A cloud platform database inspection system

Abstract

The present application relates to the field of data detection technology, and in particular to an inspection system for a cloud platform database, the system comprising a processor and a memory storing a computer program, when the computer program is executed by the processor, firstly, a plurality of first inspection indicator data lists corresponding to the database to be inspected are obtained from the target cloud platform, and an initial inspection score and an inspection weight of each first inspection indicator data list are obtained, according to the initial inspection score and the inspection weight corresponding to each first inspection indicator list, a target inspection score corresponding to the database to be inspected is obtained, and whether to perform further inspection on the database to be inspected is determined according to the size of the target inspection score; it can be seen that the present invention adds an effective inspection weight on the basis of the initial inspection score corresponding to each first inspection indicator list, so that the obtained target inspection score can more truly and accurately reflect the inspection status of the database to be inspected, thereby improving the inspection quality and effectiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of data detection, and in particular to an inspection system for a cloud platform database. Background Art

[0002] A database is a collection of large amounts of data that is stored in a computer for a long time, organized, shareable, and uniformly managed. In order to reduce operating costs, more and more databases are currently being used and managed in cloud platforms built by k8s. In order to maintain the normal operation of the database, it is necessary to conduct regular inspections of the database. The existing inspection method is generally to write a python script for the database inspection when a database inspection is required. The script can only conduct a comprehensive inspection on this database and cannot be applied to other databases. It cannot be widely used. In addition, a comprehensive inspection is conducted without determining the status of the database in use, which may result in a waste of inspection times and low inspection quality. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] A cloud platform database inspection system, the system comprising: a processor and a memory storing a computer program, when the computer program is executed by the processor, the following steps are implemented:

[0005] S100, when a cloud platform database inspection instruction is triggered, a corresponding database to be inspected is determined from a target cloud platform; the target cloud platform has several initial databases preset; the database to be inspected is any initial database among the several initial databases.

[0006] S200, according to a plurality of preset first inspection indicators, obtain a first inspection indicator data list set A corresponding to the to-be-inspected database from the target cloud platform = {A1, A2, ..., A i , ..., A m}, where A i is the i-th first inspection indicator data list, i=1, 2, ..., m, m is the number of first inspection indicator data lists, and each first inspection indicator data list includes a number of first inspection indicator data.

[0007] S300, according to A i Each first inspection indicator data in the preset inspection indicator data threshold value corresponds to each first inspection indicator data, and obtains A i The corresponding initial inspection score.

[0008] S400, according to each A iFor each of the remaining preset impact values ​​of the first inspection indicator list, a target judgment matrix is ​​constructed, and each A is obtained according to the target judgment matrix. i Corresponding inspection weight.

[0009] S500, according to each A i The corresponding initial inspection score and each A i The corresponding inspection weight is used to obtain the target inspection score corresponding to the database to be inspected.

[0010] S600: When the target inspection score is greater than the inspection score threshold, a connection with a database to be inspected is initialized and the database to be inspected is inspected to obtain a plurality of second inspection indicator data corresponding to the database to be inspected.

[0011] Compared with the prior art, the present invention has obvious beneficial effects. By means of the above technical solution, the inspection system of a cloud platform database provided by the present invention can achieve considerable technical advancement and practicality, and has wide industrial utilization value, and has at least the following beneficial effects:

[0012] The present invention provides an inspection system for a cloud platform database. When a computer program is executed by a processor, a plurality of first inspection indicator data corresponding to a database to be inspected are obtained from a target cloud platform, and an initial inspection score of each first inspection indicator data list is obtained according to a preset inspection indicator data threshold value corresponding to each first inspection indicator data. The initial inspection score obtained in this way is more reasonable and reliable and can preliminarily grasp the usage of the database to be inspected, providing a judgment basis for whether to further detect the database to be inspected; then, according to the preset influence degree value of each two first inspection indicator lists, an inspection weight corresponding to each first inspection indicator list is obtained. The inspection weight obtained in this way is more reasonable and effective. Then, based on the initial inspection score and the inspection weight corresponding to each first inspection indicator list, a target inspection score corresponding to the database to be inspected is obtained. Finally, according to the size of the target inspection score, it is determined whether to further detect the database to be inspected. On the basis of the initial inspection score corresponding to each first inspection indicator list, an effective inspection weight is added, so that the obtained target inspection score can more truly and accurately reflect the inspection status of the database to be inspected, thereby improving the inspection quality and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A flowchart of a processor executing a computer program in an inspection system of a cloud platform database provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0017] Since the database can be stored in a cloud platform or a physical container, when inspecting the database, a corresponding inspection method needs to be adopted according to the location of the database. In view of the situation where the database is stored in a cloud platform, the present invention provides an inspection system for a cloud platform database, the system comprising: a processor and a memory storing a computer program. When the computer program is executed by the processor, Figure 1 As shown, implement the following steps:

[0018] S100, when a cloud platform database inspection instruction is triggered, a corresponding database to be inspected is determined from the target cloud platform; a number of initial databases are preset in the target cloud platform; the database to be inspected is any initial database among the several initial databases; it can be understood that: when a cloud platform database inspection instruction corresponding to any initial database is triggered, the identifier of the corresponding database to be inspected is obtained from the cloud platform database inspection instruction to determine the corresponding database to be inspected.

[0019] Specifically, the initial database refers to any database pre-set in the target cloud platform; for example: MySQL database, Oracle database, Redis database, Vastbase database.

[0020] Specifically, before step S100, the following steps are also included:

[0021] S001, for any initial database, obtain the time interval between the current time and the last time a cloud platform database inspection instruction corresponding to the initial database was sent.

[0022] S002, when the time interval meets the preset inspection cycle corresponding to the initial database, the corresponding cloud platform database inspection instruction is triggered; it can be understood as: according to the preset inspection cycle, the cloud platform database inspection instruction is regularly issued.

[0023] As described above, different inspection cycles are set according to different database types, and the corresponding cloud platform database inspection instructions are triggered according to the corresponding inspection cycles. Preliminary data information of the database to be inspected can be obtained from the cloud platform on a regular basis, and it can be determined whether to perform internal inspection on the database to be inspected based on the preliminary data information, which is conducive to maintaining the normal operation of the database.

[0024] S200, according to a plurality of preset first inspection indicators, obtain a first inspection indicator data list set A corresponding to the to-be-inspected database from the target cloud platform = {A1, A2, ..., A i , ..., A m}, where A i is a first inspection indicator data list obtained from the i-th given component in the target cloud platform, i=1, 2, ..., m, and m is the number of first inspection indicator data lists.

[0025] Specifically, each first inspection indicator data list includes a number of first inspection indicator data.

[0026] Specifically, the first inspection indicator refers to any one of a metadata information indicator, a configuration information indicator, a data trend information indicator within a historical time period, and a log information indicator corresponding to the database to be inspected.

[0027] Specifically, the given components include a configuration management component, a monitoring component, and a log storage component.

[0028] Furthermore, the metadata indicators and configuration information indicators corresponding to the database to be inspected are indicators obtained from the configuration management component CMDB in the target cloud platform, the data trend information indicators within the historical time period are indicators obtained from the monitoring component Prometheus in the target cloud platform, and the log information indicators refer to indicators obtained from the log storage component ElasticSearch in the target cloud platform; wherein, the data corresponding to the metadata indicators include the name, access address, port information, specifications, database version, creation time, etc. of the database instance to be inspected; the data corresponding to the configuration information indicators include various parameter setting information, such as the number of CPUs, storage space, memory size, etc. of the primary and standby databases; the data corresponding to the data trend information indicators within the historical time period include real-time statistics of CPU usage, InnoDB lock waiting time, memory usage, etc. in the historical time period; the data corresponding to the log information indicators include slow log details, primary and standby switching logs, error logs, etc.

[0029] As mentioned above, since the data information of some indicators corresponding to the database will be stored in the relevant components in the target cloud platform, obtaining this part of the data information from the target cloud platform first is conducive to making a preliminary judgment on the usage of the inspection database, and then determining whether to conduct internal inspections on the inspection database to improve the quality and effectiveness of the inspection.

