A smart park management system and method

Abstract

The present invention relates to the technical field of air quality prediction, and more specifically to a smart park management system and method, comprising: real-time collection of park environmental parameters and reference data; evaluating the authenticity of the current environmental parameters, determining the number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, and then calculating the prediction error of the current environmental parameters; predicting the environmental parameters at the next moment after the current moment based on the actual value of the current environmental parameters and the prediction error; and determining whether the prediction result is abnormal to monitor and warn of air pollution in the park, thereby achieving air quality management in the park. The present invention can effectively identify interference data, improve the accuracy of air quality prediction, and provide reliable decision support for park management.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality prediction, and more particularly to a smart park management system and method. Background Art

[0002] In today’s context of rapid urbanization and industrialization, air quality issues are receiving increasing attention. Fine particulate matter (PM) is a key indicator of air quality, and its pollution status has a profound impact on public health. As a product of the integration of modern management concepts and technologies, smart parks are responsible for creating a green and healthy environment. Air quality monitoring is a crucial component of smart park management. Traditional air pollution monitoring methods rely heavily on manual analysis and data processing, which suffers from significant lags and makes it difficult to respond promptly to changes in air quality.

[0003] The prediction algorithm is an algorithm that predicts future data based on the historical change trend of the data. Usually, the error between the predicted value and the actual value at the previous moment of the current moment is multiplied by the error factor as the prediction error of the environmental parameter at the current moment, and then the prediction error of the environmental parameter at the current moment and the actual value at the current moment are used to predict the data at the next moment. This prediction algorithm reflects the prediction effect of the previous moment through the prediction error, and continuously adjusts the prediction value of subsequent moments based on the prediction error, so that the prediction process can continuously adapt to the changing trend and fluctuation of the data, and gradually improve the accuracy of the prediction. For example, the existing Chinese patent document with the announcement number CN118673387B discloses a high-temperature early warning method and system for pulverized coal gasification burners. This method obtains all combination sequences, calculates the preference degree of each combination sequence, and takes the change trend of the combination sequence with the highest preference degree as The prediction algorithm predicts the historical trend of the data to predict the temperature data at the next moment, thereby achieving temperature warning for the burner.

[0004] However, using the above technical solution Prediction algorithm for the park When predicting data, During the data collection and transmission process, it is susceptible to signal interference, which may cause the collected data to be transmitted incorrectly and appear untrue. Data, and if the last moment If the data is not true, then the prediction error of the calculated environmental parameters at the current moment will be inaccurate, which will eventually lead to a large deviation in the prediction results for the next moment and it will be impossible to issue a timely and accurate warning of the air pollution situation. Summary of the Invention

[0005] In order to solve the problem of air quality monitoring in the park The data is disturbed by the signal and becomes untrue, affecting The accuracy of the prediction results of the prediction algorithm leads to the problem of being unable to timely and accurately monitor and warn of air pollution in the park. The present invention proposes a smart park management method, including:

[0006] Real-time collection of the park's environmental parameters and reference data; the environmental parameters and reference data are Data; take each moment as the current moment and evaluate the authenticity of the environmental parameters at the current moment: , For the The authenticity of the environmental parameters at the current moment, For the The suspicious degree of the environmental parameters at the current moment is used to reflect the extent to which the environmental parameters are affected by noise. is the total number of reference data of environmental parameters at any moment, and Respectively The current environmental parameter and the Reference data, For the The variance of all reference data of the environmental parameters at the current moment, is a hyperparameter, is the natural exponential function, is the absolute value symbol;

[0007] Determining the number of historical environmental parameters required for calculating the prediction error of the environmental parameter at the current moment according to the authenticity of the environmental parameter at the current moment, and selecting historical environmental parameters according to the required number of historical environmental parameters to calculate the prediction error of the environmental parameter at the current moment;

[0008] pass The prediction algorithm predicts the environmental parameters at the next moment based on the prediction error and actual value of the environmental parameters at the current moment, and monitors and warns of air pollution in the park based on the prediction results to achieve air quality management in the park.

[0009] The above technical solution not only obtains direct environmental parameters but also collects reference data, providing a multi-dimensional source of information for subsequent data reliability assessments. Multiple reference data helps to more comprehensively and accurately grasp the true air quality status. Furthermore, it comprehensively considers factors such as the difference between the current environmental parameters and multiple reference data, the degree of suspicion of the current environmental parameters themselves, and the degree of dispersion of the reference data to accurately quantify the true reliability of the current environmental parameters. This can effectively identify unrealistic environmental parameters caused by interference, provide a reliable basis for subsequent predictions, and reduce the risk of prediction bias caused by erroneous data. Furthermore, considering that traditional methods usually use a fixed number of historical environmental parameters to calculate the prediction error of the current environmental parameters, this method can introduce large errors when the environmental parameters may be untrue. Here, the number of selected historical environmental parameters is dynamically adjusted according to the authenticity of the environmental parameters, more rationally utilizing historical environmental parameters to accurately calculate the prediction error, improving the accuracy and flexibility of the prediction error calculation, and enabling the subsequent prediction process to more accurately adapt to the actual data situation. Furthermore, the corrected prediction error and actual value are used in combination with the HTFE prediction algorithm to predict the environmental parameters at the next moment from the current moment, thereby realizing forward-looking monitoring and early warning of the park's air quality. This can overcome the problem of inaccurate predictions caused by data interference, predict the park's future air quality more timely and accurately, and provide reliable decision-making support for park management.

[0010] Preferably, the calculation formula for the prediction error of the environmental parameters at the current moment is:

[0011]

[0012] Where, For the The prediction error of the environmental parameters at the current moment, To calculate the The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, and In the calculation of The prediction error of the environmental parameters at the current moment is required The predicted and actual values ​​of historical environmental parameters, is a uniformly preset error factor.

[0013] Compared with simply using a fixed method or a single data point to calculate the prediction error, the above-mentioned technical solution, this calculation method based on multiple historical environmental parameters, can more comprehensively reflect the accuracy and stability of historical predictions, and can effectively reduce the impact of individual abnormal historical data or inaccurate historical predictions on the current prediction error calculation, thereby improving the reliability of the prediction error.

[0014] Preferably, the suspicious degree of the environmental parameters at the current moment satisfies the following relationship:

[0015] ;

[0016] Where, For the The suspiciousness of the environmental parameters at the current moment, and Respectively The predicted and actual values ​​of the environmental parameters at the current moment, For the The average value of the local data segment of the environmental parameters at the current moment, For the The local data segment of the environmental parameters at the current moment is The variance of all environmental parameters except the current environmental parameters, is the absolute value symbol, It is a normalization operation.

[0017] The above technical solution helps to more accurately screen out environmental parameters that may be affected by abnormal factors such as signal interference in complex campus environmental monitoring scenarios by accurately evaluating the suspiciousness of environmental parameters at each current moment.

[0018] Preferably, the local data segment of the environmental parameter is determined according to the following method: The environmental parameters at the current moment, and before the current moment The environmental parameters at each moment constitute the A local data segment of the environmental parameters at the current moment, The preset quantity.

[0019] Preferably, the number of historical environmental parameters required when calculating the prediction error of the environmental parameters at the current moment satisfies the following relationship:

[0020] , To calculate the The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, For the The authenticity of the environmental parameