Detection report management method and system based on big data

Abstract

The invention relates to the technical field of report management, and particularly discloses a detection report management method and system based on big data, and the method comprises the steps: firstly obtaining detection items, results and standard data, carrying out the classification, and constructing an index; comparing and analyzing to determine compliance, and comprehensively evaluating to construct a database; key and problem report information is generated, and subsequent feedback data is obtained; dynamically updating the database according to information comparison; and finally, providing the optimized management service and decision support information for related users. According to the method, from acquisition of the detection report data to classification arrangement and index establishment, a large amount of data can be rapidly and orderly processed, the low efficiency condition of traditional manual processing is changed, the data processing speed is increased, and the labor cost and the error rate are reduced. The compliance state is determined through comparative analysis of detection item data and standard data, whether the data meet the standard or not can be accurately found, problems can be recognized in time, and powerful support is provided for quality control and supervision.

Description

Technical Field

[0001] The present invention relates to the technical field of report management, and in particular to a detection report management method and system based on big data. Background Art

[0002] In today's society, with the rapid development of technology and the increasing demand for detections in various industries, the number of detection reports has shown an explosive growth trend. Whether it is environmental detection, product quality detection, food safety detection or various detections in the industrial production process, a large amount of detection report data has been generated.

[0003] Traditional detection report management methods mainly rely on manual processing and simple document storage. After the detection personnel complete the detection, the results are recorded in paper reports or electronic documents, and subsequent review, analysis and archiving work are also mostly carried out manually one by one. The storage and retrieval of data are extremely inconvenient, it is difficult to quickly locate and obtain the required historical detection data, and the ability to discover and analyze problems in the detection data is limited. It is impossible to timely and effectively identify abnormal situations, compliance issues and potential risk factors in the data, and it is difficult to meet the needs of modern society for the efficient management and in-depth analysis of detection reports.

[0004] With the rise of big data technology, it is of great practical significance and urgent application need to develop a detection report management method and system based on big data. Summary of the Invention

[0005] The purpose of the present invention is to provide a detection report management method based on big data, including the following steps:

[0006] Obtain various types of detection report data, where the detection report data includes detection item data, detection result data and relevant standard data;

[0007] Classify and sort the obtained detection report data, and establish a data index according to the detection type and time sequence;

[0008] Conduct a comparative analysis based on the detection item data and relevant standard data to determine the data compliance status data;

[0009] Combine the detection result data with the data compliance status data for comprehensive evaluation, and construct a detection report analysis database;

[0010] Generate key data summary information and problem report information based on the detection report analysis database;

[0011] Obtain the follow-up processing feedback data of the detection report;

[0012] Dynamically update the detection report analysis database based on the comparative analysis of key data summary information, problem report information, and subsequent processing feedback data;

[0013] Provide optimized detection report management services and decision support information to relevant users based on the updated detection report analysis database.

[0014] Furthermore, the steps of obtaining various types of detection report data include:

[0015] Establish a data connection channel with various detection devices or detection systems, and obtain the original detection data according to the predetermined data transmission protocol and format specifications;

[0016] Analyze and extract the original detection data, and identify the relevant data information of the detection items, detection results, and corresponding detection time and detection location;

[0017] Retrieve the standard data corresponding to the detection items from the standard database and the relevant regulatory document library, including limit standards and detection method standards;

[0018] Integrate and verify the obtained detection item data, detection result data, and relevant standard data.

[0019] Furthermore, the steps of determining the data compliance status data include:

[0020] For each detection item, compare the detection result data with the corresponding standard data numerically;

[0021] If the detection result is within the qualified range specified by the standard, mark it as a compliant status and assign a compliance identifier;

[0022] If it exceeds the qualified range, mark it as a non-compliant status and classify and identify it according to the degree of excess;

[0023] For detection items with logical associations, analyze whether the logical relationship between their detection results meets the expectations. If not, mark it as a logical exception status and record the exception type;

[0024] Generate a complete data compliance status data report by comprehensively judging the compliance and logical status of each detection item.

