CN118014313B - Remote monitoring system is equipped in storage based on thing networking - Google Patents
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Abstract
The invention discloses a remote monitoring system of storage equipment based on the Internet of things, and relates to the technical field of storage remote monitoring; according to the invention, the accuracy of data analysis is improved by presetting the environment reference value sets of different cargo types, a plurality of groups of corresponding monitoring points are deployed in the target storage area, the comprehensive monitoring of the environment condition of the target storage area is realized, the comprehensiveness of data acquisition is realized, and a sufficient data basis is provided for subsequent analysis, so that the problem that a remote monitoring system for storage equipment in the prior art usually only pays attention to the use condition of the storage equipment, the monitoring of the storage environment is ignored, and the safety and quality of the cargo in the storage process are ensured.
Description
Technical Field
The invention relates to the technical field of remote monitoring of storage, in particular to a remote monitoring system of storage equipment based on the Internet of things.
Background
Along with the rapid development of the internet of things technology, traditional warehouse management and monitoring gradually changes to intelligent and automatic. However, the existing remote monitoring system for warehouse equipment only usually focuses on the use condition of warehouse equipment, such as the sorting condition of sorting equipment and the use condition of a carrying robot, neglects the monitoring of warehouse environment, and has the following problems in the aspect of monitoring warehouse environment:
Because the different warehouse goods types have different requirements on the stored environment, the warehouse humiture and the dust concentration corresponding to the different goods types cannot be subjected to targeted analysis based on the specific goods types placed in the current warehouse, so that the data analysis accuracy is lower, and the storage quality of the warehouse is affected;
in addition, the existing remote monitoring system for the storage equipment can only execute corresponding measures after an alarm is triggered, the temperature and humidity and dust concentration parameter change trend of storage areas corresponding to different cargo types before the alarm is triggered each time cannot be analyzed, early warning of storage environment is achieved, and the intelligent monitoring degree is low.
Therefore, a warehousing equipment remote monitoring system based on the Internet of things is provided.
Disclosure of Invention
In view of the above, the present invention provides a remote monitoring system for warehousing equipment based on the internet of things, so as to solve the problems set forth in the above background art.
The aim of the invention can be achieved by the following technical scheme:
And a monitoring layout module: acquiring detailed goods information tables in each warehouse; presetting different environment reference value sets based on the types of cargoes, and obtaining corresponding environment reference value sets of each storage area according to the corresponding types of cargoes in each storage area; deploying X groups of temperature and humidity sensors and concentration sensors in a target storage area; wherein X > 5; acquiring temperature and humidity data and dust concentration data of a target storage area in a set monitoring time period through a plurality of groups of deployed temperature and humidity monitoring points and concentration monitoring points, and sending the temperature and humidity data and dust concentration data to a layout analysis module;
And (3) a layout analysis module: receiving temperature and humidity data and dust concentration data of different deployment points of the target storage area in a set monitoring time period, and comprehensively analyzing to obtain a storage Wen Zhuang value CT, a storage wet value CS and a storage gray value CN of the target storage area in the set monitoring time period;
the method for obtaining the CT of the storage Wen Zhuang value of the target storage area in the set monitoring time period comprises the following specific steps:
s1: analyzing the temperature values of different deployment points of the target storage area in a set monitoring time period to obtain a stored Wen Zhuang value CT of the target storage area in the set monitoring time period;
S1-101: extracting temperature values of the corresponding deployment points of the target storage area at different time points in a set monitoring time period, and taking the average value of the temperature values of the different time points as Wen Junzhi of the corresponding deployment points of the target storage area; extracting the temperature peak value with the largest value from the temperature values at different time points, and taking the temperature peak value as a temperature peak value of a corresponding deployment point of the target storage area; the temperature average value and the temperature peak value of the corresponding deployment points of the target storage area are recorded as WJc and WFc; where c represents the number of the corresponding deployment point, c=1, 2..once-a-X; calculating according to a formula WPc = WJc ×a1+WFc×a2 to obtain a storage Wen Guzhi WPc of the corresponding deployment point of the target storage area; wherein a1 and a2 are respectively the influence weight factors of the target storage area Wen Junzhi and the temperature peak value, and a2 is more than a1, and the specific value is set based on the specific cargo type of the target storage area;
S1-102: respectively calculating the temperature average value and the temperature peak value of each deployment point of the target storage area through a standard deviation formula to obtain a temperature average value and a temperature peak value which are recorded as F1 and G1; extracting reference values of temperature average difference values and temperature peak difference values from corresponding environment reference value sets based on the cargo types of the target storage areas, and marking the reference values as F2 and G2;
S1-103: based on the comparison result of the temperature average difference value and the temperature peak difference value with the corresponding reference value, corresponding calculation is carried out to obtain a stored Wen Zhuang value CT of the target storage area; i.e. by Substituting the comparison result into a corresponding formula for calculation; wherein/>Extracting from a set of environment reference values corresponding to the target storage area, and representing an allowable value of the target storage area memory Wen Guzhi WPc; qe1, qe2, qe3 and qe4 are all influence weight factors of the CT stored Wen Zhuang value, and qe1 > qe2 > qe3 > qe4;
cloud platform of internet of things: and receiving a stored Wen Zhuang value CT, a stored wet value CS and a stored gray value CN of the current target storage area in a set monitoring time period, and executing corresponding steps based on the comparison result of the parameters.
In some embodiments, the method for obtaining the stored humidity value CS of the target warehouse area in the set monitoring period includes the following specific steps:
S2: analyzing the humidity values of different deployment points of the target storage area in the set monitoring time period to obtain a stored humidity value CS of the target storage area in the set monitoring time period;
S2-201: extracting humidity values of different time points of the corresponding deployment points of the target storage area in a set monitoring time period, taking the average value of the humidity values of the different time points as the humidity average value of the corresponding deployment points of the target storage area and marking as SJc;
S2-201: substituting the wet average SJc of each deployment point of the target warehouse area into the formula Calculating to obtain a stored wet value CS of the target storage area; wherein/>Extracting from a set of environment reference values corresponding to the target storage area, and representing an optimal storage humidity value of the target storage area; /(I)Represents the wet average SJc and/>The allowable maximum difference between them; tr1 is an influence weight factor of the wet average SJc of each deployment point.
In some embodiments, the stored gray value CN of the target storage area in the set monitoring period is obtained by the following specific steps:
s3: analyzing dust concentrations of different deployment points of the target storage area in the set monitoring time period to obtain a stored gray value CN of the target storage area in the set monitoring time period;
s3-301: extracting dust concentration values of different time points of the corresponding deployment points of the target storage area in a set monitoring time period,
S3-302: taking dust concentration values of all deployment points at the same time point, and calculating the average value to obtain the dust concentration value of the target storage area at the current time point; meanwhile, calculating the dust concentration value of each time point by using a standard deviation formula to obtain a dust variation value TJ of the target storage area; meanwhile, the dust concentration value with the largest value is extracted from the dust concentration values at each time point to serve as the dust peak value TF of the target storage area, the dust concentration value with the smallest value at each time point is further extracted, the difference value is calculated with the dust peak value TF, and the calculated difference value is taken as the dust difference value TB of the target storage area after the absolute value is taken;
substituting the gray variation TJ, the gray peak value TF and the gray difference value TB of the target storage area into a formula Calculating to obtain a stored gray value CN of the target storage area; wherein/>、/>/>Extracting from a set of environment reference values corresponding to the target storage area, wherein the reference values respectively represent the gray variation value TJ, the gray peak value TF and the gray difference value TB of the target storage area; rq1, rq2 and rq3 are the influencing weight factors of the gray scale TJ, the gray peak TF and the gray difference TB, respectively, and rq2 > rq3 > rq1.
In some embodiments, the corresponding steps are executed based on the comparison result of the above parameters, and the specific step one is:
m1: extracting a reference threshold value of a stored Wen Zhuang value CT, a stored wet value CS and a stored gray value CN from a corresponding environment reference value set based on the cargo type of the target storage area;
m2: comparing the stored Wen Zhuang value CT of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a temperature abnormality alarm if the stored Wen Zhuang value CT is larger than the corresponding reference threshold value, and executing the steps M2-101;
M2-101: immediately checking whether the refrigeration equipment in the target storage area is abnormal or not, if the refrigeration equipment is abnormal, drawing a circle by taking the current target storage area as a circle center and setting the distance as a radius to obtain a screening range, selecting a maintainer with the shortest route distance as a maintainer of the current target storage area based on the route distance between each maintainer and the target storage area in the screening range, and sending a maintenance signaling to a mobile terminal of the maintainer; if the refrigeration equipment in the target storage area is in a normal use state, executing the steps M2-102;
M2-102: and acquiring various parameters of the refrigeration equipment in the target storage area corresponding to the current trigger temperature abnormality alarm time point, sending the parameters to a mobile terminal of an administrator corresponding to the target storage area, and after the administrator remotely adjusts the parameters based on the refrigeration equipment through the mobile terminal, further acquiring the storage position of various cargoes in the target storage area and adjusting the angle of various air outlets of the refrigeration equipment.
In some embodiments, the corresponding steps are executed based on the comparison result of the above parameters, and the specific step two is:
M3: comparing the stored humidity value CS of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a humidity abnormality alarm if the stored humidity value CS is larger than the corresponding reference threshold value, and executing the step M3-101;
m3-101: immediately checking whether the dehumidifying equipment in the target storage area is abnormal or not, and if the dehumidifying equipment is abnormal in use; then selecting maintenance personnel for maintenance based on the step M2-101; if the operation is in the normal use state, executing the steps M3-102;
m3-102: in the same step M2-102, after the manager remotely adjusts the parameters of the dehumidification equipment through the mobile terminal, the manager further obtains the specific cargo type of the target storage area and judges whether isolation measures need to be taken;
M4: comparing the stored gray value CN of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a dust abnormality alarm if the stored gray value CN is larger than the corresponding reference threshold value, and executing the steps M4-101;
M4-101: immediately checking whether the air purifier in the target storage area is abnormal or not, and if the air purifier is abnormal in use; selecting maintenance personnel for maintenance based on the step M2-101, and simultaneously selecting a cleaner with the shortest route distance based on the screening range as a processor of the target storage area, sending a cleaning signaling to a mobile terminal of the cleaner, and manually cleaning dust of the target storage area through the processor in the maintenance process of the maintenance personnel; if the operation is in the normal use state, executing the steps M4-102;
M4-102: and in the same step M2-102, after the administrator remotely adjusts the air purifier through the mobile terminal according to various parameters of the air purifier, the specific cargo type of the target storage area is further obtained, and whether isolation measures need to be taken or not is judged.
In some embodiments, the cloud platform of the internet of things is further configured to trigger an early warning analysis signaling and execute corresponding steps when the target storage area triggers a temperature anomaly alarm, a humidity anomaly alarm or a dust anomaly alarm, and specifically:
Y1: acquiring midpoint time of a current monitoring time period, dividing the current monitoring time period based on the midpoint time, and taking the divided first half period time as an early warning analysis time period of a target storage area;
y2: based on the specific abnormal alarm triggered by the current target storage area, if the specific abnormal alarm is triggered, further analyzing the temperature change condition of the target storage area in the early warning analysis time period;
Y2-101: acquiring temperature values of different time points of each deployment point in a current early warning analysis time period of the target storage area, and carrying out average value calculation on the temperature values of the same time point of each deployment point to serve as a bin temperature value of the target storage area at the current time point;
Based on the bin temperature values of the target storage area at different time points in the current early warning analysis time period, a line graph is built; drawing numerical points of the bin temperature values corresponding to the line graph at different time points, connecting adjacent numerical points to obtain a bin temperature line, and calculating the slope of the bin temperature line and the included angle between the bin temperature line and the horizontal line; when the included angle between the bin temperature line and the horizontal line is an acute angle, marking the slope of the bin temperature line as a first slope; when the included angle between the bin temperature line and the horizontal line is an obtuse angle, marking the bin temperature line as a second slope; summing all the values of the first slopes to obtain a first total value and marking the first total value as HF1, summing all the values of the second slopes to obtain a second total value and marking the second total value as HF2;
Connecting a forefront numerical value point and a last numerical value point in the line graph to obtain a line segment, marking the line segment as a total line, calculating the slope of the total line and an included angle between the total line and a horizontal line, marking the slope of the total line as a third slope when the included angle between the total line and the horizontal line is an acute angle, and the numerical value of the third slope is represented by a symbol HX 1; when the included angle between the main trend line and the horizontal line is an obtuse angle, marking the slope of the main trend line as a fourth slope, wherein the absolute value of the fourth slope is represented by a symbol HX 2;
Calculating the vertical distance between the highest numerical point and the lowest numerical point and marking the numerical value of the vertical distance as HF3; obtaining a temperature early warning evaluation index YJP of the target storage area by using a formula YJP= (HF 2/HF 1) multiplied by pv1+ HXz multiplied by pv2+HF3 multiplied by pv3; wherein z=1 or 2; pv1, pv2 and pv3 are respectively influence weight factors of corresponding parameters, and specific values are set based on specific cargo types of the target storage area;
Y2-102: meanwhile, taking the average value of the bin temperature values at different time points as a temperature early-warning evaluation value YJM of the target storage area;
y3: in the same way, when triggering the humidity abnormality alarm, analyzing the humidity change condition of the target storage area in the early-warning analysis time period to obtain a humidity early-warning evaluation index and a humidity early-warning evaluation value of the target storage area;
Y4: in the same way, step Y2, when triggering the dust abnormality alarm, analyzing the dust concentration change condition of the target storage area in the early warning analysis time period to obtain the concentration early warning evaluation index and the concentration early warning evaluation value of the target storage area;
Y5: and (3) integrating the corresponding early warning evaluation index and the early warning evaluation value into an established early warning model based on the corresponding early warning evaluation index and the early warning evaluation value obtained in the steps Y1 to Y4, and taking the integrated early warning evaluation index and the integrated early warning evaluation value as a corresponding early warning reference set of the target storage area.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the accuracy of data analysis is improved by presetting environment reference value sets of different cargo types, a plurality of groups of corresponding monitoring points are deployed in a target storage area, the comprehensive monitoring of the environmental conditions of the target storage area is realized, the comprehensiveness of data acquisition is realized, and a sufficient data basis is provided for subsequent analysis, so that the problem that a remote monitoring system of storage equipment in the prior art usually only pays attention to the use condition of the storage equipment, the monitoring of the storage environment is ignored, and the safety and quality of the cargo in the storage process are ensured;
According to the invention, after triggering the corresponding abnormal alarm through the target storage area, the generated abnormality is processed in time by remotely regulating and controlling and selecting the corresponding personnel through the administrator, so that the safety of goods storage is further ensured while the remote regulation and management of the target storage area is realized;
According to the invention, the corresponding abnormal alarm is triggered at the same time as the corresponding abnormal alarm is triggered in the target storage area, the parameter change condition of the target storage area in the early warning analysis time period is analyzed, the early warning evaluation index and the early warning evaluation value corresponding to the current abnormal alarm are obtained and are integrated into the established early warning model to be updated, if the early warning evaluation index and the early warning evaluation value corresponding to the early warning analysis time period in a future monitoring time period are matched with those in the early warning model, the corresponding abnormal alarm is directly triggered, the early warning of the target storage area can be realized, the intelligent degree is improved, and further deterioration of the goods storage environment is avoided.
Drawings
Further details, features and advantages of the application are disclosed in the following description of exemplary embodiments with reference to the following drawings, in which:
FIG. 1 is a schematic block diagram of the present invention;
FIG. 2 is a plot of the variation of the bin temperature values in the present invention.
Detailed Description
Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings in order to enable those skilled in the art to practice the application. The present application may be embodied in many different forms and objects and should not be limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art. The examples do not limit the application.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and/or the present specification and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1-2, a remote monitoring system for storage equipment based on internet of things comprises a monitoring layout module, a layout analysis module and an internet of things cloud platform;
The monitoring layout module is used for acquiring detailed goods information tables in all the storages; when the goods are put in storage, the detailed goods information table is synchronously updated by scanning the bar codes, and the types, the quantity and the storage positions of the goods are recorded in the information table, wherein the types of the goods include but are not limited to fresh products, medicines and electronic products; presetting different environment reference value sets based on the type of goods; obtaining a corresponding environment reference value set of each storage area according to the corresponding type of the goods in each storage; deploying X groups of temperature and humidity sensors and concentration sensors in a target storage area; wherein X > 5; acquiring temperature and humidity data and dust concentration data of a target storage area in a set monitoring time period through a plurality of groups of deployed temperature and humidity monitoring points and concentration monitoring points, and sending the temperature and humidity data and dust concentration data to a layout analysis module;
It should be noted that, different environment reference values are preset based on the types of the warehoused goods, so that the accuracy of data analysis is improved, meanwhile, a plurality of groups of temperature and humidity sensors and concentration sensors are deployed in the target warehouse area, the environmental conditions of all areas in the warehouse are ensured to be monitored comprehensively, and the coverage rate of monitoring and the accuracy of data are improved.
The layout analysis module receives temperature and humidity data and dust concentration data of different deployment points of the target storage area in a set monitoring time period, and comprehensively analyzes the temperature and humidity data and the dust concentration data to obtain a storage Wen Zhuang value CT, a storage wet value CS and a storage ash value CN of the target storage area in the set monitoring time period;
The method comprises the following specific steps:
s1: analyzing the temperature values of different deployment points of the target storage area in a set monitoring time period to obtain a stored Wen Zhuang value CT of the target storage area in the set monitoring time period;
S1-101: extracting temperature values of the corresponding deployment points of the target storage area at different time points in a set monitoring time period, and taking the average value of the temperature values of the different time points as Wen Junzhi of the corresponding deployment points of the target storage area; extracting the temperature peak value with the largest value from the temperature values at different time points, and taking the temperature peak value as a temperature peak value of a corresponding deployment point of the target storage area; the temperature average value and the temperature peak value of the corresponding deployment points of the target storage area are recorded as WJc and WFc; where c represents the number of the corresponding deployment point, c=1, 2..once-a-X; calculating according to a formula WPc = WJc ×a1+WFc×a2 to obtain a storage Wen Guzhi WPc of the corresponding deployment point of the target storage area; wherein a1 and a2 are respectively the influence weight factors of the target storage area Wen Junzhi and the temperature peak value, and a2 is more than a1, and the specific value is set based on the specific cargo type of the target storage area;
it should be noted that, through the memory Wen Guzhi of each deployment point of the target storage area, the temperature state of the target storage area at the specific deployment point is accurately estimated, so that a comprehensive scientific basis is provided for the environmental control of the target storage area.
S1-102: respectively calculating the temperature average value and the temperature peak value of each deployment point of the target storage area through a standard deviation formula to obtain a temperature average value and a temperature peak value which are recorded as F1 and G1; extracting reference values of temperature average difference values and temperature peak difference values from corresponding environment reference value sets based on the cargo types of the target storage areas, and marking the reference values as F2 and G2;
S1-103: based on the comparison result of the temperature average difference value and the temperature peak difference value with the corresponding reference value, corresponding calculation is carried out to obtain a stored Wen Zhuang value CT of the target storage area; i.e. by Substituting the comparison result into a corresponding formula for calculation; wherein/>Extracting from a set of environment reference values corresponding to the target storage area, and representing an allowable value of the target storage area memory Wen Guzhi WPc; qe1, qe2, qe3 and qe4 are all influence weight factors of the CT with the stored Wen Zhuang values, and qe1 > qe2 > qe3 > qe4, and specific values are set based on specific cargo types of the target storage area;
It should be noted that, the temperature average value and the temperature peak value of each deployment point in the target storage area are calculated through a standard deviation formula, the temperature fluctuation condition of the target storage area can be reflected according to the calculated result, when the temperature average difference value and the temperature peak difference value of the target storage area are larger, the storage temperature difference of each deployment point is represented to be larger, the risk is higher, and the calculation is performed based on the comparison result substituted into different formulas with the corresponding reference value, so that the accuracy of data analysis is improved.
S2: analyzing the humidity values of different deployment points of the target storage area in the set monitoring time period to obtain a stored humidity value CS of the target storage area in the set monitoring time period;
S2-201: extracting humidity values of different time points of the corresponding deployment points of the target storage area in a set monitoring time period, taking the average value of the humidity values of the different time points as the humidity average value of the corresponding deployment points of the target storage area and marking as SJc;
S2-201: substituting the wet average SJc of each deployment point of the target warehouse area into the formula Calculating to obtain a stored wet value CS of the target storage area; wherein/>Extracting from a set of environment reference values corresponding to the target storage area, and representing an optimal storage humidity value of the target storage area; /(I)Represents the wet average SJc and/>The allowable maximum difference between them; tr1 is an influence weight factor of the wet average SJc of each deployment point, and the specific value is set based on the specific cargo type of the target storage area;
it should be noted that, by calculating the humidity average value of each deployment point in the target storage area, the humidity condition of the whole target storage area in the set monitoring time period can be known, and comprehensive analysis is performed on the humidity data of each deployment point, so as to provide more accurate humidity data.
S3: analyzing dust concentrations of different deployment points of the target storage area in the set monitoring time period to obtain a stored gray value CN of the target storage area in the set monitoring time period;
s3-301: extracting dust concentration values of different time points of the corresponding deployment points of the target storage area in a set monitoring time period,
S3-302: taking dust concentration values of all deployment points at the same time point, and calculating the average value to obtain the dust concentration value of the target storage area at the current time point; meanwhile, calculating the dust concentration value of each time point by using a standard deviation formula to obtain a dust variation value TJ of the target storage area; meanwhile, the dust concentration value with the largest value is extracted from the dust concentration values at each time point to serve as the dust peak value TF of the target storage area, the dust concentration value with the smallest value at each time point is further extracted, the difference value is calculated with the dust peak value TF, and the calculated difference value is taken as the dust difference value TB of the target storage area after the absolute value is taken;
substituting the gray variation TJ, the gray peak value TF and the gray difference value TB of the target storage area into a formula Calculating to obtain a stored gray value CN of the target storage area; wherein/>、/>/>Extracting from a set of environment reference values corresponding to the target storage area, wherein the reference values respectively represent the gray variation value TJ, the gray peak value TF and the gray difference value TB of the target storage area; rq1, rq2 and rq3 are respectively the influence weight factors of the gray variation TJ, the gray peak TF and the gray difference TB, rq2 is larger than rq3 and larger than rq1, and the specific values are set based on the specific cargo types of the target storage area;
it should be noted that, by collecting dust concentration values of different deployment points in a set monitoring time period, the dust level in the storage area can be comprehensively known, detailed data is provided for environmental management, and the calculation of the stored gray value CN provides a quantized index to evaluate the dust control effect of the storage area, so as to ensure that the storage environment of the current cargo type meets the standard.
The internet of things cloud platform is used for receiving a stored Wen Zhuang value CT, a stored wet value CS and a stored gray value CN of a current target storage area in a set monitoring time period, and executing corresponding steps based on comparison results of the parameters; the method comprises the following steps:
m1: extracting a reference threshold value of a stored Wen Zhuang value CT, a stored wet value CS and a stored gray value CN from a corresponding environment reference value set based on the cargo type of the target storage area;
m2: comparing the stored Wen Zhuang value CT of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a temperature abnormality alarm if the stored Wen Zhuang value CT is larger than the corresponding reference threshold value, and executing the steps M2-101;
M2-101: immediately checking whether the refrigeration equipment in the target storage area is abnormal or not, if the refrigeration equipment is abnormal, drawing a circle by taking the current target storage area as a circle center and setting the distance as a radius to obtain a screening range, selecting a maintainer with the shortest route distance as a maintainer of the current target storage area based on the route distance between each maintainer and the target storage area in the screening range, and sending a maintenance signaling to a mobile terminal of the maintainer; if the refrigeration equipment in the target storage area is in a normal use state, executing the steps M2-102;
It should be noted that, at first, the refrigerating equipment in the target warehouse area is subjected to fault removal, if the use abnormality occurs, the maintainer with the shortest route distance is further analyzed and selected to go to the site for maintenance, the problem solving efficiency is improved, and the safety of goods storage is further ensured.
M2-102: acquiring various parameters of the refrigeration equipment in the target storage area corresponding to the current triggering temperature abnormality alarm time point and sending the parameters to a mobile terminal of an administrator corresponding to the target storage area, and after the administrator remotely adjusts the parameters based on the refrigeration equipment through the mobile terminal, further acquiring the storage position of various cargoes in the target storage area and adjusting the angle of various air outlets of the refrigeration equipment;
It should be noted that, if the refrigeration equipment in the target storage area is not abnormal in use, each parameter of the refrigeration equipment is further adjusted remotely through the corresponding administrator in the target storage area, and the air outlet angle of each air outlet of the refrigeration equipment is adjusted, so that the safety of storage of each cargo and the uniformity of the temperature of each cargo deployment point are further ensured, and the temperature difference between the cargo storage position and the air outlet position is avoided.
M3: comparing the stored humidity value CS of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a humidity abnormality alarm if the stored humidity value CS is larger than the corresponding reference threshold value, and executing the step M3-101;
m3-101: immediately checking whether the dehumidifying equipment in the target storage area is abnormal or not, and if the dehumidifying equipment is abnormal in use; then selecting maintenance personnel for maintenance based on the step M2-101; if the operation is in the normal use state, executing the steps M3-102;
M3-102: in the same step M2-102, after the manager remotely adjusts the parameters of the dehumidification equipment through the mobile terminal, the manager further obtains the specific cargo type of the target storage area and judges whether isolation measures need to be taken; such as a sealed container or sealed package;
M4: comparing the stored gray value CN of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a dust abnormality alarm if the stored gray value CN is larger than the corresponding reference threshold value, and executing the steps M4-101;
M4-101: immediately checking whether the air purifier in the target storage area is abnormal or not, and if the air purifier is abnormal in use; selecting maintenance personnel for maintenance based on the step M2-101, and simultaneously selecting a cleaner with the shortest route distance based on the screening range as a processor of the target storage area, sending a cleaning signaling to a mobile terminal of the cleaner, and manually cleaning dust of the target storage area through the processor in the maintenance process of the maintenance personnel; if the operation is in the normal use state, executing the steps M4-102;
M4-102: in the same step M2-102, after the administrator remotely adjusts the air purifier through the mobile terminal according to various parameters of the air purifier, the specific cargo type of the target storage area is further obtained, and whether isolation measures need to be taken or not is judged; such as a cover or seal package.
It should be noted that, based on the above steps, the target storage area is correspondingly processed after the corresponding abnormal alarm is triggered, so as to remotely adjust and manage the target storage area in time.
The cloud platform of the internet of things is further used for triggering an early warning analysis signaling and executing corresponding steps when triggering a temperature abnormality alarm, a humidity abnormality alarm or a dust abnormality alarm in a target storage area, and specifically comprises the following steps:
Y1: acquiring midpoint time of a current monitoring time period, dividing the current monitoring time period based on the midpoint time, and taking the divided first half period time as an early warning analysis time period of a target storage area;
y2: based on the specific abnormal alarm triggered by the current target storage area, if the specific abnormal alarm is triggered, further analyzing the temperature change condition of the target storage area in the early warning analysis time period;
Y2-101: acquiring temperature values of different time points of each deployment point in a current early warning analysis time period of the target storage area, and carrying out average value calculation on the temperature values of the same time point of each deployment point to serve as a bin temperature value of the target storage area at the current time point;
Based on the bin temperature values of the target storage area at different time points in the current early warning analysis time period, a line graph is built; drawing numerical points of the bin temperature values corresponding to the line graph at different time points, connecting adjacent numerical points to obtain a bin temperature line, and calculating the slope of the bin temperature line and the included angle between the bin temperature line and the horizontal line; when the included angle between the bin temperature line and the horizontal line is an acute angle, marking the slope of the bin temperature line as a first slope; when the included angle between the bin temperature line and the horizontal line is an obtuse angle, marking the bin temperature line as a second slope; summing all the values of the first slopes to obtain a first total value and marking the first total value as HF1, summing all the values of the second slopes to obtain a second total value and marking the second total value as HF2;
Connecting a forefront numerical value point and a last numerical value point in the line graph to obtain a line segment, marking the line segment as a total line, calculating the slope of the total line and an included angle between the total line and a horizontal line, marking the slope of the total line as a third slope when the included angle between the total line and the horizontal line is an acute angle, and the numerical value of the third slope is represented by a symbol HX 1; when the included angle between the main trend line and the horizontal line is an obtuse angle, marking the slope of the main trend line as a fourth slope, wherein the absolute value of the fourth slope is represented by a symbol HX 2;
Calculating the vertical distance between the highest numerical point and the lowest numerical point and marking the numerical value of the vertical distance as HF3; obtaining a temperature early warning evaluation index YJP of the target storage area by using a formula YJP= (HF 2/HF 1) multiplied by pv1+ HXz multiplied by pv2+HF3 multiplied by pv3; wherein z=1 or 2; pv1, pv2 and pv3 are respectively influence weight factors of corresponding parameters, and specific values are set based on specific cargo types of the target storage area;
Y2-102: meanwhile, taking the average value of the bin temperature values at different time points as a temperature early-warning evaluation value YJM of the target storage area;
y3: in the same way, when triggering the humidity abnormality alarm, analyzing the humidity change condition of the target storage area in the early-warning analysis time period to obtain a humidity early-warning evaluation index and a humidity early-warning evaluation value of the target storage area;
Y4: in the same way, step Y2, when triggering the dust abnormality alarm, analyzing the dust concentration change condition of the target storage area in the early warning analysis time period to obtain the concentration early warning evaluation index and the concentration early warning evaluation value of the target storage area;
y5: based on the corresponding early warning evaluation indexes and the early warning evaluation values obtained in the steps Y1 to Y4, integrating the corresponding early warning evaluation indexes and the early warning evaluation values into an established early warning model to serve as a corresponding early warning reference set of the target storage area;
The manager can optimize according to the obtained corresponding early warning evaluation index and early warning evaluation value, so that the early warning accuracy and comprehensiveness of the early warning model are further improved; adjusting the fluctuation range up and down;
if the early warning evaluation index and the early warning evaluation value of a certain monitoring time period corresponding to the early warning analysis time period are matched with those in the early warning model, the corresponding abnormal alarm is directly triggered, the early warning of the target storage area can be realized, the intelligent degree is improved, the further deterioration of the goods storage environment is avoided, and the safety of the goods storage process is further ensured.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. Storage equipment remote monitering system based on thing networking, its characterized in that includes:
And a monitoring layout module: acquiring detailed goods information tables in each warehouse; presetting different environment reference value sets based on the types of cargoes, and obtaining corresponding environment reference value sets of each storage area according to the corresponding types of cargoes in each storage area; deploying X groups of temperature and humidity sensors and concentration sensors in a target storage area; wherein X > 5; acquiring temperature and humidity data and dust concentration data of a target storage area in a set monitoring time period through a plurality of groups of deployed temperature and humidity monitoring points and concentration monitoring points, and sending the temperature and humidity data and dust concentration data to a layout analysis module;
And (3) a layout analysis module: receiving temperature and humidity data and dust concentration data of different deployment points of the target storage area in a set monitoring time period, and comprehensively analyzing to obtain a storage Wen Zhuang value CT, a storage wet value CS and a storage gray value CN of the target storage area in the set monitoring time period;
the method for obtaining the CT of the storage Wen Zhuang value of the target storage area in the set monitoring time period comprises the following specific steps:
s1: analyzing the temperature values of different deployment points of the target storage area in a set monitoring time period to obtain a stored Wen Zhuang value CT of the target storage area in the set monitoring time period;
S1-101: extracting temperature values of the corresponding deployment points of the target storage area at different time points in a set monitoring time period, and taking the average value of the temperature values of the different time points as Wen Junzhi of the corresponding deployment points of the target storage area; extracting the temperature peak value with the largest value from the temperature values at different time points, and taking the temperature peak value as a temperature peak value of a corresponding deployment point of the target storage area; the temperature average value and the temperature peak value of the corresponding deployment points of the target storage area are recorded as WJc and WFc; where c represents the number of the corresponding deployment point, c=1, 2..once-a-X; calculating according to a formula WPc = WJc ×a1+WFc×a2 to obtain a storage Wen Guzhi WPc of the corresponding deployment point of the target storage area; wherein a1 and a2 are respectively the influence weight factors of the target storage area Wen Junzhi and the temperature peak value, and a2 is more than a1, and the specific value is set based on the specific cargo type of the target storage area;
S1-102: respectively calculating the temperature average value and the temperature peak value of each deployment point of the target storage area through a standard deviation formula to obtain a temperature average value and a temperature peak value which are recorded as F1 and G1; extracting reference values of temperature average difference values and temperature peak difference values from corresponding environment reference value sets based on the cargo types of the target storage areas, and marking the reference values as F2 and G2;
S1-103: based on the comparison result of the temperature average difference value and the temperature peak difference value with the corresponding reference value, corresponding calculation is carried out to obtain a stored Wen Zhuang value CT of the target storage area; i.e. by Substituting the comparison result into a corresponding formula for calculation; wherein/>Extracting from a set of environment reference values corresponding to the target storage area, and representing an allowable value of the target storage area memory Wen Guzhi WPc; qe1, qe2, qe3 and qe4 are all influence weight factors of the CT stored Wen Zhuang value, and qe1 > qe2 > qe3 > qe4;
The method for obtaining the stored wet state value CS of the target storage area in the set monitoring time period comprises the following specific steps:
S2: analyzing the humidity values of different deployment points of the target storage area in the set monitoring time period to obtain a stored humidity value CS of the target storage area in the set monitoring time period;
S2-201: extracting humidity values of different time points of the corresponding deployment points of the target storage area in a set monitoring time period, taking the average value of the humidity values of the different time points as the humidity average value of the corresponding deployment points of the target storage area and marking as SJc;
S2-201: substituting the wet average SJc of each deployment point of the target warehouse area into the formula Calculating to obtain a stored wet value CS of the target storage area; wherein/>Extracting from a set of environment reference values corresponding to the target storage area, and representing an optimal storage humidity value of the target storage area; /(I)Represents the wet average SJc and/>The allowable maximum difference between them; tr1 is an influence weight factor of the wet average SJc of each deployment point;
The method for obtaining the stored gray value CN of the target storage area in the set monitoring time period comprises the following specific steps:
s3: analyzing dust concentrations of different deployment points of the target storage area in the set monitoring time period to obtain a stored gray value CN of the target storage area in the set monitoring time period;
s3-301: extracting dust concentration values of different time points of the corresponding deployment points of the target storage area in a set monitoring time period,
S3-302: taking dust concentration values of all deployment points at the same time point, and calculating the average value to obtain the dust concentration value of the target storage area at the current time point; meanwhile, calculating the dust concentration value of each time point by using a standard deviation formula to obtain a dust variation value TJ of the target storage area; meanwhile, the dust concentration value with the largest value is extracted from the dust concentration values at each time point to serve as the dust peak value TF of the target storage area, the dust concentration value with the smallest value at each time point is further extracted, the difference value is calculated with the dust peak value TF, and the calculated difference value is taken as the dust difference value TB of the target storage area after the absolute value is taken;
substituting the gray variation TJ, the gray peak value TF and the gray difference value TB of the target storage area into a formula Calculating to obtain a stored gray value CN of the target storage area; wherein the method comprises the steps of/>Extracting from a set of environment reference values corresponding to the target storage area, wherein the reference values respectively represent the gray variation value TJ, the gray peak value TF and the gray difference value TB of the target storage area; rq1, rq2 and rq3 are the influencing weight factors of the gray variation TJ, the gray peak TF and the gray difference TB, respectively, and rq2 > rq3 > rq1;
cloud platform of internet of things: receiving a stored Wen Zhuang value CT, a stored wet value CS and a stored gray value CN of the current target storage area in a set monitoring time period, and executing corresponding steps based on the comparison result of the parameters;
Based on the comparison result of the parameters, executing corresponding steps, wherein the specific step one is as follows:
m1: extracting a reference threshold value of a stored Wen Zhuang value CT, a stored wet value CS and a stored gray value CN from a corresponding environment reference value set based on the cargo type of the target storage area;
m2: comparing the stored Wen Zhuang value CT of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a temperature abnormality alarm if the stored Wen Zhuang value CT is larger than the corresponding reference threshold value, and executing the steps M2-101;
M2-101: immediately checking whether the refrigeration equipment in the target storage area is abnormal or not, if the refrigeration equipment is abnormal, drawing a circle by taking the current target storage area as a circle center and setting the distance as a radius to obtain a screening range, selecting a maintainer with the shortest route distance as a maintainer of the current target storage area based on the route distance between each maintainer and the target storage area in the screening range, and sending a maintenance signaling to a mobile terminal of the maintainer; if the refrigeration equipment in the target storage area is in a normal use state, executing the steps M2-102;
M2-102: acquiring various parameters of the refrigeration equipment in the target storage area corresponding to the current triggering temperature abnormality alarm time point and sending the parameters to a mobile terminal of an administrator corresponding to the target storage area, and after the administrator remotely adjusts the parameters based on the refrigeration equipment through the mobile terminal, further acquiring the storage position of various cargoes in the target storage area and adjusting the angle of various air outlets of the refrigeration equipment;
based on the comparison result of the parameters, executing corresponding steps, wherein the specific step two is as follows:
M3: comparing the stored humidity value CS of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a humidity abnormality alarm if the stored humidity value CS is larger than the corresponding reference threshold value, and executing the step M3-101;
m3-101: immediately checking whether the dehumidifying equipment in the target storage area is abnormal or not, and if the dehumidifying equipment is abnormal in use; then selecting maintenance personnel for maintenance based on the step M2-101; if the operation is in the normal use state, executing the steps M3-102;
m3-102: in the same step M2-102, after the manager remotely adjusts the parameters of the dehumidification equipment through the mobile terminal, the manager further obtains the specific cargo type of the target storage area and judges whether isolation measures need to be taken;
M4: comparing the stored gray value CN of the target storage area in the set monitoring time period with a corresponding reference threshold value, triggering a dust abnormality alarm if the stored gray value CN is larger than the corresponding reference threshold value, and executing the steps M4-101;
M4-101: immediately checking whether the air purifier in the target storage area is abnormal or not, and if the air purifier is abnormal in use; selecting maintenance personnel for maintenance based on the step M2-101, and simultaneously selecting a cleaner with the shortest route distance based on the screening range as a processor of the target storage area, sending a cleaning signaling to a mobile terminal of the cleaner, and manually cleaning dust of the target storage area through the processor in the maintenance process of the maintenance personnel; if the operation is in the normal use state, executing the steps M4-102;
M4-102: and in the same step M2-102, after the administrator remotely adjusts the air purifier through the mobile terminal according to various parameters of the air purifier, the specific cargo type of the target storage area is further obtained, and whether isolation measures need to be taken or not is judged.
2. The warehousing equipment remote monitoring system based on the internet of things according to claim 1, wherein the internet of things cloud platform is further configured to trigger an early warning analysis signaling while triggering a temperature abnormality alarm, a humidity abnormality alarm or a dust abnormality alarm in a target warehousing area, and perform the corresponding steps of:
Y1: acquiring midpoint time of a current monitoring time period, dividing the current monitoring time period based on the midpoint time, and taking the divided first half period time as an early warning analysis time period of a target storage area;
y2: based on the specific abnormal alarm triggered by the current target storage area, if the specific abnormal alarm is triggered, further analyzing the temperature change condition of the target storage area in the early warning analysis time period;
Y2-101: acquiring temperature values of different time points of each deployment point in a current early warning analysis time period of the target storage area, and carrying out average value calculation on the temperature values of the same time point of each deployment point to serve as a bin temperature value of the target storage area at the current time point;
Based on the bin temperature values of the target storage area at different time points in the current early warning analysis time period, a line graph is built; drawing numerical points of the bin temperature values corresponding to the line graph at different time points, connecting adjacent numerical points to obtain a bin temperature line, and calculating the slope of the bin temperature line and the included angle between the bin temperature line and the horizontal line; when the included angle between the bin temperature line and the horizontal line is an acute angle, marking the slope of the bin temperature line as a first slope; when the included angle between the bin temperature line and the horizontal line is an obtuse angle, marking the bin temperature line as a second slope; summing all the values of the first slopes to obtain a first total value and marking the first total value as HF1, summing all the values of the second slopes to obtain a second total value and marking the second total value as HF2;
Connecting a forefront numerical value point and a last numerical value point in the line graph to obtain a line segment, marking the line segment as a total line, calculating the slope of the total line and an included angle between the total line and a horizontal line, marking the slope of the total line as a third slope when the included angle between the total line and the horizontal line is an acute angle, and the numerical value of the third slope is represented by a symbol HX 1; when the included angle between the main trend line and the horizontal line is an obtuse angle, marking the slope of the main trend line as a fourth slope, wherein the absolute value of the fourth slope is represented by a symbol HX 2;
Calculating the vertical distance between the highest numerical point and the lowest numerical point and marking the numerical value of the vertical distance as HF3; obtaining a temperature early warning evaluation index YJP of the target storage area by using a formula YJP= (HF 2/HF 1) multiplied by pv1+ HXz multiplied by pv2+HF3 multiplied by pv3; wherein z=1 or 2; pv1, pv2 and pv3 are respectively influence weight factors of corresponding parameters, and specific values are set based on specific cargo types of the target storage area;
Y2-102: meanwhile, taking the average value of the bin temperature values at different time points as a temperature early-warning evaluation value YJM of the target storage area;
y3: in the same way, when triggering the humidity abnormality alarm, analyzing the humidity change condition of the target storage area in the early-warning analysis time period to obtain a humidity early-warning evaluation index and a humidity early-warning evaluation value of the target storage area;
Y4: in the same way, step Y2, when triggering the dust abnormality alarm, analyzing the dust concentration change condition of the target storage area in the early warning analysis time period to obtain the concentration early warning evaluation index and the concentration early warning evaluation value of the target storage area;
Y5: and (3) integrating the corresponding early warning evaluation index and the early warning evaluation value into an established early warning model based on the corresponding early warning evaluation index and the early warning evaluation value obtained in the steps Y1 to Y4, and taking the integrated early warning evaluation index and the integrated early warning evaluation value as a corresponding early warning reference set of the target storage area.
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