Environment control method and system, readable storage medium and electronic equipment

Abstract

The invention provides an environment control method and system, a readable storage medium and electronic equipment, and the method comprises the steps: obtaining environment data, the environment data at least comprises temperature data, humidity data, air quality data and pressure data, and the vibration data is collected through vibration sensors arranged on all manufacturing equipment; judging whether the environment data is abnormal or not; if not, inputting the environment data into the trained neural network model, outputting a control strategy, and controlling corresponding environment control equipment according to the control strategy so as to ensure the stability and consistency of the production environment; if yes, acquiring vibration data of each device, analyzing the vibration data, and determining a target device with an abnormal operation state; according to the method, the target equipment is controlled to stop working, an alarm is given, real-time prediction and quick response to environment changes can be achieved, the stability of the production process is ensured, in addition, shutdown processing is carried out on the equipment in an abnormal state, and the production environment is further optimized while safe production is ensured.

Description

Technical Field

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

[0002] In the process of intelligent automotive manufacturing, a stable production environment is crucial for product quality. Minor changes in environmental factors such as temperature, humidity, and vibration can affect assembly accuracy and cause product defects. Therefore, the role of the environmental control system has become increasingly prominent.

[0003] Currently, the environmental control system mainly operates based on preset parameters and rules, consists of multiple controllers, and maintains environmental stability by manually adjusting equipment. Although it has certain effects, it lacks intelligence and self - adaptability. Summary of the Invention

[0004] Based on this, embodiments of the present invention provide an environmental control method, system, readable storage medium and electronic device, aiming to solve the problem in the prior art that it is difficult to dynamically adjust to environmental changes according to fixed parameters and rules, which affects the control effect.

[0005] The first aspect of the embodiments of the present invention provides an environmental control method applied to an automotive manufacturing scenario. The method includes: Obtain environmental data, where the environmental data at least includes temperature data, humidity data, air quality data, and pressure data; Determine whether there is an abnormality in the environmental data; If it is determined that there is no abnormality in the environmental data, input the environmental data into a trained neural network model, output a control strategy, and control the corresponding environmental control device 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, obtain the vibration data of each device, and analyze the vibration data to determine the target device with an abnormal operating state, where the vibration data is collected by vibration sensors installed on each manufacturing device; Control the target device to stop working and give an alarm; The step of obtaining the vibration data of each device, analyzing the vibration data, and determining the target device with an abnormal operating state includes: Pre - process the vibration data and the environmental data, and perform time - domain analysis and correlation analysis on the pre - processed results in sequence to obtain an analysis result; According to the analysis result, determine whether the vibration of the manufacturing device affects the production environment; If it is determined that the vibration of the manufacturing device affects the production environment, analyze the vibration data to determine the target device with an abnormal operating state.

[0006] Further, the step of determining whether there is an abnormality in the environmental data includes: Obtain historical environmental data, perform data cleaning and standardization processing on the historical environmental data to obtain processed data; According to the data type, perform K-Means clustering on the processed data respectively to determine the cluster centers; Calculate the distance from the data point of the current environmental data to the cluster center, and determine whether the distance is greater than the first threshold; If it is determined that the distance is greater than the first threshold, it is determined that there is an abnormality in the current environmental data.

[0007] Further, before the step of obtaining the vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status, includes: Calculate an evaluation value according to the abnormal data and the distance from the data point of the abnormal data to the cluster center; Determine 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, then execute the step of obtaining the vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status.

[0008] Further, in the step of calculating the evaluation value according to the abnormal data and the distance from the data point of the abnormal data to 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 point of the temperature abnormal data to the cluster center, B is the average distance from the data point of the humidity abnormal data to the cluster center, C is the average distance from the data point of the air quality abnormal data to the cluster center, D is the average distance from the data point of the pressure abnormal data to the cluster center, and α, β, λ, and γ are the corresponding weight coefficients respectively.

[0009] Further, the step of obtaining the vibration data of each device, analyzing the vibration data, and determining the target device with abnormal operating status includes: Preprocess the vibration data and the environmental data, and perform time-domain analysis and correlation analysis on the results of the preprocessing in sequence to obtain analysis results; According to the analysis results, determine 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, then analyze the vibration data to determine the target device with abnormal operating status.

[0010] Further, the step of preprocessing the vibration data and the environmental data and sequentially performing time-domain analysis and correlation analysis on the preprocessing results to obtain the analysis results includes: Performing outlier removal processing, filtering processing, and data interpolation processing on the vibration data and the environmental data in sequence to obtain the preprocessing results; Plotting the waveform of the preprocessing results in the time domain, dividing several time periods according to the waveform, and determining the 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 the second preset value, determining the number of corresponding target time periods, and calculating the proportion according to 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, determining that the vibration of the manufacturing equipment affects the production environment.

[0011] Further, the step of dividing several time periods according to the waveform and determining the target time period includes: Dividing the data corresponding to each waveform into several initial time periods according to a preset time interval; Obtaining various types of data within each initial time period, and determining whether each type of data simultaneously exceeds its respective second threshold; If it is determined that each type of data simultaneously exceeds its respective second threshold, determining the corresponding initial time period as a candidate time period; Obtaining the 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, merging adjacent candidate time periods to finally obtain the target time period.

[0012] The second aspect of the embodiments of the present invention provides an environmental control system for implementing the environmental control method described in the first aspect. The system includes: An acquisition module for acquiring environmental data, where the environmental data at least includes temperature data, humidity data, air quality data, and pressure data; A first judgment module for judging whether there is an abnormality in the environmental data; An input module for, if it is determined that there is no abnormality in the environmental data, inputting the environmental data into a trained neural network model, outputting a control strategy, and controlling corresponding environmental control devices according to the control strategy to ensure the stability and consistency of the production environment; An analysis module, configured to, if it is determined that the environmental data is abnormal, obtain the vibration data of each device, analyze the vibration data, and determine the target device with abnormal operating status, where the vibration data is collected by vibration sensors installed on each manufacturing device; A control module, configured 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 judgment unit, configured to judge whether the vibration of the manufacturing device affects the production environment according to the analysis results; An analysis unit, configured to, if it is judged that the vibration of the manufacturing device affects the production environment, analyze the vibration data to determine the target device with abnormal operating status.

[0013] A third aspect of the embodiments of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the environmental control method provided in the first aspect is implemented.

[0014] A fourth aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the program, the environmental control method provided in the first aspect is implemented.

[0015] An environmental control method, system, readable storage medium, and electronic device provided in the embodiments of the present invention. The method obtains environmental data, which at least includes temperature data, humidity data, air quality data, and pressure data, where the vibration data is collected by vibration sensors installed on each manufacturing device; judges whether the environmental data is abnormal; if it is judged that the environmental data is not abnormal, the environmental data is input into a trained neural network model to output a control strategy, and according to the control strategy, the corresponding environmental control device is controlled to ensure the stability and consistency of the production environment; if it is judged that the environmental data is abnormal, 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 in addition, the device with abnormal status is shut down to ensure safe production and further optimize the production environment. Description of the Drawings

[0016] Figure 1 It is a flowchart of an environmental control method provided in Embodiment 1 of the present invention; Figure 2A structural block diagram of an environmental control system provided in the second embodiment of the present invention; Figure 3 A structural block diagram of an electronic device provided in the third embodiment of the present invention. Detailed implementation manners

[0017] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0018] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0020] Embodiment 1 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0021] In the first embodiment, an environmental control method is provided, which can be used in an electronic device, such as a computer. Please refer to Figure 1 , Figure 1 shows an implementation flowchart of an environmental control method provided in the first embodiment of the present invention, specifically including steps S01 to S05.

[0022] Step S01, obtain environmental data, where the environmental data at least includes temperature data, humidity data, air quality data, and pressure data.

[0023] Specifically, environmental data is obtained in real time through temperature sensors, humidity sensors, air quality sensors, and pressure sensors deployed in the automobile manufacturing workshop. Among them, the position information of each sensor is known.

[0024] Step S02: Determine whether the environmental data is abnormal. If not, execute Step S03; if so, execute Step S04.

[0025] It should be noted that historical environmental data is obtained, and the historical environmental data is subjected to data cleaning and standardization processing to obtain processed data. Among them, data cleaning mainly checks and processes missing values and outliers. Missing values can be filled using methods such as mean and median, or records containing missing values can be directly deleted. In addition, Z-score standardization (converting the data into a distribution with a mean of 0 and a standard deviation of 1) is used to ensure that each feature has the same weight in the clustering algorithm. According to the data types, namely temperature data, humidity data, air quality data, and pressure data, the processed data is respectively subjected to K-Means clustering to determine the cluster centers. Among them, the elbow method is used to determine the appropriate value of K. The elbow method calculates the clustering error (such as the sum of squared errors within the cluster SSE) under different values of K, and plots the relationship diagram between the value of K and SSE, and selects the value of K where the curve shows an obvious elbow as the optimal number of clusters. Calculate the distance from the data point of the current environmental data to the cluster center, and determine whether the distance is greater than the first threshold. If it is determined that the distance is greater than the first threshold, it is determined that the current environmental data is abnormal.

