A method and system for online monitoring and purification of restaurant fume

Abstract

The present invention provides a method and system for online monitoring and purification of restaurant oil fumes, wherein the method comprises: step 1: collecting environmental data and original oil fume data in an oil fume channel; step 2: calculating an oil fume severity value according to the environmental data and the original oil fume data; step 3: setting the exhaust fan speed for the exhaust fan according to the oil fume severity value; step 4: after starting the exhaust fan, judging whether the oil fume severity value exceeds a preset threshold value, if so, starting the oil fume purification system to purify the restaurant oil fume in the oil fume channel, otherwise, not starting. The method and system for online monitoring and purification of restaurant oil fumes of the present invention determine the oil fume severity value according to environmental data and original oil fume data, determine the exhaust fan speed according to the oil fume severity value and the exhaust fan speed determination library, and then make a purification judgment according to the comparison result of the oil fume severity value and the preset threshold value, thereby improving the suitability of restaurant oil fume monitoring and purification.

Description

Technical Field

[0001] The present invention relates to the technical field of smoke monitoring, and in particular to an online monitoring and purification method and system for restaurant oil smoke. Background Art

[0002] Cooking fume is one of the air pollutants, containing a variety of harmful substances that affect air quality, such as particulate matter and volatile organic compounds (VOCs). Harmful substances in cooking fume may have long-term effects on human health. Areas where the catering industry is concentrated may affect the quality of life of surrounding residents due to cooking fume emissions. Active monitoring and control of cooking fume emissions can effectively reduce the impact of the catering industry on the environment and public health, and also contribute to the sustainable development of catering companies themselves.

[0003] The invention patent with application number: CN202410381673.X discloses a fresh air system control method, equipment and storage medium based on the Internet of Things. The above invention monitors the air quality of the kitchen in real time, determines whether the kitchen fresh air system needs to be started, and adjusts the working parameters of the kitchen fresh air system to realize flexible control of the switch state of the kitchen fresh air system, which is more intelligent; by monitoring the temperature and humidity of the indoor air and outdoor air in the kitchen, the temperature and humidity of the kitchen are adjusted in a combined manner to improve the adjustment effect of the indoor temperature and humidity in the kitchen, and achieve energy saving at the same time; by monitoring the oil fume concentration in the kitchen and combining the use of kitchen appliances, it is determined whether it is necessary to remove oil fume from the exhaust gas discharged by the kitchen fresh air system, and the exhaust oil fume removal of the kitchen fresh air system is adjusted, so that the exhaust gas can be purified and removed before being discharged from the kitchen, thereby avoiding damage to the exterior wall of the kitchen building and pollution of the environment.

[0004] The above-mentioned prior art adjusts the working parameters of the kitchen fresh air system to realize flexible control of the switch state of the kitchen fresh air system to discharge oil smoke, but the above-mentioned fresh air system has only two states of "on" and "off". The wind force required by the corresponding fresh air system is different under different air quality conditions. Setting a fixed wind force for the fresh air system is not very suitable.

[0005] In view of this, there is an urgent need for an online monitoring and purification method and system for restaurant oil fume to at least solve the above-mentioned deficiencies. Summary of the invention

[0006] One of the purposes of the present invention is to provide a method and system for online monitoring and purification of restaurant oil fumes, which determines the severity of the oil fume based on the collected environmental data and the original data of the oil fume, determines the library according to the different severity of the oil fume and the introduced smoke exhaust fan speed, sets the corresponding smoke exhaust fan speed for the smoke exhaust fan, and then makes a purification judgment based on the comparison result between the severity of the oil fume and the preset threshold value, thereby improving the suitability of restaurant oil fume monitoring and purification.

[0007] The method for online monitoring and purification of restaurant fume provided by the embodiment of the present invention comprises:

[0008] Step 1: Collect environmental data and raw data of oil smoke in the oil smoke channel;

[0009] Step 2: Calculate the severity of the oil smoke in the oil smoke passage according to the collected environmental data and the original oil smoke data;

[0010] Step 3: Determine the library according to the severity of the oil smoke and the exhaust fan speed, and set the corresponding exhaust fan speed for the exhaust fan;

[0011] Step 4: After starting the smoke exhaust fan, determine whether the severity of the oil fume exceeds the preset threshold. If so, turn on the oil fume purification system to purify the catering oil fume in the oil fume passage. Otherwise, there is no need to turn on the oil fume purification system.

[0012] Preferably, step 1: collecting environmental data and raw data of oil smoke in the oil smoke channel includes:

[0013] Determine the target space corresponding to the oil smoke passage, obtain first collected data of a first gas sensor preset in the target space, and use the data as environmental data;

[0014] The second collected data of the second gas sensor in the oil fume passage is obtained and used as the oil fume original data.

[0015] Preferably, step 2: calculating the severity value of the oil fume in the oil fume passage according to the collected environmental data and the original oil fume data, comprises:

[0016] Obtaining environmental data on restaurant fume regulations;

[0017] Compare the catering oil fume standard environmental data with the environmental data to obtain a first corresponding value;

[0018] Obtaining the oil fume data evaluation template in the oil fume channel;

[0019] Obtaining a second corresponding value according to the oil fume data evaluation template and the oil fume original data;

[0020] The oil smoke severity value is calculated according to the first corresponding value and the second corresponding value.

[0021] Preferably, step 3: determining a library according to the oil fume severity value and the exhaust fan speed, and setting a corresponding exhaust fan speed for the exhaust fan, comprises:

[0022] Constructing a smoke exhaust fan speed determination library; the smoke exhaust fan speed determination library includes: one-to-one correspondence between pre-selected smoke exhaust fan speeds and pre-selected oil smoke severity values;

[0023] The oil fume severity value is compared with the pre-selected oil fume severity value. If they are consistent, the pre-selected exhaust fan speed corresponding to the pre-selected exhaust fan severity value is used as the exhaust fan speed set for the exhaust fan.

[0024] Preferably, constructing a smoke exhaust fan speed determination library includes:

[0025] Get the smoke exhaust fan startup scene record;

[0026] Parse the smoke exhaust fan startup scene record to obtain the first target extraction item; the first target extraction item includes: smoke exhaust channel data and fan startup effect data of the smoke exhaust fan startup scene;

[0027] According to the smoke exhaust channel data, determine the channel similarity value;

[0028] Determine the fan startup effect value according to the fan startup effect data of the smoke exhaust fan startup scenario;

[0029] If the channel similarity value and the fan startup effect value are both within the corresponding expectations, the second target extraction item in the corresponding exhaust fan startup scene record is extracted; the second target extraction item includes: the oil fume severity value determination data of the exhaust fan startup scene and the fan speed data corresponding to the oil fume severity value determination data;

[0030] Determine a preselected oil smoke severity value according to the oil smoke severity value determination data;

[0031] According to the oil fume severity value, the fan speed data corresponding to the data is determined to determine the pre-selected exhaust fan speed corresponding to the pre-selected oil fume severity value.

[0032] The method for online monitoring and purification of restaurant fume provided by the embodiment of the present invention further includes:

[0033] Step 5: Collect statistics on the working information of the oil fume purification system within the preset target time, conduct load analysis based on the working information, and determine the necessity of restaurant flue renovation based on the load analysis results.

[0034] Preferably, step 5: collecting statistics on the working information of the oil fume purification system within a preset target time, performing load analysis based on the working information, and determining the necessity of restaurant flue modification based on the load analysis results, including:

[0035] Analyze the working information and obtain the start and stop information of the oil fume purification system within the target duration;

[0036] Determine the start and stop timeline based on the start and stop information;

[0037] According to the start-stop time axis, the load analysis of the oil fume purification system within the target duration is carried out to obtain the load feature set;

[0038] Determine the fault probability based on the load characteristic set and the preset fault probability characteristic set library;

[0039] If the failure probability is greater than or equal to the preset failure probability threshold, it is determined that it is necessary to renovate the restaurant flue.

[0040] Preferably, step 5: collecting statistics on the working information of the oil fume purification system within a preset target time, performing load analysis based on the working information, and determining the necessity of restaurant flue modification based on the load analysis results, further includes:

[0041] Obtain target flue design records;

[0042] Based on deep learning technology, the flue design model is determined according to the target flue design records;

[0043] Determine the ideal flue model based on the flue reconstruction area information and flue design model;

[0044] According to the current flue model and the ideal flue model, the modified flue nodes and modified flue branches are determined; wherein, the modified flue branches include: removing flue branches and adding new flue branches;

[0045] Display the modified flue nodes and modified flue branches in the current flue model diagram;

[0046] Instruct the inspector to check the current flue model diagram;

[0047] Among them, obtaining the target flue design records includes:

[0048] If it is determined that it is necessary to renovate the restaurant flue, obtain the current flue model, flue renovation area information and cooking habit information;

[0049] Obtain flue design records;

[0050] Analyze the flue design records and obtain the operating information of the designed flue corresponding to the flue design records;

[0051] Determine the target cuisine based on business information;

[0052] Determine a first cooking habit feature description vector according to the target cuisine and a preset cooking habit determination library;

[0053] Determining a second cooking habit feature description vector according to the cooking habit information;

[0054] Matching the first cooking habit feature description vector and the second cooking habit feature description vector to determine a matching value;

[0055] Analyze the flue design records and obtain the design experience of designers;

[0056] Determine the target flue design record based on the matching value and design experience;

[0057] Among them, the modified flue nodes and modified flue branches are displayed in the current flue model diagram, including:

[0058] Displaying the modified flue node in the current flue model diagram based on the first display rule;

[0059] The demolished flue branch is displayed in the current flue model diagram based on the second display rule;

[0060] The newly added flue branch will be displayed in the current flue model diagram based on the third display rule.

[0061] Preferably, instructing the viewer to view the current flue model diagram includes:

[0062] Obtain the operation information after the viewer views the current flue model diagram;

[0063] Determine local flue modification instructions based on operational information;

[0064] Determine the modification constraints according to the local flue modification instructions;

[0065] The ideal flue model is re-determined based on the renovation constraints, flue renovation area information and flue design model.

[0066] An embodiment of the present invention provides an online monitoring and purification system for restaurant oil smoke, comprising:

[0067] The data acquisition subsystem is used to collect environmental data and raw data of oil smoke in the oil smoke channel;

[0068] The oil fume severity value determination subsystem is used to calculate the oil fume severity value in the oil fume channel according to the collected environmental data and oil fume original data;

[0069] The speed determination subsystem is used to set the corresponding exhaust fan speed for the exhaust fan according to the oil fume severity value and the exhaust fan speed determination library;

[0070] The purification subsystem is used to determine whether the severity of the oil fume exceeds a preset threshold after the smoke exhaust fan is started. If so, the oil fume purification system is turned on to purify the catering oil fume in the oil fume passage. Otherwise, there is no need to turn on the oil fume purification system.

[0071] Preferably, the oil smoke severity value determination subsystem includes:

[0072] Standardized environment data acquisition module, used to obtain standardized environment data of restaurant fume;

[0073] A first corresponding value acquisition module, used to compare the restaurant oil fume standard environmental data with the environmental data to obtain a first corresponding value;

[0074] A fume data evaluation template acquisition module is used to acquire the fume data evaluation template in the fume channel;

[0075] A second corresponding value acquisition module, used for acquiring a second corresponding value according to the oil fume data evaluation template and the oil fume original data;

[0076] The oil fume severity value calculation module is used to calculate the oil fume severity value according to the first corresponding value and the second corresponding value.

[0077] The beneficial effects of the present invention are:

[0078] The present invention determines the oil fume severity value based on the collected environmental data and oil fume original data, sets the corresponding smoke exhaust fan speed for the smoke exhaust fan according to the different oil fume severity values ​​and the introduced smoke exhaust fan speed determination library, and then makes a purification judgment based on the comparison result between the oil fume severity value and the preset threshold value, thereby improving the suitability of catering oil fume monitoring and purification.

[0079] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the present application documents.

[0080] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0082] Figure 1 A schematic diagram of an online monitoring and purification method for restaurant oil fume in an embodiment of the present invention;

[0083] Figure 2 It is a schematic diagram of an online monitoring and purification system for restaurant oil fumes in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0085] The embodiment of the present invention provides a method for online monitoring and purification of restaurant fume, such as Figure 1 As shown, including:

[0086] Step 1: Collect environmental data and raw oil fume data in the oil fume channel; the environmental data is the oil fume data in the corresponding connected space of the oil fume channel (for example, the back kitchen of a hotel); the raw oil fume data is the oil fume data in the oil fume channel when the exhaust fan is not enabled; the oil fume data includes: oil fume concentration, oil fume composition, oil fume temperature, and oil fume particle size;

[0087] Step 2: Calculate the oil fume severity value in the oil fume channel according to the collected environmental data and the original oil fume data; wherein the oil fume severity value is: an indicator for measuring the pollution degree of the oil fume, and the higher the oil fume severity value, the more serious the pollution degree of the oil fume;

[0088] Step 3: according to the oil fume severity value and the smoke exhaust fan speed determination library, a corresponding smoke exhaust fan speed is set for the smoke exhaust fan; wherein the smoke exhaust fan speed determination library includes a plurality of one-to-one corresponding pre-selected oil fume severity values ​​and pre-selected smoke exhaust fan speeds, and when determining the smoke exhaust fan speed, the oil fume severity value and the pre-selected oil fume severity value are compared, and the pre-selected smoke exhaust fan speed corresponding to the consistent pre-selected oil fume severity value is used as the smoke exhaust fan speed;

[0089] Step 4: After starting the exhaust fan, determine whether the fume severity value exceeds the preset threshold. If so, start the fume purification system to purify the restaurant fume in the fume channel. Otherwise, there is no need to start the fume purification system. The preset threshold is pre-set by the staff.

[0090] The working principle and beneficial effects of the above technical solution are:

[0091] The present invention determines the oil fume severity value based on the collected environmental data and oil fume original data, sets the corresponding smoke exhaust fan speed for the smoke exhaust fan according to the different oil fume severity values ​​and the introduced smoke exhaust fan speed determination library, and then makes a purification judgment based on the comparison result between the oil fume severity value and the preset threshold value, thereby improving the suitability of catering oil fume monitoring and purification.

[0092] In one embodiment, step 1: collecting environmental data and raw data of oil smoke in the oil smoke channel includes:

[0093] Determine the target space corresponding to the oil fume channel, obtain the first collected data of the first gas sensor preset in the target space, and use it as environmental data; wherein, the target space corresponding to the oil fume channel is: the smoke channel corresponds to the space for absorbing restaurant oil fume, such as: a hotel kitchen; the first gas sensor is a sensor for sensing restaurant oil fume arranged in the target space, such as: PM3009BP sensor; the first collected data is the sensing data of the first gas sensor;

[0094] The second collected data of the second gas sensor in the oil fume passage is obtained and used as the oil fume original data.

[0095] The working principle and beneficial effects of the above technical solution are:

[0096] The present invention determines environmental data through first collected data of a space corresponding to a fume channel for absorbing restaurant fume, and obtains original fume data through a second gas sensor in the fume channel, thereby improving the calculation accuracy of subsequent fume severity values.

[0097] In one embodiment, step 2: calculating the severity of the oil fume in the oil fume passage according to the collected environmental data and the original oil fume data, comprises:

[0098] Obtaining the standard environmental data of restaurant oil fumes; the standard environmental data of restaurant oil fumes refers to the oil fume emission limits set by environmental protection regulations, including: oil fume concentration, particulate matter content and the allowable emission of pollutants such as non-methane total hydrocarbons;

[0099] Compare the catering oil fume standard environmental data with the environmental data to obtain a first corresponding value; wherein the first corresponding value is: the sum of the data deviation values ​​in the environmental data that exceed the catering oil fume standard environmental data;

[0100] Obtaining an oil fume data evaluation template in the oil fume channel; wherein the oil fume data evaluation template is used to generate a pollution evaluation value by comparing the oil fume channel data, and the template includes a plurality of one-to-one corresponding pre-selected oil fume channel data and pre-selected pollution evaluation values, and the plurality of one-to-one corresponding pre-selected oil fume channel data and pre-selected pollution evaluation values ​​are determined by manually evaluating and recording the pollution situation in the oil fume channel, and the higher the pre-selected pollution evaluation value, the more serious the oil fume pollution in the corresponding oil fume channel;

[0101] According to the oil fume data evaluation template and the oil fume original data, a second corresponding value is obtained; wherein the second corresponding value is: a pollution evaluation value corresponding to the oil fume original data;

[0102] The oil fume severity value is calculated according to the first corresponding value and the second corresponding value. When calculating the oil fume severity value, the first corresponding value and the second corresponding value are normalized and summed to obtain the oil fume severity value.

[0103] The working principle and beneficial effects of the above technical solution are:

[0104] The present invention introduces the standard environmental data of restaurant oil fume and compares it with the environmental data to determine the first corresponding value. The oil fume data evaluation template in the oil fume channel is introduced, and the second corresponding value is obtained according to the oil fume data evaluation template and the original oil fume data. The first corresponding value and the second corresponding value are normalized and summed to obtain the oil fume severity value, thereby improving the accuracy of the calculation of the oil fume severity value.

[0105] In one embodiment, step 3: according to the oil fume severity value and the smoke exhaust fan speed determination library, setting the corresponding smoke exhaust fan speed for the smoke exhaust fan includes:

[0106] Constructing a smoke exhaust fan speed determination library; the smoke exhaust fan speed determination library includes: one-to-one correspondence between pre-selected smoke exhaust fan speeds and pre-selected oil smoke severity values;

[0107] The oil fume severity value is compared with the pre-selected oil fume severity value. If they are consistent, the pre-selected exhaust fan speed corresponding to the pre-selected exhaust fan severity value is used as the exhaust fan speed set for the exhaust fan.

[0108] The working principle and beneficial effects of the above technical solution are:

[0109] The present invention constructs a smoke exhaust fan speed determination library to determine the smoke exhaust fan speed set by the smoke exhaust fan, thereby improving the rationality of the smoke exhaust fan speed setting.

[0110] In one embodiment, a smoke exhaust fan speed determination library is constructed, including:

[0111] Obtain the smoke exhaust fan startup scene record; wherein the smoke exhaust fan startup scene record is: various data and time records under the smoke exhaust fan startup scenario recorded in the big data platform (such as the range hood discussion forum), including: smoke data at startup, smoke exhaust effect after startup, etc.;

[0112] Parse the smoke exhaust fan start-up scene record to obtain the first target extraction item; the first target extraction item includes: smoke exhaust channel data and fan start-up effect data of the smoke exhaust fan start-up scene; wherein the smoke exhaust channel data is: physical parameters of the smoke exhaust channel in the smoke exhaust fan start-up scene, including the size, shape and material of the channel; the fan start-up effect data of the smoke exhaust fan start-up scene is: data describing the smoke exhaust effect before and after the smoke exhaust fan is started, such as: smoke exhaust efficiency;

