CN117057483A - Catering oil smoke prediction processing method and system based on big data - Google Patents
Catering oil smoke prediction processing method and system based on big data Download PDFInfo
- Publication number
- CN117057483A CN117057483A CN202311308297.3A CN202311308297A CN117057483A CN 117057483 A CN117057483 A CN 117057483A CN 202311308297 A CN202311308297 A CN 202311308297A CN 117057483 A CN117057483 A CN 117057483A
- Authority
- CN
- China
- Prior art keywords
- oil smoke
- concentration
- monitoring
- time
- generated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000000779 smoke Substances 0.000 title claims abstract description 418
- 238000003672 processing method Methods 0.000 title claims abstract description 19
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 152
- 231100000719 pollutant Toxicity 0.000 claims abstract description 152
- 238000012544 monitoring process Methods 0.000 claims abstract description 151
- 238000000034 method Methods 0.000 claims abstract description 20
- 230000008569 process Effects 0.000 claims abstract description 5
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 30
- 238000001514 detection method Methods 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 22
- 239000006233 lamp black Substances 0.000 claims description 15
- 238000009423 ventilation Methods 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 12
- 230000000711 cancerogenic effect Effects 0.000 claims description 10
- 231100000315 carcinogenic Toxicity 0.000 claims description 10
- 230000036541 health Effects 0.000 claims description 10
- 239000000126 substance Substances 0.000 claims description 10
- 239000003517 fume Substances 0.000 claims description 9
- 238000012502 risk assessment Methods 0.000 claims description 9
- 239000000356 contaminant Substances 0.000 claims description 6
- 238000010411 cooking Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000012806 monitoring device Methods 0.000 claims description 4
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 241001083514 Gayophytum Species 0.000 claims 1
- 238000009434 installation Methods 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 claims 1
- 238000012360 testing method Methods 0.000 claims 1
- 239000003921 oil Substances 0.000 description 284
- 230000002411 adverse Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 239000008162 cooking oil Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000008157 edible vegetable oil Substances 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000003925 fat Substances 0.000 description 1
- 239000013067 intermediate product Substances 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 239000004071 soot Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
Landscapes
- Business, Economics & Management (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Dispersion Chemistry (AREA)
- Combustion & Propulsion (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Fire-Detection Mechanisms (AREA)
Abstract
The invention relates to the technical field of big data prediction and discloses a catering oil smoke prediction processing method and system based on big data. According to the invention, through the cooperation of setting monitoring points in the urban area and installing the oil smoke monitoring equipment at the oil smoke generating place, the concentration condition of the oil smoke gaseous pollutants at the oil smoke generating place is monitored in real time. Meanwhile, the concentration threshold value of the oil smoke gaseous pollutants is set, and the concentration of the oil smoke gaseous pollutants generated by the oil smoke is monitored in real time, so that a user is reminded when the food of the user is processed. When the oil smoke monitoring equipment detects that the current oil smoke gaseous pollutants generated by the oil smoke exceeds the threshold value in the process of monitoring the oil smoke gaseous pollutants generated by the oil smoke in real time, the oil smoke gaseous pollutants can be reported to monitoring points in the management area of the monitoring equipment, the monitoring points can report the alarm and report the position information to the system after receiving the report information from the oil smoke monitoring equipment, so that the position of the alarm point can be determined by related personnel quickly, and the safety of catering industry is improved.
Description
Technical Field
The invention relates to the technical field of big data prediction, in particular to a catering oil smoke prediction processing method and system based on big data.
Background
With the rapid development of economy, the demand of catering industry is rapidly increased, and each household is free from food processing, so that the quantity of food processing places such as families, food processing factories, restaurants and the like is huge. Since the fats and oils of edible oils are oxidized to decompose the lipids after being heated to chemically react during cooking, most of the intermediate products or final products are particulate matters, and most of the particulate matters are harmful to human bodies. Therefore, the knowledge of the particulate pollution and emission characteristics of cooking fume has important reference value for realizing effective control of fume pollution.
The cooking process can cause higher oil smoke concentration, and particularly when the doors and windows are closed or the ventilation is poor, the whole oil smoke can be influenced. Thus, soot pollution has become a potential hazard affecting catering practitioners and environmental pollution.
In CN111340310a, the collected data are screened, and the screened data are trained, so as to predict the oil smoke predicted value of the target, but in the oil smoke predicting process, the user behavior, ventilation frequency and the like have certain influence on the results, so that the predicting precision is reduced, and certain limitation exists; the invention reduces the interference of user behavior and ventilation frequency to oil smoke detection by measuring the oil smoke state information of the oil smoke producing area in real time with the CN111340310A, predicts and improves the prediction precision and practicability.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a catering oil smoke prediction processing method and system based on big data, which have the advantages of real-time performance and the like and solve the problem of adverse effect caused by cooking oil smoke.
(II) technical scheme
In order to solve the technical problem of adverse effect caused by oil smoke in the food processing process, the invention provides the following technical scheme:
a catering oil smoke prediction processing method based on big data comprises the following steps:
s1, setting monitoring points based on urban areas;
s2, distributing management areas of the monitoring points based on Euclidean distances according to the addresses of the monitoring points set by the roads;
s3, mounting oil smoke detection equipment in a monitoring point management area set on the basis of a road;
s4, monitoring the state condition of the oil smoke generated by the oil smoke generating place in the road in real time based on the installed oil smoke detection equipment, wherein the state condition of the oil smoke comprises the concentration value of sulfur dioxide of the oil smoke generating place and the oil smoke emission rate;
s5, the oil smoke detection equipment judges whether hidden danger exists in the current oil smoke generating place or not based on the oil smoke condition monitored in real time; when hidden danger exists, the monitored abnormal data are transmitted to monitoring points in a management area where the detection equipment is located in real time, and if the concentration value of sulfur dioxide in the current oil smoke generating place exceeds a set threshold value, the hidden danger exists is judged; the abnormal data comprise alarm information generated when the oil smoke detection equipment monitors that the concentration value of sulfur dioxide generated by the current oil smoke exceeds a threshold value in the monitoring process;
after the monitoring point receives the abnormal data from the monitoring equipment, uploading the current abnormal data, the address information of the monitoring point and the address information of the monitoring equipment to a catering oil smoke prediction system and carrying out alarm processing;
s6, the oil smoke detection equipment predicts whether hidden danger exists at the next set moment of the current oil smoke generating place in real time based on the oil smoke condition monitored in real time.
Preferably, the setting the monitoring point based on the urban area includes the following steps: the monitoring points set in the urban area comprise common monitoring points and special monitoring points;
s11, in the urban area, setting the intersection point of the urban main road and the non-main road as a special monitoring point, and setting the intersection point of the non-main road and the non-main road as a common monitoring point;
s12, numbering each monitoring point by setting a UID (user identifier) based on all the set monitoring points;
s13, storing address information of each monitoring point based on the UID number of each monitoring point;
preferably, the allocating the management area of each monitoring point based on the euclidean distance by the address of the monitoring point set on each road comprises the following steps:
setting the coordinates of a random oil smoke generating place R in the road as (x) r ,y r ) The coordinates of the common monitoring point in the road are P (x) 1 ,y 1 ) The coordinate of the special monitoring point of the intersection point of the road and the main road is Q (x 2 ,y 2 ) Setting the distance between the oil smoke generating place and the detection point as d;
；
；
when d RP ≥d RQ When the oil smoke is generated in the first monitoring point management range, when d RP <d RQ When the oil smoke generating place is in the second monitoring point management range, monitoring information can be directly reported to the special monitoring point when the oil smoke generating place is in the first monitoring point management range, and monitoring information can be reported to the common monitoring point when the oil smoke generating place is in the second monitoring point management range, and then the common monitoring point reports the monitoring information to the adjacent special monitoring point;
preferably, the method for monitoring the state of the oil smoke generated by the oil smoke generating place in the road in real time based on the installed oil smoke detection equipment comprises the following steps:
based on the change condition of the sulfur dioxide concentration, the current oil smoke gaseous pollutant concentration of the oil smoke generating place is calculated as follows:
s611, setting the ventilation frequency of a lampblack generating place as a, and setting the emission rate of the lampblack gaseous pollutants as a fixed value at a constant temperature based on a lampblack generating place lampblack gaseous pollutant mass balance equation;
the real-time concentration expression of the gaseous pollutants of the oil smoke generated by the oil smoke is as follows:
(1)
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, mg/m 3 ，C out Is the concentration mg/m of the gaseous pollutant of the outdoor lampblack 3 ，S g The emission rate of the gaseous pollutants of the oil smoke is mg/min, a is the ventilation frequency, the secondary/min and V is the volume m of the oil smoke generation place 3 T is time, min;
s612, setting the concentration C of the outdoor oil smoke gaseous pollutant when the outdoor air enters the oil smoke generating place based on the real-time concentration expression of the oil smoke gaseous pollutant of the oil smoke generating place out =0；
When the pollutant concentration t=0 generated by the oil smoke at the initial time is set as shown in the formula (1), C in，g (t)=0，t 1 For any time between the initial time and the current time, t 2 Is the current moment; then t 1 To t 2 In the time period, the dosage of the oil smoke gaseous pollutants generated by the oil smoke is as follows:
(2)
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, S g The emission rate of the gaseous pollutants of the oil smoke is a ventilation frequency, V is the volume of the oil smoke generation place, t is time, and e is the base number of natural logarithm;
from t 1 To t 2 （t 2 -t 1 The average concentration of gaseous pollutants of the oil smoke generated by the oil smoke during the period of = Δt) is:
(3)
emission rate S g The method comprises the following steps:
（4）
s62, predicting t based on average concentration and emission rate of oil smoke gaseous pollutants generated by oil smoke calculated in real time 3 The concentration of gaseous pollutants of the oil smoke generated by the oil smoke at moment is set to be unchanged, and t is predicted 3 The formula of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment is as follows:
(5)
wherein,C in，g t 3 pre-preparation To predict t 3 The concentration of gaseous pollutant of oil smoke generated by oil smoke at moment and based on predicted t 3 The concentration of gaseous pollutants of the oil smoke generated by the oil smoke at the moment is correspondingly treated; t is t 3 The moment is the set next moment;
the corresponding treatment of the gaseous pollutant concentration of the oil smoke generated by the oil smoke based on the predicted time t3 comprises
