CN111338298A - Intelligent production process monitoring system for health ring - Google Patents
Intelligent production process monitoring system for health ring Download PDFInfo
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- G—PHYSICS
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- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
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Abstract
The utility model provides a production process intelligent monitoring system for healthy circle, includes a plurality of temperature monitor module, a plurality of sensor monitor module, image acquisition module, intelligent management module, display screen and siren, temperature monitor module is used for monitoring the surface temperature of the production facility of healthy circle, sensor monitor module adopts sensor assembly to monitor the environmental information of the workshop of healthy circle, intelligent management module carries out filtering process and analysis to the monitoring information who obtains, works as make the siren report to the police when there is the monitoring information who is higher than given threshold value in the monitoring information to control image acquisition module basis the positional information of the monitoring information who is higher than given threshold value carries out image acquisition to this position. The invention has the beneficial effects that: the intelligent monitoring system is used for intelligently monitoring the production equipment of the health ring and the environment of a production workshop, and ensures the safe production of the health ring.
Description
Technical Field
The invention relates to the field of intelligent monitoring, in particular to an intelligent monitoring system for a production process of a health circle.
Background
After the physiological function of human body grows to the top, each organ declines with age and the increase of free radicals in the body, the metabolism slows down, the immunologic function declines, the adaptability to the change of external and internal environment declines, the physical strength declines and various diseases are produced. When the disease occurs, not only can great pain and affliction be brought to the individual, but also great pain is brought to the family. Meanwhile, huge losses are brought to the society and the country. The immune system, the most effective weapon of the human body to defend against the invasion of pathogens, is able to discover and eliminate the factors that cause the internal environment to fluctuate, such as: foreign bodies, foreign pathogenic microorganisms, and the like. Immunocompromised bodies are susceptible to infection or cancer; because of frequent illness, the consumption of the body is aggravated, so there are general manifestations of physical weakness, malnutrition, listlessness, fatigue, weakness, loss of appetite, sleep disorder, etc. As is well known whether the health level and state of the human body are understood by the strength and weakness of metabolism and immunity, the activity and regenerative capacity of cells must be the basis or core of metabolism and immunity; and a good tissue environment is needed to keep the activity of human cells to a certain degree.
The health ring is an anaerobic high-molecular health ring taking reasonable energy storage and transfer as a core, and MEGA as a core technology is used for acquiring and converting energy suitable for the physiological characteristics of a human body to act on the human body, so that the metabolism and the immunity of the human body are improved, and the improvement of the health level of the human body or the improvement of diseases is realized. Can produce high temperature and waste gas etc. in the production process of healthy circle, arouse accident easily carelessly a little, consequently, provide a production process intelligent monitoring system for healthy circle, carry out intelligent monitoring to the production facility of healthy circle and workshop's environment, in time early warning when appearing dangerous, can guarantee the safety in production of healthy circle.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an intelligent production process monitoring system for health circles.
The health circle is equipment which takes MEGA as a core technology to obtain and convert energy suitable for the physiological characteristics of a human body to act on the human body, so that the metabolism and the immunity of the human body are improved, and the improvement of the health level or diseases of the human body is realized. The health circle is a medical health product (applying for medical instrument certificate) sold by the applicant, and comprises the following main components: a nano-class oxygen-free high-molecular carbon prepared from hydrocarbon, tourmaline, and transition metal. The basic principle is that the oxygen-free high molecular polymer and a plurality of inorganic nano materials are scientifically and reasonably proportioned to prepare the oxygen-free nano material. The health circle has the functions of reducing water molecular groups, tourmaline, adsorbing positive ions (generating negative ions) and the like, and improves the blood system to a certain extent. The main effects are as follows: 1. the blood flow speed is accelerated; 2. the blood flow per unit time increases and hemoglobin increases; 3. enhanced cellular tissue permeability; 4. not only can improve blood microcirculation and promote metabolism, but also has the auxiliary effects of cleaning blood, generating negative ions, resisting acidification and the like. Can be used for treating diseases or diseases caused by metabolism and immunity decrease.
Specifically, a hydrocarbon (an artificial polymer compound other than phenol-formaldehyde resin and plant materials, such as waste tires and plastic products) is subjected to dry distillation (anaerobic thermal decomposition) and activation to obtain activated carbon. The active carbon comprises primary active carbon and secondary active carbon or primary and secondary active substances which are mixed and added into tourmaline. Adding transition metals, monovalent and divalent metals and oxides or salts thereof. A compound or salt.
The oxide or salt may be in powder form, or may be a component of a web, a fabric, a film, or a component of an impregnating material. When the powder is used as powder, the powder is packaged by a flexible hollow plastic tube, two ends of the powder are sealed, and a metal or other fixed connecting devices are arranged to form a ring shape, so that the powder is convenient to wear on all parts of a body. The linear section shape of the flexible plastic pipe can be round, oval, triangular or rectangular. The purpose is to increase the fluidity of human blood.
The purity of the active carbon obtained by the technology is more than 94.4 percent and is higher than that of bamboo charcoal and the like. The cation adsorption capacity is strong, and an environment with more anions is created. The material mixed by the formula and the process can generate negative ions, electromagnetic waves and far infrared rays. So as to increase blood flow and promote health. The electromagnetic wave and far infrared ray have the functions of raising the temperature of subcutaneous deep layer, dilating blood capillary and strengthening metabolism, and the negative ion has the functions of activating biological cell, regulating autonomic nerve, regulating endocrine and mental stabilization, raising immunity and relieving fatigue.
The active carbon has strong cation adsorption capacity due to its large specific surface area, especially plant carbide, and can deodorize, dehumidify, and prevent bacteria (mildew), and can generate negative ions, electromagnetic waves, and far infrared rays when being matched with tourmaline and transition metal. At present, relevant products which are sold on the market comprise products for plant carbonization, deodorization, dehumidification and mould prevention, have cation adsorbability and generate anions and far infrared rays. A generator for generating negative ions by high-voltage discharge. Clothes, bedding, mask, health products and the like which generate negative ions and far infrared rays. The optimal formula of the health ring is as follows: the mixture of oxygen-free dry distillation matter, pink tourmaline and manganese dioxide is called blood flow increasing material for short.
The core material is prepared by decomposing the artificial polymer (such as waste tire) of carbon-ammonia compound (except styrene resin) at 550 deg.C in the absence of oxygen, bagging the resultant with polyethylene bag (20 cm in length and 10cm in width), and comparing with ion energy in atmosphere of 3cm to 30cm around with ion detector (AC-I00 from Zuoteng Co.). The result is averaged for five times, the artificial macromolecule is one time higher than the active carbon generated by the plant and is 1.4 times of the tobacco carbon, the anaerobic method is more effective than the old barbecue dry distillation method, and the carbon content is high. Therefore, the negative ion generating power is strong.
It can be seen that high temperature and waste gas can be produced in the production process of the health ring, accidents are easily caused carelessly, and therefore, the intelligent production process monitoring system for the health ring is provided, intelligent monitoring is carried out on production equipment of the health ring and the environment of a production workshop, timely early warning is carried out when dangers occur, and safety production of the health ring can be guaranteed.
