CN114486656B - Dynamic environment monitoring system for medical clean room - Google Patents
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Abstract
The invention relates to a particle concentration detection technology, in particular to a dynamic environment monitoring system for a medical clean room. The system comprises: the parameter acquisition module is used for acquiring a concentration parameter sequence and an environment parameter sequence at a plurality of sampling points; the stability analysis module is used for respectively carrying out sequence fluctuation analysis on the sequences to obtain corresponding sequence fluctuation, and simultaneously obtaining the instantaneous fluctuation of the tail moment in each sequence so as to obtain the stability of each type of parameter of each sampling point; the simulated environment parameter acquisition module is used for acquiring simulated environment parameters of each unit space; the analog concentration parameter acquisition module is used for calculating the correlation between the concentration parameter at each sampling point and the environmental parameter so as to acquire the analog concentration parameter of each unit space; the early warning module is used for judging the parameter state of each unit space. The embodiment of the invention can judge the environment standard-reaching condition of each area in the clean room through data visualization, and can build special instruments and meters for environment monitoring according to the system.
Description
Technical Field
The invention relates to a particle concentration detection technology, in particular to a dynamic environment monitoring system for a medical clean room.
Background
The clean room is a room with controlled concentration of air suspended particles, and is a space with better tightness, which can control parameters such as air cleanliness, temperature, humidity, pressure difference, noise and the like according to requirements. The clean room is divided into an industrial clean room and a biological clean room, the biological clean room focuses on the quantity of suspended particles and planktonic bacteria in the air, and needs to be strictly controlled, particularly, the clean room for aseptic pharmacy needs to be monitored dynamically in real time, and is provided with a real-time particle counter and a planktonic bacteria sampling instrument.
In a clean medical area, dynamic monitoring is implemented to timely find problems existing in the clean medical area, effective measures are taken to solve the problems, adverse effects are prevented from further expanding, meanwhile, the method provides basis for further improvement of air balance, personnel behaviors and room disinfection methods, and is the primary condition for release of sterile medicines.
Therefore, it is important for pharmaceutical production to determine whether a clean room has reached a prescribed cleanliness level through dynamic monitoring of the environment.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a dynamic environmental monitoring system for a medical clean room, which adopts the following technical solutions:
one embodiment of the present invention provides a dynamic environmental monitoring system for a medical cleanroom, the system comprising the following modules:
the parameter acquisition module is used for acquiring at least one type of suspended particle concentration parameters and at least one type of environmental parameters at a plurality of sampling points of the clean room within preset time to form a plurality of concentration parameter sequences and environmental parameter sequences;
the stability analysis module is used for respectively carrying out sequence fluctuation analysis on the concentration parameter sequence and the environment parameter sequence to obtain corresponding sequence fluctuation, simultaneously obtaining the instantaneous fluctuation of the tail moment in each sequence, and obtaining the stability of each type of parameter of each sampling point according to the sequence fluctuation and the instantaneous fluctuation;
the simulated environment parameter acquisition module is used for voxelizing the clean room space to form a plurality of unit spaces and acquiring the simulated environment parameter of each unit space according to the first distance from the unit space to the sampling point, the environment parameter of the sampling point and the stability of the environment parameter;
the simulated concentration parameter acquisition module is used for calculating the correlation between the concentration parameter at each sampling point and the environmental parameter and acquiring the simulated concentration parameter of each unit space according to the first distance, the stability of the concentration parameter and the correlation;
and the early warning module is used for visualizing the simulated environment parameters and the simulated concentration parameter space, respectively comparing the visualized simulated environment parameters and the simulated concentration parameter space with corresponding early warning threshold values, and judging the parameter state of each unit space.
Preferably, the stability analysis module includes:
and the sequence fluctuation analysis unit is used for calculating the singular index and the multi-fractal spectrum of the sequence by using a multi-fractal detrending fluctuation analysis method, and taking the difference value of the multi-fractal spectrum corresponding to the maximum singular index and the multi-fractal spectrum corresponding to the minimum singular index as the sequence fluctuation.
Preferably, the stability analysis module further comprises:
and the instantaneous fluctuation analysis unit is used for acquiring a preset number of tail elements at the tail of the sequence, acquiring a differential sequence of the tail elements, and taking the average value of all values in the differential sequence as the instantaneous fluctuation.