Setting a gaseous pollutant concentration threshold of the oil smoke generated by the oil smoke;
when the concentration of the gaseous pollutants of the oil smoke generated by the oil smoke generating place exceeds a threshold value at the time t3, stopping generating the oil smoke by food processing equipment of the oil smoke generating place at the time t 4; t4 is between t2 and t 3;
s63, when the food processing equipment of the oil smoke generating place stops generating oil smoke, based on the real-time concentration expression of the oil smoke gaseous pollutants of the oil smoke generating place, the real-time concentration expression of the oil smoke gaseous pollutants of the oil smoke generating place becomes:
（6）
when the cooking equipment stops generating the oil smoke at time t=TAt the moment, the concentration of pollutants generated by the corresponding oil smoke is the largest and is C in,g (t)=C MAX And C out =0, which is taken into the above formula for the attenuation of the contaminants:
（7）
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, C MAX The method is characterized in that the maximum concentration of the gaseous pollutants of the oil smoke generated by the oil smoke is shown as a ventilation frequency, and the time for stopping the discharge of the pollutants of the oil smoke generated by the oil smoke is shown as T;
based on the real-time concentration expression of the oil smoke gaseous pollutants in the oil smoke generating place, the expression of the emission section of the oil smoke gaseous pollutants in the oil smoke generating place is as follows:
(8)
s64, carrying out corresponding treatment on the real-time concentration of the oil smoke gaseous pollutants generated by the oil smoke based on real-time monitoring and the emission condition of the oil smoke gaseous pollutants generated by the oil smoke;
s65, judging the potential hidden danger degree of the current oil smoke generating place oil smoke gaseous pollutant based on health risk assessment on the current oil smoke generating place oil smoke gaseous pollutant concentration monitored in real time;
preferably, the prediction-based t n The corresponding treatment of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment comprises the following steps:
setting a gaseous pollutant concentration threshold of the oil smoke generated by the oil smoke;
when t n The concentration of the gaseous pollutant of the oil smoke generated by the oil smoke exceeds a threshold value at the moment, and the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke exceeds the threshold value at t n Reminding a user five minutes before the moment;
when t n The concentration of the gaseous pollutant of the oil smoke generated by the oil smoke does not exceed the threshold value at any time, and no treatment is carried out;
preferably, the processing of the real-time concentration of the gaseous pollutant of the oil smoke generating place based on the real-time monitoring and the emission of the gaseous pollutant of the oil smoke generating place based on the real-time monitoring includes:
setting a threshold value of emission time required by the oil smoke gaseous pollutants in the oil smoke generating place, and judging how long the concentration of the oil smoke gaseous pollutants in the oil smoke generating place is reduced to 0 based on the expression of the emission section of the oil smoke gaseous pollutants in the oil smoke generating place;
when the emission time required by the gaseous pollutants of the oil smoke generated by the oil smoke exceeds a set threshold value, starting an auxiliary emission of an air exchange fan of the oil smoke generated by the oil smoke;
when the emission time required by the oil smoke gaseous pollutants generated by the oil smoke does not exceed the set threshold value, no treatment is carried out;
preferably, the health risk assessment based on the concentration of the gaseous pollutant of the oil smoke generated by the current real-time monitoring comprises:
health risk assessment of oil smoke generation places;
(9)
wherein RfCs is the reference concentration, is the limiting concentration of pollutants in the environment, C a Is the actual contaminant concentration;
when MOE is less than 1, the carcinogenic risk of the chemical substances in the oil fume is high, when MOE is less than or equal to 1 and less than 10, the carcinogenic risk of the chemical substances in the oil fume is low, and when MOE is more than or equal to 10, the carcinogenic risk of the chemical substances in the oil fume is negligible;
the catering oil smoke prediction processing system based on big data for realizing the catering oil smoke prediction processing method based on big data comprises the following steps: the system comprises a system interface, an area management module, a database module and a monitoring management module;
the system interface is used for providing an interface for an administrator user to log in and browse information in the system;
the regional management module is used for setting different monitoring points according to road distribution in the current city and receiving monitoring information of each monitoring point;
the monitoring management module is used for managing the addition and deletion of the monitoring equipment;
the database is used for storing the geographic position information of each monitoring point and each monitoring device.
(III) beneficial effects
Compared with the prior art, the invention provides a catering oil smoke prediction processing method and system based on big data, which have the following beneficial effects:
1. according to the invention, by arranging different monitoring points in the urban area and installing the oil smoke monitoring device at the oil smoke generating place, the system has the functions of predicting, processing and preventing restaurant oil smoke, so that the intelligent level of the existing oil smoke safety monitoring is improved; and the classification of the monitoring points is carried out based on the main road and the non-main road, so that the priority degree requirement of emergency treatment is better met, and the urban management requirement is more met.
2. According to the invention, the sulfur dioxide content in the oil smoke generated by the oil smoke is monitored in real time, the concentration of the gaseous pollutants of the oil smoke generated by the oil smoke is predicted based on the reasonable sulfur dioxide emission degree, and corresponding treatment and protection measures are made based on the predicted concentration of the gaseous pollutants of the oil smoke generated by the oil smoke, so that the hidden danger in catering oil smoke is reduced. In addition, on the calculation of the emission degree and the pollutant concentration, complicated and ingenious deduction and calculation are performed based on mass conservation, so that the oil smoke pollution is expected more accurately, and the urban oil smoke protection is better served.
