CN114764420B - Laboratory integrated lighting management system - Google Patents
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- 238000005286 illumination Methods 0.000 claims abstract description 39
- 238000004891 communication Methods 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 12
- 230000001954 sterilising effect Effects 0.000 claims abstract description 7
- 230000002159 abnormal effect Effects 0.000 claims description 52
- 230000006399 behavior Effects 0.000 claims description 24
- 238000012544 monitoring process Methods 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 20
- 230000009471 action Effects 0.000 claims description 10
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- 238000004364 calculation method Methods 0.000 claims description 8
- 238000003709 image segmentation Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 6
- 230000035772 mutation Effects 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000003064 k means clustering Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 4
- 238000011217 control strategy Methods 0.000 claims description 3
- 230000003542 behavioural effect Effects 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 11
- 230000005611 electricity Effects 0.000 abstract description 2
- 230000006855 networking Effects 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000008030 elimination Effects 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 238000004659 sterilization and disinfection Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011112 process operation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000002070 germicidal effect Effects 0.000 description 1
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- 238000012954 risk control Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
Abstract
The invention discloses a laboratory integrated lighting management system, comprising: the first control layer, the execution layer, the communication layer and the second control layer; wherein the first control layer is connected with the execution layer through a wired or wireless signal; the first control layer is connected with the communication layer through MODBUS; the communication layer is connected with the second control layer through an Ethernet or a 5G network. The method comprises the steps of carrying out centralized control and management on various lighting modes such as normal lighting, standby lighting, local lighting, ultraviolet sterilizing lamps and the like, adjusting different working modes according to actual working conditions, and meanwhile, comparing and collecting working information of equipment, personnel and lamps by adopting a computer technology, a digital technology and a networking technology, and uploading the working information to an upper computer system, wherein remote control, remote operation and the like of an integrated lighting management system can be realized through operation of the upper computer system; in addition, the workload condition of the experimental equipment is calculated through the illumination time, the electricity consumption and the like acquired by the computer technology, the digital technology and the like.
Description
Technical Field
The invention relates to the technical field of management, in particular to a laboratory integrated lighting management system.
Background
According to the experimental process requirements, scientific researchers need to operate various lighting modes in the using process of experimental environments such as laboratories, and due to various types, misoperation easily occurs in the operating process, the accuracy of experimental data is influenced, and even the personal safety and occupational health of the scientific researchers are greatly influenced. At present, the existing indoor common illumination only meets the daily illumination in a laboratory, and remote operation and control cannot be realized. The existing intelligent lighting system only realizes operation on a lighting mode and remote control in a laboratory, and does not relate to control and elimination of an ultraviolet sterilization system, an alarm system, a fire-fighting linkage system, an indoor dynamic monitoring system and safety risks. At present, scientific research institutions mostly adopt manual routine inspection modes, the labor cost is high, the inspection cannot be performed in real time, the safety risk control is delayed, and the safety risk is managed and controlled only after the risk occurs. At present, a laboratory illumination database does not exist, scientific education of scientific research institutions has no big data support, and the difficulty of scientific popularization is large and the effect is poor.
Disclosure of Invention
The present invention aims to solve, at least to some extent, one of the technical problems in the above-described technology. Therefore, the invention aims to provide a laboratory integrated illumination management system which is used for centrally controlling and managing various illumination modes such as normal illumination, standby illumination, local illumination, ultraviolet germicidal lamps and the like, adjusting different working modes according to actual working conditions, comparing and collecting working information of equipment, personnel and lamps by adopting a computer technology, a digital technology and a networking technology, uploading the working information to an upper computer system, and realizing remote control, remote operation and the like of the integrated illumination management system through the operation of the upper computer system; in addition, the workload condition of the experimental equipment is calculated through the illumination time, the electricity consumption and the like acquired by the computer technology, the digital technology and the like. The lighting system integrates a big data module, a database can be formed aiming at the lighting dynamic data, and accurate discrimination is realized through a clustering algorithm, a classifying algorithm and an association rule mining algorithm.
To achieve the above object, an embodiment of the present invention provides a laboratory integrated lighting management system, including: the first control layer, the execution layer, the communication layer and the second control layer; wherein,
the first control layer is connected with the execution layer through a wired or wireless signal;
the first control layer is connected with the communication layer through MODBUS;
the communication layer is connected with the second control layer through an Ethernet or a 5G network;
the first control layer comprises a controller and a human-computer interface;
the controller is an embedded controller and is used for:
according to the stored running program and the big data control strategy, an action instruction is sent to the execution layer, and feedback data of the execution layer are collected;
receiving an operation instruction of an upper computer;
the human-computer interface is used for displaying the running parameters and the state of the system;
the controller comprises a monitoring module, a communication module and a big data module, wherein the communication module is respectively connected with the monitoring module and the big data module.
According to some embodiments of the invention, the monitoring module is used for acquiring monitoring data and performing predictive early warning analysis by adopting a K-means behavior analysis algorithm.
According to some embodiments of the invention, the big data module stores therein an association rule algorithm, the association rule being an implication expression shaped as X-Y, wherein X and Y are disjoint sets of terms, i.eThe strength of the association rule is represented by a support index and a confidence index.
According to some embodiments of the invention, the big data module processes big data based on an association rule algorithm, including:
s1, acquiring a data set D, and setting a support degree threshold alpha;
s2, scanning the whole data set D to obtain all the data which appear as candidate frequent 1 item sets;
s3, excavating a frequent k item set; the support degree of the candidate frequent k item set is calculated by the scanning data; removing the data set with the support degree lower than the threshold value in the candidate frequent k item sets to obtain frequent k item sets; if the obtained frequent k item set is empty, directly returning the set of the frequent k-1 item set as an algorithm result, and ending the algorithm; if the obtained frequent k item set has only one item, directly returning the set of the frequent k item set as an algorithm result, and ending the algorithm; based on the frequent k item set, generating a candidate frequent k+1 item set by connection;
and S4, making k=k+1, and switching to S2.
According to some embodiments of the invention, the execution layer is configured to receive a controller action command completion action and feed back various operation data to the controller.
According to some embodiments of the invention, the executive layer comprises a lighting system, a sterilizing system, an alarm system, a fire protection linkage system and an indoor dynamic monitoring system.
According to some embodiments of the invention, the communication layer includes a remote communication module for reading all data parameters in the controller and uploading to a second control layer.
According to some embodiments of the invention, the second control layer includes a mobile terminal and an integrated management platform terminal;
the mobile terminal and the integrated management platform terminal can remotely check the running state and parameters of the system through the Internet, and can reversely control according to the authority and call the big data module to process data.
According to some embodiments of the invention, the lighting system comprises:
a plurality of illumination modules disposed at different locations in the laboratory;
the human body identification module is used for:
acquiring an actual scene image of a laboratory;
performing gridding treatment on the actual scene image, and performing image segmentation to obtain a plurality of sub-actual scene images;
acquiring a standard scene image of a laboratory;
performing gridding treatment on the standard scene image, and performing image segmentation to obtain a plurality of sub-standard scene images;
constructing a matching group by the sub-actual scene image and the corresponding sub-standard scene image;
calculating the absolute value of the difference value between the pixel value of each pixel point on the sub-actual scene image and the pixel value of the corresponding pixel point on the sub-standard scene image in the matching group, and carrying out summation calculation to obtain a difference value;
screening sub-actual scene images with the difference value larger than a preset difference value, and taking the sub-actual scene images as target sub-actual scene images;
constructing a difference image according to the position relation of the target sub-actual scene image;
performing edge contour extraction processing on the difference image to obtain contour features;
matching the contour features with preset human body contour features, and taking an image corresponding to the contour features as a human body image when the matching is successful;
an adjustment module for:
determining the current position of a human body included in the human body image according to an actual scene image;
acquiring human body key points according to the human body image, determining a human body skeleton according to the human body key points, calculating limb vector characteristics of each joint according to the human body skeleton, determining human body behaviors according to the limb vector characteristics, performing similarity calculation on the human body behaviors and preset human body behaviors in a human body behavior-working state table, and determining a working state corresponding to the preset human body behaviors with the highest similarity as a working state corresponding to the human body behaviors;
inquiring a preset lighting strategy table according to the current position and the working state to obtain a target lighting scheme;
determining a target ambient light level at a current location according to the target lighting scheme;
acquiring the current ambient light brightness of the current position;
and the illumination module corresponding to the current position adjusts the brightness according to the target ambient light brightness and the current ambient light brightness.
