword in the historical anticorrosion keyword group of the characteristic anticorrosion index and the anomaly correlation value of the characteristic anticorrosion index, and obtain an anomaly data set of the characteristic anticorrosion index.

[0112] The target anomaly anticorrosion data module includes a characteristic index anomaly value unit and a target anomaly anticorrosion data unit.

[0113] The characteristic index anomaly value unit is configured to acquire a characteristic index anomaly value of a plurality of characteristic anticorrosion indexes of anticorrosion data of each anticorrosion function module.

[0114] The target anomaly anticorrosion data unit is configured to determine data anomaly of the anticorrosion data of each anticorrosion function module according to the characteristic index anomaly value, and obtain target anomaly anticorrosion data.

[0115] The digital management module includes a digital management unit.

[0116] The digital management unit is configured to acquire the target anomaly anticorrosion data in each anticorrosion function module in a current period, process an anomaly publisher account of the anticorrosion platform in the current period, detect the target anomaly anticorrosion data in each anticorrosion function module, and digitally manage anticorrosion data of the anticorrosion platform in the current period.

[0117] It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the present application can be implemented in other particular forms without departing from the spirit or essential characteristics of the present application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims should be considered as limiting the scope of the claims with respect to the figures of the patent document.

Claims

1. A digital management method for anti-corrosion platforms 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 historical anti-corrosion data anomaly records in the anti-corrosion platform, extract historical text information of the characteristic anti-corrosion indicators from the historical anti-corrosion data anomaly records, and analyze keywords in the historical text information for anomaly correlation with the characteristic anti-corrosion indicators to obtain an anomaly data set; Step S300: acquiring anti-corrosion data of the anti-corrosion platform in the current cycle, 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; 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. The digital management method for anti-corrosion platform based on big data according to claim 1 is characterized in that: The step S100 includes: Step S101: Acquire historical anti-corrosion data abnormality records in the anti-corrosion platform, and extract historical anti-corrosion data from the historical anti-corrosion data abnormality records, wherein the historical anti-corrosion data includes the anti-corrosion function module to which the historical anti-corrosion data abnormality records belong, and data corresponding to various anti-corrosion indicators; Step S102: obtaining, from the historical anti-corrosion data abnormality records, anti-corrosion indicators marked as data abnormalities within a historical period 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 abnormality record was published in the anti-corrosion platform, and obtaining the duration between the time point and the current cycle, which is recorded as the marking duration of the historical anti-corrosion data abnormality 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. 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 indicator is the marking duration of the i-th historical anti-corrosion data abnormal record of the historical abnormal anti-corrosion indicator; j is the total number of historical anti-corrosion data abnormal records of the b-th anti-corrosion indicator; Step S105: Obtain the minimum value L of the data abnormality correlation value 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 in 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 function 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 for anti-corrosion platform based on big data according to claim 2 is characterized in that: The step S200 includes: Step S201: Acquire several characteristic anti-corrosion indicators in various anti-corrosion function modules of the anti-corrosion platform, acquire historical anti-corrosion data abnormality records of the characteristic anti-corrosion indicators, and record 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 indicator from the historical anti-corrosion data of the characteristic historical anti-corrosion data abnormality 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 indicator in the characteristic historical anti-corrosion data abnormality 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 label 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 The total number of keywords in the historical anti-corrosion keyword group of each characteristic anti-corrosion indicator in the abnormal record of each characteristic historical anti-corrosion data of the characteristic anti-corrosion indicator; H 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 for anti-corrosion platform based on big data according to claim 3 is characterized in that: The step S300 includes: Step S301: acquiring anti-corrosion data of various anti-corrosion functional modules in a 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 in 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 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: 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 correlation values ​​of several keywords in the anti-corrosion keyword groups of the several characteristic anti-corrosion indicators, wherein the characteristic abnormality correlation 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 correlation value of the xth keyword in 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 for 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 release times of the target abnormal anti-corrosion data, and obtain data release 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 an anti-corrosion platform based on big data, used to implement a digital management method for an anti-corrosion platform based on big data according to 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 historical anti-corrosion data abnormality records in the anti-corrosion platform, extract the historical anti-corrosion data from the historical anti-corrosion data abnormality records, 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 characteristic anti-corrosion indicators in the anti-corrosion platform, obtain historical anti-corrosion data abnormality records in the anti-corrosion platform, extract historical text information of the characteristic anti-corrosion indicators from the historical anti-corrosion data abnormality records, analyze keywords in the historical text information, and analyze the abnormal correlation with the characteristic anti-corrosion indicators to obtain an abnormal data set; The target abnormal anti-corrosion data module is used to perform data abnormality evaluation on 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 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 each anti-corrosion indicator in each anti-corrosion function module based on the data anomaly score of each anti-corrosion indicator to obtain a characteristic anti-corrosion indicator.

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 a number of characteristic anti-corrosion indicators in each anti-corrosion functional module 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 indicator and the abnormal correlation value of the characteristic anti-corrosion indicator to obtain the abnormal data set of the characteristic anti-corrosion indicator.

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 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 obtain 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.

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