Cooperative control method and system for multiple range hoods

Abstract

The invention discloses a multi-range hood cooperative control method and system. The method comprises the following steps: acquiring environment sensing data uploaded by a plurality of range hoods; determining a main range hood and a subordinate range hood from the plurality of range hoods according to the environment sensing data; according to the environment sensing data, determining working parameters corresponding to at least one subordinate range hood; and the main range hood sends a control instruction to the subordinate range hood to execute the working parameters. Therefore, accurate range hood group control based on multi-machine cooperative sensing can be realized, the regional lampblack purification efficiency and equipment collaboration are improved, and the purification blind area or energy waste risk caused by independent operation of a single machine is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for the coordinated control of multiple range hoods. Background Technology

[0002] With the rapid popularization of open kitchens and multi-burner cooking scenarios, users are increasingly valuing the ability to improve overall purification efficiency and energy conservation through the coordinated operation of range hood systems. Current technologies typically require each range hood to independently collect data from its own sensors and operate according to a fixed mode. Existing solutions fail to dynamically determine the master range hood based on the actual distribution of cooking fumes and to issue differentiated operating parameters to subordinate range hoods. This makes it difficult to create an effective coordinated purification airflow, resulting in low regional purification efficiency and unnecessary energy waste, thus limiting the overall performance and user experience of multi-range hood systems. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for coordinated control of multiple range hoods, which can realize precise group control of range hoods based on multi-machine coordinated sensing, improve the efficiency of regional fume purification and equipment coordination, and reduce the risk of purification blind spots or energy waste caused by the independent operation of a single machine.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a method for coordinated control of multiple range hoods, the method comprising: Acquire environmental sensor data uploaded by multiple range hoods; Based on the environmental sensor data, the main range hood and the subordinate range hood are determined from the plurality of range hoods; Based on the environmental sensing data, at least one operating parameter corresponding to the subordinate range hood is determined; The main range hood sends control commands to the subordinate range hood to execute the operating parameters.

[0005] As an optional implementation, in the first aspect of the present invention, the environmental sensing data includes at least one of oil fume concentration, ambient temperature, and ambient humidity.

[0006] As an optional implementation, in a first aspect of the invention, determining the main range hood and subordinate range hoods from the plurality of range hoods based on the environmental sensing data includes: For each range hood, the location priority of that range hood is calculated based on the environmental sensing data. The range hood with the highest location priority among all the range hoods is determined as the main range hood; For each of the range hoods other than the main range hood, calculate the correlation degree between the range hood and the main range hood; Range hoods with a correlation degree greater than a preset correlation degree threshold are identified as subordinate range hoods.

[0007] As an optional implementation, in the first aspect of the present invention, calculating the location priority of the range hood based on the environmental sensing data includes: The environmental sensor data corresponding to the range hood is input into the trained fume distance prediction model to obtain the predicted distance between the range hood and the fume generation point in the area. Calculate the average distance between the installation location of the range hood and the locations of all cooking appliances in the area to obtain the location parameters; The location priority of the range hood is obtained by calculating the product of the location parameter and the predicted distance.

[0008] As an optional implementation, in the first aspect of the present invention, the oil fume distance prediction model is a neural network model, which is trained using a training dataset that includes multiple training environment sensor data and corresponding oil fume generation location distance labels.

[0009] As an optional implementation, in the first aspect of the invention, calculating the correlation between the range hood and the main range hood includes: Calculate the installation distance between the installation location of the range hood and the installation location of the main range hood; Calculate the data similarity between the environmental sensor data of the range hood and the environmental sensor data of the main range hood; The correlation between the range hood and the main range hood is obtained by calculating the ratio of the data similarity to the installation location distance.

[0010] As an optional implementation, in the first aspect of the invention, determining at least one operating parameter corresponding to the subordinate range hood based on the environmental sensing data includes: In the preset graph structure, corresponding graph nodes are generated according to the installation position of each subordinate range hood. The environmental sensing data corresponding to the subordinate range hood of each graph node is determined as the node feature parameter corresponding to the graph node; The graph structure data, including the graph nodes and the node feature parameters, is input into the trained graph neural network to obtain the output set of working parameters; the set of working parameters includes at least one working parameter corresponding to the subordinate range hood; the graph neural network is trained using a training dataset that includes multiple training graph structure data and corresponding working parameter set annotations.

[0011] As an optional implementation, in the first aspect of the present invention, the method further includes: After executing the aforementioned working parameters, new environmental sensing data is acquired in real time; Based on the new environmental sensor data, determine whether the situation of cooking fumes in the area has improved; If not, new graph structure data will be determined based on the new environmental sensing data; The new graph structure data is input into the graph neural network to obtain a new set of working parameters; The main range hood sends control commands to the subordinate range hood to execute a new set of operating parameters.

[0012] A second aspect of this invention discloses a multi-range hood collaborative control system, the system comprising: The acquisition module is used to acquire environmental sensor data uploaded by multiple range hoods; The first determining module is used to determine the main range hood and the subordinate range hood from the plurality of range hoods based on the environmental sensing data; The second determining module is used to determine at least one operating parameter corresponding to the subordinate range hood based on the environmental sensing data. The control module is used to send control commands from the main range hood to the subordinate range hood to execute the operating parameters.

[0013] As an optional implementation, in a second aspect of the invention, the environmental sensing data includes at least one of oil fume concentration, ambient temperature, and ambient humidity.

[0014] As an optional implementation, in a second aspect of the invention, the first determining module determines the main range hood and the subordinate range hood from the plurality of range hoods based on the environmental sensing data in the following manner: For each range hood, the location priority of that range hood is calculated based on the environmental sensing data. The range hood with the highest location priority among all the range hoods is determined as the main range hood; For each of the range hoods other than the main range hood, calculate the correlation degree between the range hood and the main range hood; Range hoods with a correlation degree greater than a preset correlation degree threshold are identified as subordinate range hoods.

[0015] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module calculates the location priority corresponding to the range hood based on the environmental sensing data includes: The environmental sensor data corresponding to the range hood is input into the trained fume distance prediction model to obtain the predicted distance between the range hood and the fume generation point in the area. Calculate the average distance between the installation location of the range hood and the locations of all cooking appliances in the area to obtain the location parameters; The location priority of the range hood is obtained by calculating the product of the location parameter and the predicted distance.

[0016] As an optional implementation, in the second aspect of the present invention, the oil fume distance prediction model is a neural network model, which is trained using a training dataset that includes multiple training environment sensor data and corresponding oil fume generation location distance labels.

[0017] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module calculates the correlation between the range hood and the main range hood includes: Calculate the installation distance between the installation location of the range hood and the installation location of the main range hood; Calculate the data similarity between the environmental sensor data of the range hood and the environmental sensor data of the main range hood; The correlation between the range hood and the main range hood is obtained by calculating the ratio of the data similarity to the installation location distance.

[0018] As an optional implementation, in a second aspect of the invention, the second determining module determines the specific method by which it determines at least one operating parameter corresponding to the subordinate range hood based on the environmental sensing data, including: In the preset graph structure, corresponding graph nodes are generated according to the installation position of each subordinate range hood. The environmental sensing data corresponding to the subordinate range hood of each graph node is determined as the node feature parameter corresponding to the graph node; The graph structure data, including the graph nodes and the node feature parameters, is input into the trained graph neural network to obtain the output set of working parameters; the set of working parameters includes at least one working parameter corresponding to the subordinate range hood; the graph neural network is trained using a training dataset that includes multiple training graph structure data and corresponding working parameter set annotations.

[0019] As an optional implementation, in a second aspect of the invention, the system is further configured to perform the following steps: After executing the aforementioned working parameters, new environmental sensing data is acquired in real time; Based on the new environmental sensor data, determine whether the situation of cooking fumes in the area has improved; If not, new graph structure data will be determined based on the new environmental sensing data; The new graph structure data is input into the graph neural network to obtain a new set of working parameters; The main range hood sends control commands to the subordinate range hood to execute a new set of operating parameters.