[0026] In Step S03, the environmental data is input into the 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.

[0027] In this embodiment, the neural network model includes a convolutional neural network (CNN) and a recurrent neural network (RNN). This neural network model can predict environmental changes in the future for a period of time based on real-time data and generate an optimized control strategy. For example, when it is predicted that the air quality may deteriorate in the future, the operating state of the air purification equipment can be adjusted in advance to ensure that the air quality is maintained within the optimal range.

[0028] In Step S04, the vibration data of each device is obtained, and the vibration data is analyzed to determine the target device with abnormal operating state. Among them, the vibration data is collected by vibration sensors installed on each manufacturing device.

[0029] Prior to this, based on the abnormal data, it is initially determined whether the vibration of the device may have affected the environment. Specifically, according to the abnormal data and the distance from the data points of the abnormal data to the cluster center, an evaluation value is calculated. The calculation formula for 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 the temperature abnormal data to the cluster center, B is the average distance from the data points of the humidity abnormal data to the cluster center, C is the average distance from the data points of the air quality abnormal data to the cluster center, D is the average distance from the data points of the pressure abnormal data to the cluster center, and α, β, λ, and γ are the corresponding weight coefficients respectively. It can be understood that different weight coefficients are given according to the influence ability of the device vibration on the environmental parameters; Judge whether the evaluation value is greater than a first preset value; If it is judged that the evaluation value is greater than the first preset value, then execute the steps of obtaining the vibration data of each device and analyzing the vibration data to determine the target device with abnormal operating status.

[0030] More specifically, when it is judged that the vibration of the device may have affected the environment, the vibration data and the environmental data are preprocessed, and the results of the preprocessing are sequentially subjected to time-domain analysis and correlation analysis to obtain the 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 the results of the preprocessing. Among them, in the process of outlier removal processing, by setting a reasonable threshold range, the abnormal vibration data points exceeding this range are identified and removed. In the process of filtering processing, in order to remove high-frequency noise interference, a low-pass filter can be used, and the cut-off frequency is set at a point above the main frequency range of the device vibration. The noise in the environmental parameter data is relatively lower frequency, and median filtering or mean filtering can be used. In the process of data interpolation processing, both the vibration data and the environmental parameter data can use linear interpolation or spline interpolation; Draw the waveform of the preprocessing result in the time domain. According to the waveform, several time periods are divided, and the target time period is determined. Among them, according to a preset time interval, such as every 1 minute or shorter, the data corresponding to each waveform is divided into several initial time periods; Obtain various types of data within each initial time period, and judge whether various types of data simultaneously exceed their respective second thresholds; If it is determined that each type of data simultaneously exceeds its respective second threshold, the corresponding initial time period is determined as a candidate time period. It can be understood that for each time period, it is checked whether the vibration data exceeds the set vibration amplitude threshold and whether the environmental parameter data exceeds its corresponding threshold. If within a certain time period, the vibration data and the environmental parameter data simultaneously exceed their respective thresholds, then this time period is marked as a candidate time period where there may be a correlation; Obtain the time interval between adjacent candidate time periods and determine whether the time interval is less than a third threshold; If it is determined that the time interval is less than the third threshold, then merge the adjacent candidate time periods to finally obtain the target time period; Calculate the Pearson correlation coefficient for 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 for the Pearson correlation coefficient is:

[0031] where, is the Pearson correlation coefficient, X is the vibration data, Y j is the environmental data, j = 1, 2, 3, 4 respectively represent temperature, humidity, air quality, and pressure data, 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 's i-th observation value, is Y j 's mean. It can be understood that after calculating the Pearson correlation coefficients of temperature, humidity, air quality, and pressure data with the vibration data respectively, it is determined whether the Pearson correlation coefficient is greater than the second preset value for each type of data. The second preset value for each type is not the same because the influence of equipment vibration on different environmental parameters is different.

[0032] If it is determined that the Pearson correlation coefficient of the target time period is greater than the second preset value, then determine the number of corresponding target time periods and calculate the proportion based on the number of corresponding target time periods and the total number of target time periods; Determine whether the proportion is greater than a third preset value; If it is determined that the proportion is greater than the third preset value, then it is determined that the vibration of the manufacturing equipment affects the production environment; Based on the analysis result, determine 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, then analyze the vibration data to determine the target equipment with abnormal operating status.

[0033] Step S05, control the target device to stop working and issue an alarm.

[0034] Specifically, a visual interface can also be integrated to display various parameters of the production environment and the status of equipment in real time. In the form of graphics and reports, production managers can intuitively understand the environmental conditions and the operation of equipment. In addition, intelligent decision-making support is provided, and optimization suggestions are generated based on the analysis results of data to help production managers make scientific production decisions.

[0035] In summary, an environmental control method in the above embodiments of the present invention. This method obtains environmental data, which at least includes temperature data, humidity data, air quality data, and pressure data. Among them, vibration data is collected by vibration sensors installed on each manufacturing device; determines whether the environmental data is abnormal; if it is determined that the environmental data is not abnormal, the environmental data is input into a trained neural network model to output a control strategy, and according to the control strategy, the corresponding environmental control device is controlled to ensure the stability and consistency of the production environment; if it is determined that the environmental data is abnormal, 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. In addition, the device with abnormal status is shut down to ensure safe production and further optimize the production environment.

[0036] Embodiment 2 Please refer to Figure 2 , Figure 2 which is a structural block diagram of an environmental control system provided by Embodiment 2 of the present invention. This environmental control system 200 is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0037] 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, where: The acquisition module 21 is used to acquire environmental data, and the environmental data at least includes temperature data, humidity data, air quality data, and pressure data. Among them, the vibration data is collected by vibration sensors installed on each manufacturing device; The first judgment module 22 is used to judge whether the environmental data is abnormal; An input module 23, configured to input the environmental data into a trained neural network model to output a control strategy if it is determined that the environmental data is normal, and control corresponding environmental control devices according to the control strategy to ensure the stability and consistency of the production environment; An analysis module 24, configured to obtain vibration data of each device and analyze the vibration data to determine a target device with an abnormal operating state if it is determined that the environmental data is abnormal; A control module 25, configured to control the target device to stop working and issue an alarm.

[0038] Further, in some alternative embodiments of the present invention, the first determination module 22 includes: A data processing unit, configured to obtain historical environmental data, perform data cleaning and standardization processing on the historical environmental data to obtain processed data; A clustering unit, configured to perform K-Means clustering on the processed data respectively according to data types to determine cluster centers; A first determination unit, configured to calculate the distance from the data point of the current environmental data to the cluster center and determine whether the distance is greater than a first threshold; A first determination unit, configured to determine that the current environmental data is abnormal if it is determined that the distance is greater than the first threshold.

[0039] Further, in some alternative embodiments of the present invention, the environmental control system 200 further includes: A calculation module, configured to calculate an evaluation value according to the abnormal data and the distance from the data point of the abnormal data to the cluster center, and 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 point of the temperature abnormal data to the cluster center, B is the average distance from the data point of the humidity abnormal data to the cluster center, C is the average distance from the data point of the air quality abnormal data to the cluster center, D is the average distance from the data point of the pressure abnormal data to the cluster center, and α, β, λ, and γ are corresponding weight coefficients respectively; A second determination module, configured to determine whether the evaluation value is greater than a first preset value; An execution module, configured to execute the step of obtaining vibration data of each device and analyzing the vibration data to determine a target device with an abnormal operating state if it is determined that the evaluation value is greater than the first preset value.

[0040] Further, in some alternative embodiments of the present invention, the analysis module 24 includes: A preprocessing unit for preprocessing the vibration data and the environmental data, and performing time-domain analysis and correlation analysis on the results of the preprocessing in sequence to obtain analysis results; A second judgment unit for judging whether the vibration of the manufacturing equipment affects the production environment according to the analysis results; An analysis unit for analyzing the vibration data to determine a target device with abnormal operating status if it is judged that the vibration of the manufacturing equipment affects the production environment.

[0041] Furthermore, in some alternative embodiments of the present invention, the preprocessing unit includes: A preprocessing subunit for sequentially performing outlier removal processing, filtering processing, and data interpolation processing on the vibration data and the environmental data to obtain the results of the preprocessing; A plotting subunit for plotting the waveforms of the preprocessing results in the time domain, dividing several time periods according to the waveforms, and determining a target time period, wherein, at a preset time interval, the data corresponding to each waveform is divided into several initial time periods; Obtain various types of data within each initial time period, and judge whether each type of data simultaneously exceeds its respective second threshold; If it is judged that each type of data simultaneously exceeds its respective second threshold, then determine the corresponding initial time period as a candidate time period; Obtain the time interval between adjacent candidate time periods, and judge whether the time interval is less than a third threshold; If it is judged that the time interval is less than the third threshold, then merge adjacent candidate time periods to finally obtain the target time period; A first judgment subunit for calculating the Pearson correlation coefficient of each target time period and judging whether the Pearson correlation coefficient of the target time period is greater than a second preset value; A calculation subunit for determining the number of corresponding target time periods if it is judged that the Pearson correlation coefficient of the target time period is greater than the second preset value, and calculating a proportion according to the number of corresponding target time periods and the total number of target time periods; A second judgment subunit for judging whether the proportion is greater than a third preset value; A determination subunit for determining that the vibration of the manufacturing equipment affects the production environment if it is judged that the proportion is greater than the third preset value.

[0042] Embodiment III On the other hand, the present invention also proposes an electronic device. Please refer to Figure 3, shown is the electronic device in the third embodiment 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 above-described environment control method is implemented.

[0043] Among them, the processor 10 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments, and is used to run the program code stored in the memory 20 or process data, such as executing an access restriction program, etc.

[0044] Among them, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disc, etc. The memory 20 can be an internal storage unit of the electronic device in some embodiments, such as the hard disk of the electronic device. The memory 20 can also be an external storage device of the electronic device in other embodiments, 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. Further, the memory 20 can also include both the internal storage unit and the external storage device of the electronic device. The memory 20 can be used not only to store the application software and various types of data of the electronic device, but also to temporarily store the data that has been output or will be output.

[0045] It should be noted that Figure 3 the shown structure does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0046] The embodiment of the present invention also proposes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-described environment control method is implemented.

[0047] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0048] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing when necessary, and then stored in a computer memory.

[0049] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0050] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0051] The above embodiments merely illustrate several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several variations and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. An environmental control method, characterized in that: Applied in the 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, the vibration data of each device is obtained, and the vibration data is analyzed to determine the target device with abnormal operating status, wherein the vibration data is collected by a vibration sensor installed in each manufacturing device; Control the target device to stop working and issue an alarm; The step of acquiring 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 turn to obtain analysis results; According to 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, the vibration data is analyzed to determine the target equipment with abnormal operating status.

2. The environmental control method according to claim 1, characterized in that: The step of determining whether the environmental data is abnormal comprises: 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 from the data point of the current environment data to 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 the evaluation value according to 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 step of acquiring the vibration data of each device and analyzing the vibration data to determine the target device with abnormal operating status is executed.

4. The environmental control method according to claim 3, characterized in that: In the step of calculating the evaluation value according to the abnormal data and the distance from the data point of the abnormal data to the cluster center, the calculation formula of the evaluation value is: P = αA + βB + λC + γD Among them, 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 preprocessing the vibration data and the environmental data, and sequentially performing time domain analysis and correlation analysis on the preprocessing results to obtain the analysis results comprises: 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, 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 the second preset value, the number of corresponding target time periods is determined, and the proportion is calculated according to 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.

6. The environmental control method according to claim 5, characterized in that: The step of dividing the waveform into several time periods and determining the target time period includes: According to a preset time interval, the data corresponding to each of the waveforms is divided into a number of initial time periods; Acquire each type of data in each initial time period, and determine whether each type of data exceeds its own second threshold at the same time; If it is determined that each type of data exceeds the respective second threshold at the same time, the corresponding initial time period is determined as the 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.

7. An environmental control system, characterized in that: For implementing the environmental control method according to any one of claims 1 to 6, the system comprises: An acquisition module, used to acquire environmental data, wherein the environmental data at least includes 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, for inputting the environmental data into a trained neural network model if it is determined that the environmental data does not have an abnormality, outputting a control strategy, and controlling corresponding environmental control equipment according to the control strategy to ensure the stability and consistency of the production environment; An analysis module, for obtaining vibration data of each device if it is determined that the environmental data is abnormal, and analyzing the vibration data to determine the target device with abnormal operating status, wherein the vibration data is collected by a vibration sensor installed in each manufacturing device; A control module, used to control the target device to stop working and issue an alarm; The analysis module comprises: A preprocessing unit, used for 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; A second judgment unit, configured to judge whether the vibration of the manufacturing equipment affects the production environment according to the analysis result; The analyzing unit is used to analyze the vibration data and determine the target equipment with abnormal operating status if it is determined that the vibration of the manufacturing equipment affects the production environment.

8. 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 as described in any one of claims 1 to 6 is implemented.

9. 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 environmental control method according to any one of claims 1 to 6 is implemented.

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