According to some embodiments of the invention, the controller further comprises:
a marking module for:
acquiring historical loss data of the lighting modules at all positions;
analyzing the historical loss data to determine normal data and abnormal data;
classifying the abnormal data according to the abnormal type to obtain first abnormal data and second abnormal data;
identifying cleaning mutation data of the first abnormal data by using a K-means clustering algorithm to obtain first processing data;
performing data segmentation on the second abnormal data to obtain a plurality of sub second abnormal data;
extracting features of the second abnormal data, and determining a feature vector matrix;
according to the feature vector matrix, matching is carried out on the feature vector matrix and a preset feature vector matrix in a storage database, and history data corresponding to the successfully matched preset feature vector matrix is obtained;
performing data segmentation on the historical data to obtain a plurality of sub-historical data;
taking every two adjacent sub-historical data in a plurality of sub-historical data as a data set, recording the data set as a first data set, and determining a first association relationship between the two adjacent sub-historical data in the first data set;
taking every two adjacent sub second abnormal data in the plurality of sub second abnormal data as a data set, recording the data set as a second data set, and determining a second association relation between the two adjacent sub second abnormal data in the second data set;
comparing the first association relationship with a corresponding second association relationship, and determining a disqualified second data set when the comparison result is inconsistent; when the comparison results are consistent, determining that the second data set is qualified;
determining a qualified second data set adjacent to the unqualified second data set;
determining a first data set corresponding to the disqualified second data set as a first association set;
determining a first data group corresponding to a qualified second data group adjacent to the unqualified second data group as a second association group;
according to a third association relation between the first association group and the second association group and a qualified second data group adjacent to the unqualified second data group, carrying out data correction on the unqualified second data group to obtain second processing data;
predicting the residual life of the lighting module at each position according to the normal data, the first processing data and the second processing data, and comparing the residual life with a preset residual life threshold value respectively;
and marking the lighting module with the residual life smaller than the preset residual life.
The invention has the beneficial effects that:
1. the system has clear level, high management efficiency and reasonable function division through the first control layer, the execution layer, the communication layer and the second control layer, and realizes the prejudgment, alarm and linkage centralized control system of the illumination mode operation of the laboratory. The lighting system, the indoor dynamic monitoring system, the fire control system and the alarm system are controlled in a centralized way, so that system linkage is realized, and the experimental lighting control and risk elimination are automatically and efficiently realized.
2. Aiming at the traditional lighting system, the K-means algorithm is introduced, so that the judgment requirements of high complexity and multiple operation types of experimental process operation can be met, the database is optimized continuously through the machine learning algorithm, the recognition accuracy is improved, and the false alarm rate is reduced.
3. The real-time data monitored by the indoor dynamic monitoring system, the fire control system and the alarm system integrated by the illumination integrated control system form an illumination database of an experimental environment, an illumination prediction early warning analysis function is added through a correlation rule algorithm, and meanwhile, feedback control is added, so that a prediction alarm prompt is made before a risk fault occurs.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic diagram of a laboratory integrated lighting management system according to one embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
As shown in fig. 1, an embodiment of the present invention proposes a laboratory integrated lighting management system, including: the first control layer, the execution layer, the communication layer and the second control layer; wherein,
the first control layer is connected with the execution layer through a wired or wireless signal;
the first control layer is connected with the communication layer through MODBUS;
the communication layer is connected with the second control layer through an Ethernet or a 5G network;
the first control layer comprises a controller and a human-computer interface;
the controller is an embedded controller and is used for:
according to the stored running program and the big data control strategy, an action instruction is sent to the execution layer, and feedback data of the execution layer are collected;
receiving an operation instruction of an upper computer;
the human-computer interface is used for displaying the running parameters and the state of the system;
the controller comprises a monitoring module, a communication module and a big data module, wherein the communication module is respectively connected with the monitoring module and the big data module.
