Pollution visual data processing system and method applying cloud computing technology

Abstract

The invention discloses a pollution visual data processing system and method applying a cloud computing technology, and relates to the technical field of pollution data processing. Analyzing the correlation of the data between the monitoring stations, and determining the related monitoring station of each monitoring station; determining the contribution rate of a single monitoring station to all the monitoring stations and the contribution rate of all the monitoring stations to the single monitoring station based on the correlation of the data between the monitoring stations; determining the processing sequence of the atmospheric pollution data of the monitoring station by the cloud platform, generating a first processing result and a second processing result, and visually displaying the first processing result and the second processing result; the correlation of atmospheric pollution data among different monitoring stations is analyzed, part of monitoring station data is processed in real time, and the data processing burden of a cloud platform is reduced; real-time performance and safety are both considered, data of all monitoring stations are processed regularly, and the possibility that abnormal conditions are ignored at monitoring sites is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of pollution data processing, and in particular to a pollution visualization data processing system and method using cloud computing technology. Background Art

[0002] The atmospheric environment is closely related to human life. By monitoring atmospheric pollution, we can understand and grasp the pollution status in the environment, evaluate environmental quality and ecological risks, and provide a scientific basis for environmental protection and sustainable development. Under large-scale monitoring, a large number of standard monitoring stations and micro-monitoring stations are designed. The speed at which the monitoring stations obtain atmospheric pollution data is often very high, which requires the system to have extremely high processing efficiency and real-time performance. However, the cloud platform has limited parallel data processing capabilities and cannot take into account all monitoring stations at the first time. Therefore, how to improve the data processing effect of monitoring stations under large-scale monitoring has become an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to provide a pollution visualization data processing system and method using cloud computing technology to solve the problems raised in the above background technology.

[0004] In one aspect of the present invention, a method for processing pollution visualization data using cloud computing technology is provided, comprising:

[0005] S11, obtain air pollution data from monitoring stations;

[0006] S12, based on the air pollution data of the monitoring stations, analyzing the correlation of the data between the monitoring stations and determining the related monitoring stations of each monitoring station;

[0007] S13, based on the correlation of data between the monitoring stations, determining the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station;

[0008] S14, based on the cloud platform's parallel data processing capabilities and the correlation between data between monitoring stations, determine the cloud platform's processing order for the air pollution data of the monitoring stations, generate a first processing result and a second processing result and display them visually; the first processing result is a real-time processing result generated by the cloud platform using the air pollution data of some monitoring stations, and the second processing result is a processing result generated by the cloud platform using the air pollution data of all monitoring stations.

[0009] Air pollution data has a huge amount of data and high complexity, including various types of pollution factors, time series data, etc., which brings great pressure to data analysis and transmission. Air pollution data monitoring activities carried out in a large geographical area involve a large number of ground monitoring stations, making the characteristics of air pollution data more significant. In the case of limited cloud computing resources, in order to reflect the air pollution situation in real time, the data of some monitoring stations are analyzed to obtain the real-time air pollution situation, and the data of all monitoring stations are analyzed in spare time, taking into account the requirements of real-time and accuracy.

[0010] In step S12, analyzing the correlation of data between monitoring stations and determining the related monitoring stations of each monitoring station further comprises the following steps:

[0011] S20, executing steps S21 and S22 for i=1, 2, ..., n, where n is the number of monitoring stations, to obtain each monitoring station and a corresponding associated set, wherein the monitoring stations in the associated set are associated monitoring stations of each monitoring station;

[0012] S21, for the air pollution data A of the i-th monitoring station i , the atmospheric pollution data of the ith monitoring station is regressed through the atmospheric pollution data of other monitoring stations to obtain the first regression error e1, A i =w1A1+w2A2+…+w n A n , where w1, w2, …, w n are regression coefficients, A1, A2, …, A n Air pollution data from monitoring stations;

[0013] S22, if the regression coefficient is greater than the significance threshold, the monitoring station corresponding to the regression coefficient is added to the associated set C of the i-th monitoring station i If the regression coefficient w j If the regression coefficient is not greater than the significance threshold, then the atmospheric pollution data A of the monitoring station corresponding to the regression coefficient j Conduct significant monitoring: Air pollution data A j Remove from the input, regress the air pollution data of the i-th monitoring station through the air pollution data of other monitoring stations except i and j, and obtain the second regression error e2; j is different from i;

[0014] If the increase of e2 relative to e1 is If the air pollution data A is less than the set value, j Failed to pass the significance test, the jth monitoring station does not belong to the associated set C of the i-th monitoring station i ; If the increase in e2 If the air pollution data A is not less than the set value, jThrough saliency detection, the jth monitoring station is added to the associated set C of the i-th monitoring station. i middle.

[0015] When the regression coefficient w j If it is too small, it means that the air pollution data A j A i It may not contribute, or it may be due to atmospheric pollution data A j Its value is large, making the regression coefficient w j Too small; so the air pollution data A j Conduct a significance test to determine the air pollution data A j and the regression coefficient w j Is it really necessary?

[0016] In step S13, the step of determining the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station further includes the following steps:

[0017] S31, for the i-th monitoring station B i and associated set C i Monitoring station B k , k is different from i, use the associated set C i The air pollution data of all monitoring stations in the monitoring station B i The atmospheric pollution data is regressed to obtain the first regression value P1A i and the first regression accuracy Acc1, The first regression accuracy Acc1 is the value of all monitoring stations for a single monitoring station B. i Contribution rate B sum→i ; Change the value of i to obtain the contribution rate of all monitoring stations to any monitoring station; use the association set C i Central Monitoring Station B k The air pollution data of all monitoring stations except i The atmospheric pollution data is regressed to obtain the second regression value P2A i and the second regression accuracy Acc2, Calculate the monitoring station B based on Acc1 and Acc2 k For monitoring station B i Contribution rate B k→i , B k→i =Acc1-Acc2; when monitoring station B k Not at monitoring station B i The association set C i In the middle, B k→i If it is 0, no calculation is required;

[0018] S32, while keeping k unchanged, perform the above steps for different situations of i to obtain monitoring station B k Contribution rate to all monitoring stations B k→sum ,

[0019] S33, change the value of k, execute steps S21 and S22, and obtain the contribution rate of any monitoring station to all monitoring stations.

[0020] When some monitoring stations do not have air pollution data, partial information of the monitoring stations without air pollution data can be obtained through the air pollution data of existing monitoring stations. The amount of information of all other monitoring stations can be obtained from the existing monitoring stations as an indicator of the monitoring station, that is, the contribution rate of a single monitoring station to all monitoring stations. There is no need to obtain the specific information of all monitoring stations. The information of all monitoring stations can be obtained through some monitoring stations; the contribution rate of all monitoring stations to a single monitoring station is another indicator, reflecting the particularity of the information of a single monitoring station. If the contribution rate of all monitoring stations to a single monitoring station is low, it means that the information of a single monitoring station is difficult to obtain from other monitoring stations. It is more important to maintain real-time monitoring of a single monitoring station.

[0021] In step S14, determining the order in which the cloud platform processes the air pollution data of the monitoring station further includes the following steps:

[0022] S41, setting the initial temperature to T0, randomly selecting m monitoring stations from n monitoring stations as a set of candidate monitoring stations, where m is less than n, taking the set of candidate monitoring stations as an initial solution, determining the loss value L0 and contribution value F0 corresponding to the initial solution; and taking the initial solution as the current solution, and taking the initial temperature as the current temperature;

[0023] S42, for u=1, 2, ..., num, repeat steps S43 to S44; L is the number of cycles set;

[0024] S43, by generating disturbance on the basis of the current solution, changing the set of candidate monitoring stations, taking the set of candidate monitoring stations generated after the disturbance as a new solution, and determining the loss value L and contribution value F corresponding to the new solution;

[0025] S44, calculate the contribution value increment ΔF brought by the new solution. If the increment ΔF is less than 0, directly accept the new solution.

[0026] As the new current solution, if the increment ΔF is not less than 0, then according to the probability Accept the new solution as the new current solution, where T represents the current temperature;

[0027] S45, lowering the current temperature. If the current temperature is not less than the set temperature threshold, proceed to step S42; if the current temperature is less than the set temperature threshold, determine a set of candidate monitoring stations based on the current solution, obtain an optimization result, and complete the optimization of the air pollution data processing order of the monitoring station;

[0028] The cloud platform processes the air pollution data of the candidate monitoring station set in real time to generate the first processing result. For other monitoring stations outside the candidate monitoring station set, the air pollution data will not be processed for a long time, and abnormal situations cannot be discovered in time. Therefore, it is necessary to compensate for the optimization results to reduce the possibility of ignoring abnormal situations.

[0029] S46, compensating the optimization result, the cloud platform processes the air pollution data in real time according to the air pollution data processing order of the compensated monitoring station, and generates a first processing result.

[0030] It is characterized in that, in steps S41 and S43, the loss value and the contribution value are determined in the following manner:

[0031] In step S41, let the set of candidate monitoring stations corresponding to the initial solution be D, and the loss value L of the initial solution is calculated by the following formula: LOS i For monitoring station B i The loss rate of air pollution data, Where B D→i Denotes the set D for monitoring station B i The contribution rate of set D and monitoring station B is determined i The association set C i The intersection of i ∩D, use intersection C i The air pollution data of the monitoring station in ∩D is i Perform regression and the accuracy obtained is B D→i ; After flipping the loss value L, we get the contribution value F0;

[0032] In step S43, the loss value L and contribution value F corresponding to the new solution are generated in the same way as the loss value L0 and contribution value F0 of the initial solution;

[0033] When monitoring station B i When the air pollution data of monitoring station B is processed in real time by the cloud platform, no information is lost, so the loss rate is 0; i When the air pollution data of monitoring station B is not processed in real time by the cloud platform, the air pollution data of monitoring station B can only be reflected through the air pollution data of monitoring station B processed in real time by the cloud platform. i Part of the information will result in loss rate;

[0034] In step S43, the step of generating disturbance based on the current solution further includes the following steps:

[0035] The contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station are added together as the score of the single monitoring station; among the monitoring stations included in set D, the monitoring stations to exit set D are determined based on the scores and random numbers, and then, among the monitoring stations outside set D, the monitoring stations to join set D are determined based on the scores and random numbers.

[0036] Compensation for the optimization results also includes the following steps:

[0037] Obtain the set information and contribution value information of the candidate monitoring stations generated during the optimization process in addition to the optimization results. Let the set of candidate monitoring stations corresponding to the optimization results be G, and the set of candidate monitoring stations generated during the optimization process be G1, G2, ... GX, where X is the number of sets of candidate monitoring stations generated during the optimization process. Let the set formed by all monitoring stations be the full set U, and the complement of G in U be C. U G includes all monitoring stations outside the set G, and determines the complement set C of G1, G2, ..., GX in U respectively. U G1, C U G2, ..., C U GX, select sets GL1, GL2, ... GLX from sets G1, G2, ... GX so that the selected partial sets are in the complement set C of U U GL1, C U GL2, ..., C U GLX forms a union C U GL1∪C U GL2∪,…,∪C U GLX includes C U G, The monitoring station data contained in G, GL1, GL2, ... GLX are used as real-time processing objects of the cloud platform.

[0038] The cloud platform needs to monitor the air pollution data of all monitoring stations. The data generated by other monitoring stations outside the set G also need to be monitored regularly. This requires that the selected set must include all other monitoring stations outside the set G. After determining the set to be processed, it only needs to be updated regularly without repeated determination.

[0039] In another aspect of the present invention, there is provided a pollution visualization data processing system using cloud computing technology, comprising a ground monitoring station module, a cloud platform and a visualization module; the output end of the ground monitoring station module is connected to the input end of the cloud platform for acquiring air pollution data at the monitoring location; the output end of the cloud platform is connected to the visualization module for determining the processing order of the air pollution data of the monitoring station, performing real-time processing on the air pollution data of the monitoring station, and sending the processing results to the visualization module; the visualization module is used to render and draw the air pollution data and display the analysis results of the air pollution data.

[0040] The ground monitoring station module also includes an air pollution data acquisition unit, a data transmission unit and a time unit; the air pollution data acquisition unit is used to obtain the air pollution data of the monitoring location, the data transmission unit is used to send the air pollution data and the acquisition time to the cloud platform; the time unit is used to determine the acquisition time of the air pollution data. The cloud platform also includes an air pollution data real-time analysis unit, an air pollution data precision analysis unit, a data storage unit, a regression unit, an optimization unit, a compensation unit and an output unit; the air pollution data real-time analysis unit is used to perform real-time analysis on the air pollution data transmitted by the monitoring station; the air pollution data precision analysis unit is used to analyze the air pollution data transmitted by all monitoring stations in spare time; the data storage unit is used to store the air pollution data transmitted by the monitoring station; the regression unit is used to determine the air pollution data of the monitoring station, determine the correlation between the monitoring stations, the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station; the optimization unit is used to determine the best combination of monitoring station data that needs to be processed; the compensation unit is used to compensate the optimization result to ensure that all monitoring station data can be processed by the cloud platform; the output unit is used to send the air pollution data analysis result to the visualization module. The visualization module also includes a first visualization unit and a second visualization unit; the first visualization unit is used to render and draw the air pollution data analysis results in real time; the second visualization unit is used to render and draw the air pollution data analysis results of all monitoring stations.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are: analyzing the correlation of air pollution data between different monitoring stations, processing the data of some monitoring stations in real time, and reducing the data processing burden of the cloud platform; taking into account both real-time and security, regularly processing the data of all monitoring stations, and reducing the possibility of ignoring abnormal situations at the monitoring sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 It is a structural schematic diagram of a pollution visualization data processing system using cloud computing technology according to an embodiment of the present invention. DETAILED DESCRIPTION

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

[0045] In the embodiments of the present invention, please refer to Figure 1 , provides a pollution visualization data processing system using cloud computing technology, including: a ground monitoring station module, a cloud platform and a visualization module; the output end of the ground monitoring station module is connected to the input end of the cloud platform to obtain the air pollution data of the monitoring location; the output end of the cloud platform is connected to the visualization module to determine the processing order of the air pollution data of the monitoring station, process the air pollution data of the monitoring station in real time, and send the processing results to the visualization module; the visualization module is used to render and draw the air pollution data and display the analysis results of the air pollution data.

[0046] The ground monitoring station module also includes an air pollution data collection unit, a data transmission unit and a time unit; the air pollution data collection unit is used to obtain the air pollution data of the monitoring location, and the data transmission unit is used to send the air pollution data and the collection time to the cloud platform; the time unit is used to determine the collection time of the air pollution data.

[0047] The cloud platform also includes an air pollution data real-time analysis unit, an air pollution data precision analysis unit, a data storage unit, a regression unit, an optimization unit, a compensation unit and an output unit; the air pollution data real-time analysis unit is used to perform real-time analysis on the air pollution data transmitted by the monitoring station; the air pollution data precision analysis unit is used to analyze the air pollution data transmitted by all monitoring stations in spare time; the data storage unit is used to store the air pollution data transmitted by the monitoring station; the regression unit is used to determine the air pollution data of the monitoring station, determine the correlation between the monitoring stations, the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station; the optimization unit is used to determine the best combination of monitoring station data that needs to be processed; the compensation unit is used to compensate for the optimization results to ensure that all monitoring station data can be processed by the cloud platform; the output unit is used to send the air pollution data analysis results to the visualization module.

[0048] The visualization module also includes a first visualization unit and a second visualization unit; the first visualization unit is used to render and draw the air pollution data analysis results in real time; the second visualization unit is used to render and draw the air pollution data analysis results of all monitoring stations.

[0049] In an embodiment of the present invention, a pollution visualization data processing method using cloud computing technology is provided, comprising:

[0050] S11, obtain air pollution data from monitoring stations;

[0051] S12, based on the air pollution data of the monitoring stations, analyzing the correlation of the data between the monitoring stations and determining the related monitoring stations of each monitoring station;

[0052] The following steps are involved:

[0053] S20, executing steps S21 and S22 for i=1, 2, ..., n, where n is the number of monitoring stations, to obtain each monitoring station and a corresponding associated set, wherein the monitoring stations in the associated set are associated monitoring stations of each monitoring station;

[0054] S21, for the air pollution data A of the i-th monitoring station i , the atmospheric pollution data of the ith monitoring station is regressed through the atmospheric pollution data of other monitoring stations to obtain the first regression error e1, A i =w1A1+w2A2+…+w n A n , where w1, w2, …, w n are regression coefficients, A1, A2, …, A n Air pollution data from monitoring stations;

[0055] S22, if the regression coefficient is greater than the significance threshold, the monitoring station corresponding to the regression coefficient is added to the associated set C of the i-th monitoring station i If the regression coefficient w j If the regression coefficient is not greater than the significance threshold, then the atmospheric pollution data A of the monitoring station corresponding to the regression coefficient j Conduct significant monitoring: Air pollution data A j Remove from the input, regress the air pollution data of the i-th monitoring station through the air pollution data of other monitoring stations except i and j, and obtain the second regression error e2; j is different from i;

[0056] If the increase of e2 relative to e1 is If the air pollution data A is less than the set value, j Failed to pass the significance test, the jth monitoring station does not belong to the associated set C of the i-th monitoring station i ; If the increase in e2 If the air pollution data A is not less than the set value, j Through saliency detection, the jth monitoring station is added to the associated set C of the i-th monitoring station. i middle.

[0057] The air pollution data collected by the monitoring stations are in the form of time series. The air pollution data generated by each monitoring station at the same time point is regarded as a group of data. i As output, the data of other monitoring stations are used as input and input into the linear regression model. The regression coefficient of the linear regression model is solved iteratively with multiple sets of data at different time points. After the regression coefficient is solved iteratively, A is obtained. i The regression value of A is calculated i The regression value of A i The absolute error between them is the regression error;

[0058] The significance threshold can be set to 0.05, and the increase in e2 The setting value can be set to 5%. When the regression coefficient is less than 0.05, a significance test is performed. When the increase in e2 is less than 5%, the significance test fails and the regression coefficient is set to 0.

[0059] S13, based on the correlation of data between the monitoring stations, determining the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station, comprising the following steps:

[0060] S31, for the i-th monitoring station B i and associated set C i Monitoring station B k , k is different from i, use the associated set C iThe air pollution data of all monitoring stations in the monitoring station B i The atmospheric pollution data is regressed to obtain the first regression value P1A i and the first regression accuracy Acc1, The first regression accuracy Acc1 is the value of all monitoring stations for a single monitoring station B. i Contribution rate B sum→i ; Change the value of i to obtain the contribution rate of all monitoring stations to any monitoring station; use the association set C i Central Monitoring Station B k The air pollution data of all monitoring stations except i The atmospheric pollution data is regressed to obtain the second regression value P2A i and the second regression accuracy Acc2, Calculate the monitoring station B based on Acc1 and Acc2 k For monitoring station B i Contribution rate B k→i , B k→i =Acc1-Acc2; when monitoring station B k Not at monitoring station B i The association set C i In the middle, B k→i If it is 0, no calculation is required;

[0061] S32, while keeping k unchanged, perform the above steps for different situations of i to obtain monitoring station B k Contribution rate to all monitoring stations B k→sum ,

[0062] S33, change the value of k, execute steps S21 and S22, and obtain the contribution rate of any monitoring station to all monitoring stations.

[0063] For example, there are four monitoring stations B1, B2, B3 and B4, among which B2 and B3 belong to the associated set C1 of B1, and B2 belongs to the associated set C3 of B3, and B2 belongs to the associated set C4 of B4. For B1, by regressing B1 with B2 and B3, the contribution rate of all monitoring stations to the single monitoring station B1 can be obtained. The same is true for B2, B3 and B4. The monitoring station data in the associated set are used to regress and the contribution rate of all monitoring stations to the single monitoring stations B2, B3 and B4 can be obtained.

[0064] Contribution rate B of a single monitoring station B2 to all monitoring stations k→sumFirst, only B3 is used to regress B1. Compared with regressing B2 and B3 to B1, the regression accuracy will decrease. The decreased part is the contribution rate of B2 to B1. The contribution rate of B2 to B3 is determined in the same way. The contribution rate of B2 to B4 is 0. The contribution rate of B2 to B1, B3 and B4 is added to get the contribution rate of a single monitoring station B2 to all monitoring stations. k→sum ;

[0065] The contribution of individual monitoring stations B3 and B4 to all monitoring stations can be determined by repeating the process of B2.

[0066] S14, based on the cloud platform's parallel data processing capabilities and the correlation between data between monitoring stations, determine the cloud platform's processing order for the air pollution data of the monitoring stations, generate a first processing result and a second processing result and display them visually; the first processing result is a real-time processing result generated by the cloud platform using the air pollution data of some monitoring stations, and the second processing result is a processing result generated by the cloud platform using the air pollution data of all monitoring stations.

[0067] Determining the order in which the cloud platform processes the air pollution data of the monitoring station also includes the following steps:

[0068] S41, setting the initial temperature to T0, randomly selecting m monitoring stations from n monitoring stations as a set of candidate monitoring stations, where m is less than n, taking the set of candidate monitoring stations as an initial solution, determining the loss value L0 and contribution value F0 corresponding to the initial solution; and taking the initial solution as the current solution, and taking the initial temperature as the current temperature;

[0069] S42, for u=1, 2, ..., num, repeat steps S43 to S44; L is the number of cycles set;

[0070] S43, by generating disturbance on the basis of the current solution, changing the set of candidate monitoring stations, taking the set of candidate monitoring stations generated after the disturbance as a new solution, and determining the loss value L and contribution value F corresponding to the new solution;

[0071] S44, calculate the contribution value increment ΔF brought by the new solution. If the increment ΔF is less than 0, directly accept the new solution.

[0072] As the new current solution, if the increment ΔF is not less than 0, then according to the probability Accept the new solution as the new current solution, where T represents the current temperature;

[0073] S45, lowering the current temperature. If the current temperature is not less than the set temperature threshold, proceed to step S42; if the current temperature is less than the set temperature threshold, determine a set of candidate monitoring stations based on the current solution, obtain an optimization result, and complete the optimization of the air pollution data processing order of the monitoring station;

[0074] S46, compensating the optimization result, the cloud platform processes the air pollution data in real time according to the air pollution data processing order of the compensated monitoring station, and generates a first processing result.

[0075] Optionally, the initial temperature is set to 100, and the temperature decreases by 5 after each disturbance, that is, the current temperature changes from 100 to 95 after the first disturbance, and the temperature threshold is set to 10. When the current temperature is less than 10, the optimization of the atmospheric pollution data processing order is completed.

[0076] In step S41, let the set of candidate monitoring stations corresponding to the initial solution be D, and the loss value L of the initial solution is calculated by the following formula: LOS i For monitoring station B i The loss rate of air pollution data, Where B D→i Denotes the set D for monitoring station B i The contribution rate of set D and monitoring station B is determined i The association set C i The intersection of i ∩D, use intersection C i The air pollution data of the monitoring station in ∩D is i Perform regression and the accuracy obtained is B D→i ; After flipping the loss value L, we get the contribution value F0;

[0077] In step S43, the loss value L and contribution value F corresponding to the new solution are generated in the same way as the loss value L0 and contribution value F0 of the initial solution;

[0078] When cloud computing resources are limited, that is, the cloud platform can only process air pollution information from three monitoring stations at the same time, the set of candidate monitoring stations D corresponding to the initial solution includes B1, B2 and B3, then the loss rates of B1, B2 and B3 are 0, and the loss rate LOS4 of B4 is determined based on the regression results of B1, B2 and B3 on B4, and the final loss value L0 is LOS4; the way to flip the loss value can be selected according to the needs, and optionally, the contribution value is set to the inverse of the loss value, that is, a is the coefficient; or, the contribution value is set as a linear function of the loss value, that is, F0 = CON-bL0, CON is the bias, and b is the coefficient;

[0079] In step S43, the step of generating disturbance based on the current solution further includes the following steps:

[0080] The contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station are added together as the score of the single monitoring station; among the monitoring stations included in set D, the monitoring stations to exit set D are determined based on the scores and random numbers, and then, among the monitoring stations outside set D, the monitoring stations to join set D are determined based on the scores and random numbers.

[0081] For the sake of convenience, we continue with the previous example and add one more monitoring station B5. Now there are four monitoring stations B1, B2, B3, B4 and B5, and the set D corresponding to the initial solution includes monitoring stations B1, B2 and B3. Let f1, f2, f3, f4 and f5 represent the scores of B1, B2, B3, B4 and B5, f1 = B 1→sum +B sum→1 , f2, f3, f4 and f5 are similar to f1; let f max represents the maximum value among f1, f2, f3, f4 and f5, then three random numbers are generated in [0,f], corresponding to B1, B2 and B3 respectively; f is slightly larger than f max , to prevent the monitoring station from being always in set D; if the random number is between [0,f1], B1 continues to remain in set D, otherwise B1 exits set D. After judging the monitoring stations that need to exit set D among B1, B2, and B3, when the number of monitoring stations that exit set D is 1, a random number is generated in the interval [0,f4+f5]. When the random number is in [0,f4], B4 is added to set D to complete a disturbance; when the number of monitoring stations that exit set D is 2, B4 and B5 are added to the set together; when the number of monitoring stations that exit set D is 3, a random number is regenerated to judge the monitoring stations that need to exit set D among B1, B2, and B3.

[0082] Compensation for the optimization results also includes the following steps:

[0083] Obtain the set information and contribution value information of the candidate monitoring stations generated during the optimization process in addition to the optimization results. Let the set of candidate monitoring stations corresponding to the optimization results be G, and the set of candidate monitoring stations generated during the optimization process be G1, G2, ... GX, where X is the number of sets of candidate monitoring stations generated during the optimization process. Let the set formed by all monitoring stations be the full set U, and the complement of G in U be C. U G includes all monitoring stations outside the set G, and determines the complement set C of G1, G2, ..., GX in U respectively. U G1, C U G2, ..., C U GX, select sets GL1, GL2, ... GLX from sets G1, G2, ... GX so that the selected partial sets are in the complement of U C U GL1, C U GL2, ..., C UGLX forms a union C U GL1∪C U GL2∪,…,∪C U GLX includes C U G, The monitoring station data contained in G, GL1, GL2, ... GLX are used as real-time processing objects of the cloud platform.

[0084] The method of selecting the sets GL1, GL2, ... GLX can be sampling, or a greedy algorithm can be used to select the complement C of G in U as much as possible. U The set of monitoring stations in G. The cloud platform processes the monitoring station data contained in G, GL1, GL2, ... GLX in real time, and can determine the processing order according to the average contribution value of the sets GL1, GL2, ... GLX and the contribution value of G. When the contribution value of G is F and the average contribution value of the sets GL1, GL2, ... GLX is AF, a processing round can be generated according to the frequency of F to AF, and the order in which the cloud platform processes the monitoring station data is determined according to the processing rounds; when it is the turn of the set G, the cloud platform reads the monitoring station data in G for analysis; when it is the turn of the sets GL1, GL2, ... GLX, select a set from the sets GL1, GL2, ... GLX in order, and read the corresponding monitoring station data for analysis.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0086] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A pollution visualization data processing method using cloud computing technology, characterized in that: The following steps are involved: S11, obtain air pollution data from monitoring stations; S12, based on the air pollution data of the monitoring stations, analyzing the correlation of the data between the monitoring stations and determining the related monitoring stations of each monitoring station; S13, based on the correlation of data between the monitoring stations, determining the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station; S14, based on the cloud platform's parallel data processing capabilities and the correlation between data between monitoring stations, determine the cloud platform's processing order for the air pollution data of the monitoring stations, generate a first processing result and a second processing result and display them visually; the first processing result is a real-time processing result generated by the cloud platform using the air pollution data of some monitoring stations, and the second processing result is a processing result generated by the cloud platform using the air pollution data of all monitoring stations.

2. According to claim 1, a pollution visualization data processing method using cloud computing technology is characterized in that: In step S12, analyzing the correlation of data between monitoring stations and determining the related monitoring stations of each monitoring station further comprises the following steps: S20, executing steps S21 and S22 for i=1, 2, ..., n, where n is the number of monitoring stations, to obtain each monitoring station and a corresponding associated set, wherein the monitoring stations in the associated set are associated monitoring stations of each monitoring station; S21, for the air pollution data A of the i-th monitoring station i , the atmospheric pollution data of the ith monitoring station is regressed through the atmospheric pollution data of other monitoring stations to obtain the first regression error e1, A i =w1A1+w2A2+…+w n A n , where w1, w2, …, w n are regression coefficients, A1, A2, …, A n Air pollution data from monitoring stations; S22, if the regression coefficient is greater than the significance threshold, the monitoring station corresponding to the regression coefficient is added to the associated set C of the i-th monitoring station i If the regression coefficient w j If the regression coefficient is not greater than the significance threshold, then the atmospheric pollution data A of the monitoring station corresponding to the regression coefficient j Conduct significant monitoring: Air pollution data A j Remove from the input, regress the air pollution data of the i-th monitoring station through the air pollution data of other monitoring stations except i and j, and obtain the second regression error e2; j is different from i; If the increase of e2 relative to e1 is If the air pollution data A is less than the set value, j Failed to pass the significance test, the jth monitoring station does not belong to the associated set C of the i-th monitoring station i ; If the increase in e2 If the air pollution data A is not less than the set value, j Through saliency detection, the jth monitoring station is added to the associated set C of the i-th monitoring station. i middle.

3. The pollution visualization data processing method using cloud computing technology according to claim 2 is characterized in that: In step S13, the step of determining the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station further includes the following steps: S31, for the i-th monitoring station B i and associated set C i Monitoring station B k , k is different from i, use the associated set C i The air pollution data of all monitoring stations in the monitoring station B i The atmospheric pollution data is regressed to obtain the first regression value P1A i and the first regression accuracy Acc1, The first regression accuracy Acc1 is the value of all monitoring stations for a single monitoring station B. i Contribution rate B sum→i ; Change the value of i to obtain the contribution rate of all monitoring stations to any monitoring station; use the association set C i Central Monitoring Station B k The air pollution data of all monitoring stations except i The atmospheric pollution data is regressed to obtain the second regression value P2A i and the second regression accuracy Acc2, Calculate the monitoring station B based on Acc1 and Acc2 k For monitoring station B i Contribution rate B k→i , B k→i =Acc1-Acc2; when monitoring station B k Not at monitoring station B i The association set C i In the middle, B k→i If it is 0, no calculation is required; S32, while keeping k unchanged, perform the above steps for different situations of i to obtain monitoring station B k Contribution rate to all monitoring stations B k→sum , S33, change the value of k, execute steps S21 and S22, and obtain the contribution rate of any monitoring station to all monitoring stations.

4. The pollution visualization data processing method using cloud computing technology according to claim 3 is characterized in that: In step S14, determining the order in which the cloud platform processes the air pollution data of the monitoring station further includes the following steps: S41, setting the initial temperature to T0, randomly selecting m monitoring stations from n monitoring stations as a set of candidate monitoring stations, where m is less than n, taking the set of candidate monitoring stations as an initial solution, determining the loss value L0 and contribution value F0 corresponding to the initial solution; and taking the initial solution as the current solution, and taking the initial temperature as the current temperature; S42, for u=1, 2, ..., num, repeat steps S43 to S44; L is the number of cycles set; S43, by generating disturbance on the basis of the current solution, changing the set of candidate monitoring stations, taking the set of candidate monitoring stations generated after the disturbance as a new solution, and determining the loss value L and contribution value F corresponding to the new solution; S44, calculate the contribution value increment ΔF brought by the new solution. If the increment ΔF is less than 0, directly accept the new solution as the new current solution. If the increment ΔF is not less than 0, then Accept the new solution as the new current solution, where T represents the current temperature; S45, lowering the current temperature. If the current temperature is not less than the set temperature threshold, proceed to step S42; if the current temperature is less than the set temperature threshold, determine a set of candidate monitoring stations based on the current solution, obtain an optimization result, and complete the optimization of the air pollution data processing order of the monitoring station; S46, compensating the optimization result, the cloud platform processes the air pollution data in real time according to the air pollution data processing order of the compensated monitoring station, and generates a first processing result.

5. The method for processing pollution visualization data using cloud computing technology according to claim 4 is characterized in that: In steps S41 and S43, the loss value and contribution value are determined in the following manner: In step S41, let the set of candidate monitoring stations corresponding to the initial solution be D, and the loss value L of the initial solution is calculated by the following formula: LOS i For monitoring station B i The loss rate of air pollution data, Where B D→i Denotes the set D for monitoring station B i The contribution rate of set D and monitoring station B is determined i The association set C i The intersection of i ∩D, use intersection C i The air pollution data of the monitoring station in ∩D is i Perform regression and the accuracy obtained is B D→i ; After flipping the loss value L, we get the contribution value F0; In step S43, the loss value L and contribution value F corresponding to the new solution are generated in the same way as the loss value L0 and contribution value F0 of the initial solution; In step S43, the step of generating disturbance based on the current solution further includes the following steps: The contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station are added together as the score of the single monitoring station; among the monitoring stations included in set D, the monitoring stations to exit set D are determined based on the scores and random numbers, and then, among the monitoring stations outside set D, the monitoring stations to join set D are determined based on the scores and random numbers.

6. The pollution visualization data processing method using cloud computing technology according to claim 5 is characterized in that: Compensation for the optimization results also includes the following steps: Obtain the set information and contribution value information of the candidate monitoring stations generated during the optimization process in addition to the optimization results. Let the set of candidate monitoring stations corresponding to the optimization results be G, and the set of candidate monitoring stations generated during the optimization process be G1, G2, ... GX, where X is the number of sets of candidate monitoring stations generated during the optimization process. Let the set formed by all monitoring stations be the full set U, and the complement of G in U be C. U G includes all monitoring stations outside the set G, and determines the complement set C of G1, G2, ..., GX in U respectively. U G1, C U G2, ..., C U GX, select sets GL1, GL2, ... GLX from sets G1, G2, ... GX so that the selected partial sets are in the complement of U C U GL1, C U GL2, ..., C U GLX forms a union C U GL1∪C U GL2∪,…,∪C U GLX includes C U G, The monitoring station data contained in G, GL1, GL2, ... GLX are used as real-time processing objects of the cloud platform.

7. A pollution visualization data processing system using cloud computing technology, using a pollution visualization data processing method using cloud computing technology as described in any one of claims 1 to 6, characterized in that: include: A ground monitoring station module, a cloud platform and a visualization module; the output end of the ground monitoring station module is connected to the input end of the cloud platform to obtain the air pollution data of the monitoring location; the output end of the cloud platform is connected to the visualization module to determine the processing order of the air pollution data of the monitoring station, perform real-time processing on the air pollution data of the monitoring station, and send the processing results to the visualization module; the visualization module is used to render and draw the air pollution data and display the analysis results of the air pollution data.

8. The pollution visualization data processing system using cloud computing technology according to claim 7 is characterized in that: The ground monitoring station module also includes an air pollution data collection unit, a data transmission unit and a time unit; the air pollution data collection unit is used to obtain the air pollution data of the monitoring location, and the data transmission unit is used to send the air pollution data and the collection time to the cloud platform; the time unit is used to determine the collection time of the air pollution data.

9. The pollution visualization data processing system using cloud computing technology according to claim 7 is characterized in that: The cloud platform also includes an air pollution data real-time analysis unit, an air pollution data precision analysis unit, a data storage unit, a regression unit, an optimization unit, a compensation unit and an output unit; the air pollution data real-time analysis unit is used to perform real-time analysis on the air pollution data transmitted by the monitoring station; the air pollution data precision analysis unit is used to analyze the air pollution data transmitted by all monitoring stations in spare time; the data storage unit is used to store the air pollution data transmitted by the monitoring station; the regression unit is used to determine the air pollution data of the monitoring station, determine the correlation between the monitoring stations, the contribution rate of a single monitoring station to all monitoring stations and the contribution rate of all monitoring stations to a single monitoring station; the optimization unit is used to determine the best combination of monitoring station data that needs to be processed; the compensation unit is used to compensate for the optimization results to ensure that all monitoring station data can be processed by the cloud platform; the output unit is used to send the air pollution data analysis results to the visualization module.

10. The pollution visualization data processing system using cloud computing technology according to claim 9 is characterized in that: The visualization module also includes a first visualization unit and a second visualization unit; the first visualization unit is used to render and draw the air pollution data analysis results in real time; The second visualization unit is used to render and draw the air pollution data analysis results of all monitoring stations.

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