[0025] Furthermore, the steps of constructing the detection report analysis database include:

[0026] Using the unique identifier of the detection report as an index, associate and store the detection item data, detection result data, relevant standard data, and data compliance status data;

[0027] Classify, organize, and structurally process the stored data according to the detection type, time period, and detection agency to establish a multi-dimensional data storage architecture;

[0028] Perform data mining and statistical analysis on the detection report data, extract characteristic information corresponding to the qualification rate distribution of different detection items and the trend changes of non-compliant items, and integrate the above characteristic information into the detection report analysis database.

[0029] Further, the steps of generating the key data summary information and problem report information include:

[0030] Screen the data of key detection items from the detection report analysis database, and summarize and refine them to form key data summary information;

[0031] According to the data compliance status data, identify non-compliant detection items and their detailed information, and generate problem report information in combination with relevant standards and detection backgrounds, including problem descriptions, possible cause analyses, and preliminary recommended measures;

[0032] Adopt visualization technology to display the key data summary information and problem report information in the form of charts, graphs, and reports.

[0033] Further, the steps of obtaining the follow-up processing feedback data of the detection report include:

[0034] Establish feedback information collection channels with the users of the detection report or relevant management departments, including online feedback forms and email feedback;

[0035] Receive feedback information related to the users' questions, opinions, and implementation status of rectification measures for the detection report, and classify, organize, and preliminarily analyze it;

[0036] Associate the feedback information with the corresponding detection report to ensure the pertinence and traceability of the feedback data;

[0037] Conduct quality assessment and screening on the feedback data, remove invalid or duplicate information, and extract valuable feedback content for subsequent database update and management optimization.

[0038] Further, the steps of providing optimized detection report management services and decision support information to relevant users include:

[0039] According to the roles and needs of different users, such as detection agency managers, enterprise quality control personnel, environmental protection supervision personnel, etc., customize personalized information display interfaces and report templates;

[0040] Push key detection report information, problem warning information, trend analysis results, and decision-making suggestions based on data analysis relevant to users to assist users in decision-making and management optimization;

[0041] Provide data query and analysis tools to enable users to independently conduct in-depth mining and analysis of detection report data, meeting the personalized data exploration needs of users;

[0042] Continuously track users' usage feedback and operation behaviors on the pushed information, and further optimize the information push strategy and management service content based on users' feedback and behavior data.

[0043] This application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0044] This application also provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the computer program is executed by a processor, the steps of the above method are implemented.

[0045] The beneficial effects of this application are as follows:

[0046] From obtaining detection report data to classifying, sorting, and establishing indexes, the present invention can quickly and orderly process a large amount of data, change the low efficiency of traditional manual processing, improve the data processing speed, and reduce labor costs and error rates. By comparing and analyzing the detection item data with the standard data to determine the compliance status, it can accurately find whether the data meets the standards, timely identify problems, and provide strong support for quality control and supervision. Brief Description of the Drawings

[0047] Figure 1 It is a schematic flowchart of the method proposed in an embodiment of this application.

[0048] The realization, functional features, and advantages of the purpose of this application will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0049] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] Embodiment 1

[0051] As Figure 1 shown, this application provides a detection report management method based on big data, including the following steps:

[0052] S1, Obtain various types of detection report data, where the detection report data includes detection item data, detection result data, and relevant standard data;

[0053] S2. Classify and organize the obtained test report data, and establish a data index according to the test type and time sequence.

[0054] S3. Conduct a comparative analysis based on the test item data and relevant standard data to determine the data compliance status data.

[0055] S4. Combine the test result data and the data compliance status data for comprehensive evaluation, and construct an analysis database for test reports.

[0056] S5. Generate summary information of key data and problem report information based on the analysis database of test reports.

[0057] S6. Obtain the follow-up processing feedback data of the test report.

[0058] S7. Dynamically update the analysis database of test reports according to the comparative analysis of the summary information of key data, problem report information and follow-up processing feedback data.

[0059] S8. Provide optimized test report management services and decision support information to relevant users according to the updated analysis database of test reports.

[0060] As described in the above steps S1 - S8, the present invention can quickly and orderly process a large amount of data from obtaining test report data to classification, organization and index establishment, change the low efficiency of traditional manual processing, improve the data processing speed, and reduce the labor cost and error rate. By comparing and analyzing the test item data with the standard data to determine the compliance status, it can accurately find whether the data meets the standards, timely identify problems, and provide strong support for quality control and supervision.