3. According to the invention, the emission condition of the current oil smoke generating place oil smoke gaseous pollutant concentration is reasonably predicted by monitoring the oil smoke generating place oil smoke gaseous pollutant concentration after stopping food processing in real time, and corresponding counter measures are arranged based on the emission condition, so that hidden danger in catering oil smoke is reduced. The invention also calculates and predicts the oil smoke emission time, compares the threshold value of the emission time, adopts corresponding measures to determine whether to start the ventilator, and perfects the urban oil smoke prediction treatment better.
4. According to the invention, the MOE evaluation method is used for evaluating the gaseous pollutants of the oil smoke existing in the oil smoke generating place and reasonably reminding the user based on the evaluation result, so that the safety precaution consciousness of the user is improved; and the carcinogenic grade is evaluated, so that the early warning requirement of the health of citizens is better met.
5. The invention creates a corresponding catering oil smoke prediction processing system based on big data, so that a city administrator can conveniently monitor and manage the whole city smoke emission, the smoke emission condition of the whole city is clear at a glance through data acquisition and interface display, the development trend and risk of the city are insight, and the city management is more intelligent.
Drawings
Fig. 1 is a schematic structural diagram of a catering oil smoke prediction processing flow.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment discloses a catering oil smoke prediction processing method based on big data, which comprises the following steps:
s1, setting monitoring points based on urban areas;
s2, distributing management areas of the monitoring points based on Euclidean distances according to the addresses of the monitoring points set by the roads;
s3, mounting oil smoke detection equipment in a monitoring point management area set on the basis of a road;
s4, monitoring the state condition of the oil smoke generated by the oil smoke generating place in the road in real time based on the installed oil smoke detection equipment, wherein the state condition of the oil smoke comprises the concentration value of sulfur dioxide of the oil smoke generating place and the oil smoke emission rate;
s5, the oil smoke detection equipment judges whether hidden danger exists in the current oil smoke generating place or not based on the oil smoke condition monitored in real time; when hidden danger exists, the monitored abnormal data are transmitted to monitoring points in a management area where the detection equipment is located in real time, and if the concentration value of sulfur dioxide in the current oil smoke generating place exceeds a set threshold value, the hidden danger exists is judged; the abnormal data comprise alarm information generated when the oil smoke detection equipment monitors that the concentration value of sulfur dioxide generated by the current oil smoke exceeds a threshold value in the monitoring process;
after the monitoring point receives the abnormal data from the monitoring equipment, uploading the current abnormal data, the address information of the monitoring point and the address information of the monitoring equipment to a catering oil smoke prediction system and carrying out alarm processing;
s6, the oil smoke detection equipment predicts whether hidden danger exists at the next set moment of the current oil smoke generating place in real time based on the oil smoke condition monitored in real time.
Further, the setting of the monitoring point based on the urban area comprises the following steps: the monitoring points set in the urban area comprise common monitoring points and special monitoring points;
s11, in the urban area, setting the intersection point of the urban main road and the non-main road as a special monitoring point, and setting the intersection point of the non-main road and the non-main road as a common monitoring point;
s12, numbering each monitoring point by setting a UID (user identifier) based on all the set monitoring points;
s13, storing address information of each monitoring point based on the UID number of each monitoring point;
further, the allocating the management area of each monitoring point based on the Euclidean distance by the address of the monitoring point set on each road comprises the following steps:
setting the coordinates of a random oil smoke generating place R in the road as (x) r ,y r ) The coordinates of the common monitoring point in the road are P (x) 1 ,y 1 ) The coordinate of the special monitoring point of the intersection point of the road and the main road is Q (x 2 ,y 2 ) Setting the distance between the oil smoke generating place and the detection point as d;
；
；
when d RP ≥d RQ When the oil smoke is generated in the management range of the first monitoring point,when d RP <d RQ When the oil smoke generating place is in the second monitoring point management range, monitoring information can be directly reported to the special monitoring point when the oil smoke generating place is in the first monitoring point management range, and monitoring information can be reported to the common monitoring point when the oil smoke generating place is in the second monitoring point management range, and then the common monitoring point reports the monitoring information to the adjacent special monitoring point;
further, the oil smoke state condition of the oil smoke generating place in the road based on the installed oil smoke detection equipment is monitored in real time, and the method comprises the following steps of:
based on the change condition of the sulfur dioxide concentration, the current oil smoke gaseous pollutant concentration of the oil smoke generating place is calculated as follows:
s611, setting the ventilation frequency of a lampblack generating place as a, and setting the emission rate of the lampblack gaseous pollutants as a fixed value at a constant temperature based on a lampblack generating place lampblack gaseous pollutant mass balance equation;
the real-time concentration expression of the gaseous pollutants of the oil smoke generated by the oil smoke is as follows:
(1)
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, mg/m 3 ，C out Is the concentration mg/m of the gaseous pollutant of the outdoor lampblack 3 ，S g The emission rate of the gaseous pollutants of the oil smoke is mg/min, a is the ventilation frequency, the secondary/min and V is the volume m of the oil smoke generation place 3 T is time, min;