Preferably, the simulated environment parameter acquiring module includes:
and the simulated environment parameter calculation unit is used for acquiring a first weight of each type of environment parameter of each sampling point in a unit space, taking the first weight as the weight of the corresponding environment parameter, and carrying out weighted summation on all the similar environment parameters to obtain the simulated environment parameter.
Preferably, the simulated environment parameter acquiring module further includes:
the first weight obtaining unit is used for obtaining a first weight of each sampling point to each type of environment parameters of a unit space according to the first distance and the stability of the environment parameters, and the first distance and the stability of the environment parameters are in a negative correlation relation with the first weight.
Preferably, the analog concentration parameter obtaining module includes:
and the correlation calculation unit is used for reducing all the environmental parameters into a one-dimensional sequence for each type of concentration parameter at each sampling point, and calculating a correlation coefficient of the one-dimensional sequence and the concentration parameter sequence to be used as the correlation.
Preferably, the analog concentration parameter obtaining module further includes:
and the analog concentration parameter calculation unit is used for acquiring a second weight of each type of concentration parameter of each sampling point in a unit space, taking the second weight as the weight of the corresponding concentration parameter, and carrying out weighted summation on all the similar concentration parameters to obtain the analog concentration parameter.
Preferably, the analog concentration parameter obtaining module further includes:
a second weight obtaining unit, configured to obtain a second weight of each type of concentration parameter of the unit space for each sampling point by using the correlation, the first distance, and the stability of the concentration parameter, where the correlation and the second weight have a positive correlation.
Preferably, the early warning module includes:
and the real-time scoring acquisition unit is used for presetting a limit threshold corresponding to each parameter, accumulating the scores of the parameters when the parameters are greater than the limit threshold at continuous moments, and subtracting the attenuation scores to obtain real-time scores.
Preferably, the early warning module further comprises:
and the early warning comparison unit is used for exceeding the corresponding parameters of the unit space when the real-time score is greater than the early warning threshold value, and visualizing the exceeding result space.
The embodiment of the invention at least has the following beneficial effects:
1. based on the stability of each sampling point to each type of parameter of each unit space and obtain its weight, and then obtain simulation parameter, obtain accurate environment three-dimensional data, do benefit to the visualization of data, can judge the environment standard reaching situation of every space region in medical treatment clean room simultaneously, can also be according to this system construction environment monitoring special instrument and meter.
2. Real-time scores are obtained through accumulated scores and attenuation scores at multiple moments and are compared with an early warning threshold value, whether the parameters of each unit space reach the standard or not can be accurately judged, and false detection can be avoided to the greatest extent by considering the change of time sequences.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a system block diagram of a dynamic environmental monitoring system for a medical cleanroom according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the dynamic environmental monitoring system for medical cleanrooms according to the present invention, its specific implementation, structure, features and effects will be given with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific embodiment of the dynamic environment monitoring system for a medical clean room provided by the present invention with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a system for a dynamic environmental monitoring system for a medical cleanroom according to an embodiment of the present invention is shown, the system including the following modules: the system comprises a parameter acquisition module 100, a stability analysis module 200, a simulated environment parameter acquisition module 300, a simulated concentration parameter acquisition module 400 and an early warning module 500.
The parameter acquisition module 100 is configured to acquire at least one type of suspended particle concentration parameter and at least one type of environmental parameter at a plurality of sampling points of the clean room at preset times to form a plurality of concentration parameter sequences and environmental parameter sequences.
Specifically, the parameter acquisition module 100 includes: a suspended particle concentration parameter acquisition unit 110, an environmental parameter acquisition unit 120, and a sequence acquisition unit 130.
The suspended particle concentration parameter collecting unit 110 is configured to collect suspended particle concentration parameters of each sampling point in the clean room, in the embodiment of the present invention, the suspended particles include dust particles and floating colonies, and the dust particle number and the floating colony number in the production process in the clean room are continuously monitored in real time for 24 hours by using a particle counter and a floating colony sampling instrument, so as to obtain the dust particle concentration and the floating colony concentration.