[0061] It can also generate summary information of key data and problem report information based on the database, automatically extract key points and problems, provide intuitive and effective basis for decision-making, and assist relevant personnel to make scientific decisions quickly. Combining the test results and compliance status for comprehensive evaluation and constructing a database can comprehensively integrate information, provide rich and accurate data basis for subsequent analysis, and help to deeply explore the data value. Obtaining the follow-up processing feedback data and dynamically updating the database accordingly can enable the management system to continuously optimize according to the actual situation, maintain the timeliness and accuracy of data and analysis results, and adapt to the changing requirements.

[0062] It can also provide optimized services and decision support information to users according to the updated database, meet the personalized needs of different users, improve the user experience and the quality of management services, and enhance the pertinence and practicality of test report management.

[0063] Specifically, the steps of obtaining various test report data include:

[0064] Establish a data connection channel with various detection devices or detection systems, and obtain the original detection data according to the predetermined data transmission protocol and format specifications;

[0065] Analyze and extract the original detection data, and identify the relevant data information of the detection items, detection results, corresponding detection time, and detection location therein;

[0066] Retrieve the standard data corresponding to the detection items from the standard database and the relevant regulatory document library, including limit standards and detection method standards, and ensure the timeliness and applicability of the standard data;

[0067] Integrate and verify the obtained detection item data, detection result data, and relevant standard data to ensure the integrity and accuracy of the data.

[0068] It should be noted that the above steps compare the detection results of each detection item with the corresponding standard data, which can accurately judge whether all aspects of the smartphone meet the standards and provide an accurate basis for quality control. For example, in the smartphone detection, the standard requires that the screen resolution of a certain model of mobile phone should reach 2K (2560×1440 pixels). If the detection result shows that the screen resolution of a mobile phone is 2560×1440 pixels, within the qualified range specified by the standard, it can be marked as a compliant status and given a compliance identifier; if the resolution is only 1920×1080 pixels, lower than the standard requirement, it is marked as a non-compliant status.

[0069] For the situation beyond the qualified range, grading identification is carried out according to the degree of exceeding, which can enable producers and managers to clearly know the severity of the problem, so as to formulate targeted solutions. For example, the battery life of a smartphone is an important detection item. The standard stipulates that the continuous video playback duration of this model of mobile phone in a fully charged state should not be less than 10 hours. If the detection result of a mobile phone is 8 hours, which is slightly beyond the qualified range, it can be marked as slightly non-compliant; if the detection result is only 5 hours, with a larger degree of exceeding, it is marked as severely non-compliant. For slightly non-compliant mobile phones, it can be improved by further optimizing software energy consumption, etc.; for severely non-compliant ones, operations such as re-evaluating the battery selection need to be carried out.

[0070] Analyzing the logical relationship between the results of detection items with logical associations can discover potential unreasonable situations inside the smartphone and avoid quality hazards caused by the overall logical contradiction due to the qualification of individual indicators.

[0071] For example, there is a logical relationship between the processor performance and the graphics processing unit (GPU) performance of a smartphone. Generally speaking, a GPU with stronger performance should be paired with a high-performance processor to ensure smooth operation when running applications such as large games. If it is detected that a certain mobile phone has a very strong processor performance but a weak GPU performance, problems such as screen lag may occur when running large games. Such a situation that does not conform to the logical relationship will be marked as a logical exception state, and the type of exception will be recorded to prompt the R & D personnel to check whether it is a problem with the hardware configuration or insufficient software optimization.

[0072] Regarding the compliance and logical status judgment of each detection item, generating a complete data compliance status data report can enable managers to comprehensively understand the overall quality status of smartphones and provide strong support for decision-making. After comprehensively detecting a batch of smartphones, it involves multiple detection items such as screen resolution, battery life, processor performance, and GPU performance. The report generated after comprehensive judgment can clearly show how many mobile phones are overall qualified, which mobile phones have non-compliance problems in individual items, and which mobile phones have logical exception problems, etc. Based on the report, managers can decide whether this batch of mobile phones can be put on the market, whether rework is required, etc.

[0073] Specifically, the steps for determining the data compliance status data include:

[0074] For each detection item, numerically compare the detection result data with the corresponding standard data;

[0075] If the detection result is within the qualified range specified by the standard, mark it as a compliant state and assign a compliance identifier;

[0076] If it exceeds the qualified range, mark it as a non-compliant state and classify and identify it according to the degree of exceeding;

[0077] For detection items with logical relationships, analyze whether the logical relationship between their detection results conforms to expectations. If not, mark it as a logical exception state and record the type of exception;

[0078] Generate a complete data compliance status data report by comprehensively judging the compliance and logical status of each detection item.

[0079] For the above steps, numerically comparing the detection result with the standard data for each detection item can accurately determine whether a single item is compliant, providing an accurate basis for subsequent evaluation and decision-making. For example, the battery capacity is an important detection item, and the standard stipulates that the battery capacity of this model of mobile phone should be between 3900 - 4100 mAh. By comparing the actual detected battery capacity value with this standard range, if the detection result is 4000 mAh, it can be accurately marked as a compliant state; if the detection result is 3800 mAh, it is marked as a non-compliant state.

[0080] Grading and identifying the test results that exceed the qualified range according to the degree of exceeding helps to understand the severity of the problem more meticulously, so that relevant personnel can take corresponding measures at different levels. For example, the standard of the screen pixel density of this model of smartphone is not less than 400 ppi. If the test result of a certain mobile phone is 380 ppi, which is a slight deviation from the standard, it can be marked as slightly non-compliant; if the test result is only 350 ppi, which is seriously lower than the standard requirement, it is marked as severely non-compliant. In this way, the manufacturer can, according to the grading situation, repair or adjust the slightly non-compliant products and then put them on the market, while for the severely non-compliant products, relevant components need to be remanufactured.

[0081] For test items with logical associations, analyzing the logical relationships between their test results can discover potential unreasonable situations in the data and avoid misjudgments caused by the contradiction between the overall logic and the compliance of individual data. The processor performance and heat dissipation ability of this model of smartphone are test items with logical associations. Generally, the higher the processor performance, the more heat is generated, and the corresponding heat dissipation ability should also be stronger. If it is detected that a certain mobile phone has a very strong processor performance but a very weak heat dissipation ability, which does not conform to the normal logical relationship, it can be marked as a logical abnormal state and the type of abnormality is recorded. This may mean that there are defects in the heat dissipation design of the mobile phone. Even if the processor performance meets the standard, the mobile phone may experience problems such as freezing due to overheating during long-term use, and further investigation and improvement are required.

[0082] Generating a complete data compliance status data report based on the compliance and logical status judgments of each test item can comprehensively and systematically reflect the overall compliance of the test report data, providing a clear decision-making basis for managers. For example, after a comprehensive quality inspection of a batch of smartphones, multiple test items such as battery capacity, screen pixel density, processor performance, and heat dissipation ability are involved. By comprehensively judging the compliance and logical status of each item, the generated data compliance status data report can enable quality management personnel to clearly see the overall quality status of this batch of mobile phones. For example, how many mobile phones are completely qualified, which mobile phones have non-compliance problems in individual items, and which mobile phones have logical abnormal problems, etc. Based on this, managers can decide whether this batch of mobile phones can be directly launched into the market, or whether targeted rework or scrapping treatment is required, etc.

[0083] Specifically, the steps for constructing the test report analysis database include:

[0084] Using the unique identifier of the test report as an index, associatively storing the test item data, test result data, relevant standard data, and data compliance status data;

[0085] Classify and organize the stored data according to the detection type, time period, and detection agency, and perform structured processing to establish a multi-dimensional data storage architecture;

[0086] Perform data mining and statistical analysis on the detection report data, extract characteristic information corresponding to the pass rate distribution of different detection items and the trend changes of exceeded-standard items, and integrate the above characteristic information into the detection report analysis database to enrich the analysis value of the database.

[0087] Associate and store various types of data with a unique identifier as the index for convenient management and query. For example, each smartphone detection report has a unique code, through which the detection data such as screen resolution and battery capacity and the compliance status can be quickly associated, facilitating the overall viewing of the mobile phone quality information.

[0088] The present invention classifies and organizes data according to the detection type, time, etc., and establishes a multi-dimensional storage architecture. For example, according to the types such as functional detection and appearance detection, or classified by the quarterly detection time, it can quickly locate the mobile phone detection data within a specific type or time period, such as finding the performance detection reports of all mobile phones in a certain quarter.

[0089] Perform data mining and statistical analysis and integrate characteristic information. For example, analyze the screen pass rate distribution and the trend of battery life exceeding the standard of different batches of smartphones, provide data support for optimizing the production process and improving product quality, and help discover potential problems and improvement directions.

[0090] Specifically, the steps of generating the key data summary information and the problem report information include:

[0091] Screen out the data of key detection items from the detection report analysis database, such as the data related to environmental safety and the core indicators of product quality, and perform summarization and refinement to form the key data summary information to highlight the key detection results and trends;

[0092] According to the data compliance status data, identify the non-compliant detection items and their detailed information, and combine relevant standards and detection backgrounds to generate the problem report information, including problem description, possible cause analysis, and preliminary suggested measures;

[0093] Adopt visualization technology to display the key data summary information and the problem report information in the form of charts, graphs, and reports to improve the readability and intuitiveness of the information.

[0094] Screening and summarizing the key test item data from the test report analysis database enables relevant personnel to quickly grasp the core information of smartphone quality. For example, in smartphone testing, processor performance, battery life, and screen display effect are the core indicators affecting product quality. By screening the data of these key items, summary information of key data can be formed, such as the average processor operation speed and average battery life of a certain batch of mobile phones, which facilitates managers to quickly understand the core performance status and trends of products.

[0095] Generating problem report information based on data compliance status data, covering problem description, cause analysis, and recommended measures, helps to accurately locate and solve smartphone quality problems. If it is detected that the battery life of a certain model of smartphone does not meet the regulations, the problem report information will describe in detail the gap between the actual battery life and the standard requirements, analyze that the possible reasons may be false labeling of battery capacity or insufficient optimization of software power consumption, etc., and give preliminary recommended measures such as replacing the battery supplier or optimizing the system software, providing a direction for production improvement.

[0096] The present invention uses visualization technology to display the summary information of key data and problem report information, improving the readability and intuitiveness of the information, and facilitating understanding and decision-making. For example, presenting the key test data of smartphones, such as the pass rate of screen resolution and the compliance rate of processor performance of different batches of mobile phones, in the form of bar charts or line charts; presenting problem report information such as non-compliance of battery life in the form of reports, enabling technicians and managers to obtain information at a glance and make decisions quickly, such as whether to adjust the production process or recall problem products, etc.

[0097] Specifically, the steps of obtaining the subsequent processing feedback data of the test report include:

[0098] Establishing a feedback information collection channel with the users of the test report or relevant management departments, including online feedback forms and email feedback;

[0099] Receiving feedback information related to the users' questions, opinions, and implementation status of rectification measures regarding the test report, and classifying, sorting, and preliminarily analyzing it;

[0100] Associating the feedback information with the corresponding test report to ensure the pertinence and traceability of the feedback data;

[0101] Conducting quality assessment and screening of the feedback data, removing invalid or duplicate information, and extracting valuable feedback content for subsequent database update and management optimization.

[0102] The present invention establishes multiple feedback information collection channels, such as online feedback forms, email feedback, etc., which facilitates the users of test reports and relevant management departments to provide feedback in their preferred ways, expands the feedback sources, and improves the comprehensiveness and timeliness of feedback information collection. The received feedback information is classified, sorted, and preliminarily analyzed, making the messy feedback information well-organized, facilitating subsequent in-depth research and processing, and improving the utilization efficiency of feedback information.

[0103] The feedback information is associated with the corresponding test report to ensure the pertinence and traceability of feedback data, which helps to accurately find the root cause of problems, clarify responsibilities, and provide strong support for problem-solving. The feedback data is subject to quality assessment and screening to remove invalid or duplicate information, and valuable feedback content is extracted, ensuring the data quality for database update and management optimization, and making subsequent decisions more scientific and effective.

