Power BI-based tobacco production environment humidity monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a Power BI-based tobacco production environment humidity monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring original humidity data of a target tobacco production environment by utilizing Power BI, and preprocessing the original humidity data to obtain processed humidity data; calculating an index value of a preset monitoring index for the processed humidity data according to each production area and each specified unit time period; the distribution condition and the trend condition of the processed humidity data and the standard reaching condition of the preset monitoring indexes are visually displayed, and early warning information and decision suggestions are generated according to the index values of the preset monitoring indexes. According to the technical scheme, the deep processing capacity and the monitoring and diagnosis efficiency of mass humidity data in the tobacco production field are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for monitoring humidity in tobacco production environments based on Power BI. Background Technology

[0002] In the tobacco processing industry, ambient humidity has a direct impact on the quality of tobacco. Humidity is a core process parameter affecting the stability of tobacco moisture content, aroma retention, cigarette paper elasticity, and equipment operational reliability. Especially in the packaging workshop, excessive humidity can easily lead to mold growth in cigarettes and packaging wrinkles; while insufficient humidity can cause static electricity buildup, brittle tobacco, adhesive failure, and equipment jamming, directly affecting product qualification rates and production efficiency.

[0003] While existing traditional monitoring methods can collect a large amount of environmental humidity data, they lack the ability to perform in-depth data processing and intelligent zoning when faced with massive amounts of data, making it difficult to cope with the challenges of microclimate heterogeneity. Summary of the Invention

[0004] This invention provides a method, device, electronic device, and storage medium for monitoring humidity in tobacco production environments based on Power BI, so as to improve the ability to deeply process massive humidity data and the efficiency of monitoring and diagnosis in the tobacco production field.

[0005] According to one aspect of the present invention, a method for monitoring humidity in a tobacco production environment based on Power BI is provided, the method comprising:

[0006] The raw humidity data of the target tobacco production environment is obtained using Power BI, and the raw humidity data is preprocessed to obtain processed humidity data.

[0007] The preset monitoring index values ​​are calculated based on the processed humidity data for each production area and each designated unit time period;

[0008] The distribution and trend of the processed humidity data and the compliance status of the preset monitoring indicators are visualized, and early warning information and decision suggestions are generated based on the indicator values ​​of the preset monitoring indicators.

[0009] According to another aspect of the present invention, a humidity monitoring device for tobacco production environments based on Power BI is provided, the device comprising:

[0010] The humidity data acquisition module is used to acquire the raw humidity data of the target tobacco production environment using Power BI, and to preprocess the raw humidity data to obtain processed humidity data.

[0011] The monitoring index calculation module is used to calculate the index values ​​of preset monitoring indicators for the processed humidity data according to each production area and each specified unit time period;

[0012] The monitoring result determination module is used to visualize the distribution and trend of the processed humidity data and the compliance status of the preset monitoring indicators, and to generate early warning information and decision suggestions based on the indicator values ​​of the preset monitoring indicators.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the Power BI-based humidity monitoring method for tobacco production environments according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the Power BI-based method for monitoring humidity in tobacco production environments as described in any embodiment of the present invention.

[0018] The technical solution of this invention utilizes Power BI to acquire raw humidity data of the target tobacco production environment, and preprocesses the raw humidity data to obtain processed humidity data; calculates the index values ​​of preset monitoring indicators for each production area and each specified unit time period for the processed humidity data; visualizes the distribution, trend, and compliance status of the preset monitoring indicators of the processed humidity data, and generates early warning information and decision suggestions based on the index values ​​of the preset monitoring indicators. This approach, employing Power BI-based preprocessing of massive humidity data, calculating monitoring indicators by region and time period, and visualizing the humidity data monitoring situation, solves the problem that traditional monitoring methods lack in-depth data processing and intelligent zoning capabilities, making it difficult to cope with the challenges of microclimate heterogeneity. It improves the in-depth processing capability and monitoring and diagnostic efficiency of massive humidity data in the tobacco production field.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for monitoring humidity in a tobacco production environment based on Power BI, provided in Embodiment 1 of the present invention;

[0022] Figure 2 This is a schematic diagram of a humidity monitoring device for tobacco production environment based on Power BI, provided in Embodiment 2 of the present invention.

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the Power BI-based method for monitoring humidity in tobacco production environments according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0026] Example 1

[0027] Figure 1 This is a flowchart illustrating a Power BI-based method for monitoring humidity in a tobacco production environment, as provided in Embodiment 1 of the present invention. This embodiment is applicable to monitoring humidity in a tobacco production environment. The method can be executed by a Power BI-based humidity monitoring device for tobacco production environments. This device can be implemented in hardware and / or software and can be configured on a server. Figure 1 As shown, the method includes:

[0028] S110. Use Power BI to obtain the raw humidity data of the target tobacco production environment, and preprocess the raw humidity data to obtain the processed humidity data.

[0029] In this embodiment, the ambient humidity of the target tobacco production environment can be continuously monitored through a pre-deployed sensor network, and the collected raw humidity data can be transmitted to a central database in real time. Power BI is used to connect to the central database to obtain the raw humidity data.

[0030] The sensor network in this embodiment can be deployed in a distributed manner. High-precision humidity sensors are installed in key areas of the production workshop (such as the raw material warehouse, shredding area, drying area, flavoring area, finished product warehouse in the cigarette-making area, and production machines such as cigarette rolling machines and packaging machines in the cigarette-making and packaging area). The sensors are connected to the data acquisition terminal wirelessly or wiredly, and connected to the central database using a star or mesh topology to achieve real-time and stable data transmission, ensuring the continuity and real-time nature of humidity data. For example, a high-precision sensor network can be deployed at key workstations in the cigarette-making and packaging workshop, covering the following areas: packaging area (high-speed packaging machine operating area), cigarette rolling and packaging area (core area for cigarette forming and packaging), finished product temporary storage area (temporary storage area for packaged cigarettes or auxiliary material matching area), personnel flow passages, and areas near doors and windows (areas susceptible to external interference). The raw humidity data acquired in this embodiment also includes the corresponding timestamp.

[0031] In one optional implementation, preprocessing the raw humidity data to obtain processed humidity data may include: removing outliers, handling missing values, smoothing time series data, and performing distribution transformation on the raw humidity data to obtain processed humidity data.

[0032] This embodiment can use Power Query's three-step preprocessing workflow for data cleaning to remove interference factors such as invalid or abnormal data (e.g., null values ​​caused by sensor malfunctions, extreme values ​​exceeding the physical range). A specific preprocessing method can be exemplified as follows:

[0033] 1. Outlier removal:

[0034] Z-score method (removing deviations from the mean) (Data): When humidity data is approximately normally distributed, the Z-score for each data point can be calculated. The data points were identified as outliers.

[0035] 2. Handling missing values:

[0036] Short-term missing (continuous missing) 5 data points, i.e. (seconds), can be filled using linear interpolation. ;

[0037] Long-term deletion (continuous deletion) (Data points): can be marked as invalid data, and can also trigger sensor fault warnings and push maintenance notifications.

[0038] 3. Time series smoothing: A moving average method (window size = 5 data points, i.e., 5 seconds) can be used to smooth the data and reduce random noise. .

[0039] 4. Distribution transformation: Logarithmic transformation / power transformation is used to improve the normal distribution of the data, making it more suitable for subsequent analysis.

[0040] S120. Calculate the preset monitoring index values ​​based on the processed humidity data of each production area and each designated unit time period.

[0041] The production area can refer to areas divided according to the production process, such as raw material area, processing area, and storage area. Each area can be further subdivided into sub-areas, such as the processing area being divided into slicing and drying units. The specified unit time period can refer to a unit time period divided according to production rhythm and management needs, such as an hourly segment (1 hour), a shift segment (8 hours / shift), and a daily segment (24 hours). Preset monitoring indicators can refer to indicators such as average humidity, standard deviation, and compliance rate.

[0042] This embodiment can calculate the preset monitoring index values ​​for the target production area within each specified time period based on the processed humidity data of the target production area. The target production area can refer to one of multiple production areas. For example, it can calculate the average humidity, standard deviation, and compliance rate of the drying unit within the processing area per hour, per shift, and per day.

[0043] S130. Visualize the distribution, trend, and compliance status of the pre-set monitoring indicators of the processed humidity data, and generate early warning information and decision-making suggestions based on the indicator values ​​of the pre-set monitoring indicators.

[0044] Optionally, the distribution, trend, and compliance status of the processed humidity data and preset monitoring indicators can be visualized. This can include: generating a humidity distribution map of the target tobacco production environment based on the processed humidity data of each production area; generating a humidity change trend map of each production area based on the processed humidity data of each production area within each specified time period; and generating a humidity compliance map of each production area within each specified time period based on the index values ​​of the preset monitoring indicators of each production area within each specified time period.

[0045] In this embodiment, Power BI's charting tools can be used to create humidity distribution maps (such as heat maps, with the horizontal axis representing regions and the vertical axis representing humidity values), humidity change trend maps (with the horizontal axis representing time and the vertical axis representing humidity values, visually showing the humidity change trend of each region), and compliance rate dashboards (displaying the compliance status of each region at each time period in real time).

[0046] This embodiment can also mark anomalies, for example, by using color coding (such as green for compliance, yellow for warning, and red for anomaly) and warning icons to highlight abnormal areas, making it easier to quickly locate problems.

[0047] Optionally, generating early warning information and decision suggestions based on the preset monitoring indicators may include: generating early warning information for each production area in each specified time period to alert management personnel when the indicator value deviates from the preset range; and generating decision suggestions for each production area in each specified time period to guide the adjustment of the operating parameters of the humidification or dehumidification equipment in the production area based on the deviation of the indicator value from the preset range.

[0048] In this embodiment, humidity changes can be monitored, and if the humidity data deviates from a preset range (such as exceeding the process requirement range), the system can detect the issue. (RH), generate early warning information.

[0049] This embodiment can implement differentiated control strategies based on zoning characteristics and achieve closed-loop linkage with environmental control equipment.

[0050] For example, the target humidity requirement for the coiling and packaging areas. Within the specified range, maintain the existing air conditioning system operation; outside the specified range, adjust the air conditioning system accordingly; the finished product storage area requires a target humidity level according to process requirements. Within the designated area, the fresh air valve automatically shuts off to isolate external humid air; pedestrian passages are set up as follows: RH wide range, and integrated with the access control system, when the door is detected to be open for more than 30 seconds and a sudden change in humidity. If an event occurs, it can be identified as an "external interference event," and the auxiliary humidification unit will be immediately activated to compensate, and the event log will be recorded for traceability and analysis.

[0051] The basis for the above-mentioned zoned regulation can be obtained in the following way: collect historical humidity data of each monitoring point for 14 consecutive days, extract three key statistical features: daily average humidity value μ, humidity fluctuation coefficient and peak-valley difference, and use clustering algorithm to divide control zones with similar humidity behavior patterns.

[0052] Specifically, historical humidity time series data Where hi represents the humidity value (in %RH) at the i-th time point, and n is the total number of sampling points. Based on this, three core statistical features are extracted for cluster modeling:

[0053] Daily average humidity value : The average level of humidity;

[0054] Humidity fluctuation coefficient (CV): measures humidity stability, standard deviation , ;

[0055] Peak-valley difference : Represents the maximum intraday fluctuation range, .

[0056] Map each sensor point to a three-dimensional feature vector. , forming the feature matrix , where m is the total number of sensors. Subsequently, an unsupervised clustering algorithm is used to group X. This algorithm minimizes the intra-cluster squared error by iteratively optimizing the objective function: Where K is the preset number of clusters, Indicates the k-th cluster; Let be the centroid of the k-th cluster, which is the mean vector of all samples in that cluster. This represents the Euclidean distance. Ultimately, four humidity behavior zones can be output: the packaging area is a high stability zone (…). The roll-up area is a high-humidity fluctuation zone. The finished product temporary storage area is a low-humidity stable area. The pedestrian passageway is a high-interference area. (It is sensitive to external disturbances).

[0057] The aforementioned partitioning method based on statistical characteristics and clustering effectively identified the microclimate heterogeneity within the packaging workshop, providing a basis for differentiated environmental control.

[0058] Optionally, this embodiment can also calculate the confidence interval of the humidity data based on the treated humidity data of the target tobacco production environment to assess the reliability of the treated humidity data.

[0059] This embodiment can also construct a humidity data analysis system based on mathematical models based on data preprocessing. For example, a time series prediction model: extract the periodic characteristics of humidity changes from the processed humidity data of the target tobacco production environment; and construct a time series prediction model based on the periodic characteristics of humidity changes to predict future humidity change trends.

[0060] This embodiment can also collect data such as temperature, pressure, and wind speed to construct a multiple linear regression model, which is used to analyze the linear relationship between environmental humidity and multiple influencing factors (such as temperature, pressure, and wind speed). By solving the regression coefficients, the degree of influence of each factor on humidity can be quantified.

[0061] The technical solution of this embodiment utilizes Power BI to acquire raw humidity data of the target tobacco production environment, and preprocesses the raw humidity data to obtain processed humidity data; calculates the index values ​​of preset monitoring indicators for each production area and each specified unit time period for the processed humidity data; visualizes the distribution, trend, and compliance status of the preset monitoring indicators of the processed humidity data, and generates early warning information and decision suggestions based on the index values ​​of the preset monitoring indicators. By using the technical means of preprocessing massive humidity data based on Power BI, calculating monitoring indicators by region and time period, and visualizing the humidity data monitoring situation, this solution solves the problem that traditional monitoring methods lack in-depth data processing and intelligent zoning processing capabilities, making it difficult to cope with the challenges of microclimate heterogeneity. This improves the in-depth processing capability and monitoring and diagnostic efficiency of massive humidity data in the tobacco production field.

[0062] Example 2

[0063] Figure 2 This is a schematic diagram of a humidity monitoring device for tobacco production environments based on Power BI, provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes: a humidity data acquisition module 210, a monitoring index calculation module 220, and a monitoring result determination module 230. Wherein:

[0064] The humidity data acquisition module 210 is used to acquire the raw humidity data of the target tobacco production environment using Power BI, and to preprocess the raw humidity data to obtain processed humidity data.

[0065] The monitoring index calculation module 220 is used to calculate the index values ​​of preset monitoring indicators for the processed humidity data according to each production area and each specified unit time period;

[0066] The monitoring result determination module 230 is used to visualize the distribution and trend of the processed humidity data and the compliance status of the preset monitoring indicators, and to generate early warning information and decision suggestions based on the indicator values ​​of the preset monitoring indicators.

[0067] The technical solution of this embodiment utilizes Power BI to acquire raw humidity data of the target tobacco production environment, and preprocesses the raw humidity data to obtain processed humidity data; calculates the index values ​​of preset monitoring indicators for each production area and each specified unit time period for the processed humidity data; visualizes the distribution, trend, and compliance status of the preset monitoring indicators of the processed humidity data, and generates early warning information and decision suggestions based on the index values ​​of the preset monitoring indicators. By using the technical means of preprocessing massive humidity data based on Power BI, calculating monitoring indicators by region and time period, and visualizing the humidity data monitoring situation, this solution solves the problem that traditional monitoring methods lack in-depth data processing and intelligent zoning processing capabilities, making it difficult to cope with the challenges of microclimate heterogeneity. This improves the in-depth processing capability and monitoring and diagnostic efficiency of massive humidity data in the tobacco production field.

[0068] Optional, the humidity data acquisition module 210 can be used for:

[0069] The original humidity data is subjected to outlier removal, missing value processing, time series smoothing, and distribution transformation to obtain the processed humidity data.

[0070] Optional, the monitoring indicator calculation module 220 can be used for:

[0071] For the processed humidity data of the target production area, calculate the index values ​​of the preset monitoring indicators corresponding to the target production area in each specified unit time period.

[0072] Optionally, the monitoring result determination module 230 can be used for:

[0073] Based on the processed humidity data of each production area, a humidity distribution map of the target tobacco production environment is generated;

[0074] Based on the processed humidity data of each production area within each specified time period, a humidity change trend map is generated for each production area.

[0075] Based on the preset monitoring index values ​​for each production area within each specified time period, a humidity compliance map for each production area within each specified time period is generated.

[0076] Optionally, the monitoring result determination module 230 can also be used for:

[0077] If the indicator value deviates from the preset range, an early warning message will be generated for each production area in each specified time period to alert the management personnel.

[0078] Based on the deviation of the indicator value from the preset range, decision suggestions are generated for each production area in each specified time period to guide the adjustment of the operating parameters of the humidification or dehumidification equipment in the production area.

[0079] Optionally, the Power BI-based humidity monitoring device for tobacco production environments may further include a confidence interval calculation module, used for:

[0080] Based on the post-treatment humidity data of the target tobacco production environment, a confidence interval for the humidity data is calculated to assess the reliability of the post-treatment humidity data.

[0081] Optionally, the Power BI-based humidity monitoring device for tobacco production environments may further include a predictive model building module for:

[0082] The periodic characteristics of humidity changes are extracted from the post-treatment humidity data of the target tobacco production environment;

[0083] Based on the periodic characteristics of humidity changes, a time series prediction model is constructed to predict future humidity change trends.

[0084] The Power BI-based humidity monitoring device for tobacco production environments provided in this embodiment of the invention can execute the Power BI-based humidity monitoring method for tobacco production environments provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0085] Example 3

[0086] Figure 3 A schematic diagram of an electronic device 300 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers or various forms of mobile devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0087] like Figure 3As shown, the electronic device 300 includes at least one processor 301 and a memory, such as a read-only memory (ROM) 302 or a random access memory (RAM) 303, communicatively connected to the at least one processor 301. The memory stores computer programs executable by the at least one processor. The processor 301 can perform various appropriate actions and processes based on the computer program stored in the ROM 302 or loaded into the RAM 303 from storage unit 308. The RAM 303 can also store various programs and data required for the operation of the electronic device 300. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0088] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0089] Processor 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 301 performs the various methods and processes described above, such as a Power BI-based method for monitoring humidity in a tobacco production environment.

[0090] In some embodiments, the Power BI-based method for monitoring humidity in a tobacco production environment can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by processor 301, one or more steps of the Power BI-based method for monitoring humidity in a tobacco production environment described above can be performed. Alternatively, in other embodiments, processor 301 can be configured to perform the Power BI-based method for monitoring humidity in a tobacco production environment by any other suitable means (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0095] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0096] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0097] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0098] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for monitoring humidity in tobacco production environments based on Power BI, characterized in that, include: The raw humidity data of the target tobacco production environment is obtained using Power BI, and the raw humidity data is preprocessed to obtain processed humidity data. The preset monitoring index values ​​are calculated based on the processed humidity data for each production area and each designated unit time period; The distribution and trend of the processed humidity data and the compliance status of the preset monitoring indicators are visualized, and early warning information and decision suggestions are generated based on the indicator values ​​of the preset monitoring indicators.

2. The method according to claim 1, characterized in that, The raw humidity data is preprocessed to obtain processed humidity data, including: The original humidity data is subjected to outlier removal, missing value processing, time series smoothing, and distribution transformation to obtain the processed humidity data.

3. The method according to claim 1, characterized in that, The preset monitoring index values ​​are calculated for the processed humidity data according to each production area and each designated unit time period, including: For the processed humidity data of the target production area, calculate the index values ​​of the preset monitoring indicators corresponding to the target production area in each specified unit time period.

4. The method according to claim 3, characterized in that, The distribution and trend of the processed humidity data, as well as the compliance status of the preset monitoring indicators, are visualized, including: Based on the processed humidity data of each production area, a humidity distribution map of the target tobacco production environment is generated; Based on the processed humidity data of each production area within each specified time period, a humidity change trend map is generated for each production area. Based on the preset monitoring index values ​​for each production area within each specified time period, a humidity compliance map for each production area within each specified time period is generated.

5. The method according to claim 4, characterized in that, Based on the indicator values ​​of the preset monitoring indicators, early warning information and decision suggestions are generated, including: If the indicator value deviates from the preset range, an early warning message will be generated for each production area in each specified time period to alert the management personnel. Based on the deviation of the indicator value from the preset range, decision suggestions are generated for each production area in each specified time period to guide the adjustment of the operating parameters of the humidification or dehumidification equipment in the production area.

6. The method according to claim 1, characterized in that, Also includes: Based on the post-treatment humidity data of the target tobacco production environment, a confidence interval for the humidity data is calculated to assess the reliability of the post-treatment humidity data.

7. The method according to claim 1, characterized in that, Also includes: The periodic characteristics of humidity changes are extracted from the post-treatment humidity data of the target tobacco production environment; Based on the periodic characteristics of humidity changes, a time series prediction model is constructed to predict future humidity change trends.

8. A humidity monitoring device for tobacco production environment based on Power BI, characterized in that, include: The humidity data acquisition module is used to acquire the raw humidity data of the target tobacco production environment using Power BI, and to preprocess the raw humidity data to obtain processed humidity data. The monitoring index calculation module is used to calculate the index values ​​of preset monitoring indicators for the processed humidity data according to each production area and each specified unit time period; The monitoring result determination module is used to visualize the distribution and trend of the processed humidity data and the compliance status of the preset monitoring indicators, and to generate early warning information and decision suggestions based on the indicator values ​​of the preset monitoring indicators.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a Power BI-based method for monitoring humidity in tobacco production environments according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for monitoring humidity in a tobacco production environment based on PowerBI, as described in any one of claims 1-7.

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