s612, setting the concentration C of the outdoor oil smoke gaseous pollutant when the outdoor air enters the oil smoke generating place based on the real-time concentration expression of the oil smoke gaseous pollutant of the oil smoke generating place out =0；
The pollutant concentration C of the oil smoke is obtained by the formula (1) when the initial time t=0 is set in，g (t)=0，t 1 For any time between the initial time and the current time, t 2 Is the current moment; then t 1 To t 2 In a time period, the oil smoke generates the gaseous pollution of the oil smokeThe dosage of the substances is as follows:
(2)
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, S g The emission rate of the gaseous pollutants of the oil smoke is a ventilation frequency, V is the volume of the oil smoke generation place, t is time, and e is the base number of natural logarithm;
from t 1 To t 2 （t 2 -t 1 The average concentration of gaseous pollutants of the oil smoke generated by the oil smoke during the period of = Δt) is:
(3)
emission rate S g The method comprises the following steps:
（4）
s62, predicting t based on average concentration and emission rate of oil smoke gaseous pollutants generated by oil smoke calculated in real time 3 The concentration of gaseous pollutants of the oil smoke generated by the oil smoke at moment is set to be unchanged, and t is predicted 3 The formula of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment is as follows:
(5)
wherein,C in，g t 3 pre-preparation To predict t 3 The concentration of gaseous pollutant of oil smoke generated by oil smoke at moment and based on predicted t 3 The concentration of gaseous pollutants of the oil smoke generated by the oil smoke at the moment is correspondingly treated; t is t 3 The moment is the set next moment;
the prediction-based t 3 The corresponding treatment of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment comprises
Setting a gaseous pollutant concentration threshold of the oil smoke generated by the oil smoke;
when t 3 At moment, the concentration of the gaseous pollutant of the oil smoke generating place exceeds a threshold value, and the food processing equipment of the oil smoke generating place is at t 4 Stopping generating the oil smoke at any time; t is t 4 Between t 2 、t 3 Between them;
s63, when the food processing equipment of the oil smoke generating place stops generating oil smoke, based on the real-time concentration expression of the oil smoke gaseous pollutants of the oil smoke generating place, the real-time concentration expression of the oil smoke gaseous pollutants of the oil smoke generating place becomes:
（6）
since the time t=T of the cooking equipment stopping generating the oil smoke, the concentration of the pollutant in the corresponding oil smoke generating place is the maximum and is C in,g (t)=C MAX And C out =0, which is taken into the above formula for the attenuation of the contaminants:
（7）
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, C MAX The method is characterized in that the maximum concentration of the gaseous pollutants of the oil smoke generated by the oil smoke is shown as a ventilation frequency, and the time for stopping the discharge of the pollutants of the oil smoke generated by the oil smoke is shown as T;
based on the real-time concentration expression of the oil smoke gaseous pollutants in the oil smoke generating place, the expression of the emission section of the oil smoke gaseous pollutants in the oil smoke generating place is as follows:
(8)
s64, carrying out corresponding treatment on the real-time concentration of the oil smoke gaseous pollutants generated by the oil smoke based on real-time monitoring and the emission condition of the oil smoke gaseous pollutants generated by the oil smoke;
s65, judging the potential hidden danger degree of the current oil smoke generating place oil smoke gaseous pollutant based on health risk assessment on the current oil smoke generating place oil smoke gaseous pollutant concentration monitored in real time;
further, the prediction-based t n The corresponding treatment of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment comprises the following steps:
setting a gaseous pollutant concentration threshold of the oil smoke generated by the oil smoke;
when t n The concentration of the gaseous pollutant of the oil smoke generated by the oil smoke exceeds a threshold value at the moment, and the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke exceeds the threshold value at t n Reminding a user five minutes before the moment;
when t n The concentration of the gaseous pollutant of the oil smoke generated by the oil smoke does not exceed the threshold value at any time, and no treatment is carried out;
further, the corresponding treatment of the real-time concentration of the oil smoke gaseous pollutants generated by the oil smoke based on the real-time monitoring and the emission condition of the oil smoke gaseous pollutants generated by the oil smoke comprises the following steps:
setting a threshold value of emission time required by the oil smoke gaseous pollutants in the oil smoke generating place, and judging how long the concentration of the oil smoke gaseous pollutants in the oil smoke generating place is reduced to 0 based on the expression of the emission section of the oil smoke gaseous pollutants in the oil smoke generating place;
when the emission time required by the gaseous pollutants of the oil smoke generated by the oil smoke exceeds a set threshold value, starting an auxiliary emission of an air exchange fan of the oil smoke generated by the oil smoke;
when the emission time required by the oil smoke gaseous pollutants generated by the oil smoke does not exceed the set threshold value, no treatment is carried out;
further, the health risk assessment based on the concentration of the gaseous pollutant of the oil smoke generated by the current real-time monitoring comprises the following steps:
health risk assessment of oil smoke generation places;
(9)
wherein RfCs is the reference concentration, is the limiting concentration of pollutants in the environment, C a Is the actual contaminant concentration;
when MOE is less than 1, the carcinogenic risk of the chemical substances in the oil fume is high, when MOE is less than or equal to 1 and less than 10, the carcinogenic risk of the chemical substances in the oil fume is low, and when MOE is more than or equal to 10, the carcinogenic risk of the chemical substances in the oil fume is negligible;
the embodiment also discloses a catering oil smoke prediction processing system based on big data for realizing the catering oil smoke prediction processing method based on big data, which comprises the following steps: the system comprises a system interface, an area management module, a database module and a monitoring management module;
