CN117593166A - Worker safety production behavior management system - Google Patents
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Abstract
The invention discloses a worker safety production behavior management system, in particular to the field of safety management, which is characterized in that trend similarity is obtained by analyzing trend correlation of environmental factors and working characteristics of an alarm device by using a trend line fitting technology, and the trend similarity is compared with a preset classification threshold value, so that a state signal is generated, and the state signal is used for knowing the working state of the alarm device in advance, ensuring stable operation of the alarm device and enhancing the safety of workers; and then, extracting key features from the monitoring data according to different state signals, evaluating the importance of the unit monitoring data, generating a dynamic compression rate according to the importance of the unit monitoring data, and further dynamically compressing and sequencing the key monitoring data, thereby ensuring the reservation of the key monitoring data and timely processing key problems while saving storage resources, improving the quick response capability to the safety of operators, and enhancing the safety of the operators according to the management efficiency of the alarm device.
Description
Technical Field
The invention relates to the field of safety management, in particular to a worker safety production behavior management system.
Background
With the continuous development of technology, professional alarm devices have become powerful tools for supervising and improving the production safety of operators. The device can timely reflect the behavior harm of operators, timely report dangerous information, and is helpful for strengthening behavior management and safety operation standards. However, existing alarm devices also present challenges in practical use.
Firstly, due to the influence of the environment of use and its own factors, the alarm device may gradually change, which may not be sufficient to cause a malfunction, but gradually reduce its alarm effect, while accelerating the ageing speed of the device.
Second, the storage facilities have limited capacity, and existing alarm devices typically delete historical data in chronological order, but some monitored data may not be considered important until the deletion period has arrived. The inability to dynamically compress and save based on the importance of the monitored data results in some significant historical data that cannot be traced back.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide for the purpose of assessing the stability and overall health of an alarm device by analyzing the trend correlation of environmental factors and the operating characteristics of the alarm device using a trend line fitting technique. The process comprises the steps of firstly calculating the similarity degree of the trend, comparing the similarity degree with a preset classification threshold value, and generating a state signal which is used for knowing the working state of the alarm device in advance, ensuring the stable operation of the alarm device and enhancing the safety of operators; and then, extracting key features from the monitoring data according to different state signals, and evaluating the importance of the unit monitoring data, generating a dynamic compression rate according to the importance of the unit monitoring data, and further dynamically compressing and sequencing the key monitoring data.
In order to achieve the above purpose, the present invention provides the following technical solutions: comprising the following steps: the system comprises a self-checking fitting module, an analysis judging module, an alarm analysis module and a data storage module;
the self-checking fitting module is used for listing various environmental factors and working characteristics, analyzing and obtaining trend similarity by using a trend line fitting technology and an autocorrelation function, and transmitting the trend similarity to the analysis judging module;
the analysis and judgment module divides the alarm device into a primary state signal, a secondary state signal or a secondary state signal according to the stability degree signal, and sends the primary state signal and the secondary state signal to the alarm analysis module;
the alarm analysis module is used for extracting key features in the monitoring data, constructing a compression ratio model, generating the importance of unit monitoring data and sending the importance of the unit monitoring data to the data storage module;
and the data storage module performs corresponding compression storage on the monitoring data according to the importance degree and the trend similarity degree of the unit monitoring data.
In a preferred embodiment, the self-test fitting module operates as follows:
listing all environmental factors in the working environment of the operator, and processing all environmental factors according to a trend line fitting technology to obtain a factor graph;
listing all working characteristics of the alarm device, and processing all working characteristics according to a trend line fitting technology to obtain a characteristic curve graph;
listing each pair of environmental factors and operating characteristics using permutation and combination;
calculating an autocorrelation coefficient between each pair of environmental factors and the working characteristics;
and comparing the absolute value of the autocorrelation coefficient with a classification threshold, recording corresponding environmental factors and working characteristics if the absolute value of the autocorrelation coefficient is greater than or equal to the classification threshold, and marking the environmental factors and the working characteristics as environmental influence factors and working hidden danger characteristics respectively.
In a preferred embodiment, each pair of environmental impact factors and potential operating hazard characteristics is ranked according to the absolute value of the autocorrelation coefficients from large to small, the first pair of environmental impact factors and potential operating hazard characteristics is used for ranking, multiple autocorrelation coefficients are recorded in unit time, regression analysis is used, a time sequence is used as an independent variable, the absolute value of the autocorrelation coefficients is used as a dependent variable, a linear regression model is built based on the absolute values of the time and the autocorrelation coefficients, a regression model is fitted by a least square method, and parameters of the model are estimated, wherein the parameters comprise intercept and slope, slope is the degree of similarity of trends, and the intercept represents the change rate of the trends.
In a preferred embodiment, the analytical judgment module operates as follows:
the trend similarity degree and the similarity threshold value are compared respectively, and the specific process is as follows:
if the trend similarity is smaller than the second similarity threshold and larger than or equal to the first similarity threshold, generating a second-level state signal; if the trend similarity is smaller than the similarity threshold value I, generating a first-stage state signal; if the trend similarity is greater than the similarity threshold II, a three-level state signal is generated, and an alarm prompt is sent out.
In a preferred embodiment, the alarm analysis module operates as follows:
under the condition of obtaining a primary or secondary state signal, extracting key features in monitoring data, wherein the key features comprise alarm time points and duration, counting the number of the alarm time points in a unit, calculating to obtain unit alarm frequency, counting and accumulating the duration in unit time, and dividing the duration by the unit time to obtain a duration occupation ratio; and obtaining the importance of the unit monitoring data by carrying out weighted summation on the unit alarm frequency and the duration time occupation ratio.
In a preferred embodiment, the data storage module operates as follows:
comparing the importance degree of the unit monitoring data with an important distinguishing threshold value, and generating a low compression ratio signal when the importance degree of the unit monitoring data is larger than or equal to the important distinguishing threshold value;
after a low compression ratio signal is obtained, marking a starting time stamp and accumulating time from the first alarm sending time in unit time until the importance is smaller than a threshold value, marking an ending time stamp, recording the time between the starting time stamp and the ending time stamp as monitoring data to be saved, obtaining a dynamic compression rate according to the importance and the trend similarity degree of the unit monitoring data, compressing the monitoring data to be saved by using the dynamic compression rate, sorting the monitoring data to be saved according to the dynamic compression rate from small to large, and generating a sorting table;
and if the low compression ratio signal is not obtained, compressing the monitoring data to be stored according to the initial compression ratio.
The technical effects and advantages of the safety production behavior management system for the operators are as follows:
1. according to the invention, the stability and health condition of the alarm device are evaluated by analyzing the trend correlation between the environmental factors and the working characteristics of the alarm device. In the process, trend line fitting is firstly carried out on environmental factors and working characteristics, the trend similarity degree is obtained through calculation, and the stability of the use state of the alarm device is analyzed by evaluating the degree of the influence of the environment according to the trend similarity degree. The trend similarity is compared with a preset classification threshold value, state signals of different levels are obtained according to different threshold value settings, the state signals are used for representing the working state of the alarm device, the state of the alarm device is known in time, problems are found in advance, maintenance or replacement measures are taken, and therefore the alarm device can work stably and reliably, and the safety of analysis operators is enhanced.
2. Under the condition of obtaining a primary or secondary state signal, key features in alarm data are extracted to determine the importance of unit monitoring data, and the importance is compared with an important distinguishing threshold value, so that a low compression ratio signal is generated. When the low compression ratio signal is obtained, the monitoring data to be saved is determined according to the accumulation of the time stamp, and the monitoring data to be saved is dynamically compressed by applying the dynamic compression ratio so as to more effectively compress and save the monitoring data. This helps save storage resources while ensuring that critical monitoring data is preserved so that important data for the worker's safe production activities are not lost. In addition, by effectively compressing and ordering data, the data management efficiency is improved, and the burden of the system is reduced. Generating a low compression ratio signal is helpful for identifying and processing key problems in time, and improves the quick response capability to the safety of operators.
Drawings
Fig. 1 is a schematic structural diagram of an operator safety production behavior management system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
FIG. 1 shows a worker safety production behavior management system of the present invention, comprising: the system comprises a self-checking fitting module, an analysis judging module, an alarm analysis module and a data storage module;
the self-checking fitting module is used for listing various environmental factors and working characteristics, analyzing and obtaining trend similarity by using a trend line fitting technology and an autocorrelation function, and transmitting the trend similarity to the analysis judging module;
the analysis and judgment module divides the alarm device into a primary state signal, a secondary state signal or a secondary state signal according to the stability degree signal, and sends the primary state signal and the secondary state signal to the alarm analysis module;
the alarm analysis module is used for extracting key features in the monitoring data, constructing a compression ratio model, generating the importance of unit monitoring data and sending the importance of the unit monitoring data to the data storage module;
and the data storage module performs corresponding compression storage on the monitoring data according to the importance degree and the trend similarity degree of the unit monitoring data.
The self-checking fitting module operates as follows:
in practical application, the stability of the alarm device is important for timely and accurately giving an alarm. The normal and stable alarm device should be such that the characteristics are not or less affected by the environment, and if environmental factors have a significant impact on the operating characteristics, these factors may threaten the stability of the alarm device, resulting in an increased risk of false or false negatives. Therefore, it is reasonable to analyze the correlation of the operating characteristics of the alarm device with the environmental factors to ensure that the alarm device operates stably and reliably. By analyzing the correlation between the working characteristics and the environmental factors, the factors potentially influencing the stability of the alarm device can be identified, and a basis is provided for taking maintenance and preventive measures. Furthermore, understanding these correlations can also be used for fault diagnosis, performance optimization and predictive maintenance, helping to improve the stability of the alarm device to meet safety and production requirements.
Listing all environmental factors in the working environment of the operator, and processing all environmental factors according to a trend line fitting technology to obtain a factor graph;
listing all working characteristics of the alarm device, and processing all working characteristics according to a trend line fitting technology to obtain a characteristic curve graph;
the working characteristics are some characteristics of the health condition of the alarm device, such as sensor state, signal processing efficiency, response time and the like.
Listing each pair of environmental factors and operating characteristics using permutation and combination;
calculating an autocorrelation coefficient between each pair of environmental factors and the working characteristics;
comparing the absolute value of the autocorrelation coefficient with a classification threshold, recording corresponding environmental factors and working characteristics if the absolute value of the autocorrelation coefficient is greater than or equal to the classification threshold, and marking the environmental factors and the working characteristics as environmental influence factors and working hidden danger characteristics respectively;
sequencing each pair of environmental influence factors and potential work hazard characteristics according to the absolute value of the autocorrelation coefficients from large to small, and recording multiple autocorrelation coefficients in unit time by using the first pair of environmental influence factors and potential work hazard characteristics in sequence, wherein the time of each recording is continuous and orderly recorded according to time;
using regression analysis, taking a time sequence as an independent variable and an absolute value of an autocorrelation coefficient as a dependent variable, establishing a linear regression model based on the absolute values of time and the autocorrelation coefficient, fitting the regression model by using a least square method, and estimating parameters of the model, wherein the parameters comprise intercept and slope, the slope is the similarity degree of the trend, and the intercept represents the change rate of the trend.
The trend similarity is used to represent the trend correlation between environmental factors and operating characteristics, i.e., whether their trends are similar. The magnitude of the degree of trend similarity may represent the following: if the trend similarity is large, the change trends of the environmental factors and the working characteristics are related to each other to a certain extent, and have similar trend directions, which indicates that the environmental factors have a significant influence on the working characteristics, and the influence is consistent in time, and the factors threaten the stability of the alarm device; if the trend similarity is smaller, the correlation between the environmental factors and the change trend of the working characteristics is weaker, the alarm device is not easily influenced by the environment, and the alarm device is in a stable and controllable state which is not interfered by the external environment. This indicates that the environmental factors have less pronounced effects on the operating characteristics or that such effects are not stable in time.
Specifically, the magnitude of the degree of trend similarity reflects the degree of correlation between the environmental factors and the operating characteristics. A larger degree of trend similarity indicates a stronger correlation between them, and a smaller degree of trend similarity indicates a weaker correlation. It is helpful to understand how environmental factors affect the operating characteristics of the alarm device and can provide important information for further analysis, prediction and control.
The operation process of the analysis and judgment module is as follows:
the hidden danger degree of the clear alarm device directly relates to production safety and the safety of operators. Through knowing hidden danger degree, can help discovering and solve potential problem and trouble in advance, reduce accident risk, reduce production interruption, improve equipment's reliability, increase the security of operating personnel simultaneously. This helps to maintain the safety and stability of the production environment, ensure the physical health of the staff and reduce potential economic losses.
The trend similarity degree and the similarity threshold value are compared respectively, and the specific process is as follows:
if the trend similarity is smaller than the similarity threshold value II and is larger than or equal to the similarity threshold value I: the trend correlation between the environmental factors and the operating characteristics is represented to be moderate. The trends are not very similar but there is still some degree of correlation meaning that the environment affects the alarm means to some extent, but the effect is not very significant, generating a secondary status signal;
if the trend similarity is smaller than the similarity threshold value of one: the trend correlation between the environmental factors and the working characteristics is very weak or close to zero, the trend has almost no obvious correlation, the influence of the environment on the alarm device is very small, the alarm device is in a stable and controllable state, and a first-level state signal is generated;
if the trend similarity is greater than the similarity threshold two: the trend correlation between the environmental factors and the operating characteristics is relatively strong. The trends are related to a great extent, which shows that the environment has obvious influence on the alarm device, and the influence is stable and obvious in time, so that the state of the alarm device is easily controlled by the environment and is in a range of unstability and uncontrollable, a three-level state signal is generated, an alarm prompt is sent, replacement or maintenance is needed, and the behavior of monitoring and warning operators is not satisfied.
According to the invention, the stability and health condition of the alarm device are evaluated by analyzing the trend correlation between the environmental factors and the working characteristics of the alarm device. In the process, trend line fitting is firstly carried out on environmental factors and working characteristics, the trend similarity degree is obtained through calculation, and the stability of the use state of the alarm device is analyzed by evaluating the degree of the influence of the environment according to the trend similarity degree. The trend similarity is compared with a preset classification threshold value, state signals of different levels are obtained according to different threshold value settings, the state signals are used for representing the working state of the alarm device, the state of the alarm device is known in time, problems are found in advance, maintenance or replacement measures are taken, and therefore the alarm device can work stably and reliably, and the safety of analysis operators is enhanced.
The alarm analysis module operates as follows:
under the condition of obtaining a primary or secondary state signal, extracting key features in monitoring data, wherein the key features comprise alarm time points and duration, counting the number of the alarm time points in a unit, calculating to obtain unit alarm frequency, counting and accumulating the duration in unit time, and dividing the duration by the unit time to obtain a duration occupation ratio; the importance of the unit monitoring data is obtained by carrying out weighted summation on the unit alarming frequency and the duration occupying ratio, for example, the importance of the unit monitoring data can be obtained by calculating the following formula: z=α·pc+β·cx;
wherein Z represents the importance of unit monitoring data, pc and cx are respectively the unit alarm frequency and duration ratio, and alpha and beta are respectively the preset proportionality coefficients of the unit alarm frequency and duration ratio, and are both larger than 0.
The process of obtaining the importance of unit monitoring data aims at quantifying the alarm signal of the alarm device to evaluate the relative importance of the monitoring data. This is critical to alarm device management and monitoring. The importance of unit monitoring data can be obtained by carrying out weighted summation on the alarm frequency and the duration time proportion in unit time, and the method has the following functions: optimizing resource allocation, risk management, maintenance and repair optimization, and improving alarm accuracy. In summary, the acquisition of unit monitoring data importance helps to optimize resource management, reduce risk, improve reliability of the device, and improve efficiency and accuracy of the alarm system, thereby enhancing the ability of production and safety management.
The data storage module operates as follows:
and comparing the importance degree of the unit monitoring data with an importance distinguishing threshold value, and generating a low compression ratio signal when the importance degree of the unit monitoring data is larger than or equal to the importance distinguishing threshold value.
After the low compression ratio signal is obtained, marking a starting time stamp and accumulating time from the first alarm sending time in unit time until the importance is smaller than a threshold value, marking an ending time stamp, recording the time between the starting time stamp and the ending time stamp as monitoring data to be saved, obtaining a dynamic compression rate according to the importance and the trend similarity degree of the unit monitoring data, compressing the monitoring data to be saved by using the dynamic compression rate, sorting the monitoring data to be saved according to the dynamic compression rate from small to large, and generating a sorting table, wherein the dynamic compression rate can be calculated by the following formula:
where T represents an initial compression rate, H represents an importance discrimination threshold, Z represents unit monitor data importance, XS1, XS2 and q s The similarity threshold value is a first similarity threshold value, a second similarity threshold value and a trend similarity degree respectively,representing the correction factor.
The dynamic compression rate is used for dynamically adjusting the compression rate of the monitoring data to be stored, so that the compression of the monitoring data with higher importance of the unit monitoring data is smaller, the lost details are smaller, and the later retrospective searching of more details is facilitated.
For the case of generating the secondary status signal, the trend similarity is introduced for the purpose of performing a feasibility adjustment on the importance of the unit monitoring data obtained based on the monitoring status of the alarm device. This helps to eliminate potential interference due to the alarm device itself so that even an alarm device in a poor operating condition does not affect the alarm requirements, thereby improving the accuracy and reliability of the monitored data.
And if the low compression ratio signal is not obtained, compressing the monitoring data to be stored according to the initial compression ratio.
Under the condition of obtaining a primary or secondary state signal, key features in alarm data are extracted to determine the importance of unit monitoring data, and the importance is compared with an important distinguishing threshold value, so that a low compression ratio signal is generated. When the low compression ratio signal is obtained, the monitoring data to be saved is determined according to the accumulation of the time stamp, and the monitoring data to be saved is dynamically compressed by applying the dynamic compression ratio so as to more effectively compress and save the monitoring data. This helps save storage resources while ensuring that critical monitoring data is preserved so that important data for the worker's safe production activities are not lost. In addition, by effectively compressing and ordering data, the data management efficiency is improved, and the burden of the system is reduced. Generating a low compression ratio signal is helpful for identifying and processing key problems in time, and improves the quick response capability to the safety of operators.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may 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 may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed systems and apparatuses may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A worker-safe production behavior management system, comprising: the system comprises a self-checking fitting module, an analysis judging module, an alarm analysis module and a data storage module;
the self-checking fitting module is used for listing various environmental factors and working characteristics, analyzing and obtaining trend similarity by using a trend line fitting technology and an autocorrelation function, and transmitting the trend similarity to the analysis judging module;
the analysis and judgment module divides the alarm device into a primary state signal, a secondary state signal or a secondary state signal according to the stability degree signal, and sends the primary state signal and the secondary state signal to the alarm analysis module;
the alarm analysis module is used for extracting key features in the monitoring data, constructing a compression ratio model, generating the importance of unit monitoring data and sending the importance of the unit monitoring data to the data storage module;
and the data storage module performs corresponding compression storage on the monitoring data according to the importance degree and the trend similarity degree of the unit monitoring data.
2. A worker-safe production behavior management system according to claim 1, wherein:
the self-checking fitting module operates as follows:
listing all environmental factors in the working environment of the operator, and processing all environmental factors according to a trend line fitting technology to obtain a factor graph;
listing all working characteristics of the alarm device, and processing all working characteristics according to a trend line fitting technology to obtain a characteristic curve graph;
listing each pair of environmental factors and operating characteristics using permutation and combination;
calculating an autocorrelation coefficient between each pair of environmental factors and the working characteristics;
and comparing the absolute value of the autocorrelation coefficient with a classification threshold, recording corresponding environmental factors and working characteristics if the absolute value of the autocorrelation coefficient is greater than or equal to the classification threshold, and marking the environmental factors and the working characteristics as environmental influence factors and working hidden danger characteristics respectively.
3. A worker-safe production behavior management system according to claim 2, wherein:
sequencing each pair of environmental influence factors and potential working hazard characteristics according to the absolute value of the autocorrelation coefficient from large to small, recording multiple autocorrelation coefficients in unit time by using a first pair of environmental influence factors and potential working hazard characteristics, using regression analysis, taking a time sequence as an independent variable and the absolute value of the autocorrelation coefficient as a dependent variable, establishing a linear regression model based on the absolute values of the time and the autocorrelation coefficient, fitting the regression model by using a least square method, and estimating parameters of the model, wherein the parameters comprise intercept and slope, namely the similarity degree of trend, and the intercept represents the change rate of the trend.
4. A worker-safe production behavior management system according to claim 3, wherein:
the operation process of the analysis and judgment module is as follows:
the trend similarity degree and the similarity threshold value are compared respectively, and the specific process is as follows:
if the trend similarity is smaller than the second similarity threshold and larger than or equal to the first similarity threshold, generating a second-level state signal; if the trend similarity is smaller than the similarity threshold value I, generating a first-stage state signal; if the trend similarity is greater than the similarity threshold II, a three-level state signal is generated, and an alarm prompt is sent out.
5. The worker-safe production behavior management system according to claim 4, wherein:
the alarm analysis module operates as follows:
under the condition of obtaining a primary or secondary state signal, extracting key features in monitoring data, wherein the key features comprise alarm time points and duration, counting the number of the alarm time points in a unit, calculating to obtain unit alarm frequency, counting and accumulating the duration in unit time, and dividing the duration by the unit time to obtain a duration occupation ratio; and obtaining the importance of the unit monitoring data by carrying out weighted summation on the unit alarm frequency and the duration time occupation ratio.
6. The worker-safe production behavior management system according to claim 5, wherein:
the data storage module operates as follows:
comparing the importance degree of the unit monitoring data with an important distinguishing threshold value, and generating a low compression ratio signal when the importance degree of the unit monitoring data is larger than or equal to the important distinguishing threshold value;
after a low compression ratio signal is obtained, marking a starting time stamp and accumulating time from the first alarm sending time in unit time until the importance is smaller than a threshold value, marking an ending time stamp, recording the time between the starting time stamp and the ending time stamp as monitoring data to be saved, obtaining a dynamic compression rate according to the importance and the trend similarity degree of the unit monitoring data, compressing the monitoring data to be saved by using the dynamic compression rate, sorting the monitoring data to be saved according to the dynamic compression rate from small to large, and generating a sorting table;
and if the low compression ratio signal is not obtained, compressing the monitoring data to be stored according to the initial compression ratio.
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