A method and system for intelligent analysis and processing of dust concentration data

Abstract

The present invention relates to the field of data processing technology, and in particular to a method and system for intelligent analysis and processing of dust concentration data, the method comprising: obtaining parameters at each moment, the parameters comprising air temperature, humidity and dust concentration; calculating dust characteristic quantities of the parameters, the dust characteristic quantities being positively correlated with the dust concentration and with the ratio of the temperature to the humidity; normalizing the dust characteristic quantities, taking the obtained normalized values ​​as the approximation of the parameters, and grouping the parameters at each moment using the approximation; constructing a coordinate system regarding the dust characteristic quantities and the approximation, calculating the dust degree of the parameters within the group, and in response to the dust degree being greater than a preset threshold, losslessly storing the parameters at the corresponding moment; the present invention can judge the discrete degree of the parameters according to the dust characteristic quantities, losslessly store important parameter data, and facilitate dust monitoring and analysis of dust data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for intelligent analysis and processing of dust concentration data. Background Art

[0002] A large amount of dust is generated in some workplaces, such as construction areas, mining areas, and areas with dense human activities. In order to ensure the personal safety of personnel, it is necessary to monitor and analyze the dust concentration. By collecting data through sensors, the changing trend of dust concentration can be analyzed in real time, and the situation of excessive dust concentration or abnormal fluctuations can be discovered in time. Intelligent analysis of dust concentration data can help companies optimize the management of the working environment. Through long-term data analysis, high-incidence areas of dust concentration or high-risk workplaces can be identified, and targeted environmental improvement and control measures can be taken, which not only helps to ensure the safety and health of employees at work, but also helps companies comply with relevant safety management standards and regulations, reduce potential legal liabilities and production interruption risks, and can preventive maintenance and resource optimization.

[0003] Intelligent analysis and processing methods can use historical data and real-time monitoring results to analyze and predict dust concentration trends.

[0004] The Chinese patent document with announcement number CN114264582B discloses an ultra-low dust online monitoring and analysis control system, including: a setting module, used to set dust measurement points according to the dust monitoring area; a monitoring module, used to collect dust gas at the dust measurement points, enter the analysis space, and obtain the dust concentration at each dust measurement point; an analysis module, based on the dust concentration of each dust measurement point, determines the spatial dust dynamic law in the dust monitoring area; a control module, based on the spatial dust dynamic law, determines the spray scheme and controls the dust concentration in the dust monitoring area.

[0005] In the prior art, a large amount of data needs to be stored in the analysis of dust concentration data. The data is usually stored after lossy compression. Lossy compression will damage all stored data. The collected abnormal data is of great significance to the dust concentration analysis. When the abnormal data is damaged, it loses its analytical value. Summary of the invention

[0006] In order to solve the problem that abnormal data is damaged during compression, the present invention provides a dust concentration data intelligent analysis and processing method and system.

[0007] In the first aspect, the present invention provides a method for intelligent analysis and processing of dust concentration data, which adopts the following technical solution:

[0008] A method for intelligent analysis and processing of dust concentration data, comprising the steps of:

[0009] Obtain parameters at each moment, including air temperature, humidity, and dust concentration;

[0010] Calculate the dust characteristic quantity at each moment. The dust characteristic quantity is positively correlated with the dust concentration and the ratio of temperature and humidity.

[0011] Normalize the dust characteristic quantity, use the obtained normalized value as the approximation of the parameter, and use the approximation to group the parameters at each moment;

[0012] A coordinate system is constructed with the dust characteristic quantity as the horizontal coordinate and the approximation as the vertical coordinate, the parameters in the group are mapped into the coordinate system, the dust degree of the parameters in the group is calculated, and in response to the dust degree being greater than a preset threshold, the parameters at the corresponding moment are losslessly stored;

[0013] Among them, the calculation formula of the dustiness of the parameter is:

[0014]

[0015] in, Indicates Dustiness of moment parameters, Indicates The number of parameters in the time window, Indicates The total number of parameters in the group where the moment parameter belongs. Indicates Time parameters and group parameters The spacing, Indicates The mean value of the distance between the moment parameter and other parameters in the group, Indicates the number of parameters in the group.