[0020] A third aspect of this invention discloses another multi-range hood collaborative control system, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the multi-range hood collaborative control method disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the multi-range hood collaborative control method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires environmental sensor data uploaded by multiple range hoods and determines the master and slave range hoods accordingly. It generates operating parameters for the slave range hoods based on the environmental sensor data, and the master range hood sends control commands to execute them. This enables precise group control of range hoods based on multi-machine collaborative sensing, improves regional fume purification efficiency and equipment synergy, and reduces the risk of purification blind spots or energy waste caused by independent operation of a single machine. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a multi-range hood collaborative control method disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the structure of a multi-range hood collaborative control system disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another multi-range hood collaborative control system disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a method and system for collaborative control of multiple range hoods. By acquiring environmental sensor data uploaded by multiple range hoods and determining the master and slave range hoods accordingly, the system generates operating parameters for the slave range hoods based on the environmental sensor data. The master range hood then sends control commands to execute these commands. This enables precise group control of range hoods based on multi-machine collaborative sensing, improving regional fume purification efficiency and equipment synergy, and reducing the risk of purification blind spots or energy waste caused by independent operation of individual units. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a multi-range hood collaborative control method disclosed in an embodiment of the present invention. Figure 1 The described multi-range hood collaborative control method can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 1 As shown, the multi-range hood collaborative control method may include the following operations: 101. Obtain environmental sensor data uploaded by multiple range hoods.

[0032] Optionally, the environmental sensing data may include at least one of the following: oil fume concentration, ambient temperature, and ambient humidity.

[0033] Optionally, the environmental sensing data may include PM2.5 concentration, TVOC concentration, temperature, humidity, fan current, electrostatic voltage, or camera visual characteristics of oil fumes; this invention does not limit these parameters.

[0034] Optionally, the multiple range hoods can be 2-12 commercial / household range hoods arranged side-by-side or in a matrix within the same open kitchen or restaurant kitchen; this invention does not impose any limitations.

[0035] 102. Based on environmental sensor data, determine the main range hood and the subordinate range hoods from multiple range hoods.

[0036] Optionally, the main range hood is responsible for decision-making and issuing instructions, while the subordinate range hoods only receive and execute instructions; this invention does not impose any limitations on this.

[0037] 103. Based on environmental sensor data, determine the operating parameters corresponding to at least one subordinate range hood.

[0038] Optionally, the operating parameters may include fan speed, electrostatic field strength, light brightness, or louver angle; this invention does not impose any limitations.

[0039] 104. The main range hood sends control commands to the subordinate range hoods to execute the operating parameters.

[0040] Optionally, the transmission can be implemented based on ZigBee, Wi-Fi Mesh, LoRa, or a wired 485 bus; this invention does not limit the implementation.

[0041] As can be seen, the above-mentioned embodiments of the invention acquire environmental sensor data uploaded by multiple range hoods and determine the master range hood and slave range hoods accordingly. The operating parameters of the slave range hoods are generated based on the environmental sensor data, and the master range hood sends control commands to execute them. This enables precise group control of range hoods based on multi-machine collaborative sensing, improves the efficiency of regional fume purification and equipment synergy, and reduces the risk of purification blind spots or energy waste caused by the independent operation of a single machine.

[0042] As an optional embodiment, the step described above, determining the main range hood and the subordinate range hood from multiple range hoods based on environmental sensor data, includes: For each range hood, the location priority of that range hood is calculated based on environmental sensor data; The range hood with the highest location priority among all range hoods is determined as the primary range hood. For each range hood other than the main range hood, calculate the correlation degree between that range hood and the main range hood; Range hoods with a correlation degree greater than the preset correlation degree threshold are identified as subordinate range hoods.

[0043] Optionally, the higher the priority of the location and the closer it is to the main area where oil fumes are generated, the more suitable it is to serve as the main range hood. This invention does not impose any limitations on this.

[0044] Optionally, this correlation score combines physical distance and perceived similarity to cooking fumes, identifying range hoods with a correlation score greater than a preset threshold (e.g., 0.72) as subordinate range hoods. Optionally, range hoods that do not reach the threshold can continue to operate independently; this invention does not limit this.