[0113] According to the smoke exhaust channel data, the channel similarity value is determined; wherein the channel similarity value is: the channel parameter similarity value of the smoke exhaust channel in the smoke exhaust fan startup scene and the corresponding oil fume channel purified by the catering oil fume online monitoring and purification system;

[0114] Determine the fan startup effect value according to the fan startup effect data of the smoke exhaust fan startup scenario; wherein the fan startup effect value is the ratio of the oil fume concentration before the smoke exhaust fan is started to the oil fume concentration after the smoke exhaust fan is started;

[0115] If the channel similarity value and the fan startup effect value are both within the corresponding expectations, the second target extraction item in the corresponding exhaust fan startup scene record is extracted; the second target extraction item includes: the oil fume severity value determination data of the smoke exhaust fan startup scene and the fan speed data corresponding to the oil fume severity value determination data; wherein, the corresponding expectations of the channel similarity value and the fan startup effect value are manually preset; the oil fume severity value determination data of the smoke exhaust fan startup scene is: the ambient oil fume data and the pipeline oil fume data of the smoke exhaust fan startup scene;

[0116] Determine the preselected oil fume severity value according to the oil fume severity value determination data; wherein the determination rule of the preselected oil fume severity value is the same as the oil fume severity value;

[0117] According to the oil fume severity value, the fan speed data corresponding to the data is determined to determine the pre-selected exhaust fan speed corresponding to the pre-selected oil fume severity value.

[0118] The working principle and beneficial effects of the above technical solution are:

[0119] The present invention introduces a smoke exhaust fan startup scene record, and extracts the fan startup effect data of the smoke exhaust fan startup scene therein, determines the channel similarity value between the smoke exhaust channel and the local oil fume channel in the record and the fan startup effect value after the smoke exhaust fan is started in the record, determines that both indicators meet the corresponding smoke exhaust fan startup scene record within the expected requirements, and extracts the oil fume severity value determination data of the smoke exhaust fan startup scene therein and the fan speed data corresponding to the oil fume severity value determination data; based on the same determination rule for the oil fume severity value and according to the oil fume severity value determination data, a preselected oil fume severity value is determined, and the fan speed data corresponding to the time when the oil fume severity value determination data is generated is used as the preselected smoke exhaust fan speed corresponding to the preselected oil fume severity value, thereby improving the rationality of the construction of the smoke exhaust fan speed determination library.

[0120] The embodiment of the present invention provides a method for online monitoring and purification of restaurant fume, which also includes:

[0121] Step 5: Collect the working information of the oil fume purification system within the preset target duration, perform load analysis based on the working information, and determine the necessity of restaurant flue modification based on the load analysis results. The preset target duration is manually set, such as: 00:00-24:00 every day; the working information is: the operation record of the oil fume purification system within the preset target duration, including data such as operation time, processing efficiency, energy consumption and number of failures; load analysis refers to determining whether the oil fume purification system is overloaded (for example: there are frequent starts and stops).

[0122] The working principle and beneficial effects of the above technical solution are:

[0123] The flue structure of the oil fume system affects the smoke exhaust effect. In addition, excessive load of the oil fume purification system will also lead to an increase in the failure rate. Therefore, it is more appropriate for the present invention to collect statistics on the working information of the oil fume purification system within a preset target time, perform load analysis on the working information, and determine the necessity of restaurant flue modification.

[0124] In one embodiment, step 5: collecting statistics on the working information of the oil fume purification system within a preset target time, performing load analysis based on the working information, and determining the necessity of restaurant flue modification based on the load analysis results, including:

[0125] Analyze the working information and obtain the start and stop information of the oil fume purification system within the target duration; the start and stop information is the start and stop information of the oil fume purification system within the target duration (for example, the number of starts and stops and the working duration of each start and stop). Each time the start and stop is started and stopped, the start and stop actions are timestamped;

[0126] According to the start-stop information, a start-stop timeline is determined; wherein the start-stop timeline is: a timeline in which the start and stop actions are marked according to timestamps;

[0127] According to the start-stop time axis, the load analysis of the oil fume purification system within the target time is performed to obtain a load feature set; wherein the load features include: the number of starts and stops, the start-stop time interval, and the working time of each start and stop;

[0128] Determine the fault probability according to the load feature set and the preset fault probability feature set library; wherein the preset fault probability feature set library is a database including a plurality of one-to-one corresponding fault probability features and corresponding fault probabilities;

[0129] If the failure probability is greater than or equal to a preset failure probability threshold, it is determined that it is necessary to modify the restaurant smoke duct. The preset failure probability threshold is manually set in advance.

[0130] The working principle and beneficial effects of the above technical solution are:

[0131] The present invention parses the working information to obtain the start and stop information of the purification system, expands the start and stop information on the time axis preset by the target duration, and obtains the start and stop time axis. By analyzing the start and stop time axis, the load feature set is determined. The load feature set and the fault probability feature set in the preset fault probability feature set library are feature matched, and the corresponding probability value of the matched fault probability feature set is used as the fault probability. When the fault probability is greater than or equal to the preset fault probability threshold, it is determined that the fault probability is high, and it is necessary to carry out restaurant flue modification.

[0132] In one embodiment, step 5: collecting statistics on the working information of the oil fume purification system within a preset target time, performing load analysis based on the working information, and determining the necessity of restaurant flue modification based on the load analysis results, further includes:

[0133] Obtain a target flue design record; wherein the target flue design record is: a flue design record used for deep learning;

[0134] Based on deep learning technology, the flue design model is determined according to the target flue design records;

[0135] Determine an ideal flue model according to the flue modification area information and the flue design model; when determining the ideal flue model, input the flue modification area information into the flue design model to obtain the ideal flue model;

[0136] According to the current flue model and the ideal flue model, determine the modified flue nodes and modified flue branches; the modified flue branches include: demolished flue branches and newly added flue branches; the modified flue nodes are: key points that need to be adjusted during the flue modification process, such as: the intersection of the demolished flue branch and the original flue, and the intersection of the newly added flue branch and the original flue; the demolished flue branch is: the flue branch that needs to be demolished in the original flue; the newly added flue branch is: the flue branch that needs to be added on the basis of the original flue;

[0137] Display the modified flue nodes and modified flue branches in the current flue model diagram;

[0138] Instruct the inspector to check the current flue model diagram;

[0139] Among them, obtaining the target flue design records includes:

[0140] If it is determined that it is necessary to renovate the restaurant smoke duct, obtain the current smoke duct model, smoke duct renovation area information and cooking habit information; wherein the current smoke duct model is: a three-dimensional model of the current smoke duct pipeline; the smoke duct renovation area information is: area-related information of the smoke duct renovation area, such as: the scope of the renovation area; the cooking habit information is: cooking habit information of the cooking dishes of the catering entity that is undergoing the restaurant smoke duct renovation;

[0141] Obtain flue design records, including records of manual exhaust flue design;

[0142] Parse the flue design record to obtain the business information of the flue design record corresponding to the designed flue; wherein the business information is: the business information publicly registered by the design subject of the flue design record corresponding to the designed flue;

[0143] Determine the target cuisine according to the business information; wherein the target cuisine is: the cooking information of the design subject of the flue design corresponding to the flue design record;

[0144] Determine a first cooking habit feature description vector according to the target cuisine and a preset cooking habit determination library; wherein the preset cooking habit determination library includes a plurality of one-to-one corresponding cooking cuisines and cooking habit description features of the corresponding cooking cuisines, for example, the cooking habit description feature of Sichuan cuisine is stir-frying; the first cooking habit feature description vector is a feature description constructed based on the cooking habit description features of the cooking cuisine, and is used to characterize the overall cooking habits of the target cuisine;

[0145] Determine a second cooking habit feature description vector according to the cooking habit information; wherein the second cooking habit feature description vector is constructed according to the cooking habit information, and the construction principle is the same as the first cooking habit feature description vector;

[0146] Matching the first cooking habit feature description vector with the second cooking habit feature description vector to determine a matching value; wherein the matching value is a cosine value of the first cooking habit feature description vector and the second cooking habit feature description vector;

[0147] Analyze the flue design records to obtain the design experience of the designer; the designer refers to the flue designer; the design experience is a quantitative value that represents the designer's proficiency, which is determined based on the designer's years of experience and the number of historical designs. The quantification rules of the design experience are preset manually;

[0148] Determine the target flue design record according to the matching value and the design experience; wherein, when determining the target flue design record according to the matching value and the design experience, determine and accumulate the corresponding normalized values ​​of the matching value and the design experience according to the minimum-maximum normalization method, and take the flue design record whose cumulative value is greater than or equal to a preset cumulative value threshold as the target flue design record;

[0149] Among them, the modified flue nodes and modified flue branches are displayed in the current flue model diagram, including:

[0150] Displaying the modified flue node in the current flue model diagram based on a first display rule; wherein the first display rule is, for example, flashing display;

[0151] The demolished flue branch is displayed in the current flue model diagram based on the second display rule; wherein the second display rule is, for example, a dotted line display;

[0152] The newly added flue branch is displayed in the current flue model diagram based on the third display rule; wherein the third display rule is, for example: displaying in a color different from the current flue model.

[0153] The working principle and beneficial effects of the above technical solution are:

[0154] Different cooking habits have different requirements for flue structure. For example, Sichuan cuisine may produce a lot of oil smoke and spicy smell when cooking, so it is necessary to design a flue with good sealing and efficient oil smoke purification capacity; for example, the main cooking methods of Cantonese cuisine include steaming and stewing, etc., and it is necessary to design a flue with good water vapor permeability. Therefore, the present invention introduces the business information corresponding to the flue design record, and determines the target cuisine according to the business information. The cooking habit determination library is introduced to determine the first cooking habit feature description vector. According to the cooking habit information, the second cooking habit feature description vector is determined, and the first cooking habit feature description vector and the second cooking habit feature description vector are vector matched to determine the matching value. The matching value represents the matching degree of the flue design logic. The higher the matching value, the more matching the corresponding design logic. In addition, the design experience of the designer of the flue design is obtained. The higher the design experience, the more reasonable the flue design record is. According to the matching value and design experience, the target flue design record for deep learning is determined. After deep learning, the flue design model is obtained. The flue transformation area information is input into the flue design model to determine the ideal flue model. Compare the current flue model with the ideal flue model, and use different display methods to display the modified flue nodes, removed flue branches, and newly added flue branches in the current flue model diagram, and then instruct the viewers to view, which is more user-friendly.

[0155] In one embodiment, instructing a viewer to view the current flue model diagram includes:

[0156] Obtaining operation information of the viewer after viewing the current flue model diagram; wherein the operation information is: the operation of the viewer in the interactive system presenting the current flue model diagram, such as: double-clicking a demolished flue branch, connecting two modified flue nodes;

[0157] According to the operation information, a local flue modification instruction is determined; wherein the local flue modification instruction is determined according to the operation action determined by parsing the operation information, for example: the local flue modification instruction corresponding to connecting two modified flue nodes is: adding a flue branch between the two connected modified flue nodes, and for example: the local flue modification instruction corresponding to double-clicking a demolished flue branch is: canceling the demolition;

[0158] According to the local flue modification instruction, the modification constraints are determined; wherein the modification constraints are: preset flue design boundary conditions;

[0159] The ideal flue model is re-determined based on the renovation constraints, flue renovation area information and flue design model.

[0160] The working principle and beneficial effects of the above technical solution are:

[0161] After the renovation plan is displayed in the current flue model diagram, the viewer may have the intention to make local modifications. Therefore, it is necessary to obtain the operation information of the viewer after viewing the current flue model diagram, determine the local flue renovation instructions, determine the renovation constraints based on the local flue renovation instructions, and redetermine the ideal flue model based on the renovation constraints, flue renovation area information and flue design model, which is more applicable.

[0162] The embodiment of the present invention provides a restaurant fume online monitoring and purification system. Figure 2 As shown, including:

[0163] Data acquisition subsystem 1, used to collect environmental data and raw data of oil smoke in the oil smoke channel;

[0164] The oil fume severity value determination subsystem 2 is used to calculate the oil fume severity value in the oil fume channel according to the collected environmental data and the oil fume original data;

[0165] The speed determination subsystem 3 is used to set a corresponding exhaust fan speed for the exhaust fan according to the oil fume severity value and the exhaust fan speed determination library;

[0166] The purification subsystem 4 is used to determine whether the severity of the oil fume exceeds a preset threshold after the exhaust fan is started. If so, the oil fume purification system is turned on to purify the catering oil fume in the oil fume passage. Otherwise, there is no need to turn on the oil fume purification system.

[0167] In one embodiment, the oil smoke severity value determination subsystem includes:

[0168] Standardized environment data acquisition module, used to obtain standardized environment data of restaurant fume;

[0169] A first corresponding value acquisition module, used to compare the restaurant oil fume standard environmental data with the environmental data to obtain a first corresponding value;

[0170] A fume data evaluation template acquisition module is used to acquire the fume data evaluation template in the fume channel;

[0171] A second corresponding value acquisition module, used for acquiring a second corresponding value according to the oil fume data evaluation template and the oil fume original data;

[0172] The oil fume severity value calculation module is used to calculate the oil fume severity value according to the first corresponding value and the second corresponding value.

[0173] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for online monitoring and purification of restaurant fume, characterized in that: include: Step 1: Collect environmental data and raw data of oil smoke in the oil smoke channel; Step 2: Calculate the severity of the oil smoke in the oil smoke passage according to the collected environmental data and the original oil smoke data; Step 3: Determine the library according to the oil smoke severity value and the exhaust fan speed, and set the corresponding exhaust fan speed for the exhaust fan; Step 4: After starting the exhaust fan, determine whether the fume severity value exceeds the preset threshold. If so, start the fume purification system to purify the restaurant fume in the fume channel. Otherwise, there is no need to start the fume purification system. Step 5: Collect statistics on the working information of the oil fume purification system within the preset target time, perform load analysis based on the working information, and determine the necessity of restaurant flue renovation based on the load analysis results; The step 5: collecting statistics on the working information of the oil fume purification system within the preset target time, performing load analysis based on the working information, and determining the necessity of restaurant flue modification based on the load analysis results, including: Analyze the working information and obtain the start and stop information of the oil fume purification system within the target duration; Determine the start and stop timeline based on the start and stop information; According to the start-stop time axis, the load analysis of the oil fume purification system within the target duration is carried out to obtain the load feature set; Determine the fault probability based on the load characteristic set and the preset fault probability characteristic set library; If the failure probability is greater than or equal to the preset failure probability threshold, it is determined that it is necessary to renovate the restaurant flue.