The intelligent illumination system solves the problems of safe intelligent control, alarm and linkage control in the daily illumination process of a laboratory, improves the management level, reduces the maintenance cost, protects the lamp, prolongs the service life, achieves the energy-saving effect, and is safe to use and linked with a fire-fighting system. A traceable illumination information database is formed, and the database can meet the statistics of big laboratory data.
According to some embodiments of the present invention, the monitoring module is configured to obtain monitoring data, and perform predictive early warning analysis by using a K-means behavior analysis algorithm, including: the initialized k samples are selected as an initial cluster center a=a 1 ,a 2 ,...a k The method comprises the steps of carrying out a first treatment on the surface of the For each sample x in the dataset i Calculating the distances from the clustering center to k clustering centers and dividing the clustering center into classes corresponding to the clustering centers with the smallest distances; for each category a j Recalculating its cluster centerThe above two steps are repeated until the suspension condition is reached.
According to some embodiments of the invention, the big data module stores therein an association rule algorithm, the association rule being an implication expression shaped as X-Y, wherein X and Y are disjoint sets of terms, i.eThe strength of the association rule is represented by a support index and a confidence index.
Confidence (Confidence): the proportion of the transactions containing X and Y is expressed, namely the proportion of the transactions containing X and Y to the transactions containing X. The formula is expressed: confidence=P (X & Y)/P (X).
Support (Support): representing the proportion of transactions that contain both X and Y to all transactions. If P (X) is used to denote the proportion of X transactions, support=p (X & Y).
According to some embodiments of the invention, the big data module processes big data based on an association rule algorithm, including:
s1, acquiring a data set D, and setting a support degree threshold alpha;
s2, scanning the whole data set D to obtain all the data which appear as candidate frequent 1 item sets;
s3, excavating a frequent k item set; the support degree of the candidate frequent k item set is calculated by the scanning data; removing the data set with the support degree lower than the threshold value in the candidate frequent k item sets to obtain frequent k item sets; if the obtained frequent k item set is empty, directly returning the set of the frequent k-1 item set as an algorithm result, and ending the algorithm; if the obtained frequent k item set has only one item, directly returning the set of the frequent k item set as an algorithm result, and ending the algorithm; based on the frequent k item set, generating a candidate frequent k+1 item set by connection;
and S4, making k=k+1, and switching to S2.
According to some embodiments of the invention, the execution layer is configured to receive a controller action command completion action and feed back various operation data to the controller.
According to some embodiments of the invention, the executive layer comprises a lighting system, a sterilizing system, an alarm system, a fire protection linkage system and an indoor dynamic monitoring system.
Acquiring dynamic monitoring data based on an indoor dynamic monitoring system and transmitting the dynamic monitoring data to a first control layer;
acquiring illumination data based on an illumination system and transmitting the illumination data to a first control layer;
the first control layer analyzes the dynamic monitoring data and the illumination data, and controls the alarm system to send out an alarm prompt when abnormal conditions are determined to occur, and enables the fire-fighting linkage system to conduct linkage;
the first control layer controls the sterilization system to perform sterilization action every preset time period.
According to some embodiments of the invention, the communication layer includes a remote communication module for reading all data parameters in the controller and uploading to a second control layer.
According to some embodiments of the invention, the second control layer includes a mobile terminal and an integrated management platform terminal;
the mobile terminal and the integrated management platform terminal can remotely check the running state and parameters of the system through the Internet, and can reversely control according to the authority and call the big data module to process data.
In one embodiment, the mobile terminal is a computer system, and the integrated management platform terminal is an upper computer system.
The invention has the beneficial effects that:
1. the system has clear level, high management efficiency and reasonable function division through the first control layer, the execution layer, the communication layer and the second control layer, and realizes the prejudgment, alarm and linkage centralized control system of the illumination mode operation of the laboratory. The lighting system, the indoor dynamic monitoring system, the fire control system and the alarm system are controlled in a centralized way, so that system linkage is realized, and the experimental lighting control and risk elimination are automatically and efficiently realized.
2. Aiming at the traditional lighting system, the K-means algorithm is introduced, so that the judgment requirements of high complexity and multiple operation types of experimental process operation can be met, the database is optimized continuously through the machine learning algorithm, the recognition accuracy is improved, and the false alarm rate is reduced.
3. The real-time data monitored by the indoor dynamic monitoring system, the fire control system and the alarm system integrated by the illumination integrated control system form an illumination database of an experimental environment, an illumination prediction early warning analysis function is added through a correlation rule algorithm, and meanwhile, feedback control is added, so that a prediction alarm prompt is made before a risk fault occurs.
According to some embodiments of the invention, the lighting system comprises:
a plurality of illumination modules disposed at different locations in the laboratory;
the human body identification module is used for:
acquiring an actual scene image of a laboratory;
performing gridding treatment on the actual scene image, and performing image segmentation to obtain a plurality of sub-actual scene images;
acquiring a standard scene image of a laboratory;
performing gridding treatment on the standard scene image, and performing image segmentation to obtain a plurality of sub-standard scene images;
constructing a matching group by the sub-actual scene image and the corresponding sub-standard scene image;
calculating the absolute value of the difference value between the pixel value of each pixel point on the sub-actual scene image and the pixel value of the corresponding pixel point on the sub-standard scene image in the matching group, and carrying out summation calculation to obtain a difference value;
screening sub-actual scene images with the difference value larger than a preset difference value, and taking the sub-actual scene images as target sub-actual scene images;
constructing a difference image according to the position relation of the target sub-actual scene image;
performing edge contour extraction processing on the difference image to obtain contour features;
matching the contour features with preset human body contour features, and taking an image corresponding to the contour features as a human body image when the matching is successful;
an adjustment module for:
determining the current position of a human body included in the human body image according to an actual scene image;
acquiring human body key points according to the human body image, determining a human body skeleton according to the human body key points, calculating limb vector characteristics of each joint according to the human body skeleton, determining human body behaviors according to the limb vector characteristics, performing similarity calculation on the human body behaviors and preset human body behaviors in a human body behavior-working state table, and determining a working state corresponding to the preset human body behaviors with the highest similarity as a working state corresponding to the human body behaviors;
inquiring a preset lighting strategy table according to the current position and the working state to obtain a target lighting scheme;
determining a target ambient light level at a current location according to the target lighting scheme;
acquiring the current ambient light brightness of the current position;
and the illumination module corresponding to the current position adjusts the brightness according to the target ambient light brightness and the current ambient light brightness.
The working principle of the technical scheme is as follows: the illumination system comprises: a plurality of illumination modules disposed at different locations in the laboratory; the human body identification module is used for: acquiring an actual scene image of a laboratory; performing gridding treatment on the actual scene image, and performing image segmentation to obtain a plurality of sub-actual scene images; each grid corresponds to a sub-actual scene image. Acquiring a standard scene image of a laboratory; the standard scene image is a scene image that does not include a human body. Performing gridding treatment on the standard scene image, and performing image segmentation to obtain a plurality of sub-standard scene images; each grid corresponds to a sub-standard scene image. The size of the sub-standard scene image corresponds to the size of the sub-actual scene image. Constructing a matching group by the sub-actual scene image and the corresponding sub-standard scene image; i.e. sub-actual scene images at the same location, sub-standard scene images as a matching group. Calculating the absolute value of the difference value between the pixel value of each pixel point on the sub-actual scene image and the pixel value of the corresponding pixel point on the sub-standard scene image in the matching group, and carrying out summation calculation to obtain a difference value; screening sub-actual scene images with the difference value larger than a preset difference value, and taking the sub-actual scene images as target sub-actual scene images; constructing a difference image according to the position relation of the target sub-actual scene image; the difference image is determined from a combination of the plurality of target sub-actual scene images. Performing edge contour extraction processing on the difference image to obtain contour features; matching the contour features with preset human body contour features, and taking an image corresponding to the contour features as a human body image when the matching is successful; an adjustment module for: determining the current position of a human body included in the human body image according to an actual scene image; acquiring human body key points according to the human body image, determining a human body skeleton according to the human body key points, calculating limb vector characteristics of each joint according to the human body skeleton, determining human body behaviors according to the limb vector characteristics, performing similarity calculation on the human body behaviors and preset human body behaviors in a human body behavior-working state table, and determining a working state corresponding to the preset human body behaviors with the highest similarity as a working state corresponding to the human body behaviors; inquiring a preset lighting strategy table according to the current position and the working state to obtain a target lighting scheme; determining a target ambient light level at a current location according to the target lighting scheme; acquiring the current ambient light brightness of the current position; and the illumination module corresponding to the current position adjusts the brightness according to the target ambient light brightness and the current ambient light brightness.
The beneficial effects of the technical scheme are that: based on gridding matching, the difference image is accurately determined, the detection range is reduced, the calculated amount is reduced, and the human body is accurately identified according to the difference image. And accurately determining the human body behaviors according to the human body images, determining corresponding working states according to the human body behaviors, and setting corresponding target lighting schemes in different lighting areas based on different working states of the human body. And then control the illumination module that the current position corresponds to carries out the luminance adjustment according to target ambient light brightness and current ambient light brightness, avoid the waste of illumination resource, guarantee the rational distribution of illumination resource, improve user experience.
According to some embodiments of the invention, the controller further comprises:
a marking module for:
acquiring historical loss data of the lighting modules at all positions;
analyzing the historical loss data to determine normal data and abnormal data;
classifying the abnormal data according to the abnormal type to obtain first abnormal data and second abnormal data;
identifying cleaning mutation data of the first abnormal data by using a K-means clustering algorithm to obtain first processing data;
performing data segmentation on the second abnormal data to obtain a plurality of sub second abnormal data;
extracting features of the second abnormal data, and determining a feature vector matrix;
according to the feature vector matrix, matching is carried out on the feature vector matrix and a preset feature vector matrix in a storage database, and history data corresponding to the successfully matched preset feature vector matrix is obtained;
performing data segmentation on the historical data to obtain a plurality of sub-historical data;
taking every two adjacent sub-historical data in a plurality of sub-historical data as a data set, recording the data set as a first data set, and determining a first association relationship between the two adjacent sub-historical data in the first data set;
taking every two adjacent sub second abnormal data in the plurality of sub second abnormal data as a data set, recording the data set as a second data set, and determining a second association relation between the two adjacent sub second abnormal data in the second data set;
comparing the first association relationship with a corresponding second association relationship, and determining a disqualified second data set when the comparison result is inconsistent; when the comparison results are consistent, determining that the second data set is qualified;
determining a qualified second data set adjacent to the unqualified second data set;
determining a first data set corresponding to the disqualified second data set as a first association set;
determining a first data group corresponding to a qualified second data group adjacent to the unqualified second data group as a second association group;
according to a third association relation between the first association group and the second association group and a qualified second data group adjacent to the unqualified second data group, carrying out data correction on the unqualified second data group to obtain second processing data;
predicting the residual life of the lighting module at each position according to the normal data, the first processing data and the second processing data, and comparing the residual life with a preset residual life threshold value respectively;
and marking the lighting module with the residual life smaller than the preset residual life.
The working principle of the technical scheme is as follows: a marking module for: acquiring historical loss data of the lighting modules at all positions; analyzing the historical loss data to determine normal data and abnormal data; classifying the abnormal data according to the abnormal type to obtain first abnormal data and second abnormal data; the first anomaly data comprises mutation data. The second abnormal data is missing data. Identifying cleaning mutation data of the first abnormal data by using a K-means clustering algorithm to obtain first processing data; performing data segmentation on the second abnormal data to obtain a plurality of sub second abnormal data; extracting features of the second abnormal data, and determining a feature vector matrix; according to the feature vector matrix, matching is carried out on the feature vector matrix and a preset feature vector matrix in a storage database, and history data corresponding to the successfully matched preset feature vector matrix is obtained; performing data segmentation on the historical data to obtain a plurality of sub-historical data; taking every two adjacent sub-historical data in a plurality of sub-historical data as a data set, recording the data set as a first data set, and determining a first association relationship between the two adjacent sub-historical data in the first data set; taking every two adjacent sub second abnormal data in the plurality of sub second abnormal data as a data set, recording the data set as a second data set, and determining a second association relation between the two adjacent sub second abnormal data in the second data set; comparing the first association relationship with a corresponding second association relationship, and determining a disqualified second data set when the comparison result is inconsistent; when the comparison results are consistent, determining that the second data set is qualified; determining a qualified second data set adjacent to the unqualified second data set; determining a first data set corresponding to the disqualified second data set as a first association set; determining a first data group corresponding to a qualified second data group adjacent to the unqualified second data group as a second association group; according to a third association relation between the first association group and the second association group and a qualified second data group adjacent to the unqualified second data group, carrying out data correction on the unqualified second data group to obtain second processing data; predicting the residual life of the lighting module at each position according to the normal data, the first processing data and the second processing data, and comparing the residual life with a preset residual life threshold value respectively; and marking the lighting module with the residual life smaller than the preset residual life.
The beneficial effects of the technical scheme are that: identifying cleaning mutation data of the first abnormal data by using a K-means clustering algorithm to obtain first processing data; performing data correction on the unqualified second data set according to a third association relation between the first association set and the second association set and a qualified second data set adjacent to the unqualified second data set based on the matched historical data to obtain second processing data; the correction and the supplementation of the missing data segment in the second abnormal data are realized, and the accuracy of the data is ensured. Predicting the residual life of the lighting module at each position according to the normal data, the first processing data and the second processing data, and comparing the residual life with a preset residual life threshold value respectively; the accuracy of determining the residual life of the lighting modules at each position is improved, the lighting modules with the residual life smaller than the preset residual life are marked, corresponding lighting modules are convenient to purchase in time or replace in time, and the lighting effect is guaranteed.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A laboratory integrated lighting management system, comprising: the first control layer, the execution layer, the communication layer and the second control layer; wherein,
the first control layer is connected with the execution layer through a wired or wireless signal;
the first control layer is connected with the communication layer through MODBUS;
the communication layer is connected with the second control layer through an Ethernet or a 5G network;
the first control layer comprises a controller and a human-computer interface;
the controller is an embedded controller and is used for:
according to the stored running program and the big data control strategy, an action instruction is sent to the execution layer, and feedback data of the execution layer are collected;
receiving an operation instruction of an upper computer;
the human-computer interface is used for displaying the running parameters and the state of the system;
the controller comprises a monitoring module, a communication module and a big data module, wherein the communication module is respectively connected with the monitoring module and the big data module;
the execution layer comprises a lighting system, a sterilizing system, an alarm system, a fire-fighting linkage system and an indoor dynamic monitoring system;
the illumination system comprises:
a plurality of illumination modules disposed at different locations in the laboratory;
the human body identification module is used for:
acquiring an actual scene image of a laboratory;
performing gridding treatment on the actual scene image, and performing image segmentation to obtain a plurality of sub-actual scene images;
acquiring a standard scene image of a laboratory;
performing gridding treatment on the standard scene image, and performing image segmentation to obtain a plurality of sub-standard scene images;
constructing a matching group by the sub-actual scene image and the corresponding sub-standard scene image;
calculating the absolute value of the difference value between the pixel value of each pixel point on the sub-actual scene image and the pixel value of the corresponding pixel point on the sub-standard scene image in the matching group, and carrying out summation calculation to obtain a difference value;
screening sub-actual scene images with the difference value larger than a preset difference value, and taking the sub-actual scene images as target sub-actual scene images;
constructing a difference image according to the position relation of the target sub-actual scene image;
performing edge contour extraction processing on the difference image to obtain contour features;
matching the contour features with preset human body contour features, and taking an image corresponding to the contour features as a human body image when the matching is successful;
an adjustment module for:
determining the current position of a human body included in the human body image according to an actual scene image;
acquiring human body key points according to the human body image, determining a human body skeleton according to the human body key points, calculating limb vector characteristics of each joint according to the human body skeleton, determining human body behaviors according to the limb vector characteristics, performing similarity calculation on the human body behaviors and preset human body behaviors in a human body behavior-working state table, and determining a working state corresponding to the preset human body behaviors with the highest similarity as a working state corresponding to the human body behaviors;
inquiring a preset lighting strategy table according to the current position and the working state to obtain a target lighting scheme;
determining a target ambient light level at a current location according to the target lighting scheme;
acquiring the current ambient light brightness of the current position;
and the illumination module corresponding to the current position adjusts the brightness according to the target ambient light brightness and the current ambient light brightness.