the system interface is used for providing an interface for an administrator user to log in and browse information in the system;
the regional management module is used for setting different monitoring points according to road distribution in the current city and receiving monitoring information of each monitoring point;
the monitoring management module is used for managing the addition and deletion of the monitoring equipment;
the database is used for storing the geographic position information of each monitoring point and each monitoring device.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The catering oil smoke prediction processing method based on big data is characterized by comprising the following steps of:
s1, setting monitoring points based on urban areas;
s2, distributing management areas of the monitoring points based on Euclidean distances according to the addresses of the monitoring points set by the roads;
s3, mounting oil smoke detection equipment in a monitoring point management area set on the basis of a road;
s4, monitoring the state condition of the oil smoke generated by the oil smoke generating place in the road in real time based on the installed oil smoke detection equipment, wherein the state condition of the oil smoke comprises the concentration value of sulfur dioxide of the oil smoke generating place and the oil smoke emission rate;
s5, the oil smoke detection equipment judges whether hidden danger exists in the current oil smoke generating place or not based on the oil smoke condition monitored in real time; when hidden danger exists, the monitored abnormal data are transmitted to monitoring points in a management area where the detection equipment is located in real time; judging hidden danger if the concentration value of sulfur dioxide in the current oil smoke generating place exceeds a set threshold value; the abnormal data comprise alarm information generated when the oil smoke detection equipment monitors that the concentration value of sulfur dioxide generated by the current oil smoke exceeds a threshold value in the monitoring process;
after the monitoring point receives the abnormal data from the monitoring equipment, uploading the current abnormal data, the address information of the monitoring point and the address information of the monitoring equipment to a catering oil smoke prediction system and carrying out alarm processing;
s6, the oil smoke detection equipment predicts whether hidden danger exists at the next set moment of the current oil smoke generating place in real time based on the oil smoke condition monitored in real time.
2. The catering oil smoke prediction processing method based on big data as claimed in claim 1, wherein the method comprises the following steps: the setting of the monitoring points based on the urban area comprises the following steps: a common monitoring point and a special monitoring point;
s11, in the urban area, setting the intersection point of the urban main road and the non-main road as a special monitoring point, and setting the intersection point of the non-main road and the non-main road as a common monitoring point;
s12, numbering each monitoring point by setting a UID (user identifier) based on all the set monitoring points;
s13, storing address information of each monitoring point based on the UID number of each monitoring point.
3. The catering oil smoke prediction processing method based on big data as claimed in claim 1, wherein the method comprises the following steps: the method for allocating the management areas of the monitoring points based on the Euclidean distance according to the addresses of the monitoring points set by the roads comprises the following steps:
setting the coordinates of a random oil smoke generating place R in the road as (x) r ,y r ) The coordinates of the common monitoring point in the road are P (x) 1 ,y 1 ) The intersection point of the road and the arterial roadThe coordinate of the special monitoring point is Q (x 2 ,y 2 ) Setting the distance between the oil smoke generating place and the detection point as d;
；
；
when d RP ≥d RQ When the oil smoke is generated in the first monitoring point management range, when d RP <d RQ When the oil smoke generating place is in the second monitoring point management range, monitoring information can be directly reported to the special monitoring point when the oil smoke generating place is in the first monitoring point management range, and monitoring information can be reported to the common monitoring point when the oil smoke generating place is in the second monitoring point management range, and then the common monitoring point reports the monitoring information to the adjacent special monitoring point.
4. The catering oil smoke prediction processing method based on big data as claimed in claim 1, wherein the method comprises the following steps: the oil smoke state condition based on the oil smoke production place in the road of installation oil smoke check out test set real-time supervision includes following step:
s61, calculating the current concentration of the gaseous pollutants of the oil smoke generated by the oil smoke based on the change condition of the concentration of sulfur dioxide when food is processed;
based on the change condition of the sulfur dioxide concentration, the current oil smoke gaseous pollutant concentration of the oil smoke generating place is calculated as follows:
s611, setting the ventilation frequency of a lampblack generating place as a, and setting the emission rate of the lampblack gaseous pollutants as a fixed value at a constant temperature based on a lampblack generating place lampblack gaseous pollutant mass balance equation;
the real-time concentration expression of the gaseous pollutants of the oil smoke generated by the oil smoke is as follows:
(1)
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, mg/m 3 ，C out Is the concentration mg/m of the gaseous pollutant of the outdoor lampblack 3 ，S g The emission rate of the gaseous pollutants of the oil smoke is mg/min, a is the ventilation frequency, the secondary/min and V is the volume m of the oil smoke generation place 3 T is time, min;
s612, setting the concentration C of the outdoor oil smoke gaseous pollutant when the outdoor air enters the oil smoke generating place based on the real-time concentration expression of the oil smoke gaseous pollutant of the oil smoke generating place out =0；
The pollutant concentration C of the oil smoke is obtained by the formula (1) when the initial time t=0 is set in，g (t)=0，t 1 For any time between the initial time and the current time, t 2 Is the current moment; then t 1 To t 2 In the time period, the dosage of the oil smoke gaseous pollutants generated by the oil smoke is as follows:
(2)