[0045] As can be seen, through the above optional embodiments, by calculating the location priority of each range hood and selecting the one with the highest priority as the master range hood, and then filtering subordinate range hoods based on correlation, a precise master-slave role division based on location and data correlation is achieved, which improves the rationality and response speed of multi-range hood collaborative control and reduces the risk of control chaos caused by improper master-slave allocation.

[0046] As an optional embodiment, the step of calculating the location priority of the range hood based on environmental sensor data in the above steps includes: The environmental sensor data corresponding to the range hood is input into the trained fume distance prediction model to obtain the predicted distance between the range hood and the fume generation point in the area. Calculate the average distance between the installation location of the range hood and the locations of all cooking appliances in the area to obtain the location parameters; The location priority of the range hood is obtained by calculating the product of the location parameters and the predicted distance.

[0047] Optionally, the oil fume distance prediction model is a neural network model, which is trained using a training dataset that includes multiple training environment sensor data and corresponding oil fume generation location distance labels.

[0048] Optionally, the oil fume distance prediction model can be a 3-layer Transformer encoder (d_model=256, 6-head self-attention) + regression head, trained on 150,000 labeled oil fume distance data, with MAE<0.28m. This invention does not impose any limitations.

[0049] Optionally, the location of the cooking device can be the center coordinates of a stove, wok, or grill; this invention does not limit this.

[0050] As can be seen, through the above optional embodiments, by inputting the range hood environmental sensing data into the fume distance prediction model and combining it with the actual location parameters to calculate the location priority, a dual evaluation based on predicted distance and physical location is achieved, which improves the scientific nature and pertinence of the selection of the main range hood and reduces the risk of selecting the wrong main unit due to relying solely on physical location.

[0051] As an optional embodiment, the step of calculating the correlation between the range hood and the main range hood in the above steps includes: Calculate the installation distance between the installation location of this range hood and the installation location of the main range hood; Calculate the data similarity between the environmental sensor data of this range hood and the environmental sensor data of the main range hood; The correlation between the range hood and the main range hood is obtained by calculating the ratio of data similarity to installation location distance.

[0052] Optionally, the data similarity can be the cosine similarity of multidimensional sensor vectors (PM2.5, TVOC, temperature and humidity, etc.), which is not limited in this invention.

[0053] Optionally, a larger ratio indicates more synchronized perception and closer distance, making it more suitable for linkage; however, this invention does not impose any limitations on this.

[0054] As can be seen, through the above optional embodiments, by calculating the ratio of the distance between the installation location of the range hood and the main range hood and the similarity of environmental sensor data as the correlation degree, the accurate selection of subordinate range hoods based on both spatial and perceptual dimensions is achieved, thereby improving the tightness and effectiveness of master-slave collaboration and reducing the risk of slave control failure due to misjudgment of correlation.

[0055] As an optional embodiment, the step above, determining the operating parameters of at least one subordinate range hood based on environmental sensor data, includes: In the preset graph structure, corresponding graph nodes are generated according to the installation position of each subordinate range hood; The environmental sensor data corresponding to the subordinate range hood of each graph node is determined as the node feature parameter of the graph node. The graph structure data, which includes graph nodes and node feature parameters, is input into the trained graph neural network to obtain the output set of working parameters. Optionally, the set of working parameters includes working parameters corresponding to at least one subordinate range hood. The graph neural network is trained using a training dataset that includes multiple training graph structure data and corresponding working parameter set annotations.

[0056] Optionally, the graph structure can be a fully connected graph or a k-nearest neighbor graph (k=4), and this invention does not limit it.

[0057] Optionally, the node's feature parameters are a 12-dimensional vector (PM2.5, TVOC, temperature, humidity, current rotation speed, etc.), but this invention does not limit them.

[0058] Optionally, the graph neural network can be a 3-layer GATv2 (8-head attention, 128 hidden dimensions) + global average pooling + MLP output of the working parameter vector of each subordinate range hood, trained on 80,000 kitchen multi-machine linkage test graph data, with a fan speed prediction error of <80rpm. This invention does not limit this.

[0059] As can be seen, through the above optional embodiments, by constructing subordinate range hood location graph nodes and using environmental sensor data as node features to input graph neural networks to predict the set of working parameters, multi-machine collaborative parameter optimization based on graph structure is achieved, thereby improving the overall coordination and purification effect of subordinate range hood working parameters and reducing the risk of suboptimal group control caused by independent parameter settings.

[0060] As an optional embodiment, the method further includes the following steps: After the working parameters are executed, new environmental sensor data is acquired in real time; Based on the new environmental sensor data, determine whether the situation of cooking fumes in the area has improved; If not, new graph structure data will be determined based on the new environmental sensing data; The new graph structure data is input into the graph neural network to obtain a new set of working parameters; The main range hood sends control commands to the subordinate range hoods to execute a new set of operating parameters.

[0061] Optionally, the improvement judgment can be based on the PM2.5 decrease rate of the entire region being >5μg / m³ / min and TVOC being <1.5ppm, but this invention does not limit it.

[0062] Optionally, the scheme in this embodiment can be executed cyclically until the oil fume index reaches the standard or the maximum linkage intensity or the maximum cycle threshold is reached; this invention does not impose any limitations.

[0063] As can be seen, through the above optional embodiments, after executing the working parameters, new environmental sensor data is continuously acquired to judge the improvement of oil fume, and when the improvement is insufficient, the graph structure data is reconstructed and input into the graph neural network to generate a new set of working parameters for execution, thereby realizing continuous self-optimization group control based on closed-loop feedback, improving the stability and adaptability of long-term collaboration of multiple range hoods, and reducing the risk of repeated purification effects caused by environmental changes.

[0064] The specific implementation process of the above-described embodiments of the invention will be illustrated by a specific case: Restaurant A has six 18kW commercial range hoods (numbered M1-M6) installed side by side in its kitchen, with four stoves operating simultaneously during peak stir-frying periods.

[0065] Each range hood uploads 12 dimensions of data in real time, including PM2.5, TVOC, temperature, humidity, and current.

[0066] Location priority calculation: The average distance of the M3 fume distance prediction model to the stove is 0.68m, while the actual geometric distance is 0.71m. The product is the smallest, so M3 is determined to be the main range hood.

[0067] The correlations between the remaining 5 units and M3 are 0.89, 0.91, 0.76, 0.83, and 0.64, respectively. Only M1, M2, M4, and M5 have a correlation greater than 0.72, and are therefore identified as subordinate range hoods.

[0068] Construct a 5-node complete graph (including the master node), with node features derived from the current 12-dimensional sensor data. Input the graph into a GATv2 neural network and output the optimal linkage parameters. M1 fan 3200rpm (+15%) M2 fan 3400rpm (+22%) M4 fan 3100rpm (+10%) M5 fan 3300rpm (+18%) The main range hood M3 broadcasts commands via ZigBee Mesh, and the four slave range hoods adjust synchronously.

[0069] After 30 seconds, the PM2.5 level in the entire area dropped from 612 μg / m³ to 93 μg / m³, with an improvement rate of 17.3 μg / m³ / s, which was deemed to meet the standard, and the iteration was stopped.

[0070] The total energy consumption of the entire linkage process only increases by 12%, but the oil fume removal speed is increased by 68%, achieving optimal purification through intelligent multi-machine collaborative purification.

[0071] In one specific implementation scheme, based on the technical solution in the embodiments of the present invention, a multi-unit control method for a ductless range hood is implemented, comprising the following steps: Initialization: Each ductless range hood performs a self-test via a wireless communication module and establishes a communication connection with the control center.

[0072] Environmental monitoring: Each range hood uses a built-in environmental sensor to detect environmental parameters such as the concentration of cooking fumes, temperature, and humidity in the kitchen.

[0073] Master / Slave Determination: The control center determines the master and slave units based on the environmental monitoring data of each range hood and the preset priority rules.

[0074] Collaborative operation: The host machine formulates a collaborative operation strategy based on the overall environmental parameters of the kitchen and sends control commands to the slave machine through the wireless communication module.

[0075] Real-time adjustment: While implementing the collaborative working strategy, each range hood monitors environmental parameters in real time and feeds the data back to the control center.

[0076] Strategy optimization: The control center dynamically adjusts the collaborative working strategy based on feedback data to achieve the best oil fume purification effect.