The purpose of the invention is realized by the following technical scheme:
an intelligent monitoring system for a production process of a health circle comprises a plurality of temperature monitoring modules, a plurality of sensor monitoring modules, an image acquisition module, an intelligent management module, a display screen and an alarm, wherein each temperature monitoring module comprises a temperature monitoring unit and a position detection unit, the temperature monitoring unit is used for monitoring the surface temperature of production equipment of the health circle, the position detection unit is used for acquiring the position information of the temperature monitoring unit, each sensor monitoring module comprises a sensor monitoring unit and a GPS positioning unit, the sensor monitoring unit adopts a sensor assembly to monitor the environmental information of a production workshop of the health circle, the GPS positioning unit is used for acquiring the position information of the sensor monitoring unit, and the temperature monitoring modules and the sensor monitoring modules send the acquired monitoring information and the position information to the intelligent management module, the intelligent management module carries out filtering processing and analysis on the received monitoring information, when the monitoring information higher than a given threshold value exists in the monitoring information, the intelligent management module enables the alarm to give an alarm, controls the image acquisition module to carry out image acquisition on the position according to the position information of the monitoring information higher than the given threshold value, sends the acquired image to the intelligent management module, and the intelligent management module processes the received image and then sends the processed image to the display screen for displaying.
The beneficial effects created by the invention are as follows:
the invention is used for intelligently monitoring the environment of the production equipment and the production workshop of the health ring, the surface temperature of the production equipment of the health ring is monitored by the temperature monitoring unit, the state of the production equipment of the health ring in the production process is judged according to the surface temperature condition of the production equipment of the health ring, the abnormal phenomenon of the production equipment of the health ring can be found in time, the safety production of the health ring is ensured, the sensor monitoring module is used for monitoring the environment of the production workshop of the health circle, so that the environment condition of the production workshop of the health circle can be mastered in real time, the alarm is given in time when danger occurs, the image acquisition module is controlled to acquire images of dangerous positions, the acquired images are processed and then displayed on the display screen, so that workers can more visually know the dangerous conditions occurring in the production workshop of the health circle, the accidental injury caused by the fact that the vehicle enters the workshop without knowing the dangerous condition is avoided.
Drawings
The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a schematic diagram of the present invention.
Reference numerals:
a temperature monitoring module; a sensor monitoring module; an image acquisition module; an intelligent management module; a display screen; an alarm.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the system for intelligently monitoring the production process of a health circle in this embodiment includes a plurality of temperature monitoring modules, a plurality of sensor monitoring modules, an image acquisition module, an intelligent management module, a display screen, and an alarm, each temperature monitoring module includes a temperature monitoring unit and a position detection unit, the temperature monitoring unit is used for monitoring the surface temperature of the production equipment of the health circle, the position detection unit is used for acquiring the position information of the temperature monitoring unit, each sensor monitoring module includes a sensor monitoring unit and a GPS positioning unit, the sensor monitoring unit monitors the environmental information of the production workshop of the health circle by using a sensor component, the GPS positioning unit is used for acquiring the position information of the sensor monitoring unit, and the temperature monitoring module and the sensor monitoring module send the acquired monitoring information and position information to the intelligent management module, the intelligent management module carries out filtering processing and analysis on the received monitoring information, when the monitoring information higher than a given threshold value exists in the monitoring information, the intelligent management module enables the alarm to give an alarm, controls the image acquisition module to carry out image acquisition on the position according to the position information of the monitoring information higher than the given threshold value, sends the acquired image to the intelligent management module, and the intelligent management module processes the received image and then sends the processed image to the display screen for displaying.
This preferred embodiment is used for carrying out intelligent monitoring to the production facility of healthy circle and the environment of workshop, monitor the surface temperature of the production facility of healthy circle through the temperature monitoring unit, the state of the production facility of healthy circle in process of production is judged to the surface temperature condition of the production facility of healthy circle through the production facility of healthy circle, can in time discover the abnormal phenomenon of the production facility of healthy circle, monitor the environment of the workshop of healthy circle through sensor monitoring module, can master the environmental aspect of the workshop of healthy circle in real time, and in time early warning when dangerous appears, the safety in production of healthy circle has been guaranteed.
Preferably, the intelligent management module includes a data processing unit, a security analysis unit and an image processing unit, the data processing unit is configured to perform filtering processing on the received monitoring information, the security analysis unit compares the processed monitoring information with a given threshold, when monitoring information higher than the given threshold exists in the monitoring information, the alarm is enabled to alarm, the image acquisition module is enabled to perform image acquisition on the position according to the position information of the monitoring information higher than the given threshold, and the image processing unit is configured to process the received image.
Preferably, the image processing unit is configured to perform filtering processing on the received image, where I represents the received image, f (I, j) represents a gray value of a pixel I (I, j) at a coordinate (I, j) in the image I, and a filter function corresponding to the pixel I (I, j) is constructed as follows:
f′(i,j)＝argmin{α(i,j)*α′(i,j)+β(i,j)*β′(i,j)}
α′(i,j)＝(f′(i,j)-h(i,j))2
where f ' (I, j) represents the filtered gray scale value of the pixel I (I, j), Ω (I, j) represents the local neighborhood of (2 ω +1) × (2 ω +1) centered on the pixel I (I, j), α ' (I, j) represents the gray scale constraint factor of the pixel I (I, j), β ' (I, j) represents the edge constraint factor of the pixel I (I, j), f ' (x, y) represents the filtered gray scale value of the pixel at the coordinate (x, y) in the local neighborhood Ω (I, j), f ' (I, j +1) represents the filtered gray scale value of the pixel at the coordinate (I, j +1) in the image I, f ' (x, y +1) represents the filtered gray scale value of the pixel at the coordinate (x, y +1) in the image I, and f ' (I +1, j) represents the filtered gray scale value of the pixel at the coordinate (x, y +1) in the image IA gray value of a pixel at a coordinate (I +1, j) after filtering processing, f' (x +1, y) represents a gray value of a pixel at a coordinate (x +1, y) in an image I after filtering processing, h (I, j) represents a gray constraint reference value of the pixel I (I, j), α (I, j) represents a gray constraint weight of the pixel I (I, j), β (I, j) represents an edge constraint weight of the pixel I (I, j), and a pixel detection factor corresponding to the pixel I (I, j) is defined as
And is
The expression of (a) is:
wherein L (Ω (i, j)) represents the number of pixels in the local neighborhood Ω (i, j), k (x, y) is a value-taking function, when | f (x, y) -f (i, j) | is less than or equal to W (i, j), k (x, y) | 1, when | f (x, y) -f (i, j) |>W (I, j), k (x, y) is 0, where f (x, y) represents the gray level of the pixel at coordinate (x, y) in the local neighborhood Ω (I, j), W (I, j) is the pixel detection threshold corresponding to the pixel I (I, j), and
wherein the content of the first and second substances,
representing the mean, f, of the grey values of the pixels in the local neighborhood Ω (i, j)m(Ω (i, j)) represents the median of the gray values of the pixels in the local neighborhood Ω (i, j);
when in use
Then the values of h (i, j), α (i, j) and β (i, j) are:
α(i,j)＝1，β(i,j)0; when in use
Then h (i, j), α (i, j), and β (i, j) have values of h (i, j) to f (i, j),
α (i, j) ═ 1- β (i, j); when
Then the values of h (i, j), α (i, j) and β (i, j) are:
the preferred embodiment is used for filtering a received image, constructing a filtering function of a pixel, introducing a gray level constraint factor and an edge constraint factor into the filtering function, wherein the gray level constraint factor is used for ensuring that the gray level of a noise pixel tends to be normal in the filtering process, the edge constraint factor is used for ensuring that edge information in the image is reserved in the filtering process, defining a pixel detection factor, the pixel detection factor can effectively judge the attribute of the pixel, and determining a gray level constraint reference value, a gray level constraint weight value and an edge constraint weight value according to different attributes of the pixel, when the value of the pixel detection factor belongs to
When the pixel is a noise pixel, judging that the pixel is a noise pixel, taking the mean value of the gray values of other pixels which do not contain the noise pixel in the neighborhood pixels of the noise pixel as a gray constraint reference value of the gray constraint reference value in the filtering function, setting a gray constraint weight value to be 1, and setting a structural constraint weight value to be 0, wherein the gray constraint reference value is used for ensuring that the gray value of the noise pixel can be close to a normal value by the filtering function; when the value of the pixel detection factor belongs to
When the pixel is judged to belong to the edge area, the filtering function is mainly used for reserving the edge information in the image at the moment, the gray level constraint reference value in the filtering function is set to be the gray level value of the pixel to be filtered, namely the gray level characteristic of the edge pixel is reserved, the larger the value of the pixel detection factor in the interval is, the closer the pixel is to the edge area of the image is shown, namely the structural constraint factor in the filtering function is set to be in a mode of increasing along with the increasing of the value of the pixel detection factor, and the structural constraint factor is used for ensuring that the filtering function can effectively reserve the edge information in the image in the filtering process; when the value of the pixel detection factor belongs to
Then indicating that the gray value distribution of the pixel and the local neighborhood pixels is more balanced, and at the moment, the gray value constraint reference value in the filtering function adopts the gray value mean value of the pixels in the local neighborhood of the pixel to be filtered, and the gray value constraint weight and the structural constraint weight are both set to be the gray value constraint weight and the structural constraint weight
So that the filter function at this point ensures the smoothness of the pixel.
Preferably, let I denote the image received by the image processing unit, I ' denote the image after the filtering processing of the image I, perform information compensation on the filtered image I ', let z denote the filtered noise image, let z be I-I ', and let I bez(a, b) represents the pixel at coordinate (a, b) in the noisy image z, using the local neighborhood KzPixel-to-pixel I in (a, b)zAdjusting the gray values of (a, b), specifically:
in the formula, L (K)z(a, b)) represents a local neighborhood KzNumber of pixels in (a, b), fz' (a, b) denotes a pair of pixels IzThe adjusted gray-scale value of (a, b), fz(a, b) represents a pixel IzGrey scale value of (a, b), Kz(a,b) Is represented by a pixel IzM centered on (a, b)z(a,b)×Nz(a, b) local neighborhood of where Mz(a, b) denotes a local neighborhood KzLength of (a, b), Nz(a, b) denotes a local neighborhood KzWidth of (a, b), and Mz(a, b) and NzThe expressions of (a, b) are respectively:
where M and N represent the length and width, Ω, respectively, of the noise image zz(a, b) represents the pixel Iz(2 ω +1) × (2 ω +1) local neighborhood centered at (a, b), 0<ω<M and 0<ω<N，fz(x, y) denotes the local neighborhood Ωz(a, b) the gray value of the pixel at coordinate (x, y), fz(x +1, y) is the gray value of the pixel at coordinate (x +1, y) in the noise image z, fz(x, y +1) is the gray value of the pixel at coordinate (x, y +1) in the noise image z, fz(max) and fz(min) representing the maximum and minimum values of the pixel grey values in the noise image z, respectively;
assuming that z 'represents an image in which the gray values of pixels in the noise image z are adjusted, and I ″ represents an image in which information compensation is performed on the image I', the expression of I ″ is: i ″ + I '+ z'.
The preferred embodiment is used for performing information compensation on the filtered image, and a phenomenon of filtering weak texture detail information in the image may occur in the process of filtering the image; when information compensation is carried out on an image, firstly, the gray value of a pixel in a filtered noise image is adjusted, the gray value of the pixel is adjusted by utilizing a neighborhood pixel in a local neighborhood of the pixel, and the process of determining the neighborhood pixel participating in adjusting the gray value of the pixel is carried outIn the preferred embodiment, the neighborhood pixel range participating in the gray-scale value adjustment of the pixel is adaptively determined according to the attribute of the pixel, and when the pixel is a noise pixel, the local neighborhood K participating in the gray-scale value adjustment of the pixel is enabled to be a local neighborhood Kz(a, b) is smaller, namely, the noise pixel can be effectively removed, when the pixel is the texture detail information, the local neighborhood K participating in the gray value adjustment of the pixel is enabledzAnd (a, b) is large, namely, the texture detail information can be effectively kept.
Preferably, the sensor monitoring unit adopts sensor assembly to monitor workshop environment information, sensor assembly still includes the convergent node including the monitoring node that is used for gathering workshop environment information, the cluster head node that is used for collecting the workshop environment monitoring information that the monitoring node sent in the cluster, the convergent node is with the workshop environment monitoring information that self acquireed and the transmission of received workshop environment monitoring information after fusing to the convergent node, by the convergent node with workshop environment monitoring information sends to intelligent management module.
Preferably, the sensor assembly comprises a temperature sensor, a humidity sensor, a dust concentration sensor and a smoke concentration sensor.
Preferably, the rate of collecting the workshop environment information by the sensor assembly is adaptively adjusted, and C is set0Indicating a cluster head node, K (C)0) Indicating cluster head node C0Set of monitoring nodes in the cluster, and K (C)0)＝{ki,i＝1,2,…,N(C0) In which k isiRepresents the set K (C)0) The ith monitoring node in (1), N (C)0) Represents the set K (C)0) Number of monitoring nodes in
Indicating cluster head node C0The rate at which the plant environment information is collected at time t,
indicating a monitoring node kiThe rate of collecting the workshop environment information at time t, and
and
are all set to V0Defining a cluster head detection factor
Wherein d (C)0And t) represents a cluster head node C0Number of packets in buffer queue at time t, D (C)0) Indicating cluster head node C0The number of data packets that can be accommodated in the buffer queue;
when rho (C)0)>1 hour, cluster head node C0The speed of collecting the workshop environment information is adjusted as follows:
wherein, S (C)0) Indicating cluster head node C0Number of packets successfully transmitted to the sink node, J (C)0) Indicating cluster head node C0Number of self-collected data packets, J (k)i,C0) Indicating a monitoring node kiSuccessful transmission to cluster head node C0The number of data packets; cluster head node C0Monitoring node k of the clusteriThe speed of collecting the workshop environment information is adjusted as follows:
when rho (C)0) When the number of the cluster head nodes is less than or equal to 1, clustering the head nodes C0Rate of collecting plant environment information
And the monitoring node k of the clusteriRate of collecting plant environment information
Remain unchanged.
The preferred embodiment is used for adaptively adjusting the rate of acquiring the workshop environment information by the sensor component in the sensor monitoring unit, adjusting the rate of acquiring the workshop environment information by the cluster head node according to the transmission success rate of the cluster head node and the number of data packets in the cache queue of the cluster head node, and adjusting the rate of acquiring the workshop environment information by the monitoring node in the cluster where the cluster head node is located according to the number of data packets in the cache queue of the cluster head node, so that the number of data packets in the cache queue of the cluster head node is smaller than the number of data packets which can be accommodated by the monitoring node, and the congestion phenomenon in the process of transmitting the data packets by the cluster head node is avoided.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (6)
1. An intelligent monitoring system for a production process of a health circle is characterized by comprising a plurality of temperature monitoring modules, a plurality of sensor monitoring modules, an image acquisition module, an intelligent management module, a display screen and an alarm, wherein each temperature monitoring module comprises a temperature monitoring unit and a position detection unit, the temperature monitoring unit is used for monitoring the surface temperature of production equipment of the health circle, the position detection unit is used for acquiring the position information of the temperature monitoring unit, each sensor monitoring module comprises a sensor monitoring unit and a GPS positioning unit, the sensor monitoring unit adopts a sensor assembly to monitor the environmental information of a production workshop of the health circle, the GPS positioning unit is used for acquiring the position information of the sensor monitoring unit, and the temperature monitoring modules and the sensor monitoring modules send the acquired monitoring information and the acquired position information to the intelligent management module, the intelligent management module carries out filtering processing and analysis on the received monitoring information, when the monitoring information higher than a given threshold value exists in the monitoring information, the intelligent management module enables the alarm to give an alarm, controls the image acquisition module to carry out image acquisition on the position according to the position information of the monitoring information higher than the given threshold value, sends the acquired image to the intelligent management module, and the intelligent management module processes the received image and then sends the processed image to the display screen for displaying.
2. The system according to claim 1, wherein the intelligent management module comprises a data processing unit, a safety analysis unit and an image processing unit, the data processing unit is configured to filter the received monitoring information, the safety analysis unit compares the processed monitoring information with a given threshold, and when monitoring information higher than the given threshold exists in the monitoring information, the alarm is triggered to alarm, the image acquisition module is configured to acquire an image of the position according to the position information of the monitoring information higher than the given threshold, and the image processing unit is configured to process the received image.
3. The system according to claim 2, wherein the image processing unit is configured to perform filtering processing on the received image, where I represents the received image, f (I, j) represents a gray value of a pixel I (I, j) at a coordinate (I, j) in the image I, and the filter function corresponding to the pixel I (I, j) is constructed as follows:
f′(i,j)＝argmin{α(i,j)*α′(i,j)+β(i,j)*β′(i,j)}
α′(i,j)＝(f′(i,j)-h(i,j))2
in the formula, f '(I, j) represents a gray scale value of the pixel I (I, j) after the filtering process, Ω (I, j) represents a local neighborhood of (2 ω +1) × (2 ω +1) centered on the pixel I (I, j), and α' (I, j) represents an imageThe gray scale constraint factor of the pixel I (I, j), β '(I, j) represents the edge constraint factor of the pixel I (I, j), f' (x, y) represents the gray scale value of the pixel at the coordinate (x, y) in the local neighborhood Ω (I, j), f '(I, j +1) represents the gray scale value of the pixel at the coordinate (I, j +1) in the image I after filtering, f' (x, y +1) represents the gray scale value of the pixel at the coordinate (x, y +1) in the image I after filtering, f '(I +1, j) represents the gray scale value of the pixel at the coordinate (I +1, j) in the image I after filtering, f' (x +1, y) represents the gray scale value of the pixel at the coordinate (x +1, y) in the image I after filtering, h (I, j) represents the gray scale constraint reference value of the pixel I (I, j), α (I, j) represents the gray scale constraint factor of the pixel I (I, j), I (I, j) represents the gray scale value of the pixel I (I, j) in the edge constraint factor corresponding to the pixel I, j, β, I, j) defined as the weight value of the detected pixel I (I, j) in the local neighborhood Ω (I, j) of the local neighborhood Ω (I
And is
The expression of (a) is:
wherein L (Ω (i, j)) represents the number of pixels in the local neighborhood Ω (i, j), k (x, y) is a value-taking function, when | f (x, y) -f (i, j) | is less than or equal to W (i, j), k (x, y) | 1, when | f (x, y) -f (i, j) |>W (I, j), k (x, y) is 0, where f (x, y) represents the gray level of the pixel at coordinate (x, y) in the local neighborhood Ω (I, j), W (I, j) is the pixel detection threshold corresponding to the pixel I (I, j), and
wherein the content of the first and second substances,
representing the mean, f, of the grey values of the pixels in the local neighborhood Ω (i, j)m(Ω (i, j)) represents the median of the gray values of the pixels in the local neighborhood Ω (i, j);
when in use
Then the values of h (i, j), α (i, j) and β (i, j) are:
α (i, j) is equal to 1, β (i, j) is equal to 0, when
Then h (i, j), α (i, j), and β (i, j) have values of h (i, j) to f (i, j),
α (i, j) ═ 1- β (i, j); when
Then the values of h (i, j), α (i, j) and β (i, j) are:
4. the system according to claim 3, wherein I represents the image received by the image processing unit, I ' represents the filtered image of the image I, the filtered image I ' is compensated for information, z represents the filtered noise image, and z is I-I ', Iz(a, b) represents the pixel at coordinate (a, b) in the noisy image z, using the local neighborhood KzPixel-to-pixel I in (a, b)zAdjusting the gray values of (a, b), specifically:
in the formula, L (K)z(a, b)) represents a local neighborhood KzNumber of pixels in (a, b), fz' (a, b) denotes a pair of pixels IzThe adjusted gray-scale value of (a, b), fz(a, b) represents a pixel IzGrey scale value of (a, b), Kz(a, b) represents the pixel IzM centered on (a, b)z(a,b)×Nz(a, b) local neighborhood of where Mz(a, b) denotes a local neighborhood KzLength of (a, b), Nz(a, b) denotes a local neighborhood KzWidth of (a, b), and Mz(a, b) and NzThe expressions of (a, b) are respectively:
where M and N are the length and width, Ω, respectively, of the noise image zz(a, b) represents the pixel Iz(2 ω +1) × (2 ω +1) local neighborhood centered at (a, b), 0<ω<M and 0<ω<N，fz(x, y) denotes the local neighborhood Ωz(a, b) the gray value of the pixel at coordinate (x, y), fz(x +1, y) represents the gray value of the pixel at coordinate (x +1, y) in the noise image z, fz(x, y +1) represents the gray value of the pixel at coordinate (x, y +1) in the noise image z, fz(max) and fz(min) representing the maximum and minimum values of the pixel grey values in the noise image z, respectively;
assuming that z 'represents an image in which the gray values of pixels in the noise image z are adjusted, and I ″ represents an image in which information compensation is performed on the image I', the expression of I ″ is: i ″ + I '+ z'.
5. The intelligent production process monitoring system for the health circle as claimed in claim 4, wherein the sensor monitoring unit monitors the workshop environment information by adopting a sensor component, the sensor component comprises monitoring nodes for collecting the workshop environment information, cluster head nodes for collecting the workshop environment monitoring information sent by the monitoring nodes in the cluster, and sink nodes, the cluster head nodes transmit the workshop environment monitoring information acquired by the cluster head nodes and the received workshop environment monitoring information to the sink nodes after being fused, and the workshop environment monitoring information is sent to the intelligent management module by the sink nodes.
6. The intelligent monitoring system for the production process of the health circle as claimed in claim 5, wherein the rate of the sensor assembly acquiring the workshop environment information is adaptively adjusted, and C is set0Indicating a cluster head node, K (C)0) Indicating cluster head node C0Set of monitoring nodes in the cluster, and K (C)0)＝{ki,i＝1,2,…,N(C0) In which k isiRepresents the set K (C)0) The ith monitoring node in (1), N (C)0) Represents the set K (C)0) Number of monitoring nodes in
Indicating cluster head node C0The rate at which the plant environment information is collected at time t,
indicating a monitoring node kiThe rate of collecting the workshop environment information at time t, and
and
are all set to V0Defining a cluster head detection factor
Wherein d (C)0And t) represents a cluster head node C0Number of packets in buffer queue at time t，D(C0) Indicating cluster head node C0The number of data packets that can be accommodated in the buffer queue;
when rho (C)0)>1 hour, cluster head node C0The speed of collecting the workshop environment information is adjusted as follows:
wherein, S (C)0) Indicating cluster head node C0Number of packets successfully transmitted to the sink node, J (C)0) Indicating cluster head node C0Number of self-collected data packets, J (k)i,C0) Indicating a monitoring node kiSuccessful transmission to cluster head node C0The number of data packets; cluster head node C0Monitoring node k of the clusteriThe speed of collecting the workshop environment information is adjusted as follows:
when rho (C)0) When the number of the cluster head nodes is less than or equal to 1, clustering the head nodes C0Rate of collecting plant environment information
And the monitoring node k of the clusteriRate of collecting plant environment information
Remain unchanged.
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