Method, apparatus, device and computer readable medium for monitoring data

Abstract

The application discloses a method, device and equipment for monitoring data and a computer readable medium, and relates to the technical field of big data. A specific embodiment of the method comprises the following steps: storing land finance remote sensing data according to the types of the land finance remote sensing data, and acquiring a monitoring information configuration table of data pieces of different resolutions of the land finance remote sensing data; calling a configuration tree in a monitoring information database, and searching from a root node of the configuration tree in combination with the monitoring information configuration table of the data pieces; searching for an abnormal type node corresponding to an abnormal problem identifier in the monitoring information configuration, and verifying data in the data pieces in a verification mode of the abnormal type node to monitor the data in the data pieces. The embodiment can realize comprehensive monitoring of finance remote sensing data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data, and in particular to a method and device for monitoring data, an apparatus and a computer readable medium. BACKGROUND

[0002] With the gradual maturity of related technologies, satellite remote sensing can monitor crop types, planting areas, growth conditions and disaster data in multiple spatial scales, and even predict crop yields. These land finance remote sensing data for loan credit can play an important role in the process of applying for loans by farmers and polishing products by financial institutions.

[0003] In the process of integrating, analyzing and managing land finance remote sensing data, monitoring the state and quality of the data is an essential step.

[0004] In the process of implementing the present application, the inventors have found that at least the following problems exist in the prior art: Due to the large amount of land finance remote sensing data, it is difficult to achieve comprehensive monitoring of the finance remote sensing data. SUMMARY

[0005] Therefore, the embodiments of the present application provide a method and device for monitoring data, an apparatus and a computer readable medium, which can achieve comprehensive monitoring of finance remote sensing data.

[0006] To achieve the above-mentioned purpose, according to an aspect of an embodiment of the present application, a method for monitoring data is provided, comprising:

[0007] storing the land finance remote sensing data according to the type of the land finance remote sensing data, and obtaining a monitoring information configuration table of data pieces of different resolutions of the land finance remote sensing data;

[0008] calling a configuration tree in a monitoring information database, and searching from a root node of the configuration tree in combination with the monitoring information configuration table of the data pieces;

[0009] searching for an abnormal type node corresponding to an abnormal problem identifier in the monitoring information configuration, verifying data in the data pieces by a verification mode of the abnormal type node, and monitoring the data in the data pieces.

[0010] The storing of the land finance remote sensing data according to the type of the land finance remote sensing data comprises:

[0011] storing the land finance remote sensing data according to the data source type and / or identification result of the land finance remote sensing data.

[0012] The data source type comprises satellite remote sensing and / or customer upload.

[0013] The identification result includes cultivated land identification data and / or crop classification data.

[0014] The monitoring information configuration table of the data piece of the land finance remote sensing data at different resolutions comprises:

[0015] The land finance remote sensing data is divided into data pieces at different resolutions according to resolutions.

[0016] The monitoring information configuration table of the data piece is obtained by image data analysis.

[0017] The monitoring information configuration table involves one or more of the following data: growth monitoring data, growth early warning data, yield prediction data, total yield prediction data, yield history data, total yield history data, and disaster assessment data.

[0018] The abnormal problem identifier includes one or more of the following: storage abnormality identifier, slice abnormality identifier, statistical abnormality identifier, and value range abnormality identifier.

[0019] The configuration tree in the monitoring information database is called in combination with the monitoring information configuration table of the data piece, and the root node of the configuration tree is searched and traversed, including:

[0020] The abnormal problem identifier of the data piece is obtained in the monitoring information configuration table of the data piece.

[0021] The configuration tree in the monitoring information database is called, and the root node of the configuration tree is searched and traversed to find the abnormal problem identifier.

[0022] The configuration tree is established according to the logical relationship between storage abnormality, slice abnormality, statistical abnormality, and value range abnormality.

[0023] The statistical abnormality node and the slice abnormality node belong to the root node of the configuration tree, the value range abnormality node belongs to the statistical abnormality node, and the storage abnormality node belongs to the slice abnormality node.

[0024] The abnormal problem identifier in the monitoring information configuration is searched to find the abnormal type node corresponding to the abnormal problem identifier, and the data in the data piece is verified in the verification mode of the abnormal type node, including:

[0025] The abnormal type node corresponding to the abnormal problem identifier is searched, and the verification mode of the abnormal type node is determined.

[0026] The data in the data piece is verified in the verification mode of the abnormal type node to determine whether the data in the data piece is normal.

[0027] The abnormal problem identifier includes a slice abnormality, and the verification mode includes structure verification, same name verification, type matching, and type verification.

[0028] The abnormal problem identifier includes a statistical abnormality, and the verification manner includes a return code verification and a standard abnormal value judgment.

[0029] The abnormal problem identifier includes a value range abnormality, and the verification manner includes a standard abnormal value judgment.

[0030] The abnormal problem identifier includes a storage abnormality, and the verification manner includes a same-name verification, a type matching, and a type verification.

[0031] The method further includes:

[0032] If the abnormal type node corresponding to the abnormal problem identifier is not searched, the method further includes:

[0033] The abnormal problem identifier includes a statistical abnormality, and the abnormal type identifier subordinate to the abnormal problem identifier includes a value range abnormality.

