A QHSE management method and system based on cloud computing

Abstract

The present invention provides a QHSE management method and system based on cloud computing, which relates to the field of data processing technology. The method includes obtaining different types of management data processing result information, and performing cloud computing feature analysis based on information repetition rate to form type management information feature data; obtaining real-time management processing information, and combining the type management information feature data, performing processing management analysis to form real-time processing management requirement data; according to the real-time processing management requirement data, monitoring the processing process of the real-time management processing information, and forming real-time processing monitoring information. The method further improves the operating efficiency of the management system by using cloud computing to perform information-based intelligent processing of QHSE management information, so that the operating efficiency and operating stability of the enterprise are further improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a QHSE management method and system based on cloud computing. Background Art

[0002] The QHSE management system (the management system of the command and control organization in terms of quality and safety) is to achieve the standardized management of the enterprise QHSE, systematically integrate the quality, health, safety and environmental management modes, and create a scientific, systematic, complete and standardized information system covering the whole enterprise. The system contains basically all the important information of the enterprise, and through integration and organization, orderly management is carried out, which greatly improves the operation efficiency and stability of the enterprise.

[0003] At present, most QHSE management systems are still mainly for manual information processing. Due to the great influence of human factors, the entire management system cannot further improve its operating efficiency. Of course, there have been attempts at informatization, but due to the huge amount of data information, efficient information processing and management cannot be achieved, and thus the efficiency of the management system has not been effectively improved.

[0004] Therefore, designing a QHSE management method and system based on cloud computing, and using cloud computing to perform information-based intelligent processing of QHSE management information, further improves the operating efficiency of the management system, and further improves the operating efficiency and stability of the enterprise, is a problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide a QHSE management method based on cloud computing. By utilizing the powerful computing power of cloud computing, reasonable overlap merging feature extraction and analysis is performed on different types of management data processing result information in the enterprise, so as to obtain feature data of corresponding type of management data processing. At the same time, on the basis of obtaining feature data, matching and comparative analysis is performed on the real-time management processing information to determine the basic processing requirements of the real-time management processing information currently being performed in real time, effectively ensuring the timely and efficient processing of the real-time management processing information, and effectively comparing and monitoring the processing process of the real-time management processing information to ensure that the real-time management processing information can be processed smoothly and timely throughout the entire processing process. In this way, the entire process of the management processing information based on big data is reasonably monitored and tracked, ensuring the timeliness of the processing, effectively ensuring that the management processing data of the enterprise can be completed in time, and greatly improving the operating efficiency and stability of the enterprise under the QHSE management system.

[0006] The purpose of the present invention is also to provide a QHSE management system based on cloud computing, which is configured to perform cloud computing on historical management data processing information to obtain management information feature data, and establish reasonable processing management requirements and real-time processing tracking and detection system for real-time management processing information, thereby effectively ensuring that real-time management processing information can be processed smoothly and timely throughout the entire processing process, greatly improving the operating efficiency and stability of the enterprise under the QHSE management system, and is the material basis for achieving efficient QHSE management.

[0007] In a first aspect, the present invention provides a QHSE management method based on cloud computing, including obtaining different types of management data processing result information, and performing cloud computing feature analysis based on information repetition rate to form type management information feature data; obtaining real-time management processing information, and combining the type management information feature data to perform processing management analysis to form real-time processing management requirement data; based on the real-time processing management requirement data, monitoring the processing process of the real-time management processing information, and forming real-time processing monitoring information.

[0008] In the present invention, the method utilizes the powerful computing power of cloud computing to perform reasonable overlapping and merging feature extraction and analysis on different types of management data processing result information within the enterprise, and then obtains feature data of corresponding types of management data processing. At the same time, on the basis of obtaining the feature data, matching and comparative analysis is performed on the real-time management processing information to determine the basic processing requirements of the real-time management processing information currently being performed, effectively ensuring the timely and efficient processing of the real-time management processing information, and effectively comparing and monitoring the processing process of the real-time management processing information to ensure that the real-time management processing information can be processed smoothly and timely throughout the entire processing process. In this way, the entire process of management processing information based on big data is reasonably monitored and tracked, ensuring the timeliness of processing, effectively ensuring that the management processing data of the enterprise can be completed in a timely manner, and greatly improving the operating efficiency and stability of the enterprise under the QHSE management system.

[0009] As a possible implementation method, different types of management data processing result information are obtained, and cloud computing feature analysis based on information repetition rate is performed to form type management information feature data, including: for different types of management data processing result information, each management data processing task information and corresponding processing time information at each level are extracted; within the same type of management data processing result information, overlap analysis of the same task information is performed to form different task feature information; all task feature information corresponding to different types of management data processing result information is collected to form type management information feature data.

[0010] In the present invention, the reasonable feature information extraction of the management data processing result information with huge data volume based on cloud computing mainly considers three aspects. On the one hand, the extraction of feature data is for the original data. The more delicate the original data is divided, the more representative and independent the extracted features are. Therefore, when extracting feature information, the acquired management data processing result information is first divided into types. After all, there are large differences in the content of different types of management data processing result information. For example, the content and focus of the quality processing form and other processing forms such as equipment replacement and production safety are completely different. The type division can further improve the accuracy and rationality of feature extraction. The second aspect is that when considering feature extraction, even different management data processing result information under the same type will have different actual information descriptions. Therefore, the extraction of descriptive features can help to carry out more unified data analysis and processing. Here, the extraction of descriptive features is carried out on the one hand on the establishment of a language library corresponding to different types of management data processing result information to ensure the uniformity of the extracted description language. On the other hand, a unified format is provided for format-based language extraction to further improve the comparability and easy handling of descriptive features. Thirdly, considering that the management data processing result information mainly contains the information about the processing of the generated management data, the most direct feature that reflects this situation is the time feature, and the time feature extraction based on big data is more instructive, which provides a reference for the subsequent processing requirements for real-time management data processing.

[0011] As a possible implementation method, within the same type of management data processing result information, an overlap analysis of the same task information is performed to form different task feature information, including: within the same type of management data processing result information, task feature extraction is performed on each management data processing task information to form corresponding task description feature information; corresponding processing time information at each level for the same task description feature information within the same type of management data processing result information is merged to form the same task overlap processing information; processing time information at each level corresponding to different task description feature information that has not been merged within the same type of management data processing result information is calibrated as independent task overlap processing information; processing feature extraction is performed on the same task overlap processing information and independent task overlap processing information within the same type of management data processing result information to form corresponding task processing feature information.

[0012] In the present invention, for the data managed by QHSE, the management data processing tasks formed may have different probabilities of occurrence. Even in big data, some tasks may not appear frequently, and some may only appear once. The extraction of feature information is different for cases with high frequency of occurrence and only once. For the management data processing task information that appears repeatedly, the extraction of feature information needs to be combined and analyzed. For the management data processing task information that appears only once, independent task processing information is formed.

[0013] As a possible implementation method, processing feature extraction is performed on the same task overlapping processing information and the independent task overlapping processing information in the same type of management data processing result information respectively to form corresponding task processing feature information, including: for different same task overlapping processing information, processing feature extraction based on processing frequency is performed to form corresponding level task processing feature information; for different independent task overlapping processing information, processing feature extraction based on processing time is performed to form corresponding guide task processing feature information; all level task processing feature information and guide task processing feature information are combined to form task processing feature information.

[0014] In the present invention, for management data processing result information that appears repeatedly in historical big data, the method of extracting feature information is different from that of independent management data processing result information that only appears once, and therefore needs to be performed separately to ensure that the extracted feature information can fully reflect the processing characteristics of different management data processing result information, and provide an accurate and reasonable reference for the subsequent processing of the same management data processing tasks.

[0015] As a possible implementation method, processing features based on processing frequency are extracted for different overlapping processing information of the same task to form corresponding hierarchical task processing feature information, including: determining the total number of processing of different overlapping processing information of the same task within the same type of management data processing result information, and arranging them in descending order of the total number of processing to form an ordered set of overlapping processing information of the same task; setting different levels of total number of processing limit ranges, and classifying different overlapping processing information of the same task in the ordered set of overlapping processing information of the same task according to the total number of processing limit ranges to form a set of overlapping processing information of the same task at the same level; for different sets of overlapping processing information of the same task at the same level, the processing time range of each level is determined according to the corresponding processing time information of each level to form a processing time range of each level of the hierarchical task.

[0016] In the present invention, for the recurring management data processing task information, when extracting the overlapping processing features, it is considered that the processing time formed under the big data is stable for each level, so the extracted features are the range features containing this stability. It can be understood that for the recurring management data processing task information, the processing time at each level is discrete data information, and each of these discrete data information is valid. Therefore, when establishing the range data based on the discrete data information, it is achieved by extracting the maximum value and the minimum value. In addition, it should be noted that for the recurring management data processing tasks, the higher the frequency of occurrence, the greater the frequency of problems. Therefore, different management data processing tasks are graded by statistical division of frequency, and then the importance indication is provided for the subsequent recurrence of the same management data processing task. After all, when processing at different levels, the corresponding operators of the level need to handle more than one task. The level indication can well remind the operators to pay attention to the importance.

[0017] As a possible implementation method, processing features based on processing time are extracted for overlapping processing information of different independent tasks to form corresponding guiding task processing feature information, including: within the same type of management data processing result information, for overlapping processing information of different independent tasks, hierarchical processing time values ​​for processing at each level are determined according to the corresponding processing time information at each level; and different hierarchical processing time values ​​of overlapping processing information of independent tasks are calibrated as hierarchical processing limit times for corresponding processing levels.

[0018] In the present invention, for a management data processing task that only occurs once, there is only one corresponding processing duration for each level, and this duration data is valid. Therefore, in order to ensure that the same management data processing tasks that appear subsequently can be processed in a timely and efficient manner, the corresponding duration at each level is obtained as the longest allowed time limit.

[0019] As a possible implementation method, real-time management processing information is obtained, and combined with type management information feature data, processing management analysis is performed to form real-time processing management requirement data, including: real-time task feature extraction of real-time management processing information to form real-time task description feature information; based on the real-time task description feature information, the following matching analysis is performed with the task description feature information of the corresponding type: if there is task description feature information that matches the real-time task description feature information, then: when the task description feature information is overlapping processing information of the same task, the level corresponding to the matched task description feature information is assigned to the real-time management processing information, and the processing time range of each level of the level task corresponding to the matched task description feature information is used as the required range for the real-time management processing information to process each level; when the task description feature information is overlapping processing information of an independent task, the hierarchical processing limit time corresponding to the matched task description feature information is used as the required time for the real-time management processing information to process each level.

[0020] In the present invention, when real-time management processing information is being processed and analyzed, the first thing that needs to be clarified is the task type with the highest frequency of occurrence in history of the real-time associated processing information, so that it can be determined whether to provide a level mark of importance and reasonable processing time requirements for each level. If it is the overlapping processing information of the same task that appears frequently in historical big data, the level calibration is performed, and the time range requirements for processing at each level are provided. If it is the overlapping processing information of an independent task that only appears once in historical big data, the maximum time limit requirements for processing at each level are provided.

[0021] As a possible implementation method, the processing process of the real-time management processing information is monitored according to the real-time processing management requirement data, and real-time processing monitoring information is formed, including: if the real-time processing management requirement data is the processing time range of each level of the corresponding level task of the real-time management processing information, then: when there is any processing level where the processing time does not fall within the processing time range of the corresponding level, then a processing abnormality warning information is formed, and the real-time processing time of the corresponding level is provided; when there is no processing level where the processing time does not fall within the processing time range of the corresponding level, then normal processing information is formed, and the real-time processing time of the corresponding level is provided.

[0022] In the present invention, the required data provided by the real-time data management processing information is the level information and the requirements of the processing time range of each level. When monitoring the processing process, it is necessary to consider whether the processing time of each level does not belong to the corresponding time range. It should be noted that the processing time is not the shorter the better. After all, for multiple problems, the range provided by big data is based on facts. If it is ahead of this time range, there may be inadequate or even wrong processing.

[0023] As a possible implementation method, the processing process of the real-time management processing information is monitored according to the real-time processing management requirement data, and real-time processing monitoring information is formed, including: if the real-time processing management requirement data is the hierarchical processing limit time corresponding to the real-time management processing information, then: when the processing time at any processing level exceeds the hierarchical processing limit time of the corresponding level, a processing abnormality warning information is formed, and the real-time processing time of the corresponding level is provided; when there is no processing time at any processing level that exceeds the hierarchical processing limit time of the corresponding level, normal processing information is formed, and the real-time processing time of the corresponding level is provided.

[0024] In the present invention, the required data provided for the real-time management data processing task is only the maximum limit value of the processing time at each level. The processing time at each level is monitored with the limit value as the longest time requirement to avoid exceeding the limit time to cause processing delays and generate additional situations, thereby reducing the efficiency of the processing operation.

[0025] In a second aspect, the present invention provides a QHSE management system based on cloud computing, which is configured to: obtain different types of management data processing result information, and perform cloud computing feature analysis based on information repetition rate to form type management information feature data; obtain real-time management processing information, and perform processing management analysis in combination with the type management information feature data to form real-time processing management requirement data; monitor the processing process of the real-time management processing information based on the real-time processing management requirement data, and form real-time processing monitoring information.

[0026] In the present invention, the system is configured to perform cloud computing on historical management data processing information to obtain management information feature data, and to establish reasonable processing management requirements and real-time processing tracking and detection systems for real-time management processing information. This effectively ensures that real-time management processing information can be processed smoothly and timely throughout the entire processing process, greatly improving the operating efficiency and stability of the enterprise under the QHSE management system, and is the material basis for achieving efficient QHSE management.

[0027] The beneficial effects of a QHSE management method and system based on cloud computing provided by the present invention are as follows:

[0028] This method uses the powerful computing power of cloud computing to perform reasonable overlap merging feature extraction and analysis on different types of management data processing result information within the enterprise, and then obtains the feature data of the corresponding type of management data processing. At the same time, on the basis of obtaining the feature data, the real-time management processing information is matched and compared and analyzed to determine the basic processing requirements of the current real-time management processing information, effectively ensuring the timely and efficient processing of the real-time management processing information, and effectively comparing and monitoring the processing process of the real-time management processing information to ensure that the real-time management processing information can be processed smoothly and timely throughout the entire processing process. In this way, the entire process of management processing information based on big data is reasonably monitored and tracked, ensuring the timeliness of processing, effectively ensuring that the enterprise's management processing data can be completed in a timely manner, and greatly improving the operating efficiency and stability of the enterprise under the QHSE management system.

[0029] The system is configured to perform cloud computing on historical management data processing information to obtain management information feature data, and to establish reasonable processing management requirements and real-time processing tracking and detection for real-time management processing information. This effectively ensures that real-time management processing information can be processed smoothly and timely throughout the entire processing process, greatly improving the operating efficiency and stability of the enterprise under the QHSE management system, and is the material basis for achieving efficient QHSE management. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A step diagram of a QHSE management method based on cloud computing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] The QHSE management system is to achieve standardized QHSE management of the enterprise, systematically integrate the quality, health, safety and environmental management models, and create a scientific, systematic, complete and standardized information system covering the entire enterprise. The system contains basically all the important information of the enterprise, and through integration and organization, orderly management is carried out, which greatly improves the operating efficiency and stability of the enterprise.

[0034] At present, most QHSE management systems are still mainly for manual information processing. Due to the great influence of human factors, the entire management system cannot further improve its operating efficiency. Of course, there have been attempts at informatization, but due to the huge amount of data information, efficient information processing and management cannot be achieved, and thus the efficiency of the management system has not been effectively improved.

[0035] refer to Figure 1 The embodiment of the present invention provides a QHSE management method based on cloud computing. The method utilizes the powerful computing power of cloud computing to perform reasonable overlap merging feature extraction and analysis on different types of management data processing result information in the enterprise, thereby obtaining feature data of the corresponding type of management data processing. At the same time, on the basis of obtaining the feature data, matching and comparative analysis are performed on the real-time management processing information to determine the basic processing requirements of the real-time management processing information currently being performed, effectively ensuring the timely and efficient processing of the real-time management processing information, and effectively comparing and monitoring the processing process of the real-time management processing information to ensure that the real-time management processing information can be processed smoothly and timely throughout the entire processing process. In this way, the entire process of the management processing information based on big data is reasonably monitored and tracked, ensuring the timeliness of the processing, effectively ensuring that the management processing data of the enterprise can be completed in time, and greatly improving the operating efficiency and stability of the enterprise under the QHSE management system.

[0036] The QHSE management method based on cloud computing specifically includes the following steps:

[0037] S1: Obtain different types of management data processing result information, and perform cloud computing feature analysis based on information repetition rate to form type management information feature data.

[0038] Obtain different types of management data processing result information, and perform cloud computing feature analysis based on information repetition rate to form type management information feature data, including: for different types of management data processing result information, extract each management data processing task information and corresponding processing time information at each level; perform overlap analysis of the same task information within the same type of management data processing result information to form different task feature information; and aggregate all task feature information corresponding to different types of management data processing result information to form type management information feature data.

[0039] Based on cloud computing, the reasonable feature information extraction of management data processing result information with huge data volume mainly considers three aspects. On the one hand, the extraction of feature data is for the original data. The more delicate the original data is divided, the more representative and independent the extracted features are. Therefore, when extracting feature information, the acquired management data processing result information is first divided into types. After all, there are large differences in the content of different types of management data processing result information. For example, the content and focus of quality processing forms and other processing forms such as equipment replacement and production safety are completely different. The type division can further improve the accuracy and rationality of feature extraction. The second aspect is that when considering feature extraction, even different management data processing result information under the same type will have different actual information descriptions. Therefore, the extraction of descriptive features can help to carry out more unified data analysis and processing. Here, the extraction of descriptive features is carried out on the one hand on the establishment of language libraries corresponding to different types of management data processing result information to ensure the uniformity of the extracted description language. On the other hand, a unified format is provided for format-based language extraction to further improve the comparability and ease of processing of descriptive features. Thirdly, considering that the management data processing result information mainly contains the information about the processing of the generated management data, the most direct feature that reflects this situation is the time feature, and the time feature extraction based on big data is more instructive, which provides a reference for the subsequent processing requirements for real-time management data processing.

[0040] Within the same type of management data processing result information, overlap analysis of the same task information is performed to form different task feature information, including: within the same type of management data processing result information, task feature extraction is performed on each management data processing task information to form corresponding task description feature information; corresponding processing time information at each level for the same task description feature information within the same type of management data processing result information is merged to form the same task overlap processing information; processing time information at each level corresponding to different task description feature information that has not been merged within the same type of management data processing result information is calibrated as independent task overlap processing information; processing feature extraction is performed on the same task overlap processing information and independent task overlap processing information within the same type of management data processing result information to form corresponding task processing feature information.

[0041] For QHSE management data, the management data processing tasks may have different probabilities of occurrence. Even in big data, some tasks may not appear frequently, or even appear only once. The extraction of feature information is different for cases with high frequency and only once. For recurring management data processing task information, the extraction of feature information needs to be combined and analyzed. For management data processing task information that only appears once, independent task processing information is formed.

[0042] Processing feature extraction is performed on the same task overlap processing information and the independent task overlap processing information in the same type of management data processing result information respectively to form corresponding task processing feature information, including: for different same task overlap processing information, processing feature extraction based on processing frequency is performed to form corresponding level task processing feature information; for different independent task overlap processing information, processing feature extraction based on processing duration is performed to form corresponding guide task processing feature information; all level task processing feature information and guide task processing feature information are combined to form task processing feature information.

[0043] For management data processing result information that appears repeatedly in historical big data, the method of extracting feature information is different from that of independent management data processing result information that only appears once. Therefore, they need to be performed separately to ensure that the extracted feature information can fully reflect the processing characteristics of different management data processing result information, and provide accurate and reasonable reference for the subsequent processing of the same management data processing tasks.

[0044] For different overlapping processing information of the same task, processing features are extracted based on processing frequency to form corresponding hierarchical task processing feature information, including: determining the total number of processing of different overlapping processing information of the same task within the same type of management data processing result information, and arranging them in descending order of the total number of processing to form an ordered set of overlapping processing information of the same task; setting different levels of total number of processing limit ranges, and classifying different overlapping processing information of the same task in the ordered set of overlapping processing information of the same task according to the total number of processing limit ranges to form a set of overlapping processing information of the same task; for different sets of overlapping processing information of the same task at different levels, determining the processing time range of each level according to the corresponding processing time information of each level to form a range of processing time of each level of the hierarchical task.

[0045] For the recurring management data processing task information, when extracting the overlapping processing features, it is considered that the processing time formed under the big data is stable for each level, so the extracted features are the range features containing this stability. It can be understood that for the recurring management data processing task information, the processing time at each level is discrete data information, and each of these discrete data information is valid. Therefore, when establishing the range data based on the discrete data information, it is achieved by extracting the maximum value and the minimum value. In addition, it should be noted that for the recurring management data processing tasks, the higher the frequency of occurrence, the greater the frequency of problems. Therefore, different management data processing tasks are graded through the statistical division of frequency, and then the importance indication is provided for the subsequent recurrence of the same management data processing tasks. After all, when processing at different levels, the corresponding operators of the level need to handle more than one task. The level indication can well remind the operators to pay attention to the importance.

[0046] For different independent task overlapping processing information, processing features based on processing time are extracted to form corresponding guiding task processing feature information, including: within the same type of management data processing result information, for different independent task overlapping processing information, according to the corresponding processing time information at each level, the hierarchical processing time values ​​of each level of processing are determined; and the different hierarchical processing time values ​​of the independent task overlapping processing information are calibrated as the hierarchical processing limit time of the corresponding processing level.

[0047] For management data processing tasks that only appear once, there is only one corresponding processing duration for each level, and this duration data is valid. Therefore, in order to ensure that the same management data processing tasks that appear later can be processed in a timely and efficient manner, the corresponding duration at each level is used as the longest allowed time limit.

[0048] S2: Acquire real-time management processing information, and combine it with type management information feature data to perform processing management analysis to form real-time processing management requirement data.

[0049] Acquire real-time management processing information, and perform processing management analysis in combination with type management information feature data to form real-time processing management requirement data, including: extracting real-time task features from the real-time management processing information to form real-time task description feature information; performing the following matching analysis with the corresponding type of task description feature information based on the real-time task description feature information: if there is task description feature information that matches the real-time task description feature information, then: when the task description feature information is overlapping processing information for the same task, assign the level corresponding to the matched task description feature information to the real-time management processing information, and use the processing time range for each level of the level task corresponding to the matched task description feature information as the required range for processing at each level of the real-time management processing information; when the task description feature information is overlapping processing information for independent tasks, use the graded processing limit time corresponding to the matched task description feature information as the required time for processing at each level of the real-time management processing information.

[0050] When performing real-time management and processing information processing management and analysis, the first thing that needs to be clarified is the task type with the highest frequency of occurrence in history of the real-time associated processing information. This is so that it can be determined whether to provide a level mark of importance and reasonable processing time requirements for each level. If it is the same task overlap processing information that appears frequently in historical big data, the level calibration is performed and the time range requirements for each level of processing are provided. If it is the independent task overlap processing information that only appears once in historical big data, the maximum time limit requirements for the processing time at each level are provided.

[0051] S3: According to the real-time processing management requirement data, monitor the processing process of the real-time management processing information and form real-time processing monitoring information.

[0052] According to the real-time processing management requirement data, the processing process of the real-time management processing information is monitored, and real-time processing monitoring information is formed, including: if the real-time processing management requirement data is the processing time range of each level of the corresponding level task of the real-time management processing information, then: when there is any processing level where the processing time does not fall within the processing time range of the corresponding level, then a processing abnormality warning information is formed, and the real-time processing time of the corresponding level is provided; when there is no processing level where the processing time does not fall within the processing time range of the corresponding level, then normal processing information is formed, and the real-time processing time of the corresponding level is provided.

[0053] For real-time data management processing information, the required data is level information and the range of processing time at each level. When monitoring the processing process, it is necessary to consider whether the processing time at each level does not fall within the corresponding time range. It should be noted that the shorter the processing time, the better. After all, for multiple problems, the range provided by big data is based on facts. If it exceeds this time range, there may be inadequate or even incorrect processing.

[0054] According to the real-time processing management requirement data, the processing process of the real-time management processing information is monitored, and real-time processing monitoring information is formed, including: if the real-time processing management requirement data is the hierarchical processing limit time corresponding to the real-time management processing information, then: when the processing time at any processing level exceeds the hierarchical processing limit time of the corresponding level, a processing abnormality warning information is formed, and the real-time processing time of the corresponding level is provided; when there is no processing time at any processing level that exceeds the hierarchical processing limit time of the corresponding level, normal processing information is formed, and the real-time processing time of the corresponding level is provided.

[0055] For real-time management data processing tasks, if the required data provided is only the maximum limit value of the processing time at each level, the processing time at each level will be monitored with the limit value as the longest time requirement to avoid exceeding the limit time, resulting in processing delays and additional situations that reduce the efficiency of processing operations.

[0056] The present invention also provides a QHSE management system based on cloud computing, which is configured to obtain different types of management data processing result information, and perform cloud computing feature analysis based on information repetition rate to form type management information feature data; obtain real-time management processing information, and perform processing management analysis in combination with the type management information feature data to form real-time processing management requirement data; monitor the processing process of the real-time management processing information based on the real-time processing management requirement data, and form real-time processing monitoring information.

[0057] The system is configured to perform cloud computing on historical management data processing information to obtain management information feature data, and to establish reasonable processing management requirements and real-time processing tracking and detection for real-time management processing information. This effectively ensures that real-time management processing information can be processed smoothly and timely throughout the entire processing process, greatly improving the operating efficiency and stability of the enterprise under the QHSE management system, and is the material basis for achieving efficient QHSE management.

[0058] In summary, the cloud computing-based QHSE management method and system provided by the embodiments of the present invention have the following beneficial effects:

[0059] This method uses the powerful computing power of cloud computing to perform reasonable overlap merging feature extraction and analysis on different types of management data processing result information within the enterprise, and then obtains the feature data of the corresponding type of management data processing. At the same time, on the basis of obtaining the feature data, the real-time management processing information is matched and compared and analyzed to determine the basic processing requirements of the current real-time management processing information, effectively ensuring the timely and efficient processing of the real-time management processing information, and effectively comparing and monitoring the processing process of the real-time management processing information to ensure that the real-time management processing information can be processed smoothly and timely throughout the entire processing process. In this way, the entire process of management processing information based on big data is reasonably monitored and tracked, ensuring the timeliness of processing, effectively ensuring that the enterprise's management processing data can be completed in a timely manner, and greatly improving the operating efficiency and stability of the enterprise under the QHSE management system.

[0060] The system is configured to perform cloud computing on historical management data processing information to obtain management information feature data, and to establish reasonable processing management requirements and real-time processing tracking and detection for real-time management processing information. This effectively ensures that real-time management processing information can be processed smoothly and timely throughout the entire processing process, greatly improving the operating efficiency and stability of the enterprise under the QHSE management system, and is the material basis for achieving efficient QHSE management.

[0061] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0062] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0063] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0064] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0065] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0066] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0067] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. These three situations, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0068] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0069] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0070] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0071] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0072] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0073] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0074] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A QHSE management method based on cloud computing, characterized in that: include: Obtaining different types of management data processing result information, and performing cloud computing feature analysis based on information repetition rate to form type management information feature data; Acquire real-time management processing information, and perform processing management analysis in combination with the characteristic data of the type management information to form real-time processing management requirement data; According to the real-time processing management requirement data, monitoring the processing process of the real-time management processing information, and forming real-time processing monitoring information; The real-time management processing information is obtained, and combined with the characteristic data of the type management information, the processing management analysis is performed to form the real-time processing management requirement data, including: Extracting real-time task features from the real-time management processing information to form real-time task description feature information; According to the real-time task description feature information, the following matching analysis is performed with the task description feature information of the corresponding type: If there is a match between the task description feature information and the real-time task description feature information, then: When the task description feature information is overlapping processing information of the same task, the level corresponding to the matching task description feature information is assigned to the real-time management processing information, and the processing time range of each level of the level task corresponding to the matching task description feature information is used as the required range for the real-time management processing information to perform processing at each level; When the task description feature information is independent task overlap processing information, the hierarchical processing limit duration corresponding to the matched task description feature information is used as the required duration for each level of processing of the real-time management processing information; Obtain different types of management data processing result information, and perform cloud computing feature analysis based on information repetition rate to form type management information feature data, including: For different types of management data processing result information, extract each management data processing task information and corresponding processing time information at each level; Conducting overlap analysis of the same task information in the same type of management data processing result information to form different task feature information; Gathering all the task characteristic information corresponding to the management data processing result information of different types to form the type management information characteristic data; In the same type of management data processing result information, the overlap analysis of the same task information is performed to form different task feature information, including: Extracting task features from each management data processing task information in the management data processing result information of the same type to form corresponding task description feature information; Merging the corresponding processing time information of each level for the same task description feature information in the same type of management data processing result information to form the same task overlap processing information; Marking the processing time information at each level corresponding to the different task description feature information that have not been merged in the same type of management data processing result information as independent task overlap processing information; Processing feature extraction is performed on the same task overlap processing information and the independent task overlap processing information in the same type of management data processing result information respectively to form corresponding task processing feature information.

2. The QHSE management method based on cloud computing according to claim 1, characterized in that: The extracting processing features of the same task overlap processing information and the independent task overlap processing information in the same type of management data processing result information to form corresponding task processing feature information includes: For the overlapping processing information of different identical tasks, processing feature extraction based on processing frequency is performed to form corresponding level task processing feature information; For the overlapping processing information of different independent tasks, extract processing features based on processing duration to form corresponding guiding task processing feature information; All of the level task processing characteristic information and the guide task processing characteristic information are collected to form the task processing characteristic information.

3. The QHSE management method based on cloud computing according to claim 2 is characterized in that: The processing feature extraction based on the processing frequency is performed on the overlapping processing information of different identical tasks to form corresponding level task processing feature information, including: Determine the total number of processing of the same task overlap processing information in the same type of management data processing result information, and arrange them in descending order according to the total number of processing, to form an ordered set of the same task overlap processing information; Setting different levels of processing total number limit ranges, and classifying the different identical task coincidence processing information in the ordered identical task coincidence processing information set according to the processing total number limit ranges to form an identical task coincidence processing information set; For the overlapping processing information sets of the same tasks at different levels, the processing time ranges of the processing at each level are determined according to the corresponding processing time information of each level, so as to form the processing time ranges of each level of the level task.

4. The QHSE management method based on cloud computing according to claim 3 is characterized in that: The processing feature extraction based on the processing time of the overlapping processing information of different independent tasks to form corresponding guiding task processing feature information includes: In the same type of the management data processing result information, for the overlapping processing information of different independent tasks, the hierarchical processing duration values ​​of the processing at each level are determined according to the corresponding processing duration information at each level; The different hierarchical processing time values ​​of the independent task overlap processing information are calibrated as the hierarchical processing limit time of the corresponding processing level.

5. The QHSE management method based on cloud computing according to claim 4 is characterized in that: The step of monitoring the processing process of the real-time management processing information according to the real-time processing management requirement data and forming real-time processing monitoring information includes: If the real-time processing management requirement data is the processing time range of each level of the level task corresponding to the real-time management processing information, then: When the processing time at any processing level does not fall within the processing time range of the corresponding level, a processing abnormality warning message is generated and the real-time processing time of the corresponding level is provided; When there is no processing time at any processing level that does not fall within the processing time range of the corresponding level, normal processing information is generated and the real-time processing time of the corresponding level is provided.

6. The QHSE management method based on cloud computing according to claim 5 is characterized in that: The step of monitoring the processing process of the real-time management processing information according to the real-time processing management requirement data and forming real-time processing monitoring information includes: If the real-time processing management requirement data is the hierarchical processing limit time corresponding to the real-time management processing information, then: When the processing time at any processing level exceeds the hierarchical processing limit time of the corresponding level, a processing abnormality warning message is generated, and the real-time processing time of the corresponding level is provided; When there is no processing time at any processing level that exceeds the hierarchical processing limit time of the corresponding level, normal processing information is generated and the real-time processing time of the corresponding level is provided.

7. A QHSE management system based on cloud computing, adopting the QHSE management method based on cloud computing according to any one of claims 1 to 6, characterized in that: The cloud computing-based QHSE management system is configured to: Obtaining different types of management data processing result information, and performing cloud computing feature analysis based on information repetition rate to form type management information feature data; Acquire real-time management processing information, and perform processing management analysis in combination with the characteristic data of the type management information to form real-time processing management requirement data; According to the real-time processing management requirement data, the processing process of the real-time management processing information is monitored, and real-time processing monitoring information is formed.

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