Environmental control method, system, readable storage medium and electronic device

Abstract

The present invention provides an environmental control method, system, readable storage medium and electronic device. The method obtains environmental data, which includes at least temperature data, humidity data, air quality data and pressure data, wherein vibration data is collected by vibration sensors installed on each manufacturing equipment; determines whether there is an abnormality in the environmental data; if not, inputs the environmental data into a trained neural network model, outputs a control strategy, and controls the corresponding environmental control equipment according to the control strategy to ensure the stability and consistency of the production environment; if so, obtains the vibration data of each device, analyzes the vibration data, and determines the target device with an abnormal operating status; controls the target device to stop working and issues an alarm, thereby achieving real-time prediction and rapid response to environmental changes, ensuring the stability of the production process, and also shutting down the equipment with abnormal status to ensure safe production while further optimizing the production environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental control, and in particular relates to an environmental control method, system, readable storage medium and electronic equipment. Background Art

[0002] As automotive manufacturing becomes increasingly intelligent, a stable production environment is crucial to product quality. Even slight variations in environmental factors like temperature, humidity, and vibration can affect assembly accuracy and cause product defects. Consequently, the role of environmental control systems is becoming increasingly prominent.

[0003] Currently, environmental control systems mainly operate based on preset parameters and rules. They are composed of multiple controllers and maintain environmental stability through manual adjustment of equipment. Although they have certain effects, they lack intelligence and adaptability. Summary of the Invention

[0004] Based on this, an embodiment of the present invention provides an environmental control method, system, readable storage medium and electronic device, which aims to solve the problem in the existing technology that it is difficult to dynamically adjust to environmental changes based on fixed parameters and rules, thereby affecting the control effect.

[0005] A first aspect of an embodiment of the present invention provides an environmental control method, which is applied in an automobile manufacturing scenario. The method includes:

[0006] Acquiring environmental data, wherein the environmental data includes at least temperature data, humidity data, air quality data, and pressure data;

[0007] Determining whether the environmental data is abnormal;

[0008] If it is determined that there is no abnormality in the environmental data, the environmental data is input into the trained neural network model, a control strategy is output, and the corresponding environmental control equipment is controlled according to the control strategy to ensure the stability and consistency of the production environment;

[0009] If it is determined that the environmental data is abnormal, vibration data of each device is obtained and analyzed to determine the target device with abnormal operating status, wherein the vibration data is collected by a vibration sensor installed on each manufacturing device;

[0010] Controlling the target device to stop working and issuing an alarm;

[0011] The step of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status includes:

[0012] Preprocessing the vibration data and the environmental data, and performing time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results;

[0013] Based on the analysis results, determining whether the vibration of the manufacturing equipment affects the production environment;

[0014] If it is determined that the vibration of the manufacturing equipment affects the production environment, the vibration data is analyzed to identify target equipment with abnormal operating status.

[0015] Furthermore, the step of determining whether the environmental data is abnormal includes:

[0016] Acquire historical environmental data, and perform data cleaning and standardization on the historical environmental data to obtain processed data;

[0017] According to the data type, the processed data are clustered by K-Means to determine the cluster center;

[0018] Calculating the distance between a data point of the current environment data and the cluster center, and determining whether the distance is greater than a first threshold;

[0019] If it is determined that the distance is greater than the first threshold, it is determined that an abnormality exists in the current environmental data.

[0020] Furthermore, the step of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status includes:

[0021] Calculate an evaluation value based on the abnormal data and the distance between the data point of the abnormal data and the cluster center;

[0022] Determining whether the evaluation value is greater than a first preset value;

[0023] If it is determined that the evaluation value is greater than the first preset value, the steps of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status are performed.

[0024] Furthermore, in the step of calculating the evaluation value based on the abnormal data and the distance between the data point of the abnormal data and the cluster center, the calculation formula of the evaluation value is:

[0025] P=αA+βB+λC+γD

[0026] Where P is the evaluation value, A is the average distance from the data points of temperature anomaly data to the cluster center, B is the average distance from the data points of humidity anomaly data to the cluster center, C is the average distance from the data points of air quality anomaly data to the cluster center, D is the average distance from the data points of pressure anomaly data to the cluster center, and α, β, λ, and γ are the corresponding weight coefficients respectively.

[0027] Furthermore, the step of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status includes:

[0028] Preprocessing the vibration data and the environmental data, and performing time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results;

[0029] Based on the analysis results, determining whether the vibration of the manufacturing equipment affects the production environment;

[0030] If it is determined that the vibration of the manufacturing equipment affects the production environment, the vibration data is analyzed to identify target equipment with abnormal operating status.

[0031] Furthermore, the steps of preprocessing the vibration data and the environmental data, and performing time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results include:

[0032] The vibration data and the environmental data are sequentially subjected to outlier removal processing, filtering processing, and data interpolation processing to obtain a preprocessing result;

[0033] Draw a waveform of the preprocessing result in the time domain, divide the waveform into several time periods according to the waveform, and determine a target time period;

[0034] Calculating the Pearson correlation coefficient of each target time period, and determining whether the Pearson correlation coefficient of the target time period is greater than a second preset value;

[0035] If it is determined that the Pearson correlation coefficient of the target time period is greater than a second preset value, the number of corresponding target time periods is determined, and a proportion is calculated based on the number of corresponding target time periods and the total number of target time periods;

[0036] Determining whether the proportion is greater than a third preset value;

[0037] If it is determined that the proportion is greater than the third preset value, it is determined that the vibration of the manufacturing equipment affects the production environment.

[0038] Furthermore, the step of dividing the waveform into several time periods and determining a target time period includes:

[0039] Dividing the data corresponding to each of the waveforms into a number of initial time periods according to a preset time interval;

[0040] Acquire each type of data within each initial time period, and determine whether each type of data exceeds its respective second threshold value at the same time;

[0041] If it is determined that each type of data exceeds its respective second threshold at the same time, the corresponding initial time period is determined as a candidate time period;

[0042] Obtaining a time interval between adjacent candidate time periods, and determining whether the time interval is less than a third threshold;

[0043] If it is determined that the time interval is less than the third threshold, adjacent candidate time periods are merged to finally obtain the target time period.

[0044] A second aspect of an embodiment of the present invention provides an environmental control system for implementing the environmental control method described in the first aspect, the system comprising:

[0045] An acquisition module is used to acquire environmental data, wherein the environmental data includes at least temperature data, humidity data, air quality data and pressure data;

[0046] A first judgment module is used to judge whether the environmental data is abnormal;

[0047] An input module is configured to input the environmental data into a trained neural network model if it is determined that the environmental data does not contain any anomalies, output a control strategy, and control corresponding environmental control devices according to the control strategy to ensure the stability and consistency of the production environment;

[0048] an analysis module configured to obtain vibration data of each device if it is determined that the environmental data is abnormal, and analyze the vibration data to determine a target device with an abnormal operating status, wherein the vibration data is collected by a vibration sensor installed on each manufacturing device;

[0049] A control module, used to control the target device to stop working and issue an alarm;

[0050] The analysis module includes:

[0051] a preprocessing unit, configured to preprocess the vibration data and the environmental data, and perform time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results;

[0052] a second judging unit, configured to judge, based on the analysis result, whether the vibration of the manufacturing equipment affects the production environment;

[0053] The analyzing unit is configured to analyze the vibration data and identify target equipment with abnormal operating status if it is determined that the vibration of the manufacturing equipment affects the production environment.

[0054] A third aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the environment control method provided in the first aspect.

[0055] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the program, the environment control method provided in the first aspect is implemented.

[0056] An embodiment of the present invention provides an environmental control method, system, readable storage medium and electronic device. The method obtains environmental data, which includes at least temperature data, humidity data, air quality data and pressure data, wherein vibration data is collected by vibration sensors installed in each manufacturing equipment; determines whether there is an abnormality in the environmental data; if it is determined that there is no abnormality in the environmental data, the environmental data is input into a trained neural network model, a control strategy is output, and the corresponding environmental control equipment is controlled according to the control strategy to ensure the stability and consistency of the production environment; if it is determined that there is an abnormality in the environmental data, the vibration data of each device is obtained, and the vibration data is analyzed to determine the target device with an abnormal operating status; the target device is controlled to stop working and an alarm is issued, which can realize real-time prediction and rapid response to environmental changes, ensure the stability of the production process, and also shut down the equipment with abnormal status to ensure safe production while further optimizing the production environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of an implementation method of an environment control method provided in Example 1 of the present invention;

[0058] Figure 2 This is a structural block diagram of an environmental control system provided in Example 2 of the present invention;

[0059] Figure 3 This is a structural block diagram of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0060] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0061] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0063] Example 1

[0064] According to an embodiment of the present invention, an embodiment of an environmental control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0065] In this embodiment, an environment control method is provided, which can be used in electronic equipment, such as a computer. Figure 1 , Figure 1 The flowchart of an implementation of an environment control method provided in the first embodiment of the present invention is shown, which specifically includes steps S01 to S05.

[0066] Step S01 : Acquire environmental data, where the environmental data at least includes temperature data, humidity data, air quality data, and pressure data.

[0067] Specifically, environmental data is acquired in real time through temperature sensors, humidity sensors, air quality sensors, and pressure sensors deployed in the automobile manufacturing workshop, wherein the location information of each sensor is known.

[0068] Step S02, determine whether the environmental data is abnormal, if not, execute step S03, if yes, execute step S04.

[0069] It should be noted that historical environmental data is obtained and cleaned and standardized to obtain processed data. Data cleaning primarily involves checking for and addressing missing values ​​and outliers. Missing values ​​can be filled using methods such as the mean or median, or records with missing values ​​can be directly deleted. In addition, Z-score standardization (converting the data to a distribution with a mean of 0 and a standard deviation of 1) is used to ensure that each feature has equal weight in the clustering algorithm.

[0070] K-Means clustering was performed on the processed data based on the data type (temperature, humidity, air quality, and pressure) to determine the cluster centers. The elbow rule was used to determine the appropriate K value. The elbow rule calculates the clustering error (such as the within-cluster sum of squares (SSE)) for different K values ​​and plots the relationship between K value and SSE. The K value that shows a clear elbow in the curve is selected as the optimal number of clusters.

[0071] Calculating the distance between a data point of the current environment data and the cluster center, and determining whether the distance is greater than a first threshold;

[0072] If it is determined that the distance is greater than the first threshold, it is determined that an abnormality exists in the current environmental data.

[0073] In step S03, the environmental data is input into the trained neural network model, a control strategy is output, and corresponding environmental control equipment is controlled according to the control strategy to ensure the stability and consistency of the production environment.

[0074] In this embodiment, the neural network model includes a convolutional neural network (CNN) and a recurrent neural network (RNN). Based on real-time data, this neural network model can predict environmental changes over a period of time and generate an optimized control strategy. For example, if air quality is predicted to decline in the future, the operating status of the air purification equipment can be adjusted in advance to ensure that air quality remains within the optimal range.

[0075] In step S04, vibration data of each device is acquired and analyzed to determine target devices with abnormal operating conditions, wherein the vibration data is collected by vibration sensors installed on each manufacturing device.

[0076] Prior to this, we preliminarily judge whether the vibration of the equipment may affect the environment based on the abnormal data. Specifically, we calculate the evaluation value based on the abnormal data and the distance between the data point of the abnormal data and the center of the cluster. The calculation formula of the evaluation value is:

[0077] P=αA+βB+λC+γD

[0078] Wherein, P is the evaluation value, A is the average distance from the data point of temperature anomaly data to the cluster center, B is the average distance from the data point of humidity anomaly data to the cluster center, C is the average distance from the data point of air quality anomaly data to the cluster center, and D is the average distance from the data point of pressure anomaly data to the cluster center. α, β, λ, and γ are the corresponding weight coefficients. It can be understood that different weight coefficients are given according to the ability of equipment vibration to affect environmental parameters.

[0079] Determining whether the evaluation value is greater than a first preset value;

[0080] If it is determined that the evaluation value is greater than the first preset value, the steps of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status are performed.

[0081] More specifically, when it is determined that the vibration of the equipment may affect the environment, the vibration data and the environmental data are preprocessed, and the preprocessed results are sequentially subjected to time domain analysis and correlation analysis to obtain analysis results. It should be noted that the vibration data and the environmental data are sequentially subjected to outlier removal processing, filtering processing, and data interpolation processing to obtain preprocessed results, wherein, in the process of outlier removal processing, a reasonable threshold range is set to identify and remove abnormal vibration data points that exceed the range. In the process of filtering processing, in order to remove high-frequency noise interference, a low-pass filter can be used, and the cutoff frequency is set to a point above the main frequency range of the equipment vibration. The noise in the environmental parameter data is relatively low-frequency, and median filtering or mean filtering can be used. In the process of data interpolation processing, linear interpolation or spline interpolation can be used for both vibration data and environmental parameter data;

[0082] Draw a waveform of the preprocessing result in the time domain, divide the waveform into several time periods, and determine a target time period, wherein the data corresponding to each waveform is divided into several initial time periods according to a preset time interval, such as every 1 minute or less;

[0083] Acquire each type of data within each initial time period, and determine whether each type of data exceeds its respective second threshold value at the same time;

[0084] If it is determined that each type of data exceeds its respective second threshold at the same time, the corresponding initial time period is determined as a candidate time period. It is understandable that for each time period, the vibration data is checked to see if it exceeds the set vibration amplitude threshold, and the environmental parameter data is checked to see if it exceeds its corresponding threshold. If, within a certain time period, both the vibration data and the environmental parameter data exceed their respective thresholds at the same time, the time period is marked as a candidate time period with a possible association.

[0085] Obtaining a time interval between adjacent candidate time periods, and determining whether the time interval is less than a third threshold;

[0086] If it is determined that the time interval is less than the third threshold, the adjacent candidate time periods are merged to finally obtain the target time period;

[0087] Calculate the Pearson correlation coefficient of each target time period, and determine whether the Pearson correlation coefficient of the target time period is greater than a second preset value. The calculation formula of the Pearson correlation coefficient is:

[0088]

[0089] in, is the Pearson correlation coefficient, X is the vibration data, Y j is the environmental data, j=1,2,3,4 represents temperature, humidity, air quality, and pressure data respectively, n is the number of data points, x i is the i-th observation value of the vibration data X, is the mean of X, y ij is the environmental data Y j The i-th observation value of It's Y j Understandably, after calculating the Pearson correlation coefficients of the temperature, humidity, air quality, pressure data, and vibration data, it is determined whether the Pearson correlation coefficients are greater than the second preset values ​​of each type of data. The second preset values ​​of each type are different because the vibration of the device has different effects on different environmental parameters.

[0090] If it is determined that the Pearson correlation coefficient of the target time period is greater than a second preset value, the number of corresponding target time periods is determined, and a proportion is calculated based on the number of corresponding target time periods and the total number of target time periods;

[0091] Determining whether the proportion is greater than a third preset value;

[0092] If it is determined that the proportion is greater than a third preset value, it is determined that the vibration of the manufacturing equipment affects the production environment;

[0093] Based on the analysis results, determining whether the vibration of the manufacturing equipment affects the production environment;

[0094] If it is determined that the vibration of the manufacturing equipment affects the production environment, the vibration data is analyzed to identify target equipment with abnormal operating status.

[0095] Step S05: Control the target device to stop working and issue an alarm.

[0096] Specifically, a visual interface can be integrated to display various production environment parameters and equipment status in real time. Through graphical and report formats, production managers can intuitively understand environmental conditions and equipment operation status. Intelligent decision support is also provided, generating optimization suggestions based on data analysis results to help production managers make informed production decisions.

[0097] In summary, an environmental control method among the above embodiments of the present invention obtains environmental data, and the environmental data includes at least temperature data, humidity data, air quality data and pressure data, wherein vibration data is collected by vibration sensors installed in each manufacturing equipment; it is judged whether there is an abnormality in the environmental data; if it is judged that there is no abnormality in the environmental data, the environmental data is input into a trained neural network model, and a control strategy is output. According to the control strategy, the corresponding environmental control equipment is controlled to ensure the stability and consistency of the production environment; if it is judged that there is an abnormality in the environmental data, the vibration data of each device is obtained, and the vibration data is analyzed to determine the target device with abnormal operating status; the target device is controlled to stop working and an alarm is issued, which can realize real-time prediction and rapid response to environmental changes, ensure the stability of the production process, and also perform shutdown processing on the equipment with abnormal status to ensure safe production while further optimizing the production environment.

[0098] Example 2

[0099] See also Figure 2 , Figure 2 This is a block diagram of an environmental control system provided in Example 2 of the present invention. This environmental control system 200 is used to implement the aforementioned embodiments and preferred implementations, and details already described are omitted. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0100] Specifically, the environmental control system 200 includes: an acquisition module 21, a first judgment module 22, an input module 23, an analysis module 24 and a control module 25, wherein:

[0101] An acquisition module 21 is configured to acquire environmental data, the environmental data including at least temperature data, humidity data, air quality data, and pressure data, wherein the vibration data is collected by vibration sensors installed on each manufacturing equipment;

[0102] A first judgment module 22 is used to judge whether the environmental data is abnormal;

[0103] The input module 23 is configured to input the environmental data into the trained neural network model if it is determined that there is no abnormality in the environmental data, output a control strategy, and control the corresponding environmental control equipment according to the control strategy to ensure the stability and consistency of the production environment;

[0104] The analysis module 24 is configured to obtain vibration data of each device if it is determined that the environmental data is abnormal, and analyze the vibration data to determine the target device with abnormal operating status;

[0105] The control module 25 is used to control the target device to stop working and issue an alarm.

[0106] Furthermore, in some optional embodiments of the present invention, the first determining module 22 includes:

[0107] A data processing unit is used to obtain historical environmental data, perform data cleaning and standardization on the historical environmental data, and obtain processed data;

[0108] The clustering unit is used to perform K-Means clustering on the processed data according to the data type and determine the cluster center;

[0109] a first judging unit, configured to calculate a distance from a data point of current environmental data to the cluster center, and to judge whether the distance is greater than a first threshold;

[0110] The first determining unit is configured to determine that an abnormality exists in the current environmental data if it is determined that the distance is greater than a first threshold.

[0111] Furthermore, in some optional embodiments of the present invention, the environmental control system 200 further includes:

[0112] The calculation module is used to calculate the evaluation value based on the abnormal data and the distance between the data point of the abnormal data and the cluster center. The calculation formula of the evaluation value is:

[0113] P=αA+βB+λC+γD

[0114] Where P is the evaluation value, A is the average distance from the data point of temperature anomaly data to the cluster center, B is the average distance from the data point of humidity anomaly data to the cluster center, C is the average distance from the data point of air quality anomaly data to the cluster center, D is the average distance from the data point of pressure anomaly data to the cluster center, and α, β, λ, and γ are the corresponding weight coefficients respectively;

[0115] A second judgment module is used to judge whether the evaluation value is greater than a first preset value;

[0116] The execution module is used to execute the steps of obtaining vibration data of each device and analyzing the vibration data to determine the target device with abnormal operating status if it is determined that the evaluation value is greater than the first preset value.

[0117] Furthermore, in some optional embodiments of the present invention, the analysis module 24 includes:

[0118] a preprocessing unit, configured to preprocess the vibration data and the environmental data, and perform time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results;

[0119] a second judging unit, configured to judge, based on the analysis result, whether the vibration of the manufacturing equipment affects the production environment;

[0120] The analyzing unit is configured to analyze the vibration data and identify target equipment with abnormal operating status if it is determined that the vibration of the manufacturing equipment affects the production environment.

[0121] Furthermore, in some optional embodiments of the present invention, the pre-processing unit includes:

[0122] a preprocessing subunit, configured to sequentially perform outlier removal processing, filtering processing, and data interpolation processing on the vibration data and the environmental data to obtain a preprocessing result;

[0123] a drawing subunit, configured to draw a waveform of the preprocessing result in the time domain, divide the waveform into a number of time periods, and determine a target time period, wherein the data corresponding to each waveform is divided into a number of initial time periods according to a preset time interval;

[0124] Acquire each type of data within each initial time period, and determine whether each type of data exceeds its respective second threshold value at the same time;

[0125] If it is determined that each type of data exceeds its respective second threshold at the same time, the corresponding initial time period is determined as a candidate time period;

[0126] Obtaining a time interval between adjacent candidate time periods, and determining whether the time interval is less than a third threshold;

[0127] If it is determined that the time interval is less than the third threshold, the adjacent candidate time periods are merged to finally obtain the target time period;

[0128] a first judgment subunit, configured to calculate the Pearson correlation coefficient of each target time period, and determine whether the Pearson correlation coefficient of the target time period is greater than a second preset value;

[0129] a calculation subunit, configured to determine the number of corresponding target time periods if it is determined that the Pearson correlation coefficient of the target time period is greater than a second preset value, and calculate a proportion based on the number of corresponding target time periods and the total number of target time periods;

[0130] A second judging subunit is configured to judge whether the proportion is greater than a third preset value;

[0131] The determining subunit is configured to determine that the vibration of the manufacturing equipment affects the production environment if it is determined that the proportion is greater than a third preset value.

[0132] Example 3

[0133] Another aspect of the present invention provides an electronic device, see Figure 3 , shown is an electronic device in embodiment 3 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the environment control method as described above is implemented.

[0134] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0135] The memory 20 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 20 may be an internal storage unit of the electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 may also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Furthermore, the memory 20 may include both an internal storage unit of the electronic device and an external storage device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or is about to be output.

[0136] It should be pointed out that Figure 3 The structure shown does not constitute a limitation to the electronic device. In other embodiments, the electronic device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0137] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned environment control method when executed by a processor.

[0138] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0139] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0140] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0141] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0142] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An environmental control method, characterized in that: Applied in an automobile manufacturing scenario, the method includes: Acquiring environmental data, wherein the environmental data includes at least temperature data, humidity data, air quality data, and pressure data; Determining whether the environmental data is abnormal; If it is determined that there is no abnormality in the environmental data, the environmental data is input into the trained neural network model, a control strategy is output, and the corresponding environmental control equipment is controlled according to the control strategy to ensure the stability and consistency of the production environment; If it is determined that the environmental data is abnormal, vibration data of each device is obtained and analyzed to determine the target device with abnormal operating status, wherein the vibration data is collected by a vibration sensor installed on each manufacturing device; Controlling the target device to stop working and issuing an alarm; The step of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status includes: Preprocessing the vibration data and the environmental data, and performing time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results; Based on the analysis results, determining whether the vibration of the manufacturing equipment affects the production environment; If it is determined that the vibration of the manufacturing equipment affects the production environment, analyzing the vibration data to identify target equipment with abnormal operating status; The steps of preprocessing the vibration data and the environmental data, and performing time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results include: The vibration data and the environmental data are sequentially subjected to outlier removal processing, filtering processing, and data interpolation processing to obtain a preprocessing result; Draw a waveform of the preprocessing result in the time domain, divide the waveform into several time periods according to the waveform, and determine a target time period; Calculating the Pearson correlation coefficient of each target time period, and determining whether the Pearson correlation coefficient of the target time period is greater than a second preset value; If it is determined that the Pearson correlation coefficient of the target time period is greater than a second preset value, the number of corresponding target time periods is determined, and a proportion is calculated based on the number of corresponding target time periods and the total number of target time periods; Determining whether the proportion is greater than a third preset value; If it is determined that the proportion is greater than the third preset value, it is determined that the vibration of the manufacturing equipment affects the production environment.