[0030] Furthermore, in another embodiment, in the case where the database is stored in a physical container, it is only necessary to replace some first inspection indicator data obtained from the configuration management component CMDB, the monitoring component Prometheus and the log storage component ElasticSearch in the target cloud platform with corresponding data obtained from the host corresponding to the database in the physical container.

[0031] S300, according to A i Each first inspection indicator data in the preset inspection indicator data threshold value corresponds to each first inspection indicator data, and obtains A i The corresponding initial inspection score.

[0032] In a specific embodiment, step S300 further includes the following steps:

[0033] S301, according to A i The preset inspection index data threshold corresponding to each first inspection index data in A is obtained. i The change rate of each first inspection indicator data.

[0034] Among them, A i The change rate of the jth first inspection index data in ij Meet the following conditions:

[0035] ζij =(σ ij -λ ij ) / λ ij , where σ ij A i The target data volume corresponding to the jth first inspection index data in , λ ij A i The preset inspection indicator data threshold corresponding to the j-th first inspection indicator data in .

[0036] Specifically, the target data volume corresponding to the first inspection indicator data refers to any one of data increment, data anomaly, and data change; for example: when the first inspection indicator data detected is CPU usage, the historical data increment should be detected, that is, the target data volume is the historical data increment corresponding to the first inspection indicator data, and the corresponding preset inspection indicator data threshold is the data increment threshold; when the first inspection indicator data detected is an error log, the data anomaly volume should be detected, that is, the target data volume is the data anomaly volume corresponding to the first inspection indicator data, and the corresponding preset inspection indicator data threshold is the data anomaly volume threshold; when the first inspection indicator data detected is any metadata information, the data change volume should be detected, that is, the target data volume is the data change volume corresponding to the first inspection indicator data.

[0037] S302, according to A i The change rate of each first inspection indicator data in A is obtained i The corresponding initial inspection score S i .

[0038] Among them, A i The corresponding initial inspection score S i Meet the following conditions:

[0039] S i =(∑ n j＝1 ζ ij ) / n, where n is A i The number of corresponding first inspection indicators.

[0040] As mentioned above, by comparing each first inspection indicator data with the corresponding preset inspection indicator data threshold, the data change rate is obtained, and the initial inspection score corresponding to each first inspection indicator list is obtained according to the change situation. This is conducive to preliminarily grasping the data usage and operation status of the database to be inspected, and providing a basis for judging whether to conduct further inspections on the database to be inspected.

[0041] S400, according to each A i For each of the remaining preset impact values ​​of the first inspection indicator list, a target judgment matrix is ​​constructed, and each A is obtained according to the target judgment matrix.i The corresponding inspection weight can be understood as follows: every two first inspection indicator lists influence each other and have corresponding preset influence values; for example, when the data increment corresponding to the metadata indicator is greater, the amount of abnormal data corresponding to the log information indicator is relatively greater.

[0042] In a specific embodiment, step S400 further includes the following steps:

[0043] S401, according to each A i A fuzzy judgment matrix is ​​constructed for each of the remaining preset influence values ​​of the first inspection indicator list; it can be understood as: according to the hierarchical analysis method, a fuzzy judgment matrix composed of several preset influence values ​​is constructed, and the number of rows and columns of the fuzzy judgment matrix are equal to the number of first inspection indicator lists; those skilled in the art know the specific construction method of the fuzzy judgment matrix, which will not be repeated here.

[0044] S402, calculating the consistency ratio θ corresponding to the fuzzy judgment matrix.

[0045] Wherein, the consistency ratio θ meets the following conditions:

[0046] θ=(((L max -m) / (m-1))) / ρ, where L max is the maximum value of all preset influence values, ρ is the consistency index value corresponding to m obtained according to the preset consistency comparison table; it can be understood that: the consistency comparison table records the data of the number of first inspection indicator lists and the consistency index coefficient RI in one-to-one correspondence, for example, when n=3, the consistency index coefficient RI=0.52, and when n=4, the consistency index coefficient RI=0.89.

[0047] S403, when the consistency ratio is less than the preset ratio threshold, the fuzzy judgment matrix is ​​determined as the target judgment matrix; otherwise, each preset influence degree value is updated and returned to step S401 until the consistency ratio is less than the preset ratio threshold, and the fuzzy judgment matrix corresponding to the consistency ratio less than the preset ratio threshold is determined as the target judgment matrix; it can be understood as: obtaining the new preset influence degree value reset by the technician and updating each preset influence degree value in the fuzzy judgment matrix accordingly.

[0048] In a specific implementation, the preset ratio threshold is 0.1.

[0049] In a specific embodiment, in step S400, A is obtained by the following steps: i Corresponding inspection weights:

[0050] S404, according to A iThe m preset influence values ​​in the corresponding row i in the target judgment matrix are obtained. i The corresponding initial weight W 0 i .

[0051] Specifically, A i The corresponding initial weight W 0 i Meet the following conditions:

[0052] W 0 i =(Q1×Q2×……Q i ×……×Q m ) 1 / m , where Q i A i The i-th preset influence value in the i-th row corresponding to the target judgment matrix, Q m A i The mth preset influence degree value in the corresponding i-th row in the target judgment matrix.

[0053] S405: According to the initial weight corresponding to each first inspection indicator list, i The corresponding initial weights are normalized to obtain A i The corresponding inspection weight W i .

[0054] Specifically, A i The corresponding inspection weight W i Meet the following conditions:

[0055] W i =W 0 i / (∑ m i＝1 W 0 i ).

[0056] As mentioned above, a fuzzy judgment matrix is ​​constructed by using several preset influence degree values, and when the matrix fails the consistency test, the preset influence degree values ​​are adaptively changed until they pass the consistency test. The preset influence degree values ​​of each row are normalized to obtain the inspection weights corresponding to each inspection indicator list, and further data processing is performed on the basis of human experience to realize the effective acquisition of inspection weights.

[0057] In a parallel embodiment, when the computer program is executed by a processor, A is also obtained through the following steps: i Corresponding inspection weights:

[0058] S10, determine each A iThe preset impact degree values ​​of each remaining first inspection indicator list are determined, and the maximum value is determined from all the preset impact degree values.

[0059] S20, based on the maximum value, performing translation transformation processing on each preset influence degree value to obtain a maximum value corresponding to each preset influence degree value.

[0060] Specifically, the maximum value corresponding to the preset impact value meets the following conditions:

[0061] G=H max -g, where G is the maximum value corresponding to any preset impact value, H max is the maximum value determined among all the preset influence degree values, and g is any preset influence degree value.

[0062] As described above, the data consistency processing is performed on each preset impact value by converting extremely small data into extremely large data, so as to achieve unified dimensioning of the data, which is conducive to normalization processing and obtaining reasonable and effective normalization results.

[0063] S30, normalizing the maximum value corresponding to each preset influence degree value, and constructing a new judgment matrix according to the normalization result; it can be understood as: replacing the corresponding preset influence degree value in the fuzzy judgment matrix with the normalized value of the maximum value corresponding to each preset influence degree value, to obtain a new matrix.

[0064] S40, according to the new judgment matrix, obtain A i The corresponding inspection weight W i .

[0065] Among them, A i The corresponding inspection weight W i Meet the following conditions:

[0066]

[0067] Among them, P id =C id / ∑ m i＝1 C id , further, C id is the normalized value corresponding to the i-th row and d-th column in the new judgment matrix, d = 1, 2, ..., m.

[0068] As described above, by performing translation transformation and normalization processing on each preset influence degree value, the normalized preset influence degree value of each first inspection indicator list on any other first inspection indicator list is obtained, and based on several normalized preset influence degree values, the inspection weight corresponding to each first inspection indicator list is obtained by the entropy weight method, thereby realizing the effective acquisition of the inspection weight to an objective degree.