[0034] The abnormal problem identifier includes a slice abnormality, and the abnormal type identifier subordinate to the abnormal problem identifier includes a storage abnormality.

[0035] After the child node is verified according to the configuration tree, the method further includes:

[0036] Outputting the land finance remote sensing data, the monitoring information configuration table, and a result of verifying data in the data slice.

[0037] The outputting the land finance remote sensing data, the monitoring information configuration table, and the result of verifying data in the data slice includes:

[0038] The land finance remote sensing data is outputted in a preset display manner, and the monitoring information configuration table and the result of verifying data in the data slice are outputted, and the preset display manner includes one or more of the following: a text, a list, a pie chart, and a bar chart.

[0039] According to a second aspect of an embodiment of the present application, a device for monitoring data is provided, and the device includes:

[0040] An acquisition module is configured to store land finance remote sensing data according to a type of the land finance remote sensing data, and acquire a monitoring information configuration table of a data slice of the land finance remote sensing data with different resolutions.

[0041] A search module is configured to call a configuration tree in a monitoring information database, and search from a root node of the configuration tree in combination with the monitoring information configuration table of the data slice.

[0042] The monitoring module is configured to search for an abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration, and to verify data in the data slice in a verification manner of the abnormal type node to monitor the data in the data slice.

[0043] The acquisition module is specifically configured to split the land finance remote sensing data into data slices of different resolutions according to resolutions.

[0044] The image data analysis is performed on the data slice to obtain a monitoring information configuration table of the data slice.

[0045] The search module is specifically configured to acquire the abnormal problem identifier of the data slice from the monitoring information configuration table of the data slice.

[0046] The configuration tree in the monitoring information database is called, and the abnormal problem identifier is searched from a root node of the configuration tree.

[0047] According to a third aspect of the embodiment of the present application, an electronic device for monitoring data is provided, comprising:

[0048] One or more processors;

[0049] A storage device configured to store one or more programs,

[0050] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0051] According to a fourth aspect of the embodiment of the present application, a computer readable medium having a computer program stored thereon is provided, and the program is executed by a processor to implement the method as described above.

[0052] According to a fifth aspect of the embodiment of the present application, a computer program product is provided, comprising a computer program, and the program is executed by a processor to implement the method as described above.

[0053] An embodiment of the above invention has the following advantages or beneficial effects: according to the type of land finance remote sensing data, the land finance remote sensing data is stored, and a monitoring information configuration table of data pieces of different resolutions of the land finance remote sensing data is obtained; a configuration tree in a monitoring information database is called, and a root node of the configuration tree is searched through in combination with the monitoring information configuration table of the data pieces; an abnormal type node corresponding to an abnormal problem identifier in the monitoring information configuration is searched, and data in the data pieces is verified in a verification mode of the abnormal type node, so as to monitor the data in the data pieces. The abnormal problems easily occurring in the land finance remote sensing data involve multiple abnormal reasons, and the configuration tree represents the logical relationship of the multiple data abnormal reasons. Through the traversal of the configuration tree structure, the node type of the abnormal problems can be quickly and conveniently queried. Therefore, the data is verified in combination with the configuration tree, so that the comprehensive monitoring of the finance remote sensing data is realized.

[0054] Further effects of the above non-conventional optional mode will be described below in combination with the specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings are used to better understand the present application and do not constitute undue limitations on the present application. Among them:

[0056] Figure 1 is a schematic diagram of a segmented image of the prior art;

[0057] Figure 2 is a main flowchart of a method for monitoring data according to an embodiment of the present application;

[0058] Figure 3 is a flowchart of obtaining a monitoring information configuration table of data pieces of different resolutions of land finance remote sensing data according to an embodiment of the present application;

[0059] Figure 4 is a structural diagram of a configuration tree according to an embodiment of the present application;

[0060] Figure 5 is a flowchart of searching through a root node of a configuration tree according to an embodiment of the present application;

[0061] Figure 6 is a flowchart of verifying data in a data piece according to an embodiment of the present application;

[0062] Figure 7 is a main structural diagram of an apparatus for monitoring data according to an embodiment of the present application;

[0063] Figure 8 is an exemplary system architecture diagram to which an embodiment of the present application can be applied;

[0064] Figure 9is a structural schematic diagram of a computer system of a terminal device or a server suitable for implementing an embodiment of the present application. DETAILED DESCRIPTION

[0065] Exemplary embodiments of the present application are described herein below with reference to the accompanying drawings, in which various details of embodiments of the present application are set forth to facilitate an understanding, and should be considered as merely exemplary. Thus, it will be appreciated that various modifications and changes can be made to the embodiments described herein without departing from the scope and spirit of the application. Also, for the purpose of clarity and the brevity, the description below omits the description of well-known functions and structures. The acquisition, storage, use, processing, etc. of data in the technical solutions of the present application comply with the relevant provisions of national laws and regulations.

[0066] Currently, land finance remote sensing services are mainly used in a financial remote sensing big data platform. In order to better manage remote sensing basic raster data, remote sensing analysis result data and farmer vector land data, a financial remote sensing data management platform is established, and the platform functions include three modules of data overview, data management and data resources. Through the establishment of the financial remote sensing data management platform, remote sensing raster data and farmer vector land data are efficiently managed, remote sensing raster data statistical tasks are monitored, and remote sensing raster data quality is monitored in multiple dimensions.