[0016] The effect is that the parameters are grouped by calculating the dust characteristic quantities and approximations of the parameters, and then the dust characteristic quantities of the parameters at each moment in the group are calculated. The discrete degree of the parameters is judged according to the dust characteristic quantities, and the important parameter data is losslessly stored, which is convenient for dust monitoring and dust data analysis.

[0017] Preferably, the expression of dust characteristic quantity is:

[0018]

[0019] in, Indicates The dust characteristic quantity at the moment, Indicates Dust concentration at any time, Indicates The temperature of the air at the moment, Indicates The humidity of the air at any given moment, () represents the normalization function.

[0020] The effect is that the dust characteristic quantity is calculated by using dust concentration, air temperature and humidity data, which improves the accuracy of calculating the dust characteristic quantity and facilitates the analysis of the dust characteristic quantity.

[0021] Preferably, the expression of dust characteristic quantity is:

[0022]

[0023] in, Indicates The dust characteristic quantity at the moment, Indicates Dust concentration at any time, Indicates The temperature of the air at the moment, Indicates The humidity of the air at any given moment, () represents the normalization function, () represents the logarithmic function with base e.

[0024] Preferably, the approximate expression is:

[0025]

[0026] in, Indicates The approximation of the parameters of the moment, Indicates Dust characteristic quantity of time parameters, Indicates Dust characteristic quantity of time parameters, Indicates The dust characteristic value of the first parameter of the group where the moment parameter belongs.

[0027] Preferably, the approximate expression is:

[0028]

[0029] in, Indicates The approximation of the parameters of the moment, Indicates Dust characteristic quantity of time parameters, Indicates Dust characteristic quantity of time parameters, Indicates The dust characteristic value of the first parameter of the group where the moment parameter belongs.

[0030] The effect is that the approximation of parameters can be accurately calculated through the formula, which makes it easier to group the parameters.

[0031] Preferably, the method further includes: for the parameters in the group whose dust concentration is less than or equal to a preset threshold, calculating the mean values ​​of temperature, humidity and dust concentration, respectively, and storing the mean values.

[0032] Preferably, the method of grouping the parameters at each moment by using approximation is:

[0033] Sort the acquired parameters in chronological order;

[0034] Taking the first parameter as the basic point, the approximation of the parameters is calculated in sequence, and the parameters whose approximation is less than the preset grouping threshold are grouped into one group;

[0035] When the approximation of a parameter is greater than a preset grouping threshold, the approximation of the remaining parameters is calculated based on the parameter until the grouping is completed.

[0036] The effect is that by grouping the parameters and then analyzing the parameter data within the groups, the analysis process of the letter of commitment is refined and the accuracy of data processing is improved.

[0037] In the second aspect, a dust concentration data intelligent analysis and processing system includes: a processor; a memory, which stores computer instructions for photovoltaic power station safety warning based on artificial intelligence, and when the computer instructions are executed by the processor, the system executes the dust concentration data intelligent analysis and processing method.

[0038] The effect is: the above-mentioned dust concentration data intelligent analysis and processing method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0039] The present invention has the following technical effects:

[0040] The parameters are grouped by calculating the dust characteristic quantities and approximations of the parameters, and then the dust characteristic quantities of the parameters at each moment in the group are calculated. The discrete degree of the parameters is judged according to the dust characteristic quantities, and important parameter data is losslessly stored to facilitate dust monitoring and dust data analysis. The parameters in the group that are less than or equal to the preset threshold are compressed and stored, reducing the compression space. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0042] Figure 1 It is a flow chart of a dust concentration data intelligent analysis and processing method of the present invention.

[0043] Figure 2 It is a structural block diagram of a dust concentration data intelligent analysis and processing system of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0045] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0046] The embodiment of the present invention discloses a method for intelligent analysis and processing of dust concentration data, referring to Figure 1 , including steps S1 to S4, which are specifically as follows:

[0047] S1: Obtain parameters at each moment, including air temperature, humidity and dust concentration.

[0048] Temperature sensors, humidity sensors and dust concentration sensors are installed in areas where dust concentration needs to be monitored to collect real-time humidity data, temperature data and dust concentration data in the air respectively.

[0049] S2: Calculate the dust characteristic quantity at each moment. The dust characteristic quantity is positively correlated with the dust concentration and the ratio of temperature and humidity.

[0050] In one embodiment, the dust characteristic value is expressed as:

[0051]

[0052] in, Indicates The dust characteristic quantity at the moment, Indicates Dust concentration at any time, Indicates The temperature of the air at the moment, Indicates The humidity of the air at any given moment, () represents a normalized function. As the air temperature rises, the hot air increases the suspension time of dust, causing the air to accommodate more suspended particles, resulting in an increase in dust concentration. In an environment with high humidity, such as after rain or watering, dust particles clump together due to moisture, increasing the settling rate, causing the dust concentration in the air to decrease. In a dry environment, low humidity will cause dust particles to be more easily suspended in the air. The larger the value of the dust characteristic, the greater the dust concentration at the current moment, and the effect of temperature on dust concentration is greater than that of humidity. Reflects the impact of temperature and humidity on dust concentration throughout the data cycle.

[0053] In other embodiments, the dust characteristic value is expressed as:

[0054]

[0055] in, Indicates The dust characteristic quantity at the moment, Indicates Dust concentration at any time, Indicates The temperature of the air at the moment, Indicates The humidity of the air at any given moment, () represents the normalization function, () represents the logarithmic function with base e.

[0056] S3: normalize the dust feature quantity, use the obtained normalized value as the approximation of the parameter, and group the parameters at each moment using the approximation.

[0057] In one embodiment, the expression for the approximation is:

[0058]

[0059] in, Indicates The approximation of the parameters of the moment, Indicates Dust characteristic quantity of time parameters, Indicates Dust characteristic quantity of time parameters, Indicates The dust characteristic of the first parameter in the group where the moment parameter belongs. For The parameter at the moment is normalized with other parameters to obtain the approximation of the parameter. The larger the value, the smaller the difference between the parameter and other parameters in the same group.

[0060] The method of grouping the parameters at each moment by approximation is:

[0061] The acquired parameters are sorted in chronological order; the approximation of the parameters is calculated in sequence with the first parameter as the basic point, and the parameters with approximation less than the preset grouping threshold are grouped into one group; when the approximation of the parameter is greater than the preset grouping threshold, the approximation of the remaining parameters is calculated with the parameter as the basic point until the grouping is completed. The grouping threshold is set manually according to the actual situation, for example, the grouping threshold is 0.8.

[0062] For example, for the parameter ( , …, , , …, ),by The approximation of the following parameters is calculated in sequence as the basic point. , …, The approximation of is less than the grouping threshold 0.8, while If the approximation is greater than 0.8, then ( , …, ) into a group, and then The approximation of the following parameters is calculated for the basic points in sequence until the grouping is completed.

[0063] In one embodiment, the expression for the approximation is:

[0064]

[0065] in, Indicates The approximation of the parameters of the moment, Indicates Dust characteristic quantity of time parameters, Indicates Dust characteristic quantity of time parameters, Indicates The dust characteristic value of the first parameter of the group where the moment parameter belongs.

[0066] S4: constructing a coordinate system for dust feature quantities and approximations, calculating the dustiness of the parameters within the group, and in response to the dustiness being greater than a preset threshold, losslessly storing the parameters at the corresponding moment.

[0067] Among them, the calculation formula of the dustiness of the parameter is:

[0068]

[0069] in, Indicates Dustiness of moment parameters, Indicates The number of parameters in the time window, Indicates The total number of parameters in the group where the moment parameter belongs. Indicates The distance between the moment parameter and the parameter j within the group, that is, the Euclidean distance between the two parameters, Indicates The mean value of the distance between the moment parameter and other parameters in the group, Indicates the number of parameters in the group. Represents the summation symbol. The larger the value of The greater the outlier degree of the parameter data at the moment, the The parameters at the moment may be abnormal or have special circumstances, and the more important they are, the more they need to be adjusted. The parameters at each moment are stored losslessly. The threshold is set manually according to the actual situation, and for example, the threshold is 0.8.

[0070] It should be noted that a coordinate system for dust feature quantity and approximation is constructed, with the horizontal axis being the dust feature quantity and the vertical axis being the data approximation. The parameters in the group are mapped to the coordinate system, and an n×m window is constructed with each parameter as the center. The number of parameters in the window is .

[0071] For the parameters in the group with dust concentration less than or equal to the preset threshold, the mean values ​​of temperature, humidity, and dust concentration are calculated and stored. Then, run-length encoding is used to compress the mean data, which greatly compresses the data while retaining its reference value, making it easier to analyze and process the data. Parameters with dust concentration greater than the preset threshold are stored losslessly, which can promptly detect concentration exceeding the standard or abnormal fluctuations, and avoid safety accidents or health risks that may be caused by high dust concentrations.

[0072] In other embodiments, the parameters in the group whose dust concentration is less than or equal to a preset threshold value may be directly compressed and stored using run-length coding.

[0073] Figure 2 The structural block diagram of a dust concentration data intelligent analysis and processing system in this embodiment is schematically shown.

[0074] The present invention also provides a dust concentration data intelligent analysis and processing system. Figure 2 As shown, the dust concentration data intelligent analysis and processing system includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, the dust concentration data intelligent analysis and processing method according to the present invention is implemented.

[0075] The dust concentration data intelligent analysis and processing system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose settings and functions are known in the art and will not be described in detail here.

[0076] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0077] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0078] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent analysis and processing of dust concentration data, characterized in that: Includes steps: Obtain parameters at each moment, including air temperature, humidity, and dust concentration; Calculate the dust characteristic quantity at each moment. The dust characteristic quantity is positively correlated with the dust concentration and the ratio of temperature and humidity. The expression of the dust characteristic quantity is: Or: in, Indicates The dust characteristic quantity at the moment, Indicates Dust concentration at any time, Indicates The temperature of the air at the moment, Indicates The humidity of the air at any given moment, () represents the normalization function, () represents a logarithmic function with e as the base; the dust characteristic quantity is normalized, and the obtained normalized value is used as the approximation of the parameter, and the parameters at each moment are grouped using the approximation; A coordinate system is constructed with the dust characteristic quantity as the horizontal coordinate and the approximation as the vertical coordinate, the parameters in the group are mapped into the coordinate system, the dust degree of the parameters in the group is calculated, and in response to the dust degree being greater than a preset threshold, the parameters at the corresponding moment are losslessly stored; Among them, the calculation formula of the dustiness of the parameter is: in, Indicates Dustiness of moment parameters, Indicates The number of parameters in the time window, Indicates The total number of parameters in the group where the moment parameter belongs. Indicates Time parameters and group parameters The spacing, Indicates The mean value of the distance between the moment parameter and other parameters in the group, Indicates the number of parameters in the group.

2. The method for intelligent analysis and processing of dust concentration data according to claim 1, characterized in that: The approximate expression is: in, Indicates The approximation of the parameters of the moment, Indicates Dust characteristic quantity of time parameters, Indicates Dust characteristic quantity of time parameters, Indicates The dust characteristic value of the first parameter of the group where the moment parameter belongs.

3. The method for intelligent analysis and processing of dust concentration data according to claim 1, characterized in that: Also includes: For the parameters in the group whose dust concentration is less than or equal to the preset threshold, the mean values ​​of temperature, humidity and dust concentration are calculated and stored.

4. The method for intelligent analysis and processing of dust concentration data according to claim 1, characterized in that: The method of grouping the parameters at each moment by approximation is: Sort the acquired parameters in chronological order; Taking the first parameter as the basic point, the approximation of the parameters is calculated in sequence, and the parameters whose approximation is less than the preset grouping threshold are grouped into one group; When the approximation of a parameter is greater than a preset grouping threshold, the approximation of the remaining parameters is calculated based on the parameter until the grouping is completed.

5. A dust concentration data intelligent analysis and processing system, characterized in that: include: processor; A memory storing computer instructions for safety warning of photovoltaic power stations based on artificial intelligence. When the computer instructions are executed by the processor, the system executes a method for intelligent analysis and processing of dust concentration data according to any one of claims 1 to 4.

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