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, S g The emission rate of the gaseous pollutants of the oil smoke is a ventilation frequency, V is the volume of the oil smoke generation place, t is time, and e is the base number of natural logarithm;
within the period of Δt, t 2 -t 1 The average concentration of gaseous pollutants of the oil smoke generated by the oil smoke is as follows:
(3)
emission rate S g The method comprises the following steps:
（4）
s62, oil based on real-time calculationPrediction of average concentration and emission rate t of gaseous pollutants of smoke-generating ground smoke 3 The concentration of gaseous pollutants of the oil smoke generated by the oil smoke at moment is set to be unchanged, and t is predicted 3 The formula of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment is as follows:
(5)
wherein,C in，g t 3 pre-preparation To predict t 3 The concentration of gaseous pollutant of oil smoke generated by oil smoke at moment and based on predicted t 3 The concentration of gaseous pollutants of the oil smoke generated by the oil smoke at the moment is correspondingly treated; t is t 3 The moment is the set next moment;
the prediction-based t 3 The corresponding treatment of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment comprises
Setting a gaseous pollutant concentration threshold of the oil smoke generated by the oil smoke;
when t 3 At moment, the concentration of the gaseous pollutant of the oil smoke generating place exceeds a threshold value, and the food processing equipment of the oil smoke generating place is at t 4 Stopping generating the oil smoke at any time; t is t 4 Between t 2 、t 3 Between them;
s63, based on the real-time concentration expression of the oil smoke gaseous pollutants generated by the oil smoke, the real-time concentration expression of the oil smoke gaseous pollutants generated by the oil smoke becomes:
（6）
since the time t=T of the cooking equipment stopping generating the oil smoke, the concentration of the pollutant in the corresponding oil smoke generating place is the maximum and is C in,g (t)=C MAX And C out =0, bringing it into the above equation for contaminant attenuation:
（7）
wherein C is in，g (t) is the real-time concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the time t, C MAX The maximum concentration of the gaseous pollutant of the oil smoke generated by the oil smoke generating place is represented by a ventilation frequency, T is the moment when the oil smoke generating place stops generating the oil smoke, and T=t 4 ；
Based on the real-time concentration expression of the oil smoke gaseous pollutants in the oil smoke generating place, the expression of the emission section of the oil smoke gaseous pollutants in the oil smoke generating place is as follows:
(8)
s64, carrying out corresponding treatment on the real-time concentration of the oil smoke gaseous pollutants generated by the oil smoke based on real-time monitoring and the emission condition of the oil smoke gaseous pollutants generated by the oil smoke;
s65, judging the potential hidden danger degree of the current oil smoke generating place oil smoke gaseous pollutant based on health risk assessment on the current oil smoke generating place oil smoke gaseous pollutant concentration monitored in real time.
5. The catering oil smoke prediction processing method based on big data as claimed in claim 4, wherein the method comprises the following steps: the prediction-based t 3 The corresponding treatment of the concentration of the gaseous pollutant of the oil smoke generated by the oil smoke at the moment further comprises the following steps: will be at t 4 The user is reminded at the moment.
6. The catering oil smoke prediction processing method based on big data as claimed in claim 4, wherein the method comprises the following steps: the real-time concentration of the oil smoke gaseous pollutants generated by the oil smoke based on the real-time monitoring and the emission condition of the oil smoke gaseous pollutants generated by the oil smoke correspondingly process the oil smoke gaseous pollutants comprises the following steps:
setting a threshold value of emission time required by the oil smoke gaseous pollutants in the oil smoke generating place, and judging how long the concentration of the oil smoke gaseous pollutants in the oil smoke generating place is reduced to 0 based on the expression of the emission section of the oil smoke gaseous pollutants in the oil smoke generating place;
when the emission time required by the gaseous pollutants of the oil smoke generated by the oil smoke exceeds a set threshold value, starting an auxiliary emission of an air exchange fan of the oil smoke generated by the oil smoke;
when the emission time of the gaseous pollutant of the oil smoke generated by the oil smoke does not exceed the set threshold value, no treatment is carried out.
7. The catering oil smoke prediction processing method based on big data as claimed in claim 4, wherein the method comprises the following steps:
the health risk assessment based on the concentration of the oil smoke gaseous pollutants generated by the current real-time monitoring comprises the following steps:
health risk assessment of oil smoke generation places;
(9)
wherein RfCs is the reference concentration, is the limiting concentration of pollutants in the environment, C a Is the actual contaminant concentration;
when MOE is less than 1, the carcinogenic risk of chemical substances in the oil smoke is high, when MOE is less than or equal to 1 and less than 10, the carcinogenic risk of chemical substances in the oil smoke is low, and when MOE is more than or equal to 10, the carcinogenic risk of chemical substances in the oil smoke is negligible.
8. The catering oil smoke prediction processing method based on big data as claimed in claim 1, wherein the method comprises the following steps: the oil smoke generating place comprises one or more of various restaurants, various residential kitchens and various food processing factories.
9. A big data-based catering oil smoke prediction processing system for implementing the big data-based catering oil smoke prediction processing method as set forth in any one of claims 1 to 8, characterized in that: the system comprises a region management module, a database module and a monitoring management module;
the regional management module is used for setting different monitoring points according to road distribution in the current city and receiving monitoring information of each monitoring point;
the monitoring management module is used for managing the addition and deletion of the monitoring equipment;
the database is used for storing the geographic position information of each monitoring point and each monitoring device.
