Environment governance monitoring data transmission verification method based on big data

Abstract

The invention discloses an environmental governance monitoring data transmission verification method based on big data, and relates to the technical field of environmental governance monitoring data transmission verification, and the method comprises the following steps: analyzing the relevance between different environmental governance monitoring data based on historical environmental governance monitoring data; analyzing the environmental governance monitoring data with relevance to obtain relevance characteristics of the environmental governance monitoring data; carrying out coding transmission on the environmental governance monitoring data without relevance; verifying the transmission of the environmental governance monitoring data based on the correlation characteristics and the coding transmission; the method is used for solving the problem that environment monitoring data cannot be effectively transmitted in real time due to the fact that transmission verification is carried out mostly in a data encryption transmission mode in an existing environment monitoring data transmission verification technology.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring data transmission verification, and specifically provides a method for verifying the transmission of environmental governance monitoring data based on big data. Background Art

[0002] The environmental monitoring data transmission verification technology refers to a technical system that comprehensively applies various technical means to ensure the integrity, authenticity, reliability, and security of environmental monitoring data during the transmission process. Its core goal is to ensure the credibility of the entire data link from the collection end to the receiving end.

[0003] Existing environmental monitoring data transmission verification technologies usually encrypt environmental monitoring data into encrypted ciphertext for transmission and then decrypt it to obtain the original data to prevent the environmental monitoring data from being tampered with during transmission. However, since there is a large amount of environmental monitoring data, performing encryption operations on all of them and then decrypting and verifying is not conducive to the real-time transmission of environmental monitoring data. For example, in the patent application with the publication number CN119004550A, a method for verifying the transmission of environmental governance monitoring data based on big data is disclosed. This solution prevents the environmental monitoring data from being tampered with through data encryption transmission, and at the same time verifies the data subset through a data verification model after decryption. However, the specific verification process is not given, and the verification process for the accuracy, integrity, rationality, and logical consistency of the data subset is not clear. Existing environmental monitoring data transmission verification technologies also mostly use data encryption transmission for transmission verification, resulting in the problem that environmental monitoring data cannot be effectively transmitted in real time. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the existing technology to some extent. By normalizing historical environmental governance monitoring data to obtain normalized monitoring data, then analyzing the correlation between different environmental governance monitoring data based on the normalized monitoring data, and further analyzing the environmental governance monitoring data with correlation to obtain the correlation characteristics of the environmental governance monitoring data. At the same time, encoding and transmitting the environmental governance monitoring data without correlation, then receiving the environmental governance monitoring data, verifying the environmental governance monitoring data based on the correlation characteristics, receiving the environmental governance monitoring data again, verifying the environmental governance monitoring data based on the encoding transmission, and finally judging whether there is an abnormality in the environmental governance monitoring data during the transmission process based on the verification results, so as to solve the problem that existing environmental monitoring data transmission verification technologies mostly use data encryption transmission for transmission verification, resulting in the inability to effectively transmit environmental monitoring data in real time.

[0005] To achieve the above object, in a first aspect, the present application provides a method for verifying the transmission of environmental governance monitoring data based on big data, including the following steps:

[0006] Analyze the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data;

[0007] Analyze the correlated environmental governance monitoring data to obtain the correlation characteristics of the environmental governance monitoring data;

[0008] Encode and transmit the environmental governance monitoring data that does not have a correlation;

[0009] Verify the transmission of the environmental governance monitoring data based on the correlation characteristics and the encoding transmission.

[0010] Further, analyzing the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data includes the following sub-steps:

[0011] Normalize the historical environmental governance monitoring data to obtain normalized monitoring data;

[0012] Analyze the correlation between different environmental governance monitoring data based on the normalized monitoring data.

[0013] Further, normalizing the historical environmental governance monitoring data to obtain normalized monitoring data includes the following sub-steps:

[0014] Obtain the historical environmental governance monitoring data, named historical monitoring data;

[0015] Assign a data number to the environmental governance monitoring data, represented by the symbol P i where i is a non-zero natural number and i is the serial number of P;

[0016] Number the historical monitoring data belonging to P i represented by the symbol D(i,n), where n is a non-zero natural number and (i,n) is the serial number of D. One piece of historical monitoring data records a copy of D(i,n) corresponding to all values of i and the recording time, and the recording time is used to statistically integrate the D(i,n) recorded at the same time;

[0017] For any P i , obtain the minimum and maximum values in D(i,n), marked as min(D n ) and max(D n );

[0018] Calculate the normalized monitoring data of D(i,n) through the formula where G(i,n) is the normalized monitoring data of D(i,n).

[0019] Further, analyzing the correlation between different environmental governance monitoring data based on normalized monitoring data includes the following sub-steps:

[0020] Set a dynamic reference, marked as j, where j is a non-zero natural number and initially 1 greater than i. Starting with i = 1, obtain all G(i,n) and G(j,n);

[0021] Through the formula Calculate the sample covariance between G(i,n) and G(j,n), where S ij is the sample covariance, max(n) is the maximum value of n, is the average value of G(i,n), is the average value of G(j,n);

[0022] Through the formula Calculate the sample standard deviation of G(i,n), where S i is the sample standard deviation of G(i,n);

[0023] Through the formula Calculate the sample standard deviation of G(j,n), where S j is the sample standard deviation of G(j,n);

[0024] Through the formula Calculate P i and P j The correlation coefficient between them, where R ij is the correlation coefficient;

[0025] Compare the correlation coefficient with the first correlation threshold. If the correlation coefficient is less than the first correlation threshold, output a signal of insufficient correlation; otherwise, output a signal of sufficient correlation. If a signal of sufficient correlation is output, mark that there is a correlation between P i and P j ; otherwise, mark that there is no correlation between P i and P j ;

[0026] Increase the value of j by one and analyze the correlation coefficient between G(i,n) and G(j,n) again until j = max(n). Then increase i by one, initialize j to a value 1 greater than i, and analyze again until the correlation coefficients between all pairs of environmental governance monitoring data are calculated and compared with the first correlation threshold.

[0027] Further, analyzing the environmental governance monitoring data with correlation to obtain the correlation characteristics of the environmental governance monitoring data includes the following sub-steps:

[0028] For any P iWhen analyzing, mark the serial number i as f, query the number of environmental governance monitoring data related to P f and name it the associated quantity. Determine whether the associated quantity is greater than or equal to the first quantity threshold. If so, output an associated valid signal; if not, output an associated invalid signal;

[0029] If an associated valid signal is output, then mark the serial number i of the data number P f of the environmental governance monitoring data related to P i as h;

[0030] Establish a plane rectangular coordinate system with D(f,n) as the X-axis and D(h,n) as the Y-axis, named the associated characteristic graph. When D(f,n) is the X-axis and D(h,n) is the Y-axis, the same n represents the same recording time, and when n is the same, D(f,n) and D(h,n) form a coordinate point in the associated characteristic graph;

[0031] Conduct discrete regression analysis on the associated characteristic graph, use the least squares method to determine the best regression function, obtain the data association function, and each value of h corresponds to a data association function;

[0032] Find the minimum value and the maximum value of the residuals between different coordinate points in the associated characteristic graph and the data association function, name them the minimum residual and the maximum residual respectively, and mark the closed interval formed between the minimum residual and the maximum residual as the residual range;

[0033] The said data association function and the residual range are the associated characteristics between P f and P h ;

[0034] Furthermore, the encoding and transmission of environmental governance monitoring data without association include the following sub-steps:

[0035] For any P i , if there is no environmental governance monitoring data associated with P i or an associated invalid signal is output, then mark P i as unassociated data;

[0036] For any unassociated data, convert the unassociated data into Unicode encoding and mark it as the initial encoding;

[0037] Obtain the first digit in the initial encoding and mark it as HS;

[0038] Calculate the hash value of the initial encoding to obtain the V-th encoding, where V is a non-zero natural number and V is initially 1;

[0039] Mark the V in the V - th encoding as E, and determine whether E is equal to HS. If so, mark the E - th encoding as the transmission encoding; if not, calculate the hash value of the E - th encoding to obtain the V - th encoding. At this time, V is E + 1, and make the judgment again until the transmission encoding is obtained;

[0040] Transmit the unassociated data and the transmission encoding based on dual - channel transmission.

[0041] Furthermore, based on the association characteristics and encoding transmission, the verification of the transmission of environmental governance monitoring data includes the following sub - steps:

[0042] Receive the environmental governance monitoring data and verify the environmental governance monitoring data based on the association characteristics;

[0043] Receive the environmental governance monitoring data and verify the environmental governance monitoring data based on the encoding transmission;

[0044] Judge whether there is an abnormality in the transmission process of the environmental governance monitoring data based on the verification result.

[0045] Furthermore, receiving the environmental governance monitoring data and verifying the environmental governance monitoring data based on the association characteristics includes the following sub - steps:

[0046] Receive the environmental governance monitoring data, query whether the environmental governance monitoring data has association characteristics. If it exists, mark the environmental governance monitoring data as associated data;

[0047] When analyzing any associated data, mark it as target data, obtain the association characteristics corresponding to the target data, and at the same time obtain another associated data except the target data in the association characteristics, and mark it as reference data. Each target data corresponds to at least two reference data;

[0048] Substitute the reference data into the corresponding data association function, and mark the obtained data as verification data;

[0049] Calculate the difference between the target data and the verification data, and mark it as the verification residual;

[0050] Query whether all the verification residuals are within the corresponding residual ranges. If so, output a normal signal for the associated verification data; if not, output an abnormal signal for the associated verification data.

[0051] Furthermore, receiving the environmental governance monitoring data and verifying the environmental governance monitoring data based on the encoding transmission includes the following sub - steps:

[0052] Receive the environmental governance monitoring data and verify the environmental governance monitoring data carrying the transmission encoding based on the encoding transmission;

[0053] Name the environmental governance monitoring data carrying the transmission encoding as the data to be verified;

[0054] Extract the transmission encoding in the data to be verified, convert the remaining part to Unicode encoding and mark it as the data to be analyzed;

[0055] Obtain the first digit in the data to be analyzed, name it the hash count, and mark it as HX;

[0056] Calculate the hash value of the data to be analyzed to obtain the Vth encoding;

[0057] Mark the V in the Vth encoding as E, and determine whether E is equal to HX. If so, mark the Eth encoding as the encoding to be verified. If not, calculate the hash value of the Eth encoding to obtain the Vth encoding. At this time, V is E + 1, and make the judgment again until the encoding to be verified is obtained;

[0058] Compare whether the encoding to be verified is the same as the transmission encoding. If so, output a signal indicating that the encoding verification data is normal. If not, output a signal indicating that the encoding verification data is abnormal.

[0059] Furthermore, judging whether there is an abnormality in the environmental governance monitoring data during the transmission process based on the verification result includes the following sub-steps:

[0060] If a signal indicating that the encoding verification data is abnormal is output, mark that an abnormality occurs in the encoding to be verified during the transmission process;

[0061] Count the number of times of continuously outputting signals indicating that the associated verification data is abnormal, and mark it as the abnormal count;

[0062] Compare the abnormal count with the first abnormal threshold. If the abnormal count is less than the first abnormal threshold, output a transmission abnormality signal. Otherwise, output a relationship abnormality signal;

[0063] If a transmission abnormality signal is output, mark that an abnormality occurs in the target data during the transmission process;

[0064] If a relationship abnormality signal is output, mark that an abnormality occurs in the numerical values of the target data and the reference data is detected.

[0065] Advantages of the present invention: By normalizing historical environmental governance monitoring data, the present invention obtains normalized monitoring data, then analyzes the correlation between different environmental governance monitoring data based on the normalized monitoring data, and further analyzes the correlated environmental governance monitoring data to obtain the correlation characteristics of the environmental governance monitoring data. Then, the environmental governance monitoring data is received, and the environmental governance monitoring data is verified based on the correlation characteristics. At the same time, based on the verification result, it is judged whether there is an abnormality in the transmission process of the environmental governance monitoring data. The advantage is that there are a large number of environmental governance monitoring data, and there are environmental governance monitoring data that are correlated with each other. Through their correlation, it can be verified whether the received environmental governance monitoring data is normal, eliminating the process of data encryption and decryption, enabling real-time data transmission, and at the same time being able to preliminarily judge whether the environmental governance monitoring data has been tampered with or whether the environmental governance monitoring data has detected an abnormality, improving the effectiveness of the transmission verification of the environmental governance monitoring data and the real-time nature of the transmission of the environmental governance monitoring data;

[0066] The present invention encodes and transmits the environmental governance monitoring data that does not have a correlation, then receives the content of the encoded transmission, verifies the environmental governance monitoring data based on the encoded transmission, and finally judges whether there is an abnormality in the transmission process of the environmental governance monitoring data based on the verification result. The advantage is that for the environmental governance monitoring data that does not have a correlation, it is verified through multi-layer hash calculation, with a short processing time and strong security, further improving the effectiveness of the transmission verification of the environmental governance monitoring data and the real-time nature of the transmission of the environmental governance monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flowchart of the steps of the method of the present invention;

[0068] Figure 2 is the first correlation characteristic diagram of the present invention;

[0069] Figure 3 is the second correlation characteristic diagram of the present invention;

[0070] Figure 4 is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0072] Example 1, please refer toFigure 1 As shown in the figure, the present application provides a method for verifying the transmission of environmental governance monitoring data based on big data, including the following steps:

[0073] Step S1, analyze the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data; Step S1 includes the following sub-steps:

[0074] Step S101, perform normalization processing on historical environmental governance monitoring data to obtain normalized monitoring data;

[0075] Step S101 includes the following sub-steps:

[0076] Step S1011, obtain historical environmental governance monitoring data, named historical monitoring data;

[0077] Step S1012, assign a data number to the environmental governance monitoring data, represented by the symbol P i , where i is a non-zero natural number and i is the serial number of P;

[0078] In practical applications, the environmental governance monitoring data in this embodiment specifically refers to atmospheric environmental monitoring data. Because the changes generated by the reaction between gases usually strictly conform to chemical formulas, and the gases generated by the reaction of each mole of gas are fixed values, therefore, when performing correlation analysis later, the linear correlation can be directly evaluated. Moreover, there are other non-gas indicators in the environmental governance monitoring data, and there may also be a linear correlation between them; due to the large variety of environmental governance monitoring data, only a small part of the gases are listed in this embodiment as environmental governance monitoring data for illustration. In this embodiment, nitrogen oxides, ozone, sulfur dioxide, PM2.5, and visibility are used. The historical monitoring data is the historical data of nitrogen oxides, ozone, sulfur dioxide, PM2.5, and visibility. Through numbering, P 1 to P 5 are nitrogen oxides, ozone, sulfur dioxide, PM2.5, and visibility in sequence;

[0079] Step S1013, number the historical monitoring data belonging to P i , represented by the symbol D(i,n), where n is a non-zero natural number and (i,n) is the serial number of D. A piece of historical monitoring data records a copy of D(i,n) corresponding to all values of i and the recording time. The recording time is used to statistically integrate the D(i,n) recorded at the same time;

[0080] Step S1014, for any P i , obtain the minimum value and the maximum value in D(i,n), and mark them as min(D n ) and max(D n );

[0081] Step S1015, through the formula calculate the normalized monitoring data of D(i,n), where G(i,n) is the normalized monitoring data of D(i,n);

[0082] In practical applications, there are 1286 pieces of data in each P i , and they are numbered from D(i,1) to D(i,1286). When n is the same, it means that D(i,n) is the historical monitoring data recorded at the same moment; taking P 1 as an example, the min(D n ) in D(1,n) is obtained as 26.7 μg / m 3 , and the max(D n ) is 48.8 μg / m 3 . Among them, when n = 62, D(1,62) is 42.5 μg / m 3 , calculate to get G(1,62) = 0.7149, and the calculation result is reserved to four decimal places. Similarly, calculate each G(i,n);

[0083] Step S102, analyze the correlation between different environmental governance monitoring data based on the normalized monitoring data;

[0084] Step S102 includes the following sub-steps:

[0085] Step S1021, set a dynamic reference, marked as j, where j is a non-zero natural number and initially 1 greater than i. Starting from i = 1, obtain all G(i,n) and G(j,n);

[0086] Step S1022, through the formula calculate the sample covariance between G(i,n) and G(j,n), where S ij is the sample covariance, max(n) is the maximum value of n, is the average value of G(i,n), is the average value of G(j,n);

[0087] Step S1023, through the formula calculate the sample standard deviation of G(i,n), where S i is the sample standard deviation of G(i,n);

[0088] Step S1024, through the formula calculate the sample standard deviation of G(j,n), where S j is the sample standard deviation of G(j,n);

[0089] Step S1025, through the formula Calculate P i and P j to obtain the correlation coefficient, where R ij is the correlation coefficient;

[0090] Step S1026: Compare the correlation coefficient with the first correlation threshold. If the correlation coefficient is less than the first correlation threshold, output a signal indicating insufficient correlation; otherwise, output a signal indicating sufficient correlation. If a signal indicating sufficient correlation is output, mark that there is a correlation between P i and P j ; otherwise, mark that there is no correlation between P i and P j ;

[0091] Step S1027: Increment the value of j by one and analyze the correlation coefficient between G(i,n) and G(j,n) again. After j = max(n), increment i by one and initialize j to a value one greater than i, and then perform the analysis again until the correlation coefficients between all pairs of environmental governance monitoring data are calculated and compared with the first correlation threshold;

[0092] In practical applications, this step is to analyze whether there is a correlation between P i and other environmental governance monitoring data. With i unchanged, by increasing j in sequence, the correlation between P i and all other environmental governance monitoring data can be analyzed once. After j reaches the maximum, increment i by one and perform the correlation analysis based on another P i ; In this embodiment, the calculated correlation coefficient is the Pearson correlation coefficient, so the calculation process is not described in detail here. Generally, when the Pearson correlation coefficient reaches above 0.8, it is considered to have a correlation, so the first correlation threshold is set to 0.8.

[0093] Step S2: Analyze the environmentally governed monitoring data with correlations to obtain the correlation characteristics of the environmentally governed monitoring data. Step S2 includes the following sub-steps:

[0094] Step S201: When analyzing any P i , mark the serial number i as f, query the number of environmentally governed monitoring data that are correlated with P f , name it the correlation quantity, and determine whether the correlation quantity is greater than or equal to the first quantity threshold. If so, output a signal indicating valid correlation; if not, output a signal indicating invalid correlation;

[0095] Step S202: If a signal indicating valid correlation is output, mark the serial number i of the data number P f of the environmentally governed monitoring data that is correlated with P i as h;

[0096] In practical applications, taking P 4 as an example, at this time i = 4, that is, f = 4, query the number of environmental governance monitoring data related to P 4 . Among P 1 to P 5 , there is a relationship between P 3 and P 5 . Therefore, the associated quantity is obtained as 2. The first quantity threshold is set to 2. If the associated quantity is 1, it cannot be verified through multiple relationships during subsequent transmission verification. Therefore, the first quantity threshold is set to 2. Since the associated quantity is 2, an associated valid signal is output, and the sequence numbers of P 3 and P 5 are marked as h, that is, h = 3 or 5;

[0097] Please refer to Figures 2 to 3 as shown. In step S203, a plane rectangular coordinate system is established with D(f, n) as the X-axis and D(h, n) as the Y-axis, named the association characteristic graph. When D(f, n) is the X-axis and D(h, n) is the Y-axis, the same n represents the same recording time. When n is the same, D(f, n) and D(h, n) form a coordinate point in the association characteristic graph;

[0098] In step S204, discrete regression analysis is performed on the association characteristic graph. The least squares method is used to determine the best regression function to obtain the data association function. Each value of h corresponds to a data association function;

[0099] In step S205, the minimum and maximum values of the residuals between different coordinate points in the association characteristic graph and the data association function are found, named the minimum residual and the maximum residual respectively. The closed interval formed between the minimum residual and the maximum residual is marked as the residual range;

[0100] In step S206, the data association function and the residual range are the association characteristics between P f and P h ;

[0101] In practical applications, two association characteristic graphs are constructed with D(4, n) as the X-axis and D(3, n) and D(5, n) as the Y-axis respectively, corresponding to the association between P 4 and P 3 and the association between P 4 and P 5 respectively. They are named the first association characteristic graph and the second association characteristic graph in sequence. The first association characteristic graph constructed is as shown in Figure 2 and the third association characteristic graph is as shown in Figure 3As shown, when performing discrete regression analysis, a linear regression model is selected through the least squares method for analysis. The data association functions of the first association characteristic diagram and the second association characteristic diagram are named the first data association function and the second data association function respectively. It is analyzed that the first data association function is Y1 = 0.4472×X - 0.0331, and the second data association function is Y2 = -0.1541X + 13.506, where Y1 is the concentration of sulfur dioxide, Y2 is the visibility, and X is the concentration of PM2.5; the residual range of the first association characteristic diagram is [-2.1871, 2.6761], and the residual range of the second association characteristic diagram is [-2.3367, 2.4284].

[0102] Step S3, encode and transmit the environmental governance monitoring data that does not have relevance; Step S3 includes the following sub-steps:

[0103] Step S301, for any P i , if there is no environmental governance monitoring data associated with P i or an association invalid signal is output, then mark P i as unassociated data;

[0104] Step S302, for any unassociated data, convert the unassociated data into Unicode encoding and mark it as the initial encoding;

[0105] Step S303, obtain the first digit in the initial encoding and mark it as HS;

[0106] Step S304, calculate the hash value of the initial encoding to obtain the Vth encoding, where V is a non-zero natural number and V is initially 1;

[0107] Step S305, mark V in the Vth encoding as E, and determine whether E is equal to HS. If so, mark the Eth encoding as the transmission encoding. If not, calculate the hash value of the Eth encoding to obtain the Vth encoding. At this time, V is E + 1, and make the judgment again until the transmission encoding is obtained;

[0108] Step S306, transmit the unassociated data and the transmission encoding based on dual-channel transmission;

[0109] In practical applications, taking P 1 as an example, P 1 is only associated with P 2 , the number of associations is 1, which is less than 2. Therefore, an association invalid signal is output, that is, nitrogen oxides are unassociated data. Suppose the concentration of nitrogen oxides is measured as 42.5 μg / m 3 , and the transmitted concentration of nitrogen oxides is "42.5 μg / m 3”, the content within the quotation marks is the transmitted content, and the initial code obtained after conversion is "\u0034\u0032\u002e\u0035\u03bc\u0067\u002f\u006d\u00b3". The first digit is 0. Since HS determines the number of hash calculations, HS is not 0. HS takes the non-zero digit at the first place, that is, HS is 3. Calculate the hash value of the initial code to obtain the first code. At this time, E = 1, and E is not equal to HS. Therefore, calculate the hash value of the first code to obtain the second code. At this time, E = 2, and so on until 3 hash value calculations are performed. The final transmitted code obtained is "e6ae11e5008d0235fa646dd42a066fbd". Through different channels, "42.5μg / m 3 ” and “e6ae11e5008d0235fa646dd42a066fbd” are transmitted.

[0110] Step S4, based on the correlation characteristics and coding transmission, verify the transmission of environmental governance monitoring data; Step S4 includes the following sub-steps:

[0111] Step S401, receive the environmental governance monitoring data and verify the environmental governance monitoring data based on the correlation characteristics;

[0112] Step S401 includes the following sub-steps:

[0113] Step S4011, receive the environmental governance monitoring data, query whether the environmental governance monitoring data has correlation characteristics. If it exists, mark the environmental governance monitoring data as related data;

[0114] When analyzing any related data, mark it as the target data, obtain the correlation characteristics corresponding to the target data, and at the same time obtain another related data in the correlation characteristics except the target data, and mark it as the reference data. Each target data corresponds to at least two reference data;

[0115] Step S4013, substitute the reference data into the corresponding data correlation function, and mark the obtained data as the verification data;

[0116] Step S4014, calculate the difference between the target data and the verification data, and mark it as the verification residual;

[0117] Step S4015, query whether the verification residuals are all within the corresponding residual ranges. If so, output the normal signal of the correlation verification data. If not, output the abnormal signal of the correlation verification data;

[0118] In practical applications, when the received PM2.5 content is “62μg / m 3”, PM2.5 is relevant, so “62 μg / m 3 ” is relevant data, and the received sulfur dioxide concentration is obtained as “28.1179 μg / m 3 ”, the visibility is “4.3688 km”, among which “28.1179 μg / m 3 ” and “4.3688 km” are both reference data, “62 μg / m 3 ” is the target data. The first data correlation function between PM2.5 and sulfur dioxide is Y1 = 0.4472×X - 0.0331, and the second data correlation function between PM2.5 and visibility is Y2 = -0.1541X + 13.506. Substituting respectively, the verification data Y1 = 27.6933 and the verification data Y2 = 3.9518 are obtained. Further calculation gives the verification residuals as 28.1179 - Y1 = 0.4246 and 4.3688 - Y2 = 0.417. The corresponding residual ranges are [-2.1871, 2.6761], and the residual range corresponding to Y2 is [-2.3367, 2.4284]. By comparison, it is found that the verification residuals are all within the corresponding residual ranges, and a normal signal of correlation verification data is output;

[0119] Step S402, receive environmental governance monitoring data and verify the environmental governance monitoring data based on encoding transmission;

[0120] Step S402 includes the following sub-steps:

[0121] Step S4021, receive environmental governance monitoring data and verify the environmental governance monitoring data carrying the transmission encoding based on encoding transmission;

[0122] Step S4022, name the environmental governance monitoring data carrying the transmission encoding as the data to be verified;

[0123] Step S4023, extract the transmission encoding in the data to be verified, convert the remaining part to Unicode encoding and mark it as the data to be analyzed;

[0124] Step S4024, obtain the first digit in the data to be analyzed, name it the hash count, and mark it as HX;

[0125] Step S4025, calculate the hash value of the data to be analyzed to obtain the Vth encoding;

[0126] Step S4026, mark V in the Vth encoding as E, and determine whether E is equal to HX. If so, mark the Eth encoding as the encoding to be verified. If not, calculate the hash value of the Eth encoding to obtain the Vth encoding. At this time, V is E + 1, and make the judgment again until the encoding to be verified is obtained;

[0127] Step S4027, compare whether the encoding to be verified is the same as the transmitted encoding. If so, output a signal indicating that the encoding verification data is normal; if not, output a signal indicating that the encoding verification data is abnormal.

[0128] In practical applications, the process of encoding transmission for environmental governance monitoring data is basically the same as that in Step S3, and will not be specifically described in this embodiment. If the encoding to be verified finally calculated based on the received data to be verified is the same as the transmitted encoding, it means that there is no abnormality in the data transmission process, but it is impossible to determine whether the environmental governance monitoring data obtained by monitoring deviates from the normal range.

[0129] Step S403, based on the verification result, determine whether there is an abnormality in the transmission process of the environmental governance monitoring data.

[0130] Step S403 includes the following sub-steps:

[0131] Step S4031, if a signal indicating that the encoding verification data is abnormal is output, mark that an abnormality occurs in the encoding to be verified during the transmission process.

[0132] Step S4032, count the number of times of continuously outputting signals indicating that the associated verification data is abnormal, and mark it as the number of abnormalities.

[0133] Step S4033, compare the number of abnormalities with the first abnormality threshold. If the number of abnormalities is less than the first abnormality threshold, output a transmission abnormality signal; otherwise, output a relationship abnormality signal.

[0134] Step S4034, if a transmission abnormality signal is output, mark that an abnormality occurs in the target data during the transmission process.

[0135] Step S4035, if a relationship abnormality signal is output, mark that the numerical values of the monitored target data and the reference data are abnormal.

[0136] In practical applications, if the number of abnormalities is small, it is regarded as an error or being tampered with during one transmission. If the number of abnormalities is large, it means that the actually monitored data is abnormal and not within the normal range. For example, a sharp increase in the emission of polluting gases during a certain period will cause the monitored values of various gases in the atmosphere to deviate from the normal. The setting of the first abnormality threshold is to exclude misjudgments caused by accidental factors. Usually, after the gases in the atmosphere deviate from the normal value, they will last for a period of time. Therefore, there is no specific requirement for the setting of the first abnormality threshold, as long as it is greater than 5.

[0137] Example 3, please refer to Figure 4 as shown in Figure 4The structural schematic diagram of an electronic device is exemplified. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The memory stores computer-readable instructions. The processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a method for verifying the transmission of environmental governance monitoring data based on big data are run to achieve the following functions: analyzing the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data; analyzing the correlated environmental governance monitoring data to obtain the correlation characteristics of the environmental governance monitoring data; encoding and transmitting the environmental governance monitoring data that does not have a correlation; and verifying the transmission of the environmental governance monitoring data based on the correlation characteristics and the encoding and transmission.

[0138] In addition, when the logic instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0139] Embodiment 4, this application also provides a computer-readable storage medium. This application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method for verifying the transmission of environmental governance monitoring data based on big data are run to achieve the following functions: analyzing the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data; analyzing the correlated environmental governance monitoring data to obtain the correlation characteristics of the environmental governance monitoring data; encoding and transmitting the environmental governance monitoring data that does not have a correlation; and verifying the transmission of the environmental governance monitoring data based on the correlation characteristics and the encoding and transmission.

[0140] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0141] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces, and the indirect coupling or communication connection of the system, module and unit can be in an electrical, mechanical or other form.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for verifying environmental governance monitoring data transmission based on big data, characterized in that: The steps include: Analyze the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data; Analyze the relevant environmental governance monitoring data to obtain the relevant characteristics of the environmental governance monitoring data; Encode and transmit environmental governance monitoring data that are not relevant; Based on the correlation characteristics and coded transmission, the transmission of environmental governance monitoring data is verified.

2. According to the big data-based environmental governance monitoring data transmission verification method of claim 1, it is characterized in that: Analyzing the correlation between different environmental governance monitoring data based on historical environmental governance monitoring data includes the following sub-steps: Normalize the historical environmental governance monitoring data to obtain normalized monitoring data; Analyze the correlation between different environmental governance monitoring data based on normalized monitoring data.

3. According to the big data-based environmental governance monitoring data transmission verification method of claim 2, it is characterized in that: Normalizing the historical environmental governance monitoring data to obtain normalized monitoring data includes the following sub-steps: Obtain historical environmental governance monitoring data, named historical monitoring data; The environmental governance monitoring data is assigned a data number, and the symbol P i Represents, where i is a non-zero natural number and i is the serial number of P; P i The historical monitoring data are numbered and represented by the symbol D(i,n), where n is a non-zero natural number and (i,n) is the sequence number of D. A historical monitoring data record contains a copy of D(i,n) corresponding to all values ​​of i and the recording time, and the recording time is used to statistically integrate D(i,n) recorded at the same time; For any P i , get the minimum and maximum values ​​in D(i,n), marked as min(D n ) and max(D n ); By formula Calculate the normalized monitoring data of D(i,n), where G(i,n) is the normalized monitoring data of D(i,n).

4. According to the big data-based environmental governance monitoring data transmission verification method of claim 3, it is characterized in that: Analyzing the correlation between different environmental governance monitoring data based on normalized monitoring data includes the following sub-steps: Set a dynamic reference, marked as j, where j is a non-zero natural number and is initially 1 greater than i. Starting with i=1, obtain all G(i,n) and G(j,n); By formula Calculate the sample covariance between G(i,n) and G(j,n), where S ij is the sample covariance, max(n) is the maximum value of n, is the average value of G(i,n), is the average value of G(j,n); By formula Calculate the sample standard deviation of G(i,n), where S i is the sample standard deviation of G(i,n); By formula Calculate the sample standard deviation of G(j,n), where S j is the sample standard deviation of G(j,n); By formula Calculate P i and P j The correlation coefficient between ij is the correlation coefficient; The correlation coefficient is compared with the first correlation threshold. If the correlation coefficient is less than the first correlation threshold, an insufficient correlation signal is output, otherwise a sufficient correlation signal is output; if a sufficient correlation signal is output, P is marked. i and P j If there is a correlation between them, otherwise mark P i and P j There is no correlation between them; Add 1 to the value of j and analyze the correlation coefficient between G(i,n) and G(j,n) again until j=max(n). Then add 1 to i and initialize j to a value 1 greater than i. Perform analysis again until the correlation coefficients between all environmental governance monitoring data are calculated and compared with the first correlation threshold.

5. According to the big data-based environmental governance monitoring data transmission verification method of claim 4, it is characterized in that: Analyzing the relevant environmental governance monitoring data to obtain the relevant characteristics of the environmental governance monitoring data includes the following sub-steps: For any P i When analyzing, mark the sequence number i as f and query the f The number of environmental governance monitoring data that are correlated is named as the correlation number, and whether the correlation number is greater than or equal to the first number threshold is judged, if so, a correlation valid signal is output, if not, a correlation invalid signal is output; If the output is associated with a valid signal, it will be f Data number P of the relevant environmental governance monitoring data i The serial number i is marked as h; A plane rectangular coordinate system is established with D(f,n) as the X-axis and D(h,n) as the Y-axis, which is named as the correlation characteristic diagram. When D(f,n) is the X-axis and D(h,n) is the Y-axis, the same n means the same recording time. When n is the same, D(f,n) and D(h,n) constitute a coordinate point in the correlation characteristic diagram. Discrete regression analysis is performed on the correlation characteristic diagram, and the best regression function is determined by the least square method to obtain the data correlation function. Each value of h corresponds to a data correlation function. Find the minimum and maximum values ​​of the residuals of different coordinate points in the correlation characteristic diagram and the data correlation function, name them as the minimum residual and the maximum residual, and mark the closed interval between the minimum residual and the maximum residual as the residual range; The data correlation function and the residual range are P f With P h The correlation characteristics between them.

6. According to the big data-based environmental governance monitoring data transmission verification method of claim 5, it is characterized in that: The encoding and transmission of non-correlated environmental governance monitoring data includes the following sub-steps: For any P i If there is no environmental governance monitoring data and P i If the P i Mark as no relevant data; For any unrelated data, convert the unrelated data into Unicode encoding and mark it as the initial encoding; Get the first digit in the initial code and mark it as HS; Calculate the hash value of the initial code to obtain the Vth code, where V is a non-zero natural number and V is initially 1; Mark V in the Vth code as E, and determine whether E is equal to HS. If so, mark the Eth code as the transmission code. If not, perform hash value calculation on the Eth code to obtain the Vth code. At this time, V is E+1, and the determination is repeated until the transmission code is obtained. The unrelated data and the transmission coding are transmitted based on the dual-channel transmission.

7. According to the big data-based environmental governance monitoring data transmission verification method of claim 6, it is characterized in that: Based on the correlation characteristics and coded transmission, the verification of the transmission of environmental governance monitoring data includes the following sub-steps: Receive environmental governance monitoring data and verify the environmental governance monitoring data based on correlation characteristics; Receive environmental governance monitoring data and verify the environmental governance monitoring data based on coded transmission; Based on the verification results, determine whether there are any anomalies in the transmission process of environmental governance monitoring data.

8. According to the big data-based environmental governance monitoring data transmission verification method of claim 7, it is characterized in that: Receiving environmental governance monitoring data and verifying the environmental governance monitoring data based on correlation characteristics includes the following sub-steps: Receive environmental governance monitoring data, query whether the environmental governance monitoring data has related characteristics, and if so, mark the environmental governance monitoring data as related data; When analyzing any related data, mark it as target data, obtain the related characteristics corresponding to the target data, and at the same time obtain another related data in the related characteristics except the target data and mark it as reference data. Each target data corresponds to at least two reference data. Substitute the reference data into the corresponding data association function and mark the solved data as verification data; Calculate the difference between the target data and the validation data, marked as validation residual; Check whether the verification residuals are all within the corresponding residual range. If so, output the associated verification data normal signal; if not, output the associated verification data abnormal signal.

9. According to the big data-based environmental governance monitoring data transmission verification method of claim 8, it is characterized in that: Receiving environmental governance monitoring data and verifying the environmental governance monitoring data based on coded transmission includes the following sub-steps: Receive environmental governance monitoring data, and verify the environmental governance monitoring data carrying the transmission code based on the coded transmission; The environmental governance monitoring data carrying the transmission code is named as data to be verified; Extract the transfer code from the data to be verified, convert the remaining part into Unicode code and mark it as data to be analyzed; Get the first digit in the data to be analyzed, name it the hash number, and mark it as HX; Perform hash value calculation on the data to be analyzed to obtain the Vth code; Mark V in the Vth code as E, and determine whether E is equal to HX. If so, mark the Eth code as the code to be verified. If not, calculate the hash value of the Eth code to obtain the Vth code. At this time, V is E+1, and the determination is repeated until the code to be verified is obtained. Compare whether the code to be verified is the same as the transmission code. If so, output a normal code verification data signal; if not, output an abnormal code verification data signal.

10. The method for verifying environmental governance monitoring data transmission based on big data according to claim 9 is characterized in that: Judging whether there are any anomalies in the transmission process of environmental governance monitoring data based on the verification results includes the following sub-steps: If the code verification data abnormal signal is output, it is marked that the code to be verified is abnormal during the transmission process; Count the number of times the abnormal signal of the associated verification data is output continuously, and mark it as the abnormal number; Compare the number of abnormalities with the first abnormality threshold, if the number of abnormalities is less than the first abnormality threshold, output a transmission abnormality signal, otherwise output a relationship abnormality signal; If a transmission abnormality signal is output, it marks that the target data is abnormal during the transmission process; If an abnormal relationship signal is output, it indicates that the values ​​of the target data and the reference data are abnormal.

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