[0104] This application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0105] This application also provides a computer-readable storage medium, on which a computer program is stored. The computer program, when executed by a processor, implements the steps of the above method.

[0106] Embodiment 2

[0107] The difference between this embodiment and Embodiment 1 lies in that the step of dynamically updating the test report analysis database includes:

[0108] According to the new information and correction opinions in the feedback data, the original data, compliance judgment results, analysis conclusions, etc. in the test report analysis database are modified and improved accordingly;

[0109] The new test report data is incorporated into the test report analysis database according to the established processes and rules, and re-classified, analyzed, and integrated;

[0110] Based on the updated data, the key data summary information, problem report information, and relevant statistical analysis results are recalculated and updated to ensure the timeliness and accuracy of the database;

[0111] The update history and operation logs of the database are recorded to facilitate data traceability and auditing.

[0112] The step of providing optimized test report management services and decision support information to relevant users includes:

[0113] Customize personalized information display interfaces and report templates according to the roles and needs of different users, such as managers of testing institutions, enterprise quality control personnel, environmental protection supervision personnel, etc.;

[0114] Push key test report information, problem warning information, trend analysis results, and decision-making suggestions based on data analysis related to users to assist users in decision-making and management optimization;

[0115] Provide data query and analysis tools to enable users to independently conduct in-depth mining and analysis of test report data to meet users' personalized data exploration needs;

[0116] Continuously track users' usage feedback and operation behaviors of the pushed information, and further optimize the information push strategy and management service content according to users' feedback and behavior data.

[0117] The present invention can modify and improve the original data, compliance judgment, etc. according to the new information and correction opinions in the feedback data, and can timely correct the possible errors or outdated information in the database to ensure the accuracy and integrity of the data. For example, in the analysis database of smartphone test reports, if it is found that the previous test result of the battery life of a certain model of smartphone is incorrect, it can be corrected according to the feedback, so that subsequent decisions are based on reliable data.

[0118] It can also incorporate new test report data into the established process for reprocessing and recalculate relevant information based on the updated data to ensure that the database can timely reflect the latest test situation and provide real-time data support. For example, the test reports of new batches of smartphone products can be timely stored in the database for analysis, and enterprises can grasp the product quality dynamics in real time.

[0119] In addition, it can record the database update history and operation logs for easy data traceability and auditing. In the quality traceability of smartphones, if there are product quality problems, the evolution process of the test reports of the products in the database can be traced through the logs to clarify the responsibilities of each link, which is also conducive to the auditing of regulatory authorities.

[0120] Customizing information display interfaces and report templates according to different user roles and needs can meet diverse needs and improve the user experience. For example, managers of testing institutions are more concerned about the overall testing business situation, and the customized interface can highlight information such as testing progress and institutional performance; enterprise quality control personnel focus on specific product quality indicators, and the corresponding template will focus on this. Pushing key information, problem warnings, and decision-making suggestions to users to assist users in decision-making and management optimization. In a smartphone manufacturing enterprise, pushing warnings and improvement suggestions for unqualified battery life helps the enterprise timely adjust its production strategy and improve product quality.

[0121] Through the above steps, data query and analysis tools can be provided, enabling users to independently and deeply explore and analyze data. Mobile phone R & D personnel can use this to analyze the performance data of different models of mobile phones, providing reference for the R & D of new products and meeting the needs of personalized exploration. Continuously track user feedback and behavior data, optimize the information push strategy and management service content, and make the service more in line with user needs. For example, according to the browsing and usage feedback of users on the information of smartphone detection reports, adjust the push frequency and content focus to improve service quality and user satisfaction.

[0122] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, value library, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0123] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusively, such that a process, apparatus, article, or method including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article, or method including that element.

[0124] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are similarly included in the patent protection scope of the present invention.

Claims

1. A test report management method based on big data, characterized in that: The following steps are involved: Obtain various test report data, wherein the test report data includes test item data, test result data and related standard data; Classify and sort the acquired test report data, and establish data indexes according to test type and time sequence; Compare and analyze the test project data and relevant standard data to determine the data compliance status data; Combine test result data with data compliance status data for comprehensive evaluation and build a test report analysis database; Generate key data summary information and problem report information based on the test report analysis database; Obtain follow-up processing feedback data of the test report; Dynamically update the test report analysis database based on the comparison and analysis of key data summary information, problem report information and subsequent processing feedback data; Provide optimized test report management services and decision support information to relevant users based on the updated test report analysis database.