2. The laboratory integrated lighting management system of claim 1, wherein the monitoring module is configured to obtain monitoring data and perform predictive early warning analysis using a K-means behavioral analysis algorithm.
3. The laboratory integrated lighting management system of claim 1, wherein said big data module has stored therein an association rule algorithm, the association rule being an implication expression shaped as x→y, wherein X and Y are disjoint sets of terms, X n Y = ∅; the strength of the association rule is represented by a support index and a confidence index.
4. The laboratory integrated lighting management system of claim 3, wherein the big data module processes big data based on an association rule algorithm, comprising:
s1, acquiring a data set D, and setting a support degree threshold alpha;
s2, scanning the whole data set D to obtain all the data which appear as candidate frequent 1 item sets;
s3, excavating a frequent k item set; the support degree of the candidate frequent k item set is calculated by the scanning data; removing the data set with the support degree lower than the threshold value in the candidate frequent k item sets to obtain frequent k item sets; if the obtained frequent k item set is empty, directly returning the set of the frequent k-1 item set as an algorithm result, and ending the algorithm; if the obtained frequent k item set has only one item, directly returning the set of the frequent k item set as an algorithm result, and ending the algorithm; based on the frequent k item set, generating a candidate frequent k+1 item set by connection;
and S4, making k=k+1, and switching to S2.
5. The laboratory integrated lighting management system of claim 1, wherein the executive layer is configured to receive controller action instructions to complete actions and to feed back various operational data to the controller.
6. The laboratory integrated lighting management system of claim 1, wherein the communication layer comprises a remote communication module for reading all data parameters within the controller and uploading to a second control layer.
7. The laboratory integrated lighting management system of claim 1, wherein the second control layer comprises a mobile terminal and an integrated management platform terminal;
the mobile terminal and the integrated management platform terminal can remotely check the running state and parameters of the system through the Internet, and can reversely control according to the authority and call the big data module to process data.
8. The laboratory integrated lighting management system of claim 1, wherein the controller further comprises:
a marking module for:
acquiring historical loss data of the lighting modules at all positions;
analyzing the historical loss data to determine normal data and abnormal data;
classifying the abnormal data according to the abnormal type to obtain first abnormal data and second abnormal data;
identifying cleaning mutation data of the first abnormal data by using a K-means clustering algorithm to obtain first processing data;
performing data segmentation on the second abnormal data to obtain a plurality of sub second abnormal data;
extracting features of the second abnormal data, and determining a feature vector matrix;
according to the feature vector matrix, matching is carried out on the feature vector matrix and a preset feature vector matrix in a storage database, and history data corresponding to the successfully matched preset feature vector matrix is obtained;
performing data segmentation on the historical data to obtain a plurality of sub-historical data;
taking every two adjacent sub-historical data in a plurality of sub-historical data as a data set, recording the data set as a first data set, and determining a first association relationship between the two adjacent sub-historical data in the first data set;
taking every two adjacent sub second abnormal data in the plurality of sub second abnormal data as a data set, recording the data set as a second data set, and determining a second association relation between the two adjacent sub second abnormal data in the second data set;
comparing the first association relationship with a corresponding second association relationship, and determining a disqualified second data set when the comparison result is inconsistent; when the comparison results are consistent, determining that the second data set is qualified;
determining a qualified second data set adjacent to the unqualified second data set;
determining a first data set corresponding to the disqualified second data set as a first association set;
determining a first data group corresponding to a qualified second data group adjacent to the unqualified second data group as a second association group;
according to a third association relation between the first association group and the second association group and a qualified second data group adjacent to the unqualified second data group, carrying out data correction on the unqualified second data group to obtain second processing data;
predicting the residual life of the lighting module at each position according to the normal data, the first processing data and the second processing data, and comparing the residual life with a preset residual life threshold value respectively;
and marking the lighting module with the residual life smaller than the preset residual life.
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