[0067] Generally, the processing of remote sensing raster data by the land finance remote sensing big data platform includes slicing and statistical calculation in addition to basic picture storage. In order to enable users to intuitively see images of different resolutions when viewing land finance data by using a mouse wheel zoom, slicing processing needs to be performed on remote sensing raster image data.

[0068] Slicing is to divide a whole remote sensing raster image into images of different sizes by using an image processing algorithm, and to name and store them according to a certain file structure. For example, if an image of 1600*1600 pixel resolution is divided by 800 pixels as a unit, 4 second-level slice files of 800*800 pixels can be generated; if it is divided by 400 pixels as a unit, 16 fourth-level slice files can be generated, as shown in Figure 1 Figure 1 is a schematic diagram of dividing an image in the prior art.

[0069] The grid data slicing service is to better display and store financial images. The grid data slicing service slices remote sensing images based on the web Mercator projection method and the WGS84 coordinate system, and provides data services in the form of an interface.

[0070] Generally, this service calls raster image data in a database, slices the data in a pyramid structure, stores the processed data in a financial remote sensing image tile library, and provides the data in the form of an interface.​

[0071] The data statistical calculation service performs partition statistical calculation on land finance remote sensing resource data, meteorological data and other related data based on administrative boundaries, so as to support data of other services such as land finance remote sensing. The basic functions of the data statistical calculation service are as follows:

[0072] (1) Mean statistical calculation: support calculating the mean value of the growth status of crops in a specified area according to the input crop growth status result data; support calculating the mean value of the yield of crops in a specified area according to the input crop yield result data.

[0073] (2) Sum statistical calculation: support calculating the total yield in tons in a specified area according to the input total yield result data; support calculating the planting area of each crop in a specified area according to the input crop classification result data.

[0074] (3) Comparison statistical calculation: support calculating the number of mu of crops at each level in a specified area according to the input crop growth status result data; support calculating the number of mu of crops at each level in a specified area according to the input crop disaster result data.

[0075] (4) Cultivated land statistical calculation: support calculating the total area of cultivated land and the total area of non-cultivated land in a specified area according to the input cultivated land result data.

[0076] Currently, the following methods are used for financial remote sensing data monitoring:

[0077] (1) Obtain basic financial remote sensing data and classify the basic data;

[0078] (2) Store the basic financial remote sensing data and analyze the data to obtain intuitive information;

[0079] (3) Perform statistical calculation and visualization display on the stored and analyzed data;

[0080] (4) Perform abnormality monitoring on the analyzed and calculated data.

[0081] Input the data into the abnormality judgment module, which uses a configured normal range value to judge each type of financial remote sensing data. If the data does not meet the range, the data is marked as abnormal;

[0082] Next, input the data marked as abnormal into the abnormal data matching module to match the abnormal data information with historical abnormal information. Take the data value of the abnormal data as a, the value of the historical abnormal data as b, and the data value of the same type of abnormal data in the abnormal data as c. Calculate the matching value o = (c / a)*(c / b);

[0083] Finally, the matching value o is output to the decision information output module, and the historical abnormal information with the highest matching value is output.

[0084] The above-mentioned way of monitoring financial remote sensing data has the following shortcomings:

[0085] (1) The storage and statistical analysis tasks are not monitored, and only the stored data in the library is matched and determined for abnormality to achieve the purpose of early warning. For the overall monitoring management system, the monitoring dimension is not enough.

[0086] (2) The abnormal data information is not displayed in multiple dimensions, and the user cannot intuitively feel the amount and abnormality of the abnormal data, and the experience is poor.

[0087] (3) The single abnormality determination method based on data value does not consider that the abnormality determination methods for different types of abnormal data are not consistent, and the abnormality monitoring efficiency is low. Moreover, only threshold and historical experience value are used as the determination basis, and the accuracy of abnormality determination is insufficient, resulting in insufficient accuracy of abnormality monitoring.

[0088] In summary, it is difficult to achieve comprehensive monitoring of financial remote sensing data.

[0089] In order to solve the comprehensive monitoring of financial remote sensing data, the technical solutions in the embodiments of the present application can be used.

[0090] Referring to Figure 2 , Figure 2 is a main flow diagram of a method for monitoring data according to the embodiment of the present application, which determines a verification method in combination with a configuration tree to verify the data in the data piece. As shown in Figure 2 , the method specifically includes the following steps:

[0091] S201, according to the type of land financial remote sensing data, storing the land financial remote sensing data, and obtaining the monitoring information configuration table of the data piece with different resolutions of the land financial remote sensing data.

[0092] The land financial remote sensing data is land remote sensing data that can be processed into financial loan credit data. For example, remote sensing images in the field of agriculture can monitor crop types, planting areas and other conditions in multiple spatial scales, which can be used as the basis for financial loan credit.

[0093] The technical solutions in the embodiments of the present application are used to monitor land financial remote sensing data to ensure the safety and reliability of the basis for financial loan credit.

[0094] Considering that the amount of land financial remote sensing data is large, in order to process the land financial remote sensing data in time, it is necessary to store the land financial remote sensing data.