[0077] The beneficial effects of this specific implementation plan include: It enables intelligent linkage between multiple ductless range hoods, improving the overall efficiency of fume purification.

[0078] Through real-time monitoring and dynamic adjustments, the continuous optimization of the kitchen environment is ensured.

[0079] Suitable for various kitchen layouts, improving the applicability and flexibility of ductless range hoods.

[0080] To further elaborate on a specific implementation process of this particular implementation plan: Initialization: After each ductless range hood is turned on, it establishes a connection with the control center in the home LAN via the Wi-Fi module.

[0081] Environmental monitoring: The sensors built into each range hood monitor parameters such as the concentration of cooking fumes, temperature, and humidity in the kitchen in real time, and send the data to the control center.

[0082] Master / Slave Determination: The control center determines the master and slave units based on the detection data of each range hood and the preset priority rules (such as giving priority to the range hood closest to the cooking area).

[0083] Collaborative operation: The main unit formulates collaborative operation strategies based on the overall kitchen environment parameters, such as adjusting the fan speed and filtration efficiency of each range hood, and sends control commands to the slave units via the Wi-Fi module.

[0084] Real-time adjustment: While implementing the collaborative working strategy, each range hood monitors environmental parameters in real time and feeds the data back to the control center.

[0085] Strategy optimization: The control center dynamically adjusts collaborative work strategies based on feedback data, such as increasing fan speed during peak cooking times and decreasing fan speed after cooking is finished.

[0086] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a multi-range hood collaborative control system disclosed in an embodiment of the present invention. Figure 2 The described multi-range hood collaborative control system can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 2As shown, the multi-range hood collaborative control system may include: The acquisition module 201 is used to acquire environmental sensor data uploaded by multiple range hoods.

[0087] The first determining module 202 is used to determine the main range hood and the subordinate range hood from multiple range hoods based on environmental sensing data.

[0088] The second determining module 203 is used to determine the operating parameters of at least one subordinate range hood based on environmental sensing data.

[0089] The control module 204 is used to send control commands from the main range hood to the subordinate range hood to execute operating parameters.

[0090] As can be seen, the above-mentioned embodiments of the invention acquire environmental sensor data uploaded by multiple range hoods and determine the master range hood and slave range hoods accordingly. The operating parameters of the slave range hoods are generated based on the environmental sensor data, and the master range hood sends control commands to execute them. This enables precise group control of range hoods based on multi-machine collaborative sensing, improves the efficiency of regional fume purification and equipment synergy, and reduces the risk of purification blind spots or energy waste caused by the independent operation of a single machine.

[0091] As an optional embodiment, the environmental sensing data includes at least one of oil fume concentration, ambient temperature, and ambient humidity.

[0092] As can be seen, the above optional embodiments limit the data content of environmental sensing data to comprehensively characterize the characteristics of oil fume in the regional environment, assist in realizing precise oil fume group control based on multi-machine collaborative sensing, improve the regional oil fume purification efficiency and equipment synergy, and reduce the risk of purification blind spots or energy waste caused by independent operation of a single machine.

[0093] As an optional embodiment, the first determining module determines the main range hood and the subordinate range hood from multiple range hoods based on environmental sensor data in the following manner: For each range hood, the location priority of that range hood is calculated based on environmental sensor data; The range hood with the highest location priority among all range hoods is determined as the primary range hood. For each range hood other than the main range hood, calculate the correlation degree between that range hood and the main range hood; Range hoods with a correlation degree greater than the preset correlation degree threshold are identified as subordinate range hoods.

[0094] As can be seen, through the above optional embodiments, by calculating the location priority of each range hood and selecting the one with the highest priority as the master range hood, and then filtering subordinate range hoods based on correlation, a precise master-slave role division based on location and data correlation is achieved, which improves the rationality and response speed of multi-range hood collaborative control and reduces the risk of control chaos caused by improper master-slave allocation.

[0095] As an optional embodiment, the first determining module calculates the location priority of the range hood based on environmental sensing data in the following specific ways: The environmental sensor data corresponding to the range hood is input into the trained fume distance prediction model to obtain the predicted distance between the range hood and the fume generation point in the area. Calculate the average distance between the installation location of the range hood and the locations of all cooking appliances in the area to obtain the location parameters; The location priority of the range hood is obtained by calculating the product of the location parameters and the predicted distance.

[0096] As can be seen, through the above optional embodiments, by inputting the range hood environmental sensing data into the fume distance prediction model and combining it with the actual location parameters to calculate the location priority, a dual evaluation based on predicted distance and physical location is achieved, which improves the scientific nature and pertinence of the selection of the main range hood and reduces the risk of selecting the wrong main unit due to relying solely on physical location.

[0097] As an optional embodiment, the oil fume distance prediction model is a neural network model, which is trained using a training dataset that includes multiple training environment sensor data and corresponding oil fume generation location distance labels.

[0098] As can be seen, the model details of the oil fume distance prediction model are defined through the above optional embodiments, so as to accurately identify the distance between the range hood and the key oil fume generation location, assist in realizing precise range hood group control based on multi-machine collaborative sensing, improve the regional oil fume purification efficiency and equipment synergy, and reduce the risk of purification blind spots or energy waste caused by the independent operation of a single machine.

[0099] As an optional embodiment, the specific method by which the first determining module calculates the correlation between the range hood and the main range hood includes: Calculate the installation distance between the installation location of this range hood and the installation location of the main range hood; Calculate the data similarity between the environmental sensor data of this range hood and the environmental sensor data of the main range hood; The correlation between the range hood and the main range hood is obtained by calculating the ratio of data similarity to installation location distance.

[0100] As can be seen, through the above optional embodiments, by calculating the ratio of the distance between the installation location of the range hood and the main range hood and the similarity of environmental sensor data as the correlation degree, the accurate selection of subordinate range hoods based on both spatial and perceptual dimensions is achieved, thereby improving the tightness and effectiveness of master-slave collaboration and reducing the risk of slave control failure due to misjudgment of correlation.

[0101] As an optional embodiment, the second determining module determines the specific method of the operating parameters corresponding to at least one subordinate range hood based on environmental sensor data, including: In the preset graph structure, corresponding graph nodes are generated according to the installation position of each subordinate range hood; The environmental sensor data corresponding to the subordinate range hood of each graph node is determined as the node feature parameter of the graph node. The graph structure data, which includes graph nodes and node feature parameters, is input into the trained graph neural network to obtain the output set of working parameters. Optionally, the set of working parameters includes working parameters corresponding to at least one subordinate range hood. The graph neural network is trained using a training dataset that includes multiple training graph structure data and corresponding working parameter set annotations.

[0102] As can be seen, through the above optional embodiments, by constructing subordinate range hood location graph nodes and using environmental sensor data as node features to input graph neural networks to predict the set of working parameters, multi-machine collaborative parameter optimization based on graph structure is achieved, thereby improving the overall coordination and purification effect of subordinate range hood working parameters and reducing the risk of suboptimal group control caused by independent parameter settings.

[0103] As an optional embodiment, the system is also used to perform the following steps: After the working parameters are executed, new environmental sensor data is acquired in real time; Based on the new environmental sensor data, determine whether the situation of cooking fumes in the area has improved; If not, new graph structure data will be determined based on the new environmental sensing data; The new graph structure data is input into the graph neural network to obtain a new set of working parameters; The main range hood sends control commands to the subordinate range hoods to execute a new set of operating parameters.

[0104] As can be seen, through the above optional embodiments, after executing the working parameters, new environmental sensor data is continuously acquired to judge the improvement of oil fume, and when the improvement is insufficient, the graph structure data is reconstructed and input into the graph neural network to generate a new set of working parameters for execution, thereby realizing continuous self-optimization group control based on closed-loop feedback, improving the stability and adaptability of long-term collaboration of multiple range hoods, and reducing the risk of repeated purification effects caused by environmental changes.

[0105] Example 3 Please see Figure 3 , Figure 3 This is another multi-range hood collaborative control system disclosed in the embodiments of the present invention. Figure 3 The described multi-range hood collaborative control system is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the multi-range hood collaborative control system may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the multi-range hood collaborative control method described in Embodiment 1.

[0106] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the multi-range hood collaborative control method described in Embodiment 1.

[0107] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the multi-range hood collaborative control method described in Embodiment 1.

[0108] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0110] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0111] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0117] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0120] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0121] Finally, it should be noted that the multi-range hood collaborative control method and system disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated control of multiple range hoods, characterized in that, The method includes: Acquire environmental sensor data uploaded by multiple range hoods; Based on the environmental sensor data, the main range hood and the subordinate range hood are determined from the plurality of range hoods; Based on the environmental sensing data, determine at least one operating parameter corresponding to the subordinate range hood; The main range hood sends control commands to the subordinate range hood to execute the operating parameters.

2. The multi-range hood coordinated control method according to claim 1, characterized in that, The environmental sensing data includes at least one of the following: oil fume concentration, ambient temperature, and ambient humidity.

3. The multi-range hood coordinated control method according to claim 1, characterized in that, The step of determining the main range hood and subordinate range hoods from the plurality of range hoods based on the environmental sensing data includes: For each range hood, the location priority of the range hood is calculated based on the environmental sensing data. The range hood with the highest location priority among all the range hoods is determined as the main range hood; For each of the range hoods other than the main range hood, calculate the correlation degree between the range hood and the main range hood; Range hoods with a correlation degree greater than a preset correlation degree threshold are identified as subordinate range hoods.

4. The multi-range hood coordinated control method according to claim 3, characterized in that, The step of calculating the location priority of the range hood based on the environmental sensing data includes: The environmental sensor data corresponding to the range hood is input into the trained fume distance prediction model to obtain the predicted distance between the range hood and the fume generation point in the area. Calculate the average location distance between the installation location of the range hood and the locations of all cooking appliances in the area to obtain the location parameters; The location priority of the range hood is obtained by calculating the product of the location parameter and the predicted distance.

5. The multi-range hood coordinated control method according to claim 4, characterized in that, The oil fume distance prediction model is a neural network model, which is trained using a training dataset that includes multiple training environment sensor data and corresponding oil fume generation location distance labels.

6. The multi-range hood coordinated control method according to claim 3, characterized in that, The calculation of the correlation between the range hood and the main range hood includes: Calculate the installation distance between the installation location of the range hood and the installation location of the main range hood; Calculate the data similarity between the environmental sensor data of the range hood and the environmental sensor data of the main range hood; The correlation between the range hood and the main range hood is obtained by calculating the ratio of the data similarity to the installation location distance.

7. The multi-range hood coordinated control method according to claim 1, characterized in that, The step of determining at least one operating parameter corresponding to the subordinate range hood based on the environmental sensing data includes: In the preset graph structure, corresponding graph nodes are generated according to the installation position of each subordinate range hood. The environmental sensing data corresponding to the subordinate range hood of each graph node is determined as the node feature parameter corresponding to the graph node; The graph structure data, including the graph nodes and the node feature parameters, is input into the trained graph neural network to obtain the output set of working parameters; the set of working parameters includes at least one working parameter corresponding to the subordinate range hood; the graph neural network is trained using a training dataset that includes multiple training graph structure data and corresponding working parameter set annotations.

8. The multi-range hood collaborative control method according to claim 7, characterized in that, The method further includes: After executing the aforementioned working parameters, new environmental sensing data is acquired in real time. Based on the new environmental sensor data, determine whether the situation of cooking fumes in the area has improved; If not, new graph structure data will be determined based on the new environmental sensing data; The new graph structure data is input into the graph neural network to obtain a new set of working parameters; The main range hood sends control commands to the subordinate range hood to execute a new set of operating parameters.

9. A multi-range hood collaborative control system, characterized in that, The system includes: The acquisition module is used to acquire environmental sensor data uploaded by multiple range hoods; The first determining module is used to determine the main range hood and the subordinate range hood from the plurality of range hoods based on the environmental sensing data; The second determining module is used to determine at least one operating parameter corresponding to the subordinate range hood based on the environmental sensing data. The control module is used to send control commands from the main range hood to the subordinate range hood to execute the operating parameters.

10. A multi-range hood collaborative control system, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the multi-range hood collaborative control method as described in any one of claims 1-8.

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