2. The method for online monitoring and purification of restaurant fume according to claim 1, characterized in that: Step 1: Collect environmental data and raw data of oil smoke in the oil smoke channel, including: Determine the target space corresponding to the oil smoke passage, obtain first collected data of a first gas sensor preset in the target space, and use the data as environmental data; The second collected data of the second gas sensor in the oil fume passage is obtained and used as the oil fume original data.

3. The method for online monitoring and purification of restaurant fume according to claim 1, characterized in that: Step 2: Calculate the severity of the oil fume in the oil fume channel based on the collected environmental data and the original oil fume data, including: Obtaining the standard environmental data of restaurant fume; Compare the catering oil fume standard environmental data with the environmental data to obtain a first corresponding value; Obtaining the oil fume data evaluation template in the oil fume channel; Obtaining a second corresponding value according to the oil fume data evaluation template and the oil fume original data; The oil smoke severity value is calculated according to the first corresponding value and the second corresponding value.

4. The method for online monitoring and purification of restaurant fume according to claim 1, characterized in that: Step 3: Determine the library according to the severity of the oil smoke and the exhaust fan speed, and set the corresponding exhaust fan speed for the exhaust fan, including: Constructing a smoke exhaust fan speed determination library; the smoke exhaust fan speed determination library includes: one-to-one correspondence between pre-selected smoke exhaust fan speeds and pre-selected oil smoke severity values; The oil fume severity value is compared with the pre-selected oil fume severity value. If they are consistent, the pre-selected exhaust fan speed corresponding to the pre-selected exhaust fan severity value is used as the exhaust fan speed set for the exhaust fan.

5. The method for online monitoring and purification of restaurant fume according to claim 1, characterized in that: Also includes: Obtain target flue design records; Based on deep learning technology, the flue design model is determined according to the target flue design records; Determine the ideal flue model based on the flue reconstruction area information and flue design model; According to the current flue model and the ideal flue model, the modified flue nodes and modified flue branches are determined; wherein, the modified flue branches include: removing flue branches and adding new flue branches; Display the modified flue nodes and modified flue branches in the current flue model diagram; Instruct viewers to view the current flue model diagram.

6. The method for online monitoring and purification of restaurant fume according to claim 5, characterized in that: Instruct the viewer to view the current flue model diagram, including: Obtain the operation information after the viewer views the current flue model diagram; Determine local flue modification instructions based on operational information; Determine the modification constraints according to the local flue modification instructions; The ideal flue model is re-determined based on the renovation constraints, flue renovation area information and flue design model.

7. An online monitoring and purification system for restaurant fume, characterized in that: include: The data acquisition subsystem is used to collect environmental data and raw data of oil smoke in the oil smoke channel; The oil fume severity value determination subsystem is used to calculate the oil fume severity value in the oil fume channel according to the collected environmental data and oil fume original data; The speed determination subsystem is used to set the corresponding exhaust fan speed for the exhaust fan according to the oil fume severity value and the exhaust fan speed determination library; The purification subsystem is used to judge whether the severity of the oil fume exceeds the preset threshold after the exhaust fan is started. If so, the oil fume purification system is turned on to purify the restaurant oil fume in the oil fume channel. Otherwise, there is no need to turn on the oil fume purification system. A restaurant fume online monitoring and purification system also performs the following operations: Collect statistics on the working information of the oil fume purification system within the preset target time, conduct load analysis based on the working information, and determine the necessity of restaurant flue renovation based on the load analysis results; The statistical operation information of the oil fume purification system within the preset target time, the load analysis is performed according to the operation information, and the necessity of restaurant smoke duct reconstruction is determined according to the load analysis result, including: Analyze the working information and obtain the start and stop information of the oil fume purification system within the target duration; Determine the start and stop timeline based on the start and stop information; According to the start-stop time axis, the load analysis of the oil fume purification system within the target duration is carried out to obtain the load feature set; Determine the fault probability based on the load characteristic set and the preset fault probability characteristic set library; If the failure probability is greater than or equal to the preset failure probability threshold, it is determined that it is necessary to renovate the restaurant flue.

8. The online monitoring and purification system for restaurant fume according to claim 7, characterized in that: The subsystem for determining the severity of oil smoke includes: Standardized environment data acquisition module, used to obtain standardized environment data of restaurant fume; A first corresponding value acquisition module, used to compare the restaurant oil fume standard environmental data with the environmental data to obtain a first corresponding value; A fume data evaluation template acquisition module is used to acquire the fume data evaluation template in the fume channel; A second corresponding value acquisition module, used for acquiring a second corresponding value according to the oil fume data evaluation template and the oil fume original data; The oil fume severity value calculation module is used to calculate the oil fume severity value according to the first corresponding value and the second corresponding value.

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