An ecological environment parameter monitoring method, device and system

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method, device and system for monitoring ecological environment parameters. It acquires ecological environment parameter sequences of multiple initial dimensions, and obtains the dynamic change characteristics of each position according to the change trends of each position in the ecological environment parameter sequences. Based on the association of the dynamic change characteristics between the initial dimensions, and combining the number of associated initial dimensions associated with each initial dimension, it obtains the leading role of each initial dimension in the ecological environment, and thus combines the dynamic change characteristics of each position in each initial dimension to obtain the ecological contribution degree of each position in each initial dimension; uses the ecological contribution degree as a weight to weight the initial covariance matrix in the principal component analysis, realizes the correction of the covariance matrix, reduces the dimension of the initial dimension, reduces data redundancy, significantly reduces the complexity of the ecological environment parameter data while maintaining the main characteristics of the data, and improves the data processing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method, device and system for monitoring ecological environment parameters. Background Art

[0002] In the process of monitoring ecological environment parameters, existing monitoring methods mostly have problems such as insufficient real-time performance, limited coverage, and low data processing efficiency, making it difficult to meet the multi-dimensional monitoring requirements of complex ecological environments. Currently, it mainly relies on sensor networks, remote sensing technology and Internet of Things architectures. By collecting key parameters such as temperature, humidity, and atmospheric composition in the environment, combined with big data processing, the accuracy and efficiency of ecological environment monitoring are improved, providing a scientific basis for environmental protection decision-making.

[0003] In the prior art, ecological environment monitoring usually involves real-time monitoring of multiple ecological environment parameters. Ecological environment parameters involve a large number of multi-dimensional variables, and the amount of data is extremely large, resulting in high data processing difficulty. Summary of the Invention

[0004] In order to solve the technical problem of high data processing difficulty caused by the large amount of data of ecological environment parameters, the purpose of the present invention is to provide a method, device and system for monitoring ecological environment parameters. The specific technical solutions adopted are as follows:

[0005] In the first aspect of the present invention, a method for monitoring ecological environment parameters is provided, including:

[0006] Obtain ecological environment parameter sequences of multiple initial dimensions, and obtain dynamic change characteristics of each position according to the change trend of each position in the ecological environment parameter sequence;

[0007] Based on the difference between the degree of association of the overall data of each initial dimension with the overall data of other initial dimensions and the degree of association of local data, combined with the number of associated initial dimensions associated with each initial dimension, obtain the leading role of each initial dimension in the ecological environment; the degree of association is obtained from the dynamic change characteristics;

[0008] According to the leading role of each initial dimension in the ecological environment and the dynamic change characteristics of each position in each initial dimension, obtain the ecological contribution degree of each position in each initial dimension;

[0009] Use the ecological contribution degree of each position in each initial dimension as a weight to weight the initial covariance matrix in the principal component analysis, and reduce the dimension of the initial dimension.

[0010] In an exemplary embodiment, obtaining the dynamic change characteristics of each position according to the change trend of each position in the ecological environment parameter sequence includes:

[0011] Segment the first ecological environment parameter sequence to obtain multiple data segments; the first ecological environment parameter sequence is an ecological environment parameter sequence of a first initial dimension, and the first initial dimension is any one of the initial dimensions;

[0012] Based on the change situation of the data in the first data segment, obtain the long-term change situation of the first position; the first data segment is any one of the data segments in the first ecological environment parameter sequence; the first position is the position of any one parameter in the first data segment;

[0013] Based on the difference between the actual value and the fitted value of the first position, obtain the short-term volatility of the first position;

[0014] Fuse the long-term change situation and the short-term volatility of the first position to obtain the dynamic change characteristics of the first position.

[0015] In an exemplary embodiment, based on the change situation of the data in the first data segment, obtaining the long-term change situation of the first position includes:

[0016] Perform linear fitting on the first data segment to obtain the first slope of the fitted line of the first data segment;

[0017] Taking the first position as the demarcation point, perform linear fitting on the data before the first position and the data after the first position in the first data segment respectively to obtain the second slope of the fitted line of the data before and the third slope of the fitted line of the data after;

[0018] According to the first slope and the slope difference, obtain the long-term change situation of the first position; the long-term change situation is directly proportional to the slope difference and directly proportional to the first slope; the slope difference is the difference between the second slope and the third slope.

[0019] In an exemplary embodiment, based on the difference between the actual value and the fitted value of the first position, obtaining the short-term volatility of the first position includes:

[0020] According to the difference between the actual value and the fitted value of the first position, obtain the trend deviation degree of the first position;

[0021] Obtain the difference in the trend deviation degree between two adjacent positions within the preset neighborhood range of the first position, and further obtain the overall situation of the difference in the trend deviation degree within the preset neighborhood range of the first position to obtain the short-term volatility of the first position.

[0022] In an exemplary embodiment, based on the difference between the degree of association of the overall data of each initial dimension with the overall data of other initial dimensions and the degree of association of the local data, combined with the number of associated initial dimensions associated with each initial dimension, obtain the leading role of each initial dimension in the ecological environment, including:

[0023] Obtain the first correlation degree between the first initial dimension and the second initial dimension, where the first correlation degree is the correlation degree between the overall sequence of dynamic change characteristics of the first initial dimension and the second initial dimension; the overall sequence of dynamic change characteristics includes the dynamic change characteristics of all positions corresponding to the initial dimension; the first initial dimension is any one of the initial dimensions, and the second initial dimension is any other initial dimension different from the first initial dimension;

[0024] Obtain the first local sequence of dynamic change characteristics corresponding to the first initial dimension and the second local sequence of dynamic change characteristics corresponding to the second initial dimension within the preset neighborhood range of the second position; the second position is the position of any parameter in the first initial dimension;

[0025] Obtain the second correlation degree between the first initial dimension and the second initial dimension at the second position, where the second correlation degree is the correlation degree between the first local sequence of dynamic change characteristics and the second local sequence of dynamic change characteristics;

[0026] Based on the difference between the first correlation degree and the second correlation degree, obtain the true variability of the second position of the first initial dimension;

[0027] Based on the true variability and the number of associated initial dimensions associated with the first initial dimension, obtain the leading role of the first initial dimension in the ecological environment.

[0028] In an exemplary embodiment, obtaining the true variability of the second position of the first initial dimension based on the difference between the first correlation degree and the second correlation degree includes:

[0029] Obtain the first correlation degree between the first initial dimension and each other initial dimension, and the difference between the first correlation degree and the second correlation degree between the first initial dimension and each other initial dimension at the second position, and calculate the average value to obtain the overall correlation difference of the second position of the first initial dimension with respect to all other initial dimensions;

[0030] Based on the overall correlation difference of the second position of the first initial dimension with respect to all other initial dimensions, obtain the true variability of the second position of the first initial dimension; the true variability is inversely proportional to the overall correlation difference.

[0031] In an exemplary embodiment, the process of obtaining the associated initial dimensions associated with the first initial dimension includes: taking the initial dimensions corresponding to the first correlation degrees greater than the preset threshold among the first correlation degrees between the first initial dimension and each other initial dimension as the associated initial dimensions associated with the first initial dimension;

[0032] Based on the described true variability and the number of associated initial dimensions associated with the first initial dimension, obtaining the leading role of the first initial dimension in the ecological environment includes:

[0033] Calculating the mean of the true variability of each position of the first initial dimension to obtain the overall true variability of the first initial dimension;

[0034] Based on the overall true variability of the first initial dimension and the number of associated initial dimensions associated with the first initial dimension, obtaining the leading role of the first initial dimension in the ecological environment; the leading role is directly proportional to the overall true variability and directly proportional to the number of associated initial dimensions.

[0035] In an exemplary embodiment, weighting the initial covariance matrix in the principal component analysis includes:

[0036] Constructing an ecological contribution matrix according to the ecological contribution degrees of each position in each initial dimension, and obtaining the transposed matrix of the ecological contribution matrix;

[0037] Calculating the product of the ecological contribution matrix, the transposed matrix and the initial covariance matrix to obtain the weighted covariance matrix.

[0038] In a second aspect of the present invention, there is provided an ecological environment parameter monitoring device, and the ecological environment parameter monitoring device includes a unit for executing the above-mentioned ecological environment parameter monitoring method.

[0039] In a third aspect of the present invention, there is provided an ecological environment parameter monitoring system, including: a memory and a processor; the memory is connected to the processor; the memory is used for storing program instructions; the processor is used for implementing the above-mentioned ecological environment parameter monitoring method when the program instructions are executed.

[0040] The present invention has the following beneficial effects: By analyzing the change trends of each position in the ecological environment parameter sequences of each initial dimension, the dynamic change characteristics of each position are obtained, which helps to capture the time trends and fluctuation laws of the ecological environment parameters and can comprehensively understand the dynamic evolution of the ecosystem. Then, the differences between the overall data correlation degrees and the local data correlation degrees of each initial dimension and other initial dimensions are analyzed, and combined with the number of associated initial dimensions associated with each initial dimension, the leading role of each initial dimension in the ecological environment is obtained, and the interaction relationship between the ecological environment parameters is further analyzed to provide support for dimensionality reduction. According to the leading role of each initial dimension in the ecological environment and the dynamic change characteristics of each position in each initial dimension, the ecological contribution degrees of each position in each initial dimension are obtained, and the ecological contribution degrees highlight the core parameter indicators. Finally, the main initial covariance matrix is weighted according to the ecological contribution degrees of each position in each initial dimension to realize the correction of the covariance matrix, reduce data redundancy through dimensionality reduction, significantly reduce the complexity of the ecological environment parameter data while maintaining the main characteristics of the data, and improve the data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of a method for monitoring ecological environment parameters provided by an embodiment of the present invention;

[0042] Figure 2 is a flowchart for obtaining the dynamic change characteristics provided by an embodiment of the present invention;

[0043] Figure 3 is a flowchart for obtaining the long-term change situation provided by an embodiment of the present invention;

[0044] Figure 4 is a flowchart for obtaining the short-term volatility provided by an embodiment of the present invention;

[0045] Figure 5 is a flowchart for obtaining the overall leading role provided by an embodiment of the present invention;

[0046] Figure 6 is a flowchart for obtaining the true variability provided by an embodiment of the present invention;

[0047] Figure 7 is a flowchart for calculating the leading role provided by an embodiment of the present invention;

[0048] Figure 8 is a flowchart for weighting the initial covariance matrix in the principal component analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes the specific implementation manner, structure, features and effects of the present invention in detail in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0050] 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. All data information collected in this application has been obtained with full consent and authorization, and the collection, use and processing of relevant information need to comply with relevant laws, regulations and standards in the relevant regions.

[0051] The application scenario of an ecological environment parameter monitoring method provided in this embodiment is as follows: A variety of sensors are set in the monitoring area to detect various types of ecological environment parameters. The specific types and quantities of sensors are set according to actual needs. For example: air quality sensors (such as: PM2.5 sensors, PM10 sensors), temperature sensors, humidity sensors, wind speed sensors, harmful gas sensors (such as: CO sensors, CO2 sensors, SO2 sensors, NO2 sensors, O3 sensors), water body dissolved oxygen sensors, soil moisture content sensors, etc. Representative sites are determined as monitoring points in the monitoring area, and the above various types of sensors are set at the monitoring points. Each sensor is signal-connected to a data processing device through a data transmission module, which can be a wireless signal connection or a wired signal connection. In an exemplary embodiment, the data transmission module is a wireless communication module powered by solar energy to ensure reliable signal transmission.

[0052] Each sensor collects ecological environment parameters at the same sampling frequency, and moreover, each sensor collects synchronously. Then, at a certain sampling moment, ecological environment parameters of each type can be obtained. The specific value of the sampling frequency is set according to actual needs. For example: once an hour. The data collection period is set according to actual needs, such as 1 month. Therefore, the data collection period includes multiple sampling moments. Then, for any sensor, multiple ecological environment parameters can be obtained within the data collection period.

[0053] As Figure 1 shown, this embodiment provides an ecological environment parameter monitoring method, including:

[0054] Step 1: Obtain ecological environment parameter sequences of multiple initial dimensions, and obtain the dynamic change characteristics of each position according to the change trends of each position in the ecological environment parameter sequences;

[0055] Step 2: Based on the differences between the correlation degrees of the overall data and the local data of each initial dimension with other initial dimensions, and combining the number of associated initial dimensions associated with each initial dimension, obtain the dominant role of each initial dimension in the ecological environment;

[0056] Step 3: According to the dominant role of each initial dimension in the ecological environment and the dynamic change characteristics of each position in each initial dimension, obtain the ecological contribution degree of each position in each initial dimension;

[0057] Step 4: Use the ecological contribution degree of each position in each initial dimension as a weight to weight the initial covariance matrix in the principal component analysis and reduce the dimension of the initial dimension.

[0058] The following is a specific description of each step.

[0059] Step 1: Obtain the ecological environment parameter sequences of multiple initial dimensions, and according to the change trends of each position in the ecological environment parameter sequences, obtain the dynamic change characteristics of each position.

[0060] Set various types of sensors as each initial dimension, and the data information of as many initial dimensions as there are types of sensors will be obtained.

[0061] Obtain the ecological environment parameter sequences of each initial dimension, that is, the ecological environment parameter sequences detected by various sensors. For the ecological environment parameter sequence of any one initial dimension, the ecological environment parameter sequence is composed of the ecological environment parameters at each sampling moment in time series. Moreover, the number of ecological environment parameters included in the ecological environment parameter sequences of each initial dimension is the same. Since the ecological environment parameter sequence is a data sequence composed of multiple data in time series, each position in the ecological environment parameter sequence represents each data point, and the ecological environment parameters at the same position in the ecological environment parameter sequences of each initial dimension are the data at the same sampling moment.

[0062] There are many ecological environment parameters to be monitored in the ecological environment. During the process of dimensionality reduction, when compressing high-dimensional data into a few key feature dimensions, it is necessary to maintain the core description ability of the ecological environment changes. By analyzing the dynamic change characteristics of the ecological environment parameters, identifying the time series trends and fluctuation rules of the parameters, it is possible to more comprehensively understand the dynamic evolution of the ecosystem in the monitoring area and the performance of the parameters in the ecological environment, capture the environmental change patterns, and timely identify potential ecological problems. Therefore, according to the change trends of each position in the ecological environment parameter sequence, obtain the dynamic change characteristics of each position.

[0063] In an exemplary embodiment, as Figure 2 shown, a specific acquisition process of the dynamic change characteristics is given, including:

[0064] Step 1-1: Segment the first ecological environment parameter sequence to obtain multiple data segments.

[0065] Since the processing procedures for the ecological environment parameter sequences of each initial dimension are the same, for the sake of convenience of description, the first initial dimension is set as any one of the initial dimensions below. The ecological environment parameter sequence of the first initial dimension is set as the first ecological environment parameter sequence.

[0066] Segment the first ecological environment parameter sequence to obtain multiple data segments. In an exemplary embodiment, in order to improve the accuracy and reliability of data processing, a specific segmentation method is given as follows:

[0067] The change of ecological environment parameters has natural periodic characteristics. Affected by the natural laws of the earth, through the periodic analysis of ecological environment parameters, the natural fluctuation patterns on a long time scale can be identified, providing a basis for the long-term monitoring and prediction of the ecological environment.

[0068] Therefore, use the fast Fourier transform to obtain the periodic characteristics of the first ecological environment parameter sequence. Specifically: use the fast Fourier transform to process the first ecological environment parameter sequence, then calculate the amplitude of each frequency, identify the frequency with the largest amplitude, and the corresponding period is the main period of the first ecological environment parameter sequence.

[0069] According to the obtained main period, segment the first ecological environment parameter sequence to obtain multiple data segments, and the length of each data segment is the data length corresponding to the main period.

[0070] Step 1-2: Based on the change situation of the data in the first data segment, obtain the long-term change situation of the first position.

[0071] The long-term change trend of ecological environment parameters, that is, the long-term change situation, can reflect the evolution of the ecosystem, predict future environmental changes, and can reveal the regular changes of ecological environment parameters (such as temperature, precipitation, pollutant concentration, etc.) over time, so as to identify potential environmental problems. For example, the long-term downward trend of water resources may indicate the arrival of a regional water crisis. Therefore, it is necessary to analyze the long-term change trend of ecological environment parameters.

[0072] For the sake of convenience of description, the first data segment is set as any one of the data segments in the first ecological environment parameter sequence, and the first position is the position of any one parameter in the first data segment, that is, the first position is any one data point in the first data segment.

[0073] In an exemplary embodiment, as Figure 3 shown, a specific acquisition process of the long-term change situation is given as follows:

[0074] Step 1-2-1: Perform linear fitting on the first data segment to obtain the first slope of the fitting line of the first data segment.

[0075] Perform linear fitting on the first data segment. Use the most common least squares method to plot the fitting line of the first data segment, and then obtain the slope of the fitting line of the first data segment, which is defined as the first slope. The first slope characterizes the overall change trend of the first data segment.

[0076] Step 1-2-2: Take the first position as the demarcation point, and perform linear fitting on the data before and after the first position in the first data segment respectively to obtain the second slope of the fitting line of the data before the first position and the third slope of the fitting line of the data after the first position.

[0077] In the first data segment, taking the first position as the demarcation point, the data in the first data segment can be divided into two parts. Among them, the data before the first position in the first data segment is used as the first segmented data, and the data after the first position in the first data segment is used as the second segmented data.

[0078] Use the least squares method to perform linear fitting on the first segmented data to obtain the slope of the fitting line, which is defined as the second slope; use the least squares method to perform linear fitting on the second segmented data to obtain the slope of the fitting line, which is defined as the third slope. The second slope characterizes the change trend of the data before the first position in the first data segment, and the third slope characterizes the change trend of the data after the first position in the first data segment.

[0079] It should be understood that if the first position is the first position in the first data segment, since there is no data before it, then the second slope is set to 0 at this time. If the first position is the second position in the first data segment, since there is only one data before it, then the second slope is also set to 0 at this time; similarly, if the first position is the last position or the penultimate position in the first data segment, then the third slope is set to 0 at this time.

[0080] Step 1-2-3: Obtain the long-term change situation of the first position according to the first slope and the slope difference.

[0081] Obtain the difference between the second slope and the third slope, which is defined as the slope difference. In an exemplary embodiment, the difference between the second slope and the third slope is specifically the absolute value of the difference between the second slope and the third slope.

[0082] Since the larger the absolute value of the difference between the second slope and the third slope, the greater the difference in the change trends of the data before and after the first position in the first data segment, and the greater the change in the long-term change trend of the first position, that is, the greater the long-term change situation. Since the first slope represents the overall change trend of the first data segment, the greater the overall change trend, the greater the long-term change situation of the first position. Therefore, the long-term change situation is proportional to the slope difference and proportional to the first slope.

[0083] In an exemplary embodiment, obtain the absolute value of the first slope, and then calculate the product of the absolute value of the first slope and the slope difference, and use this product as the long-term change situation of the first position.

[0084] By using the above method, the long-term change situations of each position in the first data segment are obtained, and then the long-term change situations of each position in each data segment are obtained.

[0085] Step 1-3: Based on the difference between the actual value and the fitted value of the first position, obtain the short-term volatility of the first position.

[0086] Analyzing the short-term fluctuation law of ecological environment parameters can reflect the changes in the ecological environment, predict and mitigate sudden environmental events, and usually reflects the direct impact of weather changes, climate phenomena or local natural events. For example, the short-term fluctuations of parameters such as temperature, humidity or air pollutant concentration can reveal instantaneous weather changes, temporary fluctuations in air quality, or the impacts brought by short-term climate phenomena (such as heatwaves, precipitation, etc.).

[0087] To make the short-term change characteristics of ecological environment parameters more obvious, based on the differences between the actual values and the fitted values of each position, obtain the short-term volatility of each position.

[0088] In an exemplary embodiment, as Figure 4 shown, the following is a specific acquisition process of the short-term volatility:

[0089] Step 1-3-1: Based on the difference between the actual value and the fitted value of the first position, obtain the trend deviation degree of the first position.

[0090] Since the first position is the position of any parameter in the first data segment, and the first data segment is any data segment in the first ecological environment parameter sequence, then it can be understood that the first position is the position of any parameter in the first ecological environment parameter sequence.

[0091] Obtain the actual value of the first position. The actual value of the first position is the numerical value of the ecological environment parameter at the first position in the first ecological environment parameter sequence, which is the actual numerical value detected by the sensor.

[0092] Obtain the fitted value at the first position. The fitted value is the value on the fitted straight line corresponding to the first position after linearly fitting the first data segment.

[0093] Then, there may be a certain difference between the actual value and the fitted value at the first position. The greater the difference, the greater the deviation of the fitted value from the actual value, that is, the greater the trend deviation degree at the first position. Therefore, based on the difference between the actual value and the fitted value at the first position, the trend deviation degree at the first position is obtained.

[0094] In an exemplary embodiment, calculate the absolute value of the difference between the actual value and the fitted value at the first position. This absolute value of the difference is defined as the trend deviation degree at the first position. Thus, the trend deviation degrees at all positions in the first ecological environment parameter sequence are obtained.

[0095] Step 1-3-2: Obtain the difference in trend deviation degrees between two adjacent positions within the preset neighborhood range of the first position, and further obtain the overall situation of the trend deviation degree difference within the preset neighborhood range of the first position to obtain the short-term volatility of the first position.

[0096] Preset a neighborhood range. The length of this preset neighborhood range is set according to the actual situation, such as 21 data points. Obtain the preset neighborhood range for each position. In an exemplary embodiment, each position is at the center of the corresponding preset neighborhood range. The short-term volatility of each position is characterized by the overall situation of the trend deviation degrees within the preset neighborhood range corresponding to each position.

[0097] It should be understood that for several starting positions in the first ecological environment parameter sequence, since the number of positions before them is very small and it is impossible to ensure that all data within the preset neighborhood range can be obtained centered on them, then all positions from them to the first position are taken as the first half of their preset neighborhood range, and the second half is still obtained in the original way. For example: If the preset neighborhood range is 21, then for the 5th position, normally, the data of 10 positions before and after it need to be obtained. Since there are only 4 positions before the 5th position, then obtain the first 4 positions of the 5th position, the 5th position, and the 10 positions after the 5th position as the preset neighborhood range of the 5th position. Another example: For the 1st position, normally, the data of 10 positions before and after it need to be obtained. Since there are no other positions before the 1st position, then obtain the 1st position and the 10 positions after the 1st position as the preset neighborhood range of the 1st position.

[0098] Similarly, for several positions at the end of the first ecological environment parameter sequence, since the number of positions after them is very small and it is impossible to ensure that all data within the preset neighborhood range can be obtained centered on them, then all positions from them to the last position are taken as the second half of their preset neighborhood range, while the first half is still obtained in the original way. For example: if the preset neighborhood range is 21, then for the fifth last position, normally, data of 10 positions before and after it need to be obtained. Since there are only 4 positions after the fifth last position, then the 4 positions after the fifth last position, the fifth last position, and the 10 positions before the fifth last position are taken as the preset neighborhood range of the fifth last position. Another example: for the last position, normally, data of 10 positions before and after it need to be obtained. Since there are no other positions after the last position, then the last position and the 10 positions before the last position are taken as the preset neighborhood range of the last position.

[0099] Obtain the difference in trend deviation degrees between two adjacent positions within the preset neighborhood range of the first position. In an exemplary embodiment, the difference in trend deviation degrees is the absolute value of the difference between the trend deviation degrees of two adjacent positions within the preset neighborhood range of the first position. It should be understood that the trend deviation degree of the first position is also involved in the calculation of the difference in trend deviation degrees between two adjacent positions within its preset neighborhood range.

[0100] Then, obtain the overall situation of the difference in trend deviation degrees within the preset neighborhood range of the first position. In an exemplary embodiment, the overall situation is the average value of the differences in trend deviation degrees, and the short-term volatility of the first position is obtained.

[0101] In an exemplary embodiment, the calculation formula for short-term volatility is as follows:

[0102] ;

[0103] Wherein, represents the short-term volatility of the th position; represents the number of positions within the preset neighborhood range of the th position; represents the trend deviation degree of the th position within the preset neighborhood range of the th position; represents the trend deviation degree of the th position within the preset neighborhood range of the th position.

[0104] represents the th position within the preset neighborhood range of the The absolute value of the difference between the trend deviation degree of a position and the trend deviation degree of the position.

[0105] Denote the average value of the absolute value of the difference in trend deviation degrees within the preset neighborhood range of a position, representing the short-term volatility of a position. The larger this value, the greater the short-term volatility.

[0106] Steps 1-4: Integrate the long-term change situation and short-term volatility of the first position to obtain the dynamic change characteristics of the first position.

[0107] Comprehensively consider the long-term change situation and short-term volatility of the first position to obtain the dynamic change characteristics of the first position. In an exemplary embodiment, normalize the long-term change situation of the first position and normalize the short-term volatility of the first position, and then calculate the product of the normalized long-term change situation of the first position and the normalized short-term volatility of the first position, and use this product as the dynamic change characteristics of the first position.

[0108] The normalization in this embodiment, as well as the norm normalization function, can be specifically set according to the actual situation. For example: the maximum-minimum normalization method can be adopted, or the following common methods can be used: , represents the processing object, and exp represents the exponential function with the natural constant e as the base.

[0109] Step 2: Based on the difference between the degree of association of each initial dimension with the overall data of other initial dimensions and the degree of association with the local data, and combined with the number of associated initial dimensions associated with each initial dimension, obtain the dominant role of each initial dimension in the ecological environment.

[0110] Based on the results of the dynamic characteristic analysis, further explore the internal correlations between ecological environment parameters, such as the impact of meteorological parameters on pollutant diffusion or the interaction between water quality indicators. Incorporate the dynamic change characteristics into the overall ecological environment system, construct an interaction model between parameters, and thus identify the key parameters that have the greatest impact on ecological environment monitoring.

[0111] In an exemplary embodiment, as Figure 5 shown, the process of obtaining the dominant role of each initial dimension in the ecological environment includes:

[0112] Step 2-1: Obtain the first degree of association between the first initial dimension and the second initial dimension.

[0113] For ease of explanation, assume that the second initial dimension is any other initial dimension different from the first initial dimension.

[0114] Obtain the overall sequence of dynamic change features of the first initial dimension, which is defined as the first overall sequence of dynamic change features. The first overall sequence of dynamic change features includes the dynamic change features of all positions of the first initial dimension, and is obtained by arranging the dynamic change features of all positions of the first initial dimension according to the sorting of each position.

[0115] Obtain the overall sequence of dynamic change features of the second initial dimension, which is defined as the second overall sequence of dynamic change features. The second overall sequence of dynamic change features includes the dynamic change features of all positions of the second initial dimension, and is obtained by arranging the dynamic change features of all positions of the second initial dimension according to the sorting of each position.

[0116] Obtain the first degree of association between the first initial dimension and the second initial dimension, that is, the degree of association between the first overall sequence of dynamic change features and the second overall sequence of dynamic change features.

[0117] The degree of association is the correlation situation between two overall sequences of dynamic change features, and can be calculated by calculation methods such as correlation coefficient and similarity. In an exemplary embodiment, a specific calculation process is provided as follows:

[0118] First, obtain the consistency of the dynamic change laws of the first overall sequence of dynamic change features and the second overall sequence of dynamic change features: In the monitoring and analysis of ecological environment parameters, the dynamic change laws between parameters and their consistency help to reveal the internal associations in the ecological environment system. The changes in the dynamic change features of parameters reflect their fluctuations over time and the connections between them, thereby extracting the correlations between ecological environment parameters:

[0119] ;

[0120] where represents the dynamic change consistency between the th initial dimension and the th initial dimension; represents the overall sequence of dynamic change features of the th initial dimension; represents the overall sequence of dynamic change features of the th initial dimension; represents the DTW distance (Dynamic Time Warping distance) between the overall sequence of dynamic change features of the th initial dimension and the overall sequence of dynamic change features of the th initial dimension, representing the difference between the ecological environment parameters of the two initial dimensions. The smaller the DTW distance, the more consistent the dynamic change laws of the ecological environment parameters of the two initial dimensions over time.

[0121] Then, obtain the parameter correlation between the first overall sequence of dynamic change features and the second overall sequence of dynamic change features, that is, ecological interactivity, which is also the degree of association: By analyzing the consistency of the dynamic change rules of the parameters, measure the association strength between the parameters:

[0122] ;

[0123] Wherein, represents the ecological interactivity, that is, the degree of association, between the th initial dimension and the th initial dimension; represents the correlation coefficient between the overall sequence of dynamic change features of the th initial dimension and the overall sequence of dynamic change features of the th initial dimension. In an exemplary embodiment, the correlation coefficient here is the Pearson correlation coefficient; represents a normalization function.

[0124] In other embodiments, the Pearson correlation coefficient or cosine similarity of the two overall sequences of dynamic change features can also be directly normalized as the degree of association between the two overall sequences of dynamic change features.

[0125] Through the above process, obtain the first degree of association between the first initial dimension and each of the other initial dimensions.

[0126] Step 2-2: Obtain the first local sequence of dynamic change features corresponding to the first initial dimension within the preset neighborhood range of the second position, and the second local sequence of dynamic change features corresponding to the second initial dimension.

[0127] The preset second position is the position of any parameter in the first initial dimension, and its essential meaning is the same as that of the first position in the above text, both representing the position of any parameter in the first initial dimension.

[0128] Since the preset neighborhood range of the second position contains multiple positions, and each position has a dynamic change feature, therefore, obtain multiple dynamic change features within the preset neighborhood range of the second position in the first initial dimension, that is, multiple dynamic change features corresponding to the first initial dimension within the preset neighborhood range of the second position. These dynamic change features form the first local sequence of dynamic change features according to the sorting of each position. Since the positions in each initial dimension correspond to each other, therefore, obtain multiple dynamic change features corresponding to the second initial dimension within the preset neighborhood range of the second position, that is, multiple dynamic change features at the corresponding positions in the second initial dimension within the preset neighborhood range of the second position. These dynamic change features form the second local sequence of dynamic change features according to the sorting of each position.

[0129] Step 2-3: Obtain the second correlation degree of the first initial dimension and the second initial dimension at the second position.

[0130] Obtaining the second correlation degree of the first initial dimension and the second initial dimension at the second position means obtaining the correlation degree between the first local sequence of dynamic change features and the second local sequence of dynamic change features. The process of obtaining the second correlation degree is the same as the method for obtaining the first correlation degree in Step 2-1, and will not be repeated here.

[0131] Through the above process, the second correlation degree of the first initial dimension and each other initial dimension at the second position can be obtained, and then the second correlation degree of the first initial dimension and each other initial dimension at each position can be obtained.

[0132] Step 2-4: Obtain the true variability of the second position of the first initial dimension according to the difference between the first correlation degree and the second correlation degree.

[0133] In ecological environment monitoring, systematic analysis of various ecological environment parameters to clarify which ecological environment parameters play a dominant role in environmental changes and which ecological environment parameter changes can significantly affect the state of the ecosystem not only helps to optimize the monitoring system but also improves the accuracy of decision-making. On this basis, dimensionality reduction processing is carried out to reduce data redundancy and improve data processing efficiency.

[0134] In the process of dimensionality reduction of ecological environment parameters, first, the accuracy of ecological environment parameters must be ensured. When there are abnormal fluctuations in the data values of the ecological environment parameters of a certain initial dimension, the reasons may be equipment failures, external factor interferences leading to real environmental changes, etc. In the case of equipment failures or data collection errors, it is unlikely that monitoring equipment across the entire ecological environment will malfunction simultaneously, which is accidental. When the environment truly changes, due to the interrelationships among ecological environment parameters, such as an increase in temperature leading to a decrease in air flow and less diffusion of pollutants, resulting in an increase in the concentration of a certain gas. Therefore, through the correlation relationships among ecological environment parameters, observe whether the ecological environment parameters represent real ecological environment changes. Then, according to the difference between the first correlation degree and the second correlation degree, obtain the true variability of the second position of the first initial dimension. In an exemplary embodiment, as Figure 6 shown, a specific process for obtaining the true variability is given:

[0135] Step 2-4-1: Obtain the first correlation degree between the first initial dimension and each other initial dimension, and the difference between the first initial dimension and the second correlation degree of each other initial dimension at the second position, and calculate the mean value to obtain the overall correlation difference of the second position of the first initial dimension with respect to all other initial dimensions.

[0136] For any other initial dimension, obtain the first degree of association between the first initial dimension and this other initial dimension, and the second degree of association between the first initial dimension and this other initial dimension at the second position. Then, obtain the difference between the first degree of association between the first initial dimension and this other initial dimension and the second degree of association between the first initial dimension and this other initial dimension at the second position, to obtain the association difference of the second position of the first initial dimension with respect to this other initial dimension. Then, calculate the average value of the association differences of the second position of the first initial dimension with respect to each of the other initial dimensions, to obtain the overall association difference of the second position of the first initial dimension with respect to all the other initial dimensions.

[0137] Step 2-4-2: Obtain the true variability of the second position of the first initial dimension based on the overall association difference of the second position of the first initial dimension with respect to all the other initial dimensions.

[0138] The smaller the overall association difference of the second position of the first initial dimension with respect to all the other initial dimensions, the less it indicates that the relationship of the first initial dimension has changed, which means that the first initial dimension can better represent the real ecological environment change, and the greater the true variability. Therefore, based on the overall association difference of the second position of the first initial dimension with respect to all the other initial dimensions, obtain the true variability of the second position of the first initial dimension, and the true variability is inversely proportional to the overall association difference.

[0139] In an exemplary embodiment, the calculation formula for the true variability is given as follows:

[0140] ;

[0141] Among them, represents the true variability of the th position of the th initial dimension; represents the number of other initial dimensions except the th initial dimension; represents the degree of association between the th initial dimension and the th initial dimension at the th position; here, is the general level as a reference in this formula; represents the overall association difference of the th position of the th initial dimension with respect to all the other initial dimensions. The smaller it is, the more it indicates that the th initial dimension represents the real ecological environment change, and the greater the true variability of the th initial dimension.

[0142] Step 2-5: Obtain the leading role of the first initial dimension in the ecological environment based on the true variability and the number of associated initial dimensions associated with the first initial dimension.

[0143] By analyzing the impact of ecological environment parameters on the ecological environment, key ecological environment parameters important for ecological environment monitoring can be obtained. Each ecological environment parameter in ecological environment monitoring represents the change of the ecological environment. If there is a strong association between a certain ecological environment and multiple other ecological environment parameters, it indicates that this ecological environment parameter is an important evaluation index in the monitoring of the ecological environment and is representative. At the same time, the overall level of the true variability of ecological environment parameters represents the true environmental change, which is of great significance for evaluating the ecological environment.

[0144] First, it is necessary to obtain the associated initial dimensions associated with the first initial dimension. In an exemplary embodiment, a preset threshold is set, and this preset threshold is used to compare with the first association degree between the first initial dimension and each other initial dimension to obtain the associated initial dimensions having an association relationship with the first initial dimension. The numerical range of this preset threshold is 0-1, and the specific value is set according to actual needs, such as 0.7.

[0145] Compare the first association degree between the first initial dimension and each other initial dimension with the preset threshold, obtain the first association degrees greater than the preset threshold from them, and then obtain the initial dimensions corresponding to the first association degrees greater than the preset threshold. The obtained these initial dimensions have a strong association with the first dimension. Take the obtained these initial dimensions as the associated initial dimensions associated with the first initial dimension. Then, obtain the number of associated initial dimensions. Therefore, the more the number of associated initial dimensions, the more representative the corresponding initial dimension is, and the more significant it is for evaluating the ecological environment.

[0146] Based on the true variability and the number of associated initial dimensions associated with the first initial dimension, obtain the leading role of the first initial dimension in the ecological environment. In an exemplary embodiment, as Figure 7 shown, the following gives a specific obtaining process of the leading role:

[0147] Step 2-5-1: Calculate the mean value of the true variability of each position of the first initial dimension to obtain the overall true variability of the first initial dimension;

[0148] Step 2-5-2: Based on the overall true variability of the first initial dimension and the number of associated initial dimensions associated with the first initial dimension, obtain the leading role of the first initial dimension in the ecological environment.

[0149] The overall true variability of the first initial dimension reflects the overall situation of the true variability of each position in the first initial dimension. The greater the overall true variability, the more dominant the first initial dimension is in the ecological environment. Therefore, the dominant role is proportional to the overall true variability and proportional to the number of associated initial dimensions.

[0150] In an exemplary embodiment, the following gives the calculation formula for the dominant role:

[0151] ;

[0152] where, represents the dominant role of the -th initial dimension in the ecological environment; represents the number of associated initial dimensions of the -th initial dimension. The larger this value, the more strongly associated initial dimensions the -th initial dimension has with the ecological environment. Then, through the change of the ecological environment parameters of the -th initial dimension, the possible changes of more ecological environment parameters in the ecological environment can be inferred, and the greater the dominant role in the ecological environment; represents the number of ecological environment parameters in the ecological environment parameter sequence of the -th initial dimension.

[0153] represents the overall true variability of the -th initial dimension. The larger the value, the more reference significance the -th initial dimension has for monitoring the ecological environment.

[0154] Step 3: Obtain the ecological contribution degree of each position in each initial dimension according to the dominant role of each initial dimension in the ecological environment and the dynamic change characteristics of each position in each initial dimension.

[0155] Analyzing the contribution degree of each initial dimension to the ecological environment can highlight the core indicators in the ecological environment and reduce data redundancy. If the short-term dynamic change characteristics of the initial dimension are strong, it indicates that the ecological environment may change and needs to be focused on for analysis. Therefore, according to the dominant role of each initial dimension in the ecological environment and the dynamic change characteristics of each position in each initial dimension, the ecological contribution degree of each position in each initial dimension is obtained. In an exemplary embodiment, the following gives the calculation formula for the ecological contribution degree:

[0156] ;

[0157] where, represents the ecological contribution degree of the -th position of the -th initial dimension, Indicates the dynamical variation characteristics of the position of the

[0158] By using the above process, the ecological contribution degrees of each position of each initial dimension are obtained.

[0159] Step 4: Use the ecological contribution degrees of each position in each initial dimension as weights to weight the initial covariance matrix in the principal component analysis, and reduce the dimension of the initial dimension.

[0160] To reduce the complexity and redundancy of ecological environment parameters while retaining the most representative information on ecological environment changes, data reduction is performed on ecological environment parameters. Ecological environment monitoring usually involves a large number of ecological environment parameters, and there are often strong correlations and redundant information among these ecological environment parameters. Directly processing these high-dimensional data will increase the computational difficulty and storage pressure. Data reduction can extract the most important features in ecological environment parameters. Principal component analysis can effectively discover the principal components in the data and reduce the dimension of the feature space by projecting high-dimensional data into a new low-dimensional space, thereby removing redundancy and improving data processing efficiency. Therefore, using the ecological contribution degrees of each position in each initial dimension as weights and introducing them into the principal component analysis to weight the initial covariance matrix in the principal component analysis and reduce the dimension of the initial dimension can preferentially retain the information of ecological environment parameters that have a greater impact on the ecological environment during the dimension reduction process.

[0161] In an exemplary embodiment, as Figure 8 shown, a specific implementation process for weighting the initial covariance matrix in the principal component analysis is given:

[0162] Step 4-1: According to the ecological contribution degrees of each position in each initial dimension, construct an ecological contribution degree matrix and obtain the transpose matrix of the ecological contribution degree matrix.

[0163] Construct an ecological environment parameter matrix according to each position in each initial dimension. For example, each row of the matrix represents each initial dimension, and each column represents each position. Then, according to the ecological contribution degrees of each position in each initial dimension, construct an ecological contribution degree matrix in the same format. The ecological contribution degree matrix characterizes that different positions in different initial dimensions have different influences on the dimension reduction result.

[0164] Step 4-2: Calculate the product of the ecological contribution degree matrix, the transpose matrix, and the initial covariance matrix to obtain the weighted covariance matrix.

[0165] After obtaining the weighted covariance matrix, dimensionality reduction is performed according to the implementation process of the principal component analysis algorithm. Specifically: calculate the eigenvalues and eigenvectors of the weighted covariance matrix, select the eigenvectors corresponding to the top preset number of largest eigenvalues as the principal components, and project the original data onto these principal components, thereby achieving dimensionality reduction. This not only retains the key information of the original data but also takes into account the contribution degrees of each initial dimension through weighting, thus achieving efficient and high-quality dimensionality reduction for ecological environment monitoring.

[0166] Therefore, the dimensionality reduction process compresses the data in the ecological environment parameters from a high-dimensional space to a lower dimension, retains the most representative feature information in the parameters, and reflects the main change trends of the ecological environment, thereby achieving high-quality monitoring of the ecological environment parameters. The dimensionality-reduced data has a lower dimension and can more intuitively display the change laws of the ecological environment parameters. The principal components after dimensionality reduction can be used as new monitoring indicators for visualization and subsequent analysis. Through forms such as charts, heatmaps, and trend charts, the changes in the ecological environment parameters can be clearly displayed, helping decision-makers quickly identify the key driving factors of environmental changes. The dimensionality-reduced data can not only assist in analyzing the current ecological environment state but also be used for long-term dynamic monitoring and trend analysis. Through time series analysis of the principal components, the periodicity, long-term trends, and sudden changes in the ecological environment parameter changes can be identified, thereby providing early warnings for the ecological environment.

[0167] This embodiment also provides an ecological environment parameter monitoring device, which includes units for executing the above-mentioned ecological environment parameter monitoring method. Each unit can be a software unit corresponding to each method step or a hardware circuit for executing each method step, which is not limited in this embodiment.

[0168] This embodiment also provides an ecological environment parameter monitoring system, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-mentioned ecological environment parameter monitoring method embodiment when the program instructions are executed.

[0169] In an exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the steps in the above-mentioned ecological environment parameter monitoring method embodiment.

[0170] It should be noted that: the above-mentioned sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0171] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized.

Claims

1. An ecological environment parameter monitoring method, characterized in that, Including: Obtain ecological environment parameter sequences of multiple initial dimensions, and obtain the dynamic change characteristics of each position according to the change trends of each position in the ecological environment parameter sequences; Based on the differences between the overall data correlation degree and the local data correlation degree of each initial dimension and other initial dimensions, and combining the number of associated initial dimensions associated with each initial dimension, obtain the leading role of each initial dimension in the ecological environment; the correlation degree is obtained from the dynamic change characteristics; According to the leading role of each initial dimension in the ecological environment and the dynamic change characteristics of each position in each initial dimension, obtain the ecological contribution degree of each position in each initial dimension; Use the ecological contribution degree of each position in each initial dimension as a weight to weight the initial covariance matrix in the principal component analysis and reduce the dimension of the initial dimension; According to the change trends of each position in the ecological environment parameter sequence, obtain the dynamic change characteristics of each position, including: Segment the first ecological environment parameter sequence to obtain multiple data segments; the first ecological environment parameter sequence is the ecological environment parameter sequence of the first initial dimension, and the first initial dimension is any one of the initial dimensions; Based on the change situation of the data in the first data segment, obtain the long-term change situation of the first position; the first data segment is any data segment in the first ecological environment parameter sequence; the first position is the position of any parameter in the first data segment; Based on the difference between the actual value and the fitted value of the first position, obtain the short-term volatility of the first position; Fuse the long-term change situation and the short-term volatility of the first position to obtain the dynamic change characteristics of the first position; Calculation formula of ecological contribution degree: ； Among them, represents the ecological contribution degree of the -th position of the -th initial dimension, represents the dynamic change characteristics of the -th position of the -th initial dimension, represents the leading role of the -th initial dimension on the ecological environment, and norm represents the normalization function; Based on the differences between the overall data correlation degree and the local data correlation degree of each initial dimension and other initial dimensions, and combining the number of associated initial dimensions associated with each initial dimension, obtain the leading role of each initial dimension in the ecological environment, including: Obtain the first correlation degree between the first initial dimension and the second initial dimension, where the first correlation degree is the correlation degree between the overall sequence of dynamic change characteristics of the first initial dimension and the second initial dimension; the overall sequence of dynamic change characteristics includes the dynamic change characteristics of all positions corresponding to the initial dimension; the first initial dimension is any one of the initial dimensions, and the second initial dimension is any other initial dimension different from the first initial dimension; Obtain the first local sequence of dynamic change characteristics corresponding to the first initial dimension and the second local sequence of dynamic change characteristics corresponding to the second initial dimension within the preset neighborhood range of the second position; the second position is the position of any parameter in the first initial dimension; Obtain the second correlation degree between the first initial dimension and the second initial dimension at the second position, where the second correlation degree is the correlation degree between the first local sequence of dynamic change characteristics and the second local sequence of dynamic change characteristics; According to the difference between the first correlation degree and the second correlation degree, obtain the true variability of the second position of the first initial dimension; Based on the described true variability and the number of associated initial dimensions associated with the first initial dimension, obtain the dominant role of the first initial dimension in the ecological environment.

2. The ecological environment parameter monitoring method according to claim 1, characterized in that Based on the change situation of the data in the first data segment, obtain the long-term change situation of the first position, including: Perform a linear fit on the first data segment to obtain the first slope of the fitted line of the first data segment; Taking the first position as the demarcation point, perform a linear fit on the data before the first position and the data after the first position in the first data segment respectively, to obtain the second slope of the fitted line of the previous data and the third slope of the fitted line of the subsequent data; Based on the first slope and the slope difference, obtain the long-term change situation of the first position; the long-term change situation is directly proportional to the slope difference and directly proportional to the first slope; the slope difference is the difference between the second slope and the third slope.

3. The ecological environment parameter monitoring method according to claim 2, characterized in that, Based on the difference between the actual value and the fitted value of the first position, obtain the short-term volatility of the first position, including: Based on the difference between the actual value and the fitted value of the first position, obtain the trend deviation degree of the first position; Obtain the difference in the trend deviation degrees of two adjacent positions within the preset neighborhood range of the first position, and further obtain the overall situation of the difference in the trend deviation degrees of the preset neighborhood range of the first position, to obtain the short-term volatility of the first position.

4. The ecological environment parameter monitoring method according to claim 1, characterized in that Based on the difference between the first association degree and the second association degree, obtain the true variability of the second position of the first initial dimension, including: Obtain the difference between the first association degree of the first initial dimension and each other initial dimension, and the second association degree of the first initial dimension and each other initial dimension at the second position, and calculate the mean value to obtain the overall association difference of the second position of the first initial dimension with respect to all other initial dimensions; Based on the overall association difference of the second position of the first initial dimension with respect to all other initial dimensions, obtain the true variability of the second position of the first initial dimension; the true variability is inversely proportional to the overall association difference.

5. The ecological environment parameter monitoring method according to claim 1, characterized in that, The process of obtaining the associated initial dimensions associated with the first initial dimension includes: taking the initial dimensions corresponding to the first association degrees greater than the preset threshold among the first association degrees of the first initial dimension and each other initial dimension as the associated initial dimensions associated with the first initial dimension; Based on the described true variability and the number of associated initial dimensions associated with the first initial dimension, obtain the dominant role of the first initial dimension in the ecological environment, including: Calculate the mean value of the true variabilities of each position of the first initial dimension to obtain the overall true variability of the first initial dimension; Based on the overall true variability of the first initial dimension and the number of associated initial dimensions associated with the first initial dimension, obtain the dominant role of the first initial dimension in the ecological environment; the dominant role is directly proportional to the overall true variability and directly proportional to the number of associated initial dimensions.

6. The ecological environment parameter monitoring method according to claim 1, characterized in that, Weight the initial covariance matrix in the principal component analysis, including: Based on the ecological contribution degrees of each position in each initial dimension, construct an ecological contribution degree matrix, and obtain the transposed matrix of the ecological contribution degree matrix; Calculate the product of the ecological contribution degree matrix, the transposed matrix, and the initial covariance matrix to obtain the weighted covariance matrix.

7. An ecological environment parameter monitoring device, characterized in that, The ecological environment parameter monitoring device includes a unit for executing the ecological environment parameter monitoring method according to any one of claims 1-6.

8. An ecological environment parameter monitoring system, characterized in that it includes: A memory and a processor; The memory is connected to the processor; The memory is used for storing program instructions; The processor is used for implementing the ecological environment parameter monitoring method according to any one of claims 1-6 when the program instructions are executed.

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