The environment parameter collecting unit 120 is configured to collect environment parameters of each sampling point in the clean room, in an embodiment of the present invention, the environment parameters include values of wind speed, temperature, humidity, and pressure difference, and the wind speed, the temperature, the humidity, and the pressure difference are monitored by the wind speed sensor, the temperature and humidity sensor, and the pressure difference sensor, respectively.
The monitoring of each parameter requires sensor deployment at a plurality of sampling points to acquire the parameter at the plurality of sampling points. The number of sampling points and the arrangement thereof are determined according to the area and the cleanliness level of the root region. The sampling points are generally evenly arranged on a horizontal plane at a height of 0.8m from the ground. When the sampling points are more than 5 points, the sampling points are arranged in a layering way in an area with the height of 0.8-1.5m from the ground, but each layer is not less than 5 points.
The embodiment of the invention adopts a CRMS ultra-clean room dynamic monitoring system to acquire various parameters.
The sequence acquiring unit 130 is configured to acquire a parameter sequence of each type of parameter of each sampling point within a preset time. For the sensor at each sampling point, a real-time monitoring sequence is obtained, taking the number concentration monitoring of dust particles as an example, the data refreshing frequency of the sensor is 1s, namely, the monitoring is carried out once per second, the sampling flow is 2.83L/min, and the data can be obtained more than or equal to
The number concentration (pcs/L) of the particles with the diameter can obtain the dust particle concentration sequence within the diameter range within preset time.
As an example, the preset time in the embodiment of the present invention is 30 seconds.
And the stability analysis module 200 is configured to perform sequence fluctuation analysis on the concentration parameter sequence and the environmental parameter sequence, respectively, to obtain corresponding sequence fluctuation, obtain instantaneous fluctuation at a last time in each sequence, and obtain stability of each type of parameter of each sampling point according to the sequence fluctuation and the instantaneous fluctuation.
For dynamic monitoring, the monitoring of the number concentration of dust particles and the number concentration of floating colonies is easily influenced by the environment to cause great change of errors, and the sensor may have errors to cause false alarm, so the invention performs fluctuation analysis on the dynamic sequence.
Specifically, the stability analysis module 200 includes: a sequence fluctuation analysis unit 210, a transient fluctuation analysis unit 220, and a stability calculation unit 230.
And the sequence fluctuation analysis unit 210 is configured to calculate a singular index and a multi-fractal spectrum of the sequence by using a multi-fractal detrending fluctuation analysis method, and use a difference value between the multi-fractal spectrum corresponding to the maximum singular index and the multi-fractal spectrum corresponding to the minimum singular index as the sequence fluctuation.
The multi-fractal singular spectrums can finely describe the internal dynamic characteristics of the time sequence. Calculating singular index of sequence by utilizing multi-fractal detrending fluctuation analysis method
And multifractal Spectroscopy
And acquiring the multi-fractal dimension of the sequence. The multi-fractal spectrum is a sequence, and the dimension of the multi-fractal spectrum
The calculation is as follows:
wherein,
the maximum singular index is expressed as the maximum singular index,
representing the minimum singular index.
The larger the dimension of the multi-fractal spectrum is, the more severe the signal fluctuation is, and the stronger the multi-fractal characteristic is.
And an instantaneous fluctuation analysis unit 220, configured to obtain a preset number of end elements at the end of the sequence, obtain a differential sequence of the end elements, and take an average value of all values in the differential sequence as an instantaneous fluctuation.
A preset number N of end elements is obtained at the end of the sequence to characterize the temporal variations of the sequence. And carrying out difference calculation on the N values, namely subtracting the previous value from the next value adjacent to the previous value in the sequence, taking the absolute value of the previous value to obtain a difference sequence, and then solving the average value of all difference values in the difference sequence as the instantaneous fluctuation U.
As an example, the preset number in the embodiment of the present invention is 5.
The instantaneous fluctuation is a single value, namely, the performance of a certain time point is greatly different from that of the previous time period, the value can reflect the instantaneous sequence change, and the larger the value is, the larger the instantaneous change is.
And a stability calculating unit 230 for calculating the stability of each type of parameter of each sampling point.
Calculating the stability T of each type of parameter of each sampling point by using the sequence fluctuation and the instantaneous fluctuation:
the more the stability T approaches to 0, the more unstable the sequence, and the closer to 1, the more stable the sensor value is, and the more accurate the result is.