[0069] In another specific embodiment, when the computer program is executed by the processor, the following steps are also performed to determine A i Corresponding inspection weights:

[0070] S50, A obtained according to the target judgment matrix i The corresponding inspection weight and A obtained according to the new judgment matrix i The average value of the corresponding inspection weight is determined as A i The corresponding new inspection weight is based on A i The corresponding new inspection weight executes step S500.

[0071] In the above, the inspection weights obtained by subjective and objective methods are combined to obtain the weights of each indicator list to make up for the shortcomings of the two methods. This avoids the subjectivity of the hierarchical analysis method and overcomes the objectivity of the entropy weight method, making each determined inspection weight more reasonable and reliable.

[0072] S500, according to each A i The corresponding initial inspection score and each A i The corresponding inspection weight is used to obtain the target inspection score corresponding to the database to be inspected.

[0073] Specifically, the target inspection score meets the following conditions:

[0074] Y=∑ m i＝1 (S i ×W i ), where Y is the target inspection score.

[0075] S600: When the target inspection score is greater than the inspection score threshold, a connection with a database to be inspected is initialized and the database to be inspected is inspected to obtain a plurality of second inspection indicator data corresponding to the database to be inspected.

[0076] Specifically, the second inspection indicator corresponding to the second inspection indicator data refers to the target inspection parameter indicator and library table information indicator corresponding to the database to be inspected; for example, the target inspection parameter indicator includes the QPS indicator, TPS indicator, lock wait times, lock wait time, full table scan times per second, slow query times per second, slow sort times, etc. of the database to be inspected; the library table information indicator includes the disk temporary table usage rate, disk temporary table creation rate, and primary keyless table information in the database to be inspected.

[0077] As mentioned above, by considering the inspection weights corresponding to each first inspection indicator list, the final target inspection score is obtained, which can reflect the overall data situation and historical operation status of the database to be inspected. When the target inspection score is large, the inspection program is started, and an internal deep inspection is performed on the database to be inspected to obtain data corresponding to several second inspection indicators. When the target inspection score is small, there is no need to perform an in-depth inspection of the database to be inspected, which reduces the number of inspections and thus improves the quality and effectiveness of the inspection.

[0078] In a specific embodiment, after step S600, the following steps are also included:

[0079] S700, according to the scoring rules corresponding to each first inspection indicator data and each second inspection indicator data, obtain the final score corresponding to each first inspection indicator data and each second inspection indicator data; for example, when the lock waiting times are less than the preset waiting times threshold, the corresponding score is 0, otherwise it is 10, and an alarm prompt message is generated at this time; when there is no abnormal information in the error log, the corresponding score is 0, when there is a warning message, the corresponding score is 7 points, and when there is an error message, the corresponding score is 10; when the CPU usage is below 60%, the corresponding score is 0, when it is 60%-80%, the corresponding score is 5, and when it is above 80%, the corresponding score is 8.

[0080] S800: When the final score corresponding to any first inspection indicator data or any second inspection indicator data is greater than the corresponding score threshold, a corresponding alarm prompt information is generated.

[0081] As described above, according to the scoring rules corresponding to each inspection indicator, the detection status of each inspection indicator data is scored to reflect the abnormality of each inspection indicator data and the usage of the database to be inspected, and an alarm prompt message is issued when the usage is poor, so that the staff can take maintenance measures in time, thereby ensuring the subsequent normal use of the database.

[0082] Although some specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A cloud platform database inspection system, characterized in that: The system comprises: a processor and a memory storing a computer program, and when the computer program is executed by the processor, the following steps are implemented: S100, when a cloud platform database inspection instruction is triggered, a corresponding database to be inspected is determined from a target cloud platform; the target cloud platform is preset with a number of initial databases; the database to be inspected is any initial database among the number of initial databases; S200, according to a plurality of preset first inspection indicators, obtain a first inspection indicator data list set A corresponding to the to-be-inspected database from the target cloud platform = {A1, A2, ..., A i , ..., A m }, where A i is a first inspection indicator data list obtained from the i-th given component in the target cloud platform, i=1, 2, ..., m, m is the number of first inspection indicator data lists, and each first inspection indicator data list includes a number of first inspection indicator data; the first inspection indicator refers to any one of the metadata information indicator, configuration information indicator, data trend information indicator in the historical time period and log information indicator corresponding to the database to be inspected; S300, according to A i Each first inspection indicator data in the preset inspection indicator data threshold value corresponds to each first inspection indicator data, and obtains A i The corresponding initial inspection score; S400, according to each A i For each of the remaining preset impact values ​​of the first inspection indicator list, a target judgment matrix is ​​constructed, and each A is obtained according to the target judgment matrix. i Corresponding inspection weight; The step S400 further includes the following steps: S401, according to each A i For each of the remaining preset impact degree values ​​in the first inspection indicator list, a fuzzy judgment matrix is ​​constructed; S402, calculating the consistency ratio θ corresponding to the fuzzy judgment matrix; Wherein, the consistency ratio θ meets the following conditions: θ=(((L max -m) / (m-1))) / ρ, where L max is the maximum value among all preset impact degree values, and ρ is the consistency index value corresponding to m obtained according to the preset consistency comparison table; S403, when the consistency ratio is less than the preset ratio threshold, the fuzzy judgment matrix is ​​determined as the target judgment matrix; otherwise, each preset influence degree value is updated and returned to step S401 until the consistency ratio is less than the preset ratio threshold, and the fuzzy judgment matrix corresponding to the consistency ratio less than the preset ratio threshold is determined as the target judgment matrix; S500, according to each A i The corresponding initial inspection score and each A i Corresponding inspection weight, obtain the target inspection score corresponding to the database to be inspected; S600: When the target inspection score is greater than the inspection score threshold, a connection with a database to be inspected is initialized and the database to be inspected is inspected to obtain a plurality of second inspection indicator data corresponding to the database to be inspected.

2. The cloud platform database inspection system according to claim 1, characterized in that: The following steps are also included before step S100: S001, for any initial database, obtain the time interval between the current time and the last time a cloud platform database inspection instruction corresponding to the initial database was sent; S002, when the time interval meets the preset inspection cycle corresponding to the initial database, the corresponding cloud platform database inspection instruction is triggered.

3. The inspection system for cloud platform database according to claim 1, characterized in that: Step S300 also includes the following steps: S301, according to A i The preset inspection index data threshold corresponding to each first inspection index data in A is obtained. i The change rate of each first inspection indicator data in; Among them, A i The change rate of the jth first inspection index data in ij Meet the following conditions: ζ ij =(σ ij -λ ij ) / λ ij , where σ ij A i The target data volume corresponding to the jth first inspection index data in , λ ij A i The preset inspection index data threshold value corresponding to the j-th first inspection index data in ; S302, according to A i The change rate of each first inspection indicator data in A is obtained i The corresponding initial inspection score S i ; Among them, A i The corresponding initial inspection score S i Meet the following conditions: S i =(∑ n j=1 ζ ij ) / n, where n is A i The number of corresponding first inspection indicators.

4. The cloud platform database inspection system according to claim 1, characterized in that: In step S400, A is obtained by the following steps: i Corresponding inspection weights: S404, according to A i The m preset influence values ​​in the corresponding row i in the target judgment matrix are obtained. i The corresponding initial weights; S405: According to the initial weight corresponding to each first inspection indicator list, i The corresponding initial weights are normalized to obtain A i Corresponding inspection weight.

5. The cloud platform database inspection system according to claim 1, characterized in that: The second inspection index corresponding to the second inspection index data refers to the target inspection parameter index and the database table information index corresponding to the database to be inspected.

6. The cloud platform database inspection system according to claim 1, characterized in that: After step S600, the following steps are also included: S700, obtaining final scores corresponding to each first inspection indicator data and each second inspection indicator data according to scoring rules corresponding to each first inspection indicator data and each second inspection indicator data; S800: When the final score corresponding to any first inspection indicator data or any second inspection indicator data is greater than the corresponding score threshold, a corresponding alarm prompt information is generated.

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