2. The environmental control method according to claim 1, characterized in that: The step of determining whether the environmental data is abnormal includes: Acquire historical environmental data, and perform data cleaning and standardization on the historical environmental data to obtain processed data; According to the data type, the processed data are clustered by K-Means to determine the cluster center; Calculating the distance between a data point of the current environment data and the cluster center, and determining whether the distance is greater than a first threshold; If it is determined that the distance is greater than the first threshold, it is determined that an abnormality exists in the current environmental data.

3. The environmental control method according to claim 2, characterized in that: The step of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status includes: Calculate an evaluation value based on the abnormal data and the distance between the data point of the abnormal data and the cluster center; Determining whether the evaluation value is greater than a first preset value; If it is determined that the evaluation value is greater than the first preset value, the steps of obtaining vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status are performed.

4. The environmental control method according to claim 3, characterized in that: In the step of calculating the evaluation value based on the abnormal data and the distance between the data point of the abnormal data and the cluster center, the calculation formula of the evaluation value is: P=αA+βB+λC+γD Where P is the evaluation value, A is the average distance from the data points of temperature anomaly data to the cluster center, B is the average distance from the data points of humidity anomaly data to the cluster center, C is the average distance from the data points of air quality anomaly data to the cluster center, D is the average distance from the data points of pressure anomaly data to the cluster center, and α, β, λ, and γ are the corresponding weight coefficients respectively.

5. The environmental control method according to claim 4, characterized in that: The step of dividing the waveform into several time periods and determining a target time period includes: Dividing the data corresponding to each of the waveforms into a number of initial time periods according to a preset time interval; Acquire each type of data within each initial time period, and determine whether each type of data exceeds its respective second threshold value at the same time; If it is determined that each type of data exceeds its respective second threshold at the same time, the corresponding initial time period is determined as a candidate time period; Obtaining a time interval between adjacent candidate time periods, and determining whether the time interval is less than a third threshold; If it is determined that the time interval is less than the third threshold, adjacent candidate time periods are merged to finally obtain the target time period.

6. An environmental control system, characterized in that: For implementing the environmental control method according to any one of claims 1 to 5, the system comprises: An acquisition module is used to acquire environmental data, wherein the environmental data includes at least temperature data, humidity data, air quality data and pressure data; A first judgment module is used to judge whether the environmental data is abnormal; An input module is configured to input the environmental data into a trained neural network model if it is determined that the environmental data does not contain any anomalies, output a control strategy, and control corresponding environmental control devices according to the control strategy to ensure the stability and consistency of the production environment; an analysis module configured to obtain vibration data of each device if it is determined that the environmental data is abnormal, and analyze the vibration data to determine a target device with an abnormal operating status, wherein the vibration data is collected by a vibration sensor installed on each manufacturing device; A control module, used to control the target device to stop working and issue an alarm; The analysis module includes: a preprocessing unit, configured to preprocess the vibration data and the environmental data, and perform time domain analysis and correlation analysis on the preprocessing results in sequence to obtain analysis results; a second judging unit, configured to judge, based on the analysis result, whether the vibration of the manufacturing equipment affects the production environment; The analyzing unit is configured to analyze the vibration data and identify target equipment with abnormal operating status if it is determined that the vibration of the manufacturing equipment affects the production environment.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the environment control method according to any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the program, the environment control method according to any one of claims 1 to 5 is implemented.

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