The simulated environment parameter acquiring module 300 is configured to voxelize the clean room space to form a plurality of unit spaces, and acquire the simulated environment parameter of each unit space according to a first distance from the unit space to the sampling point, the environment parameter of the sampling point, and the stability of the environment parameter.
Specifically, the simulated environment parameter obtaining module 300 includes: a first weight acquisition unit 310 and a simulated environment parameter calculation unit 320.
The first weight obtaining unit 310 is configured to obtain a first weight of each type of environment parameter of the unit space for each sampling point according to the first distance and the stability of the environment parameter, where the first distance and the stability of the environment parameter both have a negative correlation with the first weight.
The clean room space is voxelized, the whole space is uniformly divided into a plurality of voxels, each voxel is taken as a unit space, and preferably, each sampling point is located at the central position of the unit space.
As an example, an embodiment of the present invention divides the clean room space into 50 × 60 unit spaces.
For each unit space, the unit space center point is obtained
To the center of the unit space where each sampling point is located
All the first distances are subjected to range normalization, and dimensions are unified.
For each type of environment parameter corresponding to each unit space, calculating the influence weight of each sampling point on the unit space
：
Acquiring the normalized weight of each sampling point as a first weight:
wherein,
a first weight of the ith sampling point to a certain type of environment parameter corresponding to the unit space,
indicating the ith sampling point to the unit spaceThe influence weight of a corresponding certain type of environment parameter.
The simulated environment parameter calculating unit 320 is configured to obtain a first weight of each type of environment parameter of the unit space for each sampling point, take the first weight as a weight of a corresponding environment parameter, and perform weighted summation on all the similar environment parameters to obtain a simulated environment parameter.
The specific calculation formula is as follows:
wherein,
representing a simulated environmental parameter for a certain unit space,
representing the environmental parameter of the ith sample point.
The simulated environment parameters of each unit space can be acquired through data of historical preset time sequences at each moment.
And the simulated concentration parameter obtaining module 400 is configured to calculate a correlation between the concentration parameter at each sampling point and the environmental parameter, and obtain a simulated concentration parameter of each unit space according to the first distance, the stability of the concentration parameter, and the correlation.
Specifically, the analog concentration parameter obtaining module 400 includes:
and a correlation calculation unit 410, configured to, for each type of concentration parameter at each sampling point, reduce all the environmental parameters into a one-dimensional sequence, and calculate a correlation coefficient between the one-dimensional sequence and the concentration parameter sequence as a correlation.
The Canonical Correlation Analysis (CCA) is a multivariate statistical analysis method that reflects the overall correlation between two sets of indices using the correlation between pairs of synthetic variables. For the same sampling point, an environment parameter sequence is obtained, in the embodiment of the invention, 4 sequences of wind speed, temperature, humidity and pressure difference are marked as [ M,4], wherein M represents the sampling times in the preset time, and 4 represents the wind speed, the temperature, the humidity and the pressure difference respectively. The CCA is used for firstly reducing the dimension of the environment variable sequence to obtain a [ M,1] sequence, namely a sequence of a single numerical value with the length of M, wherein the numerical value is a value of four environment variable values after dimension reduction. And (3) obtaining the correlation coefficient of the reduced-dimension sequence and the concentration sequence by utilizing a Pearson correlation coefficient method, wherein the numerical value is between-1 and 1, the strong negative correlation is-1, the strong positive correlation is +1, and 0 represents no relation. Then the correlation Corr is obtained by taking the absolute value of the correlation coefficient, and the larger the Corr is, the more the particle concentration at the sampling point is influenced by the environmental parameters.
The second weight obtaining unit 420 is configured to obtain a second weight of each type of concentration parameter of the unit space for each sampling point by using the correlation, the first distance, and the stability of the concentration parameter, where the correlation and the second weight have a positive correlation.
Obtaining the influence weight of each sampling point on each type of concentration parameter of the unit space by using the correlation, the first distance and the stability of the concentration parameter
：
Normalizing it to obtain a second weight
。
The analog concentration parameter calculating unit 430 is configured to obtain a second weight of each type of concentration parameter of each sampling point in the unit space, use the second weight as a weight of a corresponding concentration parameter, and perform weighted summation on all the similar concentration parameters to obtain an analog concentration parameter.