10. The big data based catering fume prediction processing system of claim 9, further comprising a system interface for providing an interface to an administrator user to log in and view information in the system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311308297.3A CN117057483B (en) | 2023-10-11 | 2023-10-11 | Catering oil smoke prediction processing method and system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311308297.3A CN117057483B (en) | 2023-10-11 | 2023-10-11 | Catering oil smoke prediction processing method and system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117057483A true CN117057483A (en) | 2023-11-14 |
| CN117057483B CN117057483B (en) | 2024-01-26 |
Family
ID=88666628
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311308297.3A Active CN117057483B (en) | 2023-10-11 | 2023-10-11 | Catering oil smoke prediction processing method and system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117057483B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117783430A (en) * | 2023-11-30 | 2024-03-29 | 江苏易达检测科技有限公司 | Catering lampblack online monitoring system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180165931A1 (en) * | 2016-12-14 | 2018-06-14 | Nanjing Avatarmind Robot Technology Co., Ltd. | Robot security inspection method based on environment map and robot thereof |
| CN113554363A (en) * | 2021-09-23 | 2021-10-26 | 国网江苏省电力有限公司营销服务中心 | Power customer service work order processing method and system based on grid system monitoring |
| CN115314517A (en) * | 2021-05-08 | 2022-11-08 | 浙江歆雅环保科技有限公司 | Oil smoke on-line monitoring system applied to catering industry |
| CN115829807A (en) * | 2022-11-29 | 2023-03-21 | 南京景泓智能制造技术研究院有限公司 | Real-time online monitoring system for catering users |
| CN116484250A (en) * | 2023-02-24 | 2023-07-25 | 广州正虹环境科技有限公司 | Catering lampblack online monitoring and early warning method based on map display and related device |
-
2023
- 2023-10-11 CN CN202311308297.3A patent/CN117057483B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180165931A1 (en) * | 2016-12-14 | 2018-06-14 | Nanjing Avatarmind Robot Technology Co., Ltd. | Robot security inspection method based on environment map and robot thereof |
| CN115314517A (en) * | 2021-05-08 | 2022-11-08 | 浙江歆雅环保科技有限公司 | Oil smoke on-line monitoring system applied to catering industry |
| CN113554363A (en) * | 2021-09-23 | 2021-10-26 | 国网江苏省电力有限公司营销服务中心 | Power customer service work order processing method and system based on grid system monitoring |
| CN115829807A (en) * | 2022-11-29 | 2023-03-21 | 南京景泓智能制造技术研究院有限公司 | Real-time online monitoring system for catering users |
| CN116484250A (en) * | 2023-02-24 | 2023-07-25 | 广州正虹环境科技有限公司 | Catering lampblack online monitoring and early warning method based on map display and related device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117783430A (en) * | 2023-11-30 | 2024-03-29 | 江苏易达检测科技有限公司 | Catering lampblack online monitoring system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117057483B (en) | 2024-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117057483B (en) | Catering oil smoke prediction processing method and system based on big data | |
| CN116300652B (en) | Power control cabinet on-line monitoring system based on data analysis | |
| KR101470617B1 (en) | A system and a method for assessing and reducing air pollution by regulating airflow ventilation | |
| CN114821946B (en) | Fire disaster early warning method, monitoring terminal and system for transformer substation alternating current power supply | |
| Pal et al. | Effect of road traffic noise pollution on human work efficiency in government offices, private organizations, and commercial business centres in Agartala City using fuzzy expert system: A case study | |
| CN108053594A (en) | A kind of fire alarm method for pinpointing territory element | |
| CN110601374A (en) | Power utilization management system and power utilization data monitoring method | |
| CN110953687B (en) | Health degree evaluation method and system of air conditioner and storage medium | |
| CN111507603A (en) | Method and system for evaluating current environmental risk situation of in-production enterprise | |
| CN117114406A (en) | Emergency event intelligent early warning method and system based on equipment data aggregation | |
| CN118097875A (en) | Regional fire safety early warning system and method based on Internet | |
| Ouache et al. | Evidential reasoning and machine learning-based framework for assessment and prediction of human error factors-induced fire incidents | |
| CN115098577A (en) | Building carbon emission data management cloud center system | |
| CN117709842A (en) | Fire safety control method based on Internet of things large model calculation | |
| CN117409526A (en) | Electrical fire extremely early warning and monitoring system and fire extinguishing method | |
| CN117221361A (en) | Informationized intelligent park management system | |
| CN113295589B (en) | Raise dust monitoring method, device and system | |
| JP6961138B1 (en) | Odor reduction system and odor reduction method | |
| CN213516872U (en) | Indoor accuse cigarette environment intelligence on-line monitoring system | |
| EP4392987A1 (en) | Health assessment generation based on voc detection | |
| CN113408938A (en) | Online monitoring method and system for enterprise pollution source, electronic equipment and storage medium | |
| CN113282639B (en) | Gas leakage data monitoring method and system, intelligent terminal and storage medium | |
| CN118213003B (en) | Sulfur trioxide monitoring-based early warning management method and system | |
| CN118135475B (en) | Fire-fighting hidden danger detection and early warning system and method based on Internet of things | |
| CN117053323B (en) | Air purification system based on indoor air proportion self-balancing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |