CN112351071B - Traditional chinese medicine production remote monitering system based on cloud calculates - Google Patents

Traditional chinese medicine production remote monitering system based on cloud calculates Download PDF

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CN112351071B
CN112351071B CN202011086828.5A CN202011086828A CN112351071B CN 112351071 B CN112351071 B CN 112351071B CN 202011086828 A CN202011086828 A CN 202011086828A CN 112351071 B CN112351071 B CN 112351071B
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谢志坚
贾向东
陈晓阳
张贺
王珍玉
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Cr Sanjiu Zaozhuang Pharmaceutical Co ltd
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Abstract

The utility model provides a traditional chinese medicine production process remote monitering system based on cloud, includes first remote monitering module, second remote monitering module, information transmission module and remote monitoring center, first remote monitering module is used for carrying out safety monitoring to traditional chinese medicine production facility, second remote monitering module is used for carrying out safety monitoring to traditional chinese medicine workshop, information transmission module is used for data and image transmission to the remote monitoring center with first remote monitering module and second remote monitering module collection, the remote monitoring center is used for handling and the analysis to received data and image to judge whether traditional chinese medicine production facility normally operates and whether safe in traditional chinese medicine production workshop. The invention has the beneficial effects that: through the remote monitoring to traditional chinese medicine production facility and traditional chinese medicine workshop for when the staff can know the traditional chinese medicine production circumstances in real time, guaranteed the safety of traditional chinese medicine production and gone on.

Description

Traditional chinese medicine production remote monitering system based on cloud calculates
Technical Field
The invention relates to the field of traditional Chinese medicine production, in particular to a cloud computing-based remote traditional Chinese medicine production monitoring system.
Background
With the wide application of modern information technology, image technology and sensor technology in the production process of traditional Chinese medicines, the traditional production mode of traditional Chinese medicines is converted into the digital and intelligent production mode of traditional Chinese medicines. As a complex system, the production process of the traditional Chinese medicine can influence the quality of traditional Chinese medicine products by all units and links forming the system, so that an effective monitoring system is established for the production process of the traditional Chinese medicine, and the method has important significance for ensuring the safe production of the traditional Chinese medicine.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a remote traditional Chinese medicine production monitoring system based on cloud computing.
The purpose of the invention is realized by the following technical scheme:
a traditional Chinese medicine production remote monitoring system based on cloud computing comprises a first remote monitoring module, a second remote monitoring module, an information transmission module and a remote monitoring center, wherein the first remote monitoring module comprises a first data monitoring unit, a first video monitoring unit and a safety monitoring unit, the first data monitoring unit is used for collecting operation data of traditional Chinese medicine production equipment, the first video monitoring unit is used for collecting video images of the traditional Chinese medicine production equipment, the safety monitoring unit is used for collecting infrared images of the traditional Chinese medicine production equipment, the first remote monitoring module transmits the collected data and images to the remote monitoring center through the information transmission module, the second remote monitoring module comprises a second data monitoring unit and a second video monitoring unit, the second data monitoring unit adopts a sensor node to collect environmental parameter data of a traditional Chinese medicine production workshop, the second video monitoring unit is used for acquiring video images of the traditional Chinese medicine production workshop, the second remote monitoring module transmits acquired data and images to the remote monitoring center through the information transmission module, the remote monitoring center comprises a first safety management unit, a second safety management unit, a first information display unit and a second information display unit, the first safety management unit compares received operation data with preset operation data of traditional Chinese medicine production equipment, when the operation data is not equal to the operation data of the given traditional Chinese medicine production equipment, the traditional Chinese medicine production equipment is judged to have operation faults and perform early warning, the first safety management unit compares the received environmental parameter data with a given safety threshold, when the received environmental parameter data exceeds the given safety threshold, the traditional Chinese medicine production workshop is judged to have danger and perform early warning, the second safety management unit is used for processing and analyzing the received infrared image so as to judge whether the production equipment normally operates or not, and giving an early warning when the production equipment is judged to have an operation fault, the first information display unit is used for displaying the received operation data and video image of the traditional Chinese medicine production equipment, and the second information display unit is used for displaying the received environmental parameter data and video image in the traditional Chinese medicine production workshop.
Preferably, the second data monitoring unit collects environmental parameter data of the traditional Chinese medicine production workshop by adopting sensor nodes with a clustering structure, data transmission is carried out between cluster head nodes in a multi-hop transmission mode, and CH is setiDenotes the ith cluster head node, L (CH) in the second data monitoring uniti) Indicating cluster head node CHiAnd L (CH)i)＝ {CHi，j|d(CHi，CHi，j) R (CH) }, wherein, CHi，jIndicating cluster head node CHiJ-th neighbor cluster head node of d (CH)i，CHi，j) Indicating cluster head node CHiAnd neighbor cluster head node CHi，jThe distance between the sensor node and the cluster head node is R (CH), the communication radiuses of the sensor node and the cluster head node in the second data monitoring unit are equal;
given a transmission period τ, and
Figure GDA0002975876170000021
wherein, trRepresenting the time length from the R-th round of clustering to the (R +1) -th round of clustering, R representing the number of current clustering rounds, and setting cluster head nodes CH at intervals of tauiSelecting a next-hop cluster head node from the neighbor cluster head nodes for data transmission, setting r (e) to represent the e-th transmission period in the r-th round of clustering, and defining
Figure GDA0002975876170000022
Indicates the cluster head node CH from the initial stage of the r-th round clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jCoefficient of reliability of transmission data, and
Figure GDA0002975876170000023
wherein, S (CH)i，CHi，jR (e)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jAmount of data successfully transmitted, N (CH)i，CHi，jR (e)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jThe amount of data transmitted;
definition of P (CH)iAnd r (e)) represents a set L (CH) from an initial stage of the r-th round of clustering to a transmission period r (e)i) And a transmission attribute detection coefficient corresponding to the neighbor cluster head node in (1), and
Figure GDA0002975876170000024
definition of W (CH)i，jAnd r (e)) represents a neighbor cluster head node CHi，jBecomes a cluster head node CH in the transmission period r (e)iPriority of the next hop cluster head node, when P (CH)iWhen r (e) is not less than P, then W (CH)i，jAnd r (e)) is represented by:
Figure GDA0002975876170000025
where P denotes a given transmission property detection threshold, and P is 0.4, E (CH)i，jAnd r (e)) represents a neighbor cluster head node CHi，jValue of residual energy by transmission period r (E), E0(CHi，j) Indicating neighbor cluster head node CHi，jThe initial energy values of the sensor nodes and the cluster head nodes in the second data monitoring unit are equal;
when P (CH)iAnd r (e) < P, then W (CH)i，jAnd r (e)) is represented by:
Figure GDA0002975876170000031
in the formula (I), the compound is shown in the specification,
Figure GDA0002975876170000032
indicates the cluster head node CH from the initial stage of the r-th round clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jAnd correcting the reliability coefficient of the transmission data.
Preferably, the cluster head node CH is from the initial stage of the r-th round of clustering to the time of the transmission period r (e)iTo neighbor cluster head node CHi，jCorrection of reliability coefficients of transmitted data
Figure GDA0002975876170000033
The value of (c) is determined in the following manner:
(1) when neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000034
When it is, then
Figure GDA0002975876170000035
Figure GDA0002975876170000036
(2) When neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000037
Then, the cluster head node CH is pairediTo neighbor cluster head node CHi，jThe reliability of data transmission is secondarily detected by setting L (CH)i，j) Indicating neighbor cluster head node CHi，jNeighbor cluster head node set, CHi，j，gIndicating neighbor cluster head node CHi，jR(s) represents the s transmission period in the r round of clustering, mu (CH)i，j，g，CHi，jAnd r (s)) represents the period from the initial stage of the r-th round of clustering to the transmission cyclePeriod r(s) time neighbor cluster head node CHi，j，gTo neighbor cluster head node CHi，jStatistical coefficients of the transmitted data, and
Figure GDA0002975876170000038
Figure GDA0002975876170000039
wherein, N (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gTo neighbor cluster head node CHi，jAmount of data transmitted, θ (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gAnd neighbor cluster head node CHi，jA transmission attribute judgment function therebetween, and
Figure GDA00029758761700000310
wherein the content of the first and second substances,
Figure GDA00029758761700000311
represents the neighbor cluster head node CH from the initial stage of the r-th round clustering to the transmission period r(s)i，j，gTo neighbor cluster head node CHi，jReliability coefficient of the transmission data;
then
Figure GDA00029758761700000312
The values of (A) are:
when neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA00029758761700000313
Figure GDA0002975876170000048
Then judging the neighbor cluster head node CHi，jIs a malicious cluster head node, at this time order
Figure GDA0002975876170000042
When neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000043
Figure GDA0002975876170000044
Then judging the neighbor cluster head node CHi，jFrom the initial stage of the r-th round of clustering to the transmission period r (e), there is channel congestion or channel interference phenomenon, at this time, the order is
Figure GDA0002975876170000045
Figure GDA0002975876170000046
Wherein, N (CH)i，CHi，jR (s)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r(s)iTo neighbor cluster head node CHi，jAmount of data transmitted, ρ (N (CH)i，CHi，j，r(s))，N(CHi，CHi，jR (e)) represents a judgment function, and
Figure GDA0002975876170000047
in the set L (CH)i) The neighbor cluster head node with the maximum priority is selected to become a cluster head node CHiAnd the cluster head node of the next hop in the transmission period r (e) performs data transmission.
Preferably, the second safety management unit is configured to process and analyze the received infrared image, and includes an image processing unit and an image analysis unit, where the image processing unit is configured to perform filtering processing and target segmentation on the received infrared image, and the image analysis unit is configured to analyze the target image obtained by segmentation, so as to determine whether the traditional Chinese medicine production device is faulty in operation.
Preferably, the image processing unit performs target segmentation on the filtered device image by using an OTSU algorithm to obtain a device area image in the device image, determines an optimal threshold value of the OTSU algorithm by using a particle swarm algorithm, and defines a fitness function of the particle swarm algorithm as a maximum inter-class variance, where a larger fitness function value of the particle indicates that a better optimization result of the particle is.
The beneficial effects created by the invention are as follows:
the remote monitoring of the traditional Chinese medicine production equipment and the traditional Chinese medicine production workshop is comprehensively carried out from the aspects of data and images, so that the safety of the traditional Chinese medicine production is ensured while workers can know the production condition of the traditional Chinese medicine in real time; the sensor nodes with the clustering structure are adopted to safely monitor the environment of the traditional Chinese medicine production workshop, the set transmission mode among the cluster head nodes can effectively balance the energy consumption of the cluster head nodes, and the reliability of data transmission among the cluster head nodes is improved; the method has the advantages that the infrared image is adopted to carry out safety monitoring on the traditional Chinese medicine production equipment, the OTSU algorithm is adopted to carry out segmentation on the collected infrared image, the particle swarm algorithm is adopted to determine the optimal threshold of the OTSU algorithm, and the particle swarm algorithm is adopted to solve the problem that the traditional particle swarm algorithm is easy to fall into local optimization to influence the optimization precision.
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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.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the cloud computing-based remote monitoring system for traditional Chinese medicine production of the embodiment includes a first remote monitoring module, a second remote monitoring module, an information transmission module and a remote monitoring center, where the first remote monitoring module includes a first data monitoring unit, a first video monitoring unit and a safety monitoring unit, the first data monitoring unit is used to collect operation data of traditional Chinese medicine production equipment, the first video monitoring unit is used to collect video images of the traditional Chinese medicine production equipment, the safety monitoring unit is used to collect infrared images of the traditional Chinese medicine production equipment, the first remote monitoring module transmits the collected data and images to the remote monitoring center through the information transmission module, the second remote monitoring module includes a second data monitoring unit and a second video monitoring unit, and the second data monitoring unit adopts a sensor node to collect environmental parameter data of a traditional Chinese medicine production workshop, the second video monitoring unit is used for acquiring video images of the traditional Chinese medicine production workshop, the second remote monitoring module transmits acquired data and images to the remote monitoring center through the information transmission module, the remote monitoring center comprises a first safety management unit, a second safety management unit, a first information display unit and a second information display unit, the first safety management unit compares received operation data with preset operation data of traditional Chinese medicine production equipment, when the operation data is not equal to the operation data of the given traditional Chinese medicine production equipment, the traditional Chinese medicine production equipment is judged to have operation faults and perform early warning, the first safety management unit compares the received environmental parameter data with a given safety threshold, when the received environmental parameter data exceeds the given safety threshold, the traditional Chinese medicine production workshop is judged to have danger and perform early warning, the second safety management unit is used for processing and analyzing the received infrared image so as to judge whether the production equipment normally operates or not, and giving an early warning when the production equipment is judged to have an operation fault, the first information display unit is used for displaying the received operation data and video image of the traditional Chinese medicine production equipment, and the second information display unit is used for displaying the received environmental parameter data and video image in the traditional Chinese medicine production workshop.
The preferred embodiment comprehensively carries out remote monitoring on the traditional Chinese medicine production equipment and the traditional Chinese medicine production workshop from the aspects of data and images, so that the working personnel can know the production condition of the traditional Chinese medicine in real time and simultaneously ensure the safe operation of the traditional Chinese medicine production; the sensor nodes with the clustering structure are adopted to safely monitor the environment of the traditional Chinese medicine production workshop, the set transmission mode among the cluster head nodes can effectively balance the energy consumption of the cluster head nodes, and the reliability of data transmission among the cluster head nodes is improved; the method has the advantages that the infrared image is adopted to carry out safety monitoring on the traditional Chinese medicine production equipment, the OTSU algorithm is adopted to carry out segmentation on the collected infrared image, the particle swarm algorithm is adopted to determine the optimal threshold of the OTSU algorithm, and the particle swarm algorithm is adopted to solve the problem that the traditional particle swarm algorithm is easy to fall into local optimization to influence the optimization precision.
Preferably, the second data monitoring unit collects environmental parameter data of the traditional Chinese medicine production workshop by adopting sensor nodes with a clustering structure, data transmission is carried out between cluster head nodes in a multi-hop transmission mode, and CH is setiDenotes the ith cluster head node, L (CH) in the second data monitoring uniti) Indicating cluster head node CHiAnd L (CH)i)＝ {CHi，j|d(CHi，CHi，j) R (CH) }, wherein, CHi，jIndicating cluster head node CHiJ-th neighbor cluster head node of d (CH)i，CHi，j) Indicating cluster head node CHiAnd neighbor cluster head node CHi，jThe distance between the sensor node and the cluster head node is R (CH), the communication radiuses of the sensor node and the cluster head node in the second data monitoring unit are equal;
given a transmission period τ, and
Figure GDA0002975876170000061
wherein, trRepresenting the time length from the R-th round of clustering to the (R +1) -th round of clustering, R representing the number of current clustering rounds, and setting cluster head nodes CH at intervals of tauiSelecting a next-hop cluster head node from the neighbor cluster head nodes for data transmission, setting r (e) to represent the e-th transmission period in the r-th round of clustering, and defining
Figure GDA0002975876170000062
Indicates the cluster head node CH from the initial stage of the r-th round clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jCoefficient of reliability of transmission data, and
Figure GDA0002975876170000063
wherein, S (CH)i，CHi，jR (e)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jAmount of data successfully transmitted, N (CH)i，CHi，jR (e)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jThe amount of data transmitted;
definition of P (CH)iAnd r (e)) represents a set L (CH) from an initial stage of the r-th round of clustering to a transmission period r (e)i) And a transmission attribute detection coefficient corresponding to the neighbor cluster head node in (1), and
Figure GDA0002975876170000064
definition of W (CH)i，jAnd r (e)) represents a neighbor cluster head node CHi，jBecomes a cluster head node CH in the transmission period r (e)iPriority of the next hop cluster head node, when P (CH)iWhen r (e) is not less than P, then W (CH)i，jAnd r (e)) is represented by:
Figure GDA0002975876170000071
where P denotes a given transmission property detection threshold, and P is 0.4, E (CH)i，jAnd r (e)) represents a neighbor cluster head node CHi，jValue of residual energy by transmission period r (E), E0(CHi，j) Indicating neighbor cluster head node CHi，jThe initial energy values of the sensor nodes and the cluster head nodes in the second data monitoring unit are equal;
when P (CH)iAnd r (e) < P, then W (CH)i，jAnd r (e)) is represented by:
Figure GDA0002975876170000072
in the formula (I), the compound is shown in the specification,
Figure GDA0002975876170000073
indicates the cluster head node CH from the initial stage of the r-th round clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jAnd correcting the reliability coefficient of the transmission data.
Preferably, the cluster head node CH is from the initial stage of the r-th round of clustering to the time of the transmission period r (e)iTo neighbor cluster head node CHi，jCorrection of reliability coefficients of transmitted data
Figure GDA0002975876170000074
The value of (c) is determined in the following manner:
(1) when neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000075
When it is, then
Figure GDA0002975876170000076
Figure GDA0002975876170000077
(2) When neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000078
Then, the cluster head node CH is pairediTo neighbor cluster head node CHi，jThe reliability of data transmission is secondarily detected by setting L (CH)i，j) Indicating neighbor cluster head node CHi，jNeighbor cluster head node set, CHi，j，gIndicating neighbor cluster head node CHi，jR(s) represents the s transmission period in the r round of clustering, mu (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gTo neighbor cluster head node CHi，jStatistical coefficients of the transmitted data, and
Figure GDA0002975876170000079
Figure GDA00029758761700000710
wherein, N (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gTo neighbor cluster head node CHi，jAmount of data transmitted, θ (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gAnd neighbor cluster head node CHi，jA transmission attribute judgment function therebetween, and
Figure GDA0002975876170000081
wherein the content of the first and second substances,
Figure GDA0002975876170000082
represents the neighbor cluster head node CH from the initial stage of the r-th round clustering to the transmission period r(s)i，j，gTo neighbor cluster head node CHi，jA transmission data reliability coefficient;
then
Figure GDA0002975876170000083
The values of (A) are:
when neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000084
Figure GDA00029758761700000812
Then judging the neighbor cluster head node CHi，jIs a malicious cluster head node, at this time order
Figure GDA0002975876170000086
When neighbor cluster head node CHi，jSatisfy the requirement of
Figure GDA0002975876170000087
Figure GDA0002975876170000088
Then judging the neighbor cluster head node CHi，jFrom the initial stage of the r-th round of clustering to the transmission period r (e), there is channel congestion or channel interference phenomenon, at this time, the order is
Figure GDA0002975876170000089
Figure GDA00029758761700000810
Wherein, N (CH)i，CHi，jR (s)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r(s)iTo neighbor cluster head node CHi，jAmount of data transmitted, ρ (N (CH)i，CHi，j，r(s))，N(CHi，CHi，jR (e)) represents a judgment function, and
Figure GDA00029758761700000811
in the set L (CH)i) The neighbor cluster head node with the maximum priority is selected to become a cluster head node CHiAnd the cluster head node of the next hop in the transmission period r (e) performs data transmission.
In the preferred embodiment, the sensor nodes with a clustering structure are used for collecting the environmental parameter data of the traditional Chinese medicine production workshop, the sensor nodes are used for collecting the environmental parameter data of the traditional Chinese medicine production workshop, and the cluster head nodes transmit the collected environmental parameter data in a multi-hop transmission mode, so that the energy consumption of the sensor nodes and the cluster head nodes can be effectively saved, and the life cycle of a wireless sensor network is prolonged; in order to increase the reliability of data transmission between cluster head nodes, the preferred embodiment sets a transmission cycle, and selects a new neighbor cluster head node as a next-hop cluster head node in each transmission cycle according to the specific situation of data transmission between the cluster head node and the neighbor cluster head node, thereby ensuring the efficiency and reliability of data transmission of the cluster head node; compared with the traditional method for selecting the next hop cluster head node, the preferred embodiment defines the priority of the neighbor cluster head node as the next hop cluster head node, the energy value item in the priority is used for ensuring that the selected next hop cluster head node has higher residual energy value, and the reliability coefficient item in the priority is used for ensuring that the cluster head node and the selected next hop cluster head node have higher data transmission efficiency and reliability, the traditional method usually measures the reliability of the cluster head node by calculating the success rate of data transmission between the cluster head nodes, however, when the success rate of data transmission between the cluster head nodes is lower, the cluster head node may be a malicious node, or channel congestion or channel interference occurs in the previous transmission process, thereby causing the condition of lower data transmission efficiency and reliability, and the channel congestion phenomenon or channel interference phenomenon does not exist all the time, however, the method of calculating the reliability coefficient according to the success rate of data transmission between the cluster head node and the neighboring cluster head node can cause the phenomenon of channel congestion and channel interference which occur before to affect the probability that the neighboring cluster head node is selected as the next hop cluster head node, thereby easily causing the phenomenon of energy imbalance between the cluster head nodes, aiming at the situation, the preferred embodiment carries out secondary detection on the neighboring cluster head node with smaller reliability coefficient value, when the neighboring cluster head node of the neighboring cluster head node has smaller efficiency and reliability in the previous transmission period to transmit data to the neighboring cluster head node, the neighboring cluster head node is indicated as a malicious node, at this time, the reliability coefficient of the malicious node is made equal to 0, namely, the malicious node is prevented from being selected as the next hop cluster head node, when the neighboring cluster head node of the neighboring cluster head node has a higher reliability coefficient in the previous transmission period, indicating that the neighbor cluster head node is a case of a lower reliability coefficient due to a channel congestion or channel interference phenomenon, therefore, the preferred embodiment corrects the reliability coefficient of the neighboring cluster head node, and determines the number of times that the neighboring cluster head node is not selected as the next-hop cluster head node by measuring the data amount transmitted from the cluster head node to the neighboring cluster head node, the more the number of times, it indicates that the phenomenon that the neighbor cluster head node is not selected as the next-hop cluster head node due to the channel interference or the channel congestion phenomenon is maintained for a longer time, and at this time, the influence of the channel interference or the channel congestion phenomenon on the neighbor cluster head node should be reduced, namely, the reliability coefficient value of the neighbor cluster head node is increased, so that the cluster head node participates in data transmission again, and the energy consumption among the cluster head nodes is balanced.
Preferably, the second safety management unit is configured to process and analyze the received infrared image, and includes an image processing unit and an image analysis unit, where the image processing unit is configured to perform filtering processing and target segmentation on the received infrared image, and the image analysis unit is configured to analyze the target image obtained by segmentation, so as to determine whether the traditional Chinese medicine production device is faulty in operation.
Preferably, the image processing unit performs target segmentation on the filtered device image by using an OTSU algorithm to obtain a device area image in the device image, determines an optimal threshold value of the OTSU algorithm by using a particle swarm algorithm, and defines a fitness function of the particle swarm algorithm as a maximum inter-class variance, where a larger fitness function value of the particle indicates that a better optimization result of the particle is.
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 (3)

1. A cloud computing-based remote monitoring system for a traditional Chinese medicine production process is characterized by comprising a first remote monitoring module, a second remote monitoring module, an information transmission module and a remote monitoring center, wherein the first remote monitoring module comprises a first data monitoring unit, a first video monitoring unit and a safety monitoring unit, the first data monitoring unit is used for collecting operation data of traditional Chinese medicine production equipment, the first video monitoring unit is used for collecting video images of the traditional Chinese medicine production equipment, the safety monitoring unit is used for collecting infrared images of the traditional Chinese medicine production equipment, the first remote monitoring module transmits the collected data and images to the remote monitoring center through the information transmission module, the second remote monitoring module comprises a second data monitoring unit and a second video monitoring unit, the second data monitoring unit adopts a sensor node to collect environmental parameter data of a traditional Chinese medicine production workshop, the second video monitoring unit is used for acquiring video images of the traditional Chinese medicine production workshop, the second remote monitoring module transmits acquired data and images to the remote monitoring center through the information transmission module, the remote monitoring center comprises a first safety management unit, a second safety management unit, a first information display unit and a second information display unit, the first safety management unit compares received operation data with preset operation data of traditional Chinese medicine production equipment, when the operation data is not equal to the operation data of the given traditional Chinese medicine production equipment, the traditional Chinese medicine production equipment is judged to have operation faults and perform early warning, the first safety management unit compares the received environmental parameter data with a given safety threshold, when the received environmental parameter data exceeds the given safety threshold, the traditional Chinese medicine production workshop is judged to have danger and perform early warning, the second safety management unit is used for processing and analyzing the received infrared image so as to judge whether the production equipment normally operates or not, and giving an early warning when the production equipment is judged to have an operation fault, the first information display unit is used for displaying the received operation data and video image of the traditional Chinese medicine production equipment, and the second information display unit is used for displaying the received environmental parameter data and video image in the traditional Chinese medicine production workshop;
the second data monitoring unit adopts sensor nodes with a clustering structure to acquire environmental parameter data of a traditional Chinese medicine production workshop, data transmission is carried out between cluster head nodes in a multi-hop transmission mode, and CH is setiDenotes the ith cluster head node, L (CH) in the second data monitoring uniti) Indicating cluster head node CHiAnd L (CH)i)＝{CHi，j|d(CHi，CHi，j) R (CH) }, wherein, CHi，jIndicating cluster head node CHiJ-th neighbor cluster head node of d (CH)i，CHi，j) Indicating cluster head node CHiAnd neighbor cluster head node CHi，jThe distance between the sensor node and the cluster head node is R (CH), the communication radiuses of the sensor node and the cluster head node in the second data monitoring unit are equal;
given a transmission period τ, and
Figure FDA0003020329470000011
wherein, trRepresenting the time length from the R-th round of clustering to the (R +1) -th round of clustering, R representing the number of current clustering rounds, and setting cluster head nodes CH at intervals of tauiSelecting a next-hop cluster head node from the neighbor cluster head nodes for data transmission, setting r (e) to represent the e-th transmission period in the r-th round of clustering, and defining
Figure FDA0003020329470000012
Indicates the cluster head node CH from the initial stage of the r-th round clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jCoefficient of reliability of transmission data, and
Figure FDA0003020329470000021
wherein, S (CH)i，CHi，jR (e)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jAmount of data successfully transmitted, N (CH)i，CHi，jR (e)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jThe amount of data transmitted;
definition of P (CH)iAnd r (e)) represents a set L (CH) from an initial stage of the r-th round of clustering to a transmission period r (e)i) And a transmission attribute detection coefficient corresponding to the neighbor cluster head node in (1), and
Figure FDA0003020329470000022
definition of W (CH)i，jAnd r (e)) represents a neighbor cluster head node CHi，jBecomes a cluster head node CH in the transmission period r (e)iPriority of the next hop cluster head node, when P (CH)iWhen r (e) is not less than P, then W (CH)i，jAnd r (e)) is represented by:
Figure FDA0003020329470000023
where P denotes a given transmission property detection threshold, and P is 0.4, E (CH)i，jR (e)) indicates the neighbor cluster head node CH by the transmission period r (e)i，jResidual energy value of, E0(CHi，j) Indicating neighbor cluster head node CHi，jThe initial energy values of the sensor nodes and the cluster head nodes in the second data monitoring unit are equal;
when P (CH)iAnd r (e) < P, then W (CH)i，jAnd r (e)) is represented by:
Figure FDA0003020329470000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003020329470000025
indicates the cluster head node CH from the initial stage of the r-th round clustering to the transmission period r (e)iTo neighbor cluster head node CHi，jA correction value of a reliability coefficient of the transmission data;
from the initial stage of the r-th round of clustering to the time of the transmission period r (e)iTo neighbor cluster head node CHi，jCorrection of reliability coefficients of transmitted data
Figure FDA0003020329470000026
The value of (c) is determined in the following manner:
(1) when neighbor cluster head node CHi，jSatisfy the requirement of
Figure FDA0003020329470000027
When it is, then
Figure FDA0003020329470000028
Figure FDA0003020329470000029
(2) When neighbor cluster head node CHi，jSatisfy the requirement of
Figure FDA0003020329470000031
Then, the cluster head node CH is pairediTo neighbor cluster head node CHi，jThe reliability of data transmission is secondarily detected by setting L (CH)i，j) Indicating neighbor cluster head node CHi，jNeighbor cluster head node set, CHi，j，gIndicating neighbor cluster head node CHi，jR(s) represents the s transmission period in the r round of clustering, mu (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gTo neighbor cluster head node CHi，jStatistical coefficients of the transmitted data, and
Figure FDA0003020329470000032
Figure FDA0003020329470000033
wherein, N (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gTo neighbor cluster head node CHi，jAmount of data transmitted, θ (CH)i，j，g，CHi，jAnd r (s)) represents neighbor cluster head node CH from initial stage of r-th round clustering to transmission period r(s)i，j，gAnd neighbor cluster head node CHi，jA transmission attribute judgment function therebetween, and
Figure FDA0003020329470000034
wherein the content of the first and second substances,
Figure FDA0003020329470000035
represents the neighbor cluster head node CH from the initial stage of the r-th round clustering to the transmission period r(s)i，j，gTo neighbor cluster head node CHi，jReliability coefficient of the transmission data;
then
Figure FDA0003020329470000036
The values of (A) are:
when neighbor cluster head node CHi，jSatisfy the requirement of
Figure FDA0003020329470000037
Figure FDA0003020329470000038
Then judging the neighbor cluster head node CHi，jIs a malicious cluster head node, at this time order
Figure FDA0003020329470000039
When neighbor cluster head node CHi，jSatisfy the requirement of
Figure FDA00030203294700000310
Figure FDA00030203294700000311
Then judging the neighbor cluster head node CHi，jFrom the initial stage of the r-th round of clustering to the transmission period r (e), there is channel congestion or channel interference phenomenon, at this time, the order is
Figure FDA00030203294700000312
Figure FDA00030203294700000313
Wherein, N (CH)i，CHi，jR (s)) represents the cluster head node CH from the initial stage of the r-th round of clustering to the transmission period r(s)iTo neighbor cluster head node CHi，jAmount of data transmitted, ρ (N (CH)i，CHi，j，r(s))，N(CHi，CHi，jR (e)) represents a judgment function, and
Figure FDA0003020329470000041
in the set L (CH)i) The neighbor cluster head node with the maximum priority is selected to become a cluster head node CHiAnd the cluster head node of the next hop in the transmission period r (e) performs data transmission.
2. The cloud-computing-based remote monitoring system for the production process of traditional Chinese medicines, as claimed in claim 1, wherein the second security management unit is configured to process and analyze the received infrared image, and comprises an image processing unit and an image analysis unit, the image processing unit is configured to perform filtering processing and target segmentation on the received infrared image, and the image analysis unit is configured to analyze the segmented target image, so as to determine whether the traditional Chinese medicine production equipment is faulty in operation.
3. The cloud-computing-based remote monitoring system for the production process of traditional Chinese medicines, as recited in claim 2, characterized in that the image processing unit performs target segmentation on the filtered device image by using an OTSU algorithm to obtain a device region image in the device image, determines the optimal threshold value of the OTSU algorithm by using a particle swarm algorithm, defines the fitness function of the particle swarm algorithm as the maximum between-class variance, and a larger fitness function value of the particle indicates that the optimization result of the particle is better.
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