The specific calculation formula is as follows:
wherein,
a parameter representing the simulated concentration of a certain unit space,
the concentration parameter of the ith sample point is shown.
And the analog concentration parameter of each unit space can be acquired through data of historical preset time sequences at each moment.
And the early warning module 500 is used for visualizing the simulated environment parameters and the simulated concentration parameter space, comparing the visualized simulated environment parameters and the simulated concentration parameter space with corresponding early warning threshold values respectively, and judging the parameter state of each unit space.
For a traditional environment monitoring system, whether particle pollution exists or not is judged only through a threshold or times of continuously exceeding the threshold, the former easily causes frequent false alarm, the latter is easily influenced by environment and sensor precision, and the times need manual debugging or are difficult to trigger conditions, so that manpower is wasted.
Specifically, the early warning module 500 includes:
and a real-time score obtaining unit 510, configured to preset a limit threshold corresponding to each parameter, and when the parameter is greater than the limit threshold at all consecutive times, accumulate the scores of the parameter, and subtract the decay score to obtain a real-time score.
Taking the concentration of floating colonies as an example, first, a threshold value of the concentration of floating colonies is set
Then, the real-time alert score is calculated according to the following heat algorithm
：
Wherein
For the cumulative score indicator, when the concentration of the floating colony number bodies is larger than the concentration threshold of the floating colony number bodies at the initial moment
When the concentration is 1, the concentration is still greater than the threshold value of several volume concentrations of planktonic colonies at the next successive time
The method comprises the following steps:
And the early warning comparison unit 520 is used for exceeding the corresponding parameter at the unit space when the real-time score is greater than the early warning threshold value, and visualizing the exceeding result space.
Setting an early warning threshold
When the real-time score is larger than the early warning threshold value
In time, the particle contamination in the unit space exceeds the standard.
As an example, the warning threshold value in the embodiment of the present invention is 25.
By the method, whether the parameters of each spatial voxel reach the standard or not is judged, and the false detection can be avoided to the greatest extent by considering the change of the time sequence.
It should be noted that, in the embodiment of the present invention, the spatial data visualization rendering technology uses a SuperMap spatial data visualization tool, and in other embodiments, spatial visualization technologies that can achieve the same effect, such as an ArcGIS tool, may also be used.
The environment of the current dynamic monitoring is visualized, and the environment can be fused with the modern Web technology, so that more visualization effects can be displayed in the current medical clean room.
In summary, the embodiment of the present invention includes the following modules: the system comprises a parameter acquisition module 100, a stability analysis module 200, a simulated environment parameter acquisition module 300, a simulated concentration parameter acquisition module 400 and an early warning module 500.
Specifically, the parameter collecting module 100 is configured to collect at least one type of suspended particle concentration parameter and at least one type of environmental parameter at a plurality of sampling points of the clean room within a preset time to form a plurality of concentration parameter sequences and environmental parameter sequences; the stability analysis module 200 is configured to obtain corresponding sequence fluctuations by performing sequence fluctuation analysis on the concentration parameter sequence and the environmental parameter sequence, obtain instantaneous fluctuations at the last time in each sequence, and obtain the stability of each type of parameter of each sampling point according to the sequence fluctuations and the instantaneous fluctuations; the simulated environment parameter acquiring module 300 is configured to voxelize a clean room space to form a plurality of unit spaces, and acquire a simulated environment parameter of each unit space according to a first distance from the unit space to a sampling point, an environment parameter of the sampling point, and stability of the environment parameter; the simulated concentration parameter obtaining module 400 is configured to calculate a correlation between a concentration parameter at each sampling point and an environmental parameter, and obtain a simulated concentration parameter of each unit space according to the first distance, the stability of the concentration parameter, and the correlation; the early warning module 500 is configured to visualize the simulated environment parameter and the simulated concentration parameter space, compare the visualized simulated environment parameter and the simulated concentration parameter space with corresponding early warning thresholds, and determine the parameter state of each unit space. The embodiment of the invention can judge the environment standard-reaching condition of each area in the clean room through data visualization, and can also use the system for special instruments and meters for environment monitoring.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A dynamic environmental monitoring system for a medical cleanroom, the system comprising the following modules:
the parameter acquisition module is used for acquiring at least one type of suspended particle concentration parameter and at least one type of environmental parameter at a plurality of sampling points of the clean room within preset time to form a plurality of concentration parameter sequences and environmental parameter sequences;
the stability analysis module is used for respectively carrying out sequence fluctuation analysis on the concentration parameter sequence and the environment parameter sequence to obtain corresponding sequence fluctuation, simultaneously obtaining the instantaneous fluctuation of the tail moment in each sequence, and obtaining the stability of each type of parameter of each sampling point according to the sequence fluctuation and the instantaneous fluctuation;
the simulated environment parameter acquisition module is used for voxelizing the clean room space to form a plurality of unit spaces and acquiring the simulated environment parameter of each unit space according to the first distance from the unit space to the sampling point, the environment parameter of the sampling point and the stability of the environment parameter;
the simulated concentration parameter acquisition module is used for calculating the correlation between the concentration parameter at each sampling point and the environmental parameter and acquiring the simulated concentration parameter of each unit space according to the first distance, the stability of the concentration parameter and the correlation;
the early warning module is used for visualizing the simulated environment parameters and the simulated concentration parameter space, respectively comparing the visualized simulated environment parameters and the simulated concentration parameter space with corresponding early warning threshold values, and judging the parameter state of each unit space;
the simulated environment parameter acquisition module comprises:
the simulation environment parameter calculation unit is used for acquiring a first weight of each type of environment parameter of each sampling point in a unit space, taking the first weight as the weight of the corresponding environment parameter, and carrying out weighted summation on all the same type of environment parameters to obtain the simulation environment parameter;
the simulated environment parameter acquisition module further comprises:
a first weight obtaining unit, configured to obtain a first weight of each type of environment parameter of a unit space for each sampling point according to the first distance and the stability of the environment parameter, where the first distance and the stability of the environment parameter both have a negative correlation with the first weight;
the analog concentration parameter acquisition module comprises:
the correlation calculation unit is used for reducing the dimensions of all the environmental parameters into a one-dimensional sequence for each type of concentration parameters at each sampling point, and calculating a correlation coefficient of the one-dimensional sequence and the concentration parameter sequence to be used as the correlation;
the analog concentration parameter obtaining module further comprises:
the analog concentration parameter calculation unit is used for acquiring a second weight of each type of concentration parameter of each sampling point in a unit space, taking the second weight as the weight of the corresponding concentration parameter, and performing weighted summation on all the similar concentration parameters to obtain the analog concentration parameter;
the analog concentration parameter obtaining module further comprises:
a second weight obtaining unit, configured to obtain a second weight of each type of concentration parameter of the unit space for each sampling point by using the correlation, the first distance, and the stability of the concentration parameter, where the correlation and the second weight have a positive correlation.
2. The system of claim 1, wherein the stability analysis module comprises:
and the sequence fluctuation analysis unit is used for calculating the singular index and the multi-fractal spectrum of the sequence by using a multi-fractal detrending fluctuation analysis method, and taking the difference value of the multi-fractal spectrum corresponding to the maximum singular index and the multi-fractal spectrum corresponding to the minimum singular index as the sequence fluctuation.
3. The system of claim 1, wherein the stability analysis module further comprises:
and the instantaneous fluctuation analysis unit is used for acquiring a preset number of tail elements at the tail of the sequence, acquiring a differential sequence of the tail elements, and taking the average value of all values in the differential sequence as the instantaneous fluctuation.
4. The system of claim 1, wherein the early warning module comprises:
and the real-time scoring acquisition unit is used for presetting a limit threshold corresponding to each parameter, accumulating the scores of the parameters when the parameters are greater than the limit threshold at continuous moments, and subtracting the attenuation scores to obtain real-time scores.
5. The system of claim 4, wherein the early warning module further comprises:
and the early warning comparison unit is used for exceeding the corresponding parameters at the unit space when the real-time score is greater than the early warning threshold value, and visualizing the exceeding result space.
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| CN115144663A (en) * | 2022-07-18 | 2022-10-04 | 济宁市海豚科技有限公司 | Integrated machine room environment detection device |
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