2. The test report management method based on big data according to claim 1, characterized in that: The steps of obtaining various test report data include: Establish data connection channels with various testing equipment or testing systems, and obtain original testing data according to the predetermined data transmission protocol and format specifications; Parse and extract the original test data to identify the test items, test results, and the corresponding test time and test location; Retrieve standard data corresponding to the test items from the standard database and relevant regulatory document library, including limit standards and test method standards; Integrate and verify the acquired test item data, test result data and related standard data.

3. The test report management method based on big data according to claim 1, characterized in that: The step of determining data compliance status data comprises: For each test item, the test result data is numerically compared with the corresponding standard data; If the test result is within the qualified range specified by the standard, it will be marked as compliant and given a compliance mark; If it exceeds the qualified range, it will be marked as non-compliant and graded according to the degree of excess; For test items with logical associations, analyze whether the logical relationship between the test results meets expectations. If not, mark them as logical abnormalities and record the abnormality type. Comprehensively judge the compliance and logical status of each test item to generate a complete data compliance status report.

4. The test report management method based on big data according to claim 1, characterized in that: The step of constructing a test report analysis database comprises: Using the unique identifier of the test report as an index, the test item data, test result data, relevant standard data, and data compliance status data are stored in an associated manner; Classify and organize the stored data according to the test type, time period, and testing agency, and establish a multi-dimensional data storage architecture; Data mining and statistical analysis are performed on the test report data to extract characteristic information corresponding to the distribution of qualified rates of different test items and trend changes of items exceeding the standard, and the above characteristic information is integrated into the test report analysis database.

5. The test report management method based on big data according to claim 4 is characterized in that: The step of generating key data summary information and problem report information includes: Filter out the data of key test items from the test report analysis database, summarize and refine them, and form key data summary information; Based on the data compliance status data, identify the non-compliant test items and their detailed information, and generate problem report information in combination with relevant standards and test background, including problem description, possible cause analysis and preliminary recommended measures; Visualization technology is used to display key data summary information and problem report information in the form of charts, graphs and reports.

6. The test report management method based on big data according to claim 5, characterized in that: The step of obtaining the subsequent processing feedback data of the test report includes: Establish feedback information collection channels with test report users or relevant management departments, including online feedback forms and email feedback; Receive users' questions, opinions, and feedback on the implementation of corrective measures regarding the test report, and classify and conduct preliminary analysis; Associate the feedback information with the corresponding test report to ensure the pertinence and traceability of the feedback data; Conduct quality assessment and screening of feedback data, remove invalid or duplicate information, and extract valuable feedback content for subsequent database updates and management optimization.

7. The test report management method based on big data according to claim 6 is characterized in that: The step of providing optimized test report management services and decision support information to relevant users includes: Customize personalized information display interface and report templates according to different user roles and needs; Push key test report information, problem warning information, trend analysis results and decision-making suggestions based on data analysis to users; Provide data query and analysis tools; Continue to track user feedback and operational behavior on push information, and further optimize information push strategies and management service content based on user feedback and behavioral data.

8. A test report management system based on big data, characterized in that: include: A first acquisition module is used to acquire various test report data, wherein the test report data includes test item data, test result data and related standard data; Establish a module to classify and organize the acquired test report data and establish a data index according to the test type and time sequence; The analysis module is used to compare and analyze the test project data and relevant standard data to determine the data compliance status data; A construction module is used to combine the test result data with the data compliance status data for comprehensive evaluation and to build a test report analysis database; A generation module, used to generate key data summary information and problem report information based on the test report analysis database; The second acquisition module is used to obtain the subsequent processing feedback data of the test report; An update module is used to dynamically update the test report analysis database based on the comparison and analysis of key data summary information, problem report information and subsequent processing feedback data; The optimization module is used to provide optimized test report management services and decision support information to relevant users based on the updated test report analysis database.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a test report management method based on big data described in claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a test report management method based on big data described in claims 1-7 are implemented.

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