[0095] In an embodiment of the present application, the land finance remote sensing data is stored according to the data source type and / or the recognition result of the land finance remote sensing data.

[0096] The data source type of the land finance remote sensing data includes satellite remote sensing and / or customer upload. Satellite remote sensing refers to directly obtained satellite remote sensing data; customer upload refers to data collected from the client end. For example, farmer-delineated plot data obtained from a channel end application (APP).

[0097] The recognition result is the result of recognizing the land finance remote sensing data by a machine learning model or other means. The recognition result includes cultivated land recognition data and / or crop classification data. For example, cultivated land recognition data and crop classification data can be obtained by AI model recognition and classification.

[0098] Referring to Figure 3 , Figure 3 is a flowchart of a process for configuring a monitoring information table of data pieces of land finance remote sensing data with different resolutions according to an embodiment of the present application. Specifically, the process includes the following steps:

[0099] S301, the land finance remote sensing data is divided into data pieces with different resolutions according to the resolution.

[0100] In order to facilitate users to intuitively view images with different resolutions, the land finance remote sensing data is divided into data pieces with different resolutions according to the resolution. As an example, the land finance remote sensing data is divided into two data pieces with different resolutions according to a resolution of 800 pixels and a resolution of 400 pixels, respectively.

[0101] It should be noted that the numerical value of the resolution can be pre-set according to the specific application scenario.

[0102] S302, for each data piece, image data analysis is performed to obtain a monitoring information table of the data piece.

[0103] For data pieces with different resolutions, image data analysis is performed to obtain a monitoring information table of the data piece. For example, image data analysis is performed to obtain right vector block data and crop classification data of the data piece, and thus the yield per unit and total yield of a certain crop within the scope of the farmer's plot can be calculated.

[0104] In an embodiment of the present application, the data of the data piece includes one or more of the following data: growth monitoring data, growth warning data, yield prediction data, total yield prediction data, yield history data, total yield history data, and disaster assessment data. Therefore, the monitoring information table involves one or more of the following data: growth monitoring data, growth warning data, yield prediction data, total yield prediction data, yield history data, total yield history data, and disaster assessment data.

[0105] In Figure 3 Embodiments of the application, for each data slice to obtain monitoring information configuration table, to achieve monitoring data.

[0106] S202, call monitoring information database configuration tree, combined with the monitoring information configuration table of data slice, from the root node of the configuration tree search.

[0107] In embodiments of the application, the use of configuration tree to achieve monitoring data. Configuration tree stored in the monitoring information database, configuration tree represents the logical relationship between a plurality of data abnormal reasons.

[0108] The following for land finance remote sensing data, illustratively described configuration tree.

[0109] Land finance remote sensing data usually need to be stored, slicing and statistical operations. Among them, the abnormal problems that are prone to occur generally include four, storage exception, slicing exception, statistical exception and value range exception.

[0110] Storage exception (SV): involving the existence of the same name file, storage type is not in the system support type library, and file suffix and file real type does not match one or more.

[0111] Slicing exception (SL): the number of slices and structure does not meet the expected, determine the slice file storage is wrong.

[0112] Statistical exception (ST): statistical task returns check code display abnormal, statistical data exists value range exception.

[0113] Value range exception (RG): the use of standard abnormal value determination, and the same type of historical value of the mean more than two standard deviation, then determine the value range exception.

[0114] It can be seen that the above four kinds of abnormal type of determination method exists correlation, the above four kinds of abnormal type of determination method in the tree structure configuration in MySQL database, the above tree structure as configuration tree.

[0115] In an embodiment of the application, the configuration tree is established according to the logical relationship between the storage exception, slicing exception, statistical exception and value range exception.

[0116] Referring to Figure 4 , Figure 4is a structural schematic diagram of a configuration tree according to an embodiment of the present application. The root node (Root) has two nodes, namely a statistical anomaly (ST) and a slice anomaly (SL). The statistical anomaly (ST) has two nodes, namely a value range anomaly (RG) and a return check code. The slice anomaly (SL) has two nodes, namely a storage anomaly (SV) and a structure check. The value range anomaly (RG) has one node, namely a standard anomaly value judgment. The storage anomaly (SV) has three nodes, namely a same name check, a type matching, and a type verification.

[0117] That is, the root node of the configuration tree has a statistical anomaly node and a slice anomaly node, the statistical anomaly node has a value range anomaly node, and the slice anomaly node has a storage anomaly node.

[0118] In an embodiment of the present application, the abnormal problems that are prone to occur in land finance remote sensing data include a storage anomaly, a slice anomaly, a statistical anomaly, and a value range anomaly. Moreover, the above abnormal problems are associated. For example, the statistical anomaly (ST) can be caused by a statistical task anomaly or a statistical data value range anomaly, and therefore the statistical anomaly contains the value range anomaly. For another example, the slice anomaly (SL) includes the storage anomaly (SV). The above association relationship can be abstracted as a configuration tree, and the abnormal problems are taken as nodes. The type of the node in which the abnormal problem occurs can be conveniently and quickly queried by traversing the tree structure.

[0119] Referring to Figure 5 , Figure 5 is a flowchart of root node traversal and search of a configuration tree according to an embodiment of the present application. Specifically, the following steps are included.

[0120] S501, acquiring, in a data slice monitoring information configuration table, an abnormal problem identifier of the data slice.

[0121] The abnormal problem identifier of the data slice is recorded in the data slice monitoring information configuration table. Therefore, the abnormal problem identifier of the data slice can be acquired in the data slice monitoring information configuration table.

[0122] In an embodiment of the present application, the abnormal problem identifier includes one or more of the following: a storage anomaly identifier, a slice anomaly identifier, a statistical anomaly identifier, and a value range anomaly identifier.

[0123] It can be understood that one or more abnormal problem identifiers of the data slice are recorded in the data slice monitoring information configuration table, and the abnormal problem identifier is learned and recorded in the process of monitoring the data slice.

[0124] S502, calling a configuration tree in a monitoring information database, and traversing and searching the abnormal problem identifier from the root node of the configuration tree.

[0125] For each abnormal problem identifier, the configuration tree in the monitoring information database can be called and searched from the root node of the configuration tree. The purpose of the search is to determine the abnormal type node in the configuration tree. A plurality of abnormal type nodes are included in the configuration tree, and the data pieces can be checked according to a preset checking mode for the abnormal type node.

[0126] In Figure 5 Embodiments, the configuration tree is called and traversed based on the abnormal problem identifier.

[0127] S203, the abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration is searched, and the data in the data piece is checked according to the checking mode of the abnormal type node to monitor the data in the data piece.

[0128] The purpose of traversing the root node is to search for the abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration. The abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration is searched, and the data in the data piece is checked according to the checking mode of the abnormal type node to monitor the data in the data piece.

[0129] Referring to Figure 6 , Figure 6 is a flowchart of checking the data in the data piece according to an embodiment of the application. Specifically, the following steps are included:

[0130] S601, the abnormal type node corresponding to the abnormal problem identifier is searched, and the checking mode of the abnormal type node is determined.

[0131] In the configuration tree, the abnormal type node can be searched based on the abnormal problem identifier, and the checking mode of the abnormal type node is determined.

[0132] S602, the data in the data piece is checked according to the checking mode of the abnormal type node to determine whether the data in the data piece is normal.

[0133] After determining the checking mode of the abnormal type node, the data in the data piece is checked according to the checking mode of the abnormal type node to determine whether the data in the data piece is normal

[0134] As an example, the abnormal problem identifier includes a slice exception. In Figure 4 The configuration tree, the abnormal problem identifier is searched from the root node, and the SL node is searched.

[0135] One of the sub-nodes of SL is structure checking, and the corresponding shell script is executed. For the output directory structure of the slice file, the folder hierarchy and the number of files under each hierarchy are checked to determine whether the data in the data piece is normal. Then, the final result is returned and stored in the monitoring information database;

[0136] Another node of the SL is the SV, indicating that a storage exception judgment needs to be made for the sliced data. At this time, the child nodes of the SV are continuously traversed, and the same name check, type matching, and type verification shell scripts are executed. The type matching needs to be compared with the configured support file type in the database; the type verification uses the Magic Number of the image file hexadecimal encoding start to judge the type, to determine whether the data in the data slice is normal. Finally, the interface is called to return the results of the checks to the monitoring information database.

[0137] In Figure 6 Embodiments, the checking mode of the exception type node is determined by traversing the configuration tree. Then, the data in the data slice is checked in the above checking mode.

[0138] In an embodiment of the application, for different exception problem identifications, corresponding checking modes are selected to implement data checking in the data slice.

[0139] The exception problem identification includes a slice exception, and the checking mode includes structure checking, same name checking, type matching, and type verification.

[0140] The exception problem identification includes a statistical exception, and the checking mode includes return code checking and standard exception value judgment.

[0141] The exception problem identification includes a value range exception, and the checking mode includes standard exception value judgment.

[0142] The exception problem identification includes a storage exception, and the checking mode includes same name checking, type matching, and type verification.

[0143] In an embodiment of the application, if the exception type node corresponding to the exception problem identification is not searched, the exception type node is continuously searched according to the exception type identification under the exception problem identification in the configuration tree.

[0144] Continuing to refer to Figure 4 The exception problem identification includes a value range exception. The exception type node corresponding to the exception problem identification is not searched in the first layer node under the root node, and the exception type node is searched in the nodes under the second layer node of the root node, i.e., the nodes under the statistical exception node and the nodes under the slice exception.

[0145] In an embodiment of the application, the exception problem identification includes a statistical exception, and the exception type identification under the exception problem identification includes a value range exception.

[0146] The exception problem identification includes a slice exception, and the exception type identification under the exception problem identification includes a storage exception.

[0147] In an embodiment of the present application, after the sub-nodes are checked according to the configuration tree, the land finance remote sensing data, the monitoring information configuration table and the result of checking the data in the data piece can also be output for the user to refer. For example, the React front-end project calling interface is used to display the front-end page, and the display of the land finance remote sensing data, the monitoring information configuration table and the result of checking the data in the data piece is realized.

[0148] In an embodiment of the present application, the land finance remote sensing data can also be output in a preset display mode, and the monitoring information configuration table and the result of checking the data in the data piece are output. The preset display mode includes one or more of the following: text, list, pie chart and bar chart. As an example, the Echarts plug-in is used to realize the above-mentioned preset display mode.

[0149] In the above-mentioned embodiment of the present application, the land finance remote sensing data is stored according to the type of the land finance remote sensing data, and the monitoring information configuration table of the data piece of different resolutions of the land finance remote sensing data is obtained; the configuration tree in the monitoring information database is called, and the search is performed from the root node of the configuration tree in combination with the monitoring information configuration table of the data piece; the abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration is searched, the data in the data piece is checked in the checking mode of the abnormal type node, and the data of the data piece is monitored. The abnormal problems of the land finance remote sensing data are related to multiple abnormal reasons, and the configuration tree represents the logical relationship of multiple data abnormal reasons. Through the traversal of the configuration tree structure, the node type of the abnormal problem can be conveniently and quickly queried. Therefore, the data is checked in combination with the configuration tree, and thus the comprehensive monitoring of the finance remote sensing data is realized.

[0150] Referring to Figure 7 , Figure 7 is the main structure diagram of the monitoring data device according to the embodiment of the present application, and the monitoring data device can realize the monitoring data method, as shown in Figure 7 , the monitoring data device specifically includes:

[0151] The acquisition module 701 is configured to store the land finance remote sensing data according to the type of the land finance remote sensing data, and obtain the monitoring information configuration table of the data piece of different resolutions of the land finance remote sensing data.

[0152] The search module 702 is configured to call the configuration tree in the monitoring information database, and perform the search from the root node of the configuration tree in combination with the monitoring information configuration table of the data piece.

[0153] The monitoring module 703 is configured to search the abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration, check the data in the data piece in the checking mode of the abnormal type node, and monitor the data of the data piece.

[0154] In an embodiment of the present application, the acquisition module 701 is specifically configured to store the land finance remote sensing data according to the data source type and / or the identification result of the land finance remote sensing data.

[0155] In an embodiment of the present application, the data source type comprises satellite remote sensing and / or customer uploading.

[0156] The identification result comprises cultivated land identification data and / or crop classification data.

[0157] In an embodiment of the present application, the acquisition module 701 is specifically configured to split the land finance remote sensing data into data pieces of different resolutions according to the resolution.

[0158] The image data analysis is used to obtain a monitoring information configuration table of the data piece.

[0159] In an embodiment of the present application, the monitoring information configuration table involves one or more of the following data: growth monitoring data, growth early warning data, yield prediction data, total yield prediction data, yield history data, total yield history data and disaster assessment data.

[0160] In an embodiment of the present application, the abnormal problem identification comprises one or more of the following: storage abnormality identification, slice abnormality identification, statistical abnormality identification and value range abnormality identification.

[0161] In an embodiment of the present application, the search module 702 is specifically configured to obtain the abnormal problem identification of the data piece from the monitoring information configuration table of the data piece.

[0162] The configuration tree in the monitoring information database is called to search the abnormal problem identification from the root node of the configuration tree.

[0163] In an embodiment of the present application, the configuration tree is established according to the logical relationship among the storage abnormality, the slice abnormality, the statistical abnormality and the value range abnormality.

[0164] In an embodiment of the present application, the statistical abnormality node and the slice abnormality node are subordinate to the root node of the configuration tree, the value range abnormality node is subordinate to the statistical abnormality node, and the storage abnormality node is subordinate to the slice abnormality node.

[0165] In an embodiment of the present application, the monitoring module 703 is specifically configured to search the abnormal type node corresponding to the abnormal problem identification, and determine the verification mode of the abnormal type node.

[0166] The data in the data piece is verified according to the verification mode of the abnormal type node to determine whether the data in the data piece is normal.

[0167] In one embodiment of the present application, the abnormal problem identifier comprises a slice exception, and the verification manner comprises structure verification, same name verification, type matching and type verification.

[0168] The abnormal problem identifier comprises a statistical exception, and the verification manner comprises return code verification and standard exception value judgment.

[0169] The abnormal problem identifier comprises a value range exception, and the verification manner comprises standard exception value judgment.

[0170] The abnormal problem identifier comprises a storage exception, and the verification manner comprises same name verification, type matching and type verification.

[0171] In one embodiment of the present application, the monitoring module 703 is further configured to, if the abnormal type node corresponding to the abnormal problem identifier is not searched, continue to search the abnormal type node according to the abnormal type identifier subordinate to the abnormal problem identifier in the configuration tree.

[0172] In one embodiment of the present application, the abnormal problem identifier comprises a statistical exception, and the abnormal type identifier subordinate to the abnormal problem identifier comprises a value range exception.

[0173] The abnormal problem identifier comprises a slice exception, and the abnormal type identifier subordinate to the abnormal problem identifier comprises a storage exception.

[0174] In one embodiment of the present application, the monitoring module 703 is further configured to output the land finance remote sensing data, the monitoring information configuration table and the result of verifying the data in the data slice.

[0175] In one embodiment of the present application, the monitoring module 703 is specifically configured to output the land finance remote sensing data in a preset display manner, and output the monitoring information configuration table and the result of verifying the data in the data slice, wherein the preset display manner comprises one or more of the following: text, list, pie chart and bar chart.

[0176] Figure 8 An exemplary system architecture 800 is shown, which can apply the method for monitoring data or the apparatus for monitoring data according to the embodiments of the present application.

[0177] As shown in Figure 8 The system architecture 800 can include terminal devices 801, 802 and 803, a network 804 and a server 805. The network 804 is used to provide a communication link medium between the terminal devices 801, 802 and 803 and the server 805. The network 804 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0178] The user can use the terminal devices 801, 802, 803 to interact with the server 805 through the network 804 to receive or send messages and the like. Various communication client applications can be installed on the terminal devices 801, 802, 803, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, and the like (only as examples).

[0179] The terminal devices 801, 802, 803 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0180] The server 805 can be a server providing various services, such as a background management server providing support for a user to browse a shopping website using the terminal devices 801, 802, 803 (only as an example). The background management server can analyze and process received product information query requests and the like, and feed back the processing results (such as target push information, product information - only as examples) to the terminal devices.

[0181] It should be noted that the method for monitoring data provided by the embodiments of the present application is generally executed by the server 805, and accordingly, the device for monitoring data is generally provided in the server 805.

[0182] It should be understood that Figure 8 The number of terminal devices, networks, and servers in

[0183] A computing program product of an embodiment of the present application includes a computer program, which, when executed by a processor, implements the method for monitoring data provided by the embodiments of the present application.

[0184] Reference is made below to Figure 9 which shows a structural schematic diagram of a computer system 900 of a terminal device suitable for use to implement the embodiments of the present application. Figure 9 The terminal device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0185] As Figure 9As shown, the computer system 900 includes a central processing unit (CPU) 901 which can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 902 or loaded into a random access memory (RAM) 903 from a storage section 908. In the RAM 903, various programs and data required for the operation of the system 900 are also stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0186] Connected to the I / O interface 905 are an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a display device such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as necessary. A removable recording medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 910 as necessary, so that a computer program read therefrom is installed into the storage section 908 as necessary.

[0187] In particular, the processes described above with reference to the flow charts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flow charts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable recording medium 911. When the computer program is executed by the central processing unit (CPU) 901, the above-described functions defined in the system of the present disclosure are performed.

[0188] It should be noted that the computer-readable medium shown in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present application, the computer-readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0189] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code containing one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different order than that shown in the drawings. For example, two blocks that are shown in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0190] The modules described in the embodiments of the present application can be implemented in software or hardware. The modules described can be arranged in a processor, for example, a processor can include an obtaining module, a searching module and a monitoring module. In some cases, the names of the modules do not limit the modules themselves, for example, the obtaining module can also be described as "a monitoring information configuration table for storing land finance remote sensing data according to types of the land finance remote sensing data and obtaining data pieces of different resolutions of the land finance remote sensing data".

[0191] As another aspect, the present application also provides a computer readable medium, which can be included in the device described in the above embodiments or exist separately without being assembled into the device. The computer readable medium carries one or more programs, which, when executed by the device, cause the device to include:

[0192] storing land finance remote sensing data according to types of the land finance remote sensing data and obtaining a monitoring information configuration table of data pieces of different resolutions of the land finance remote sensing data;

[0193] calling a configuration tree in a monitoring information database and searching from a root node of the configuration tree in combination with the monitoring information configuration table of the data pieces;

[0194] searching for an abnormal type node corresponding to an abnormal problem identifier in the monitoring information configuration table and verifying data in the data pieces in a verification mode of the abnormal type node to monitor the data in the data pieces.

[0195] According to the technical scheme of the embodiments of the present application, land finance remote sensing data is stored according to types of the land finance remote sensing data and a monitoring information configuration table of data pieces of different resolutions of the land finance remote sensing data is obtained; a configuration tree in a monitoring information database is called and searching is performed from a root node of the configuration tree in combination with the monitoring information configuration table of the data pieces; an abnormal type node corresponding to an abnormal problem identifier in the monitoring information configuration table is searched for and data in the data pieces is verified in a verification mode of the abnormal type node to monitor the data in the data pieces. Abnormal problems of land finance remote sensing data are related to multiple abnormal reasons and the configuration tree represents logical relationships of multiple data abnormal reasons. By traversing the configuration tree structure, a node type of an abnormal problem can be quickly and conveniently queried. Therefore, data is verified in combination with the configuration tree, thereby realizing comprehensive monitoring of finance remote sensing data.

[0196] The foregoing detailed description has set forth various embodiments of the devices and / or processes via the use of specific terminology. However, embodiments thereof can be practiced with the exact description not being presented in detail. The term "device" should be understood to encompass devices operating in various modes, such as active mode, sleep mode, hibernate mode, and the like. The terms "coupled" and "connected," along with derivatives thereof, can be used. It should be understood that these terms are not intended as synonyms for each other. Rather, particular circuitry that can be said to be coupled to, or connected with, other circuitry can be some of the other circuitry that can communicate in some way, while other circuitry can be some of the other circuitry that can not be in communication with that particular circuitry.

Claims

1. A method of monitoring data, characterized by, The method comprises the following steps: According to the type of land finance remote sensing data, the land finance remote sensing data is stored, and a monitoring information configuration table of data pieces with different resolutions of the land finance remote sensing data is obtained; In the data piece monitoring information configuration table, the abnormal problem identifier of the data piece is obtained; The configuration tree in the monitoring information database is called, and the abnormal problem identifier is searched from the root node of the configuration tree; the configuration tree is established according to the logical relationship between storage abnormality, slice abnormality, statistical abnormality and value range abnormality; the statistical abnormality node and the slice abnormality node belong to the root node of the configuration tree; the value range abnormality node belongs to the statistical abnormality node; and the storage abnormality node belongs to the slice abnormality node; The configuration tree in the monitoring information database is called, and the search is performed from the root node of the configuration tree in combination with the data piece monitoring information configuration table; The abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration is searched, the data in the data piece is verified in the verification mode of the abnormal type node, and the data in the data piece is monitored.

2. The method of claim 1, wherein, The method comprises the following steps: According to the type of land finance remote sensing data, the land finance remote sensing data is stored, and a monitoring information configuration table of data pieces with different resolutions of the land finance remote sensing data is obtained; 3. The method of claim 2, wherein, According to the type of land finance remote sensing data, the land finance remote sensing data is stored, and a monitoring information configuration table of data pieces with different resolutions of the land finance remote sensing data is obtained; The data source type includes satellite remote sensing and / or customer upload; 4. The method of claim 1, wherein, The identification result includes cultivated land identification data and / or crop classification data. The method comprises the following steps: The land finance remote sensing data is divided into data pieces with different resolutions according to the resolution; 5. The method of claim 4, wherein, Image data analysis is performed on the data pieces to obtain the monitoring information configuration table of the data pieces.

6. The method of claim 1, wherein, The monitoring information configuration table involves one or more of the following data: growth monitoring data, growth warning data, yield prediction data, total yield prediction data, yield history data, total yield history data and disaster assessment data.

7. The method of claim 1, wherein, The abnormal problem identifier includes one or more of the following: storage abnormality identifier, slice abnormality identifier, statistical abnormality identifier and value range abnormality identifier. The method comprises the following steps: The abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration is searched, the data in the data piece is verified in the verification mode of the abnormal type node, and the data in the data piece is monitored.

8. The method of claim 1, wherein, The abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration is searched, the data in the data piece is verified in the verification mode of the abnormal type node, and the data in the data piece is monitored. The abnormal problem identifier includes slice abnormality, and the verification mode includes structure verification, same name verification, type matching and type verification; The abnormal problem identifier includes statistical abnormality, and the verification mode includes return code verification and standard abnormal value judgment; The abnormal problem identifier includes value range abnormality, and the verification mode includes standard abnormal value judgment; 9. The method of claim 1, wherein, The abnormal problem identifier includes storage abnormality, and the verification mode includes same name verification, type matching and type verification. The method further comprises: If the abnormal problem identifier does not correspond to an abnormal type node, the abnormal type node is searched according to an abnormal type identifier subordinate to the abnormal problem identifier in the configuration tree.

10. The method of claim 9, wherein, The abnormal problem identifier includes a statistical abnormality, and the abnormal type identifier subordinate to the abnormal problem identifier includes a value range abnormality. The abnormal problem identifier includes a slice abnormality, and the abnormal type identifier subordinate to the abnormal problem identifier includes a storage abnormality.

11. The method of claim 1, wherein, After the checking of the sub-nodes in the configuration tree, the method further includes: outputting the land finance remote sensing data, the monitoring information configuration table, and a result of checking data in the data slice.

12. The method of claim 11, wherein, The outputting of the land finance remote sensing data, the monitoring information configuration table, and the result of checking data in the data slice includes: outputting the land finance remote sensing data in a preset display mode, and outputting the monitoring information configuration table and the result of checking data in the data slice, the preset display mode including one or more of the following: text, list, pie chart, and bar chart.

13. An apparatus for monitoring data, the apparatus comprising: The method includes: an acquisition module configured to store land finance remote sensing data according to a type of the land finance remote sensing data, and acquire a monitoring information configuration table of a data slice of the land finance remote sensing data at different resolutions; a search module configured to acquire an abnormal problem identifier of the data slice from the monitoring information configuration table of the data slice; a calling module configured to search the abnormal problem identifier from a root node of a configuration tree in a monitoring information database, the configuration tree being established according to a logical relationship among a storage abnormality, a slice abnormality, a statistical abnormality, and a value range abnormality, the root node of the configuration tree having a statistical abnormality node and a slice abnormality node subordinate thereto, the statistical abnormality node having a value range abnormality node subordinate thereto, and the slice abnormality node having a storage abnormality node subordinate thereto; a monitoring module configured to search an abnormal type node corresponding to the abnormal problem identifier in the monitoring information configuration, and check data in the data slice in a checking mode of the abnormal type node to monitor the data in the data slice.

14. The apparatus for monitoring data according to claim 13, wherein, The acquisition module is specifically configured to divide the land finance remote sensing data into data slices at different resolutions according to resolutions. The monitoring information configuration table of the data slice is acquired by image data analysis.

15. An electronic device for monitoring data, characterized in that The method includes: one or more processors; a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method in any one of claims 1-12.

16. A computer readable medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method in any one of claims 1-12.

17. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the method in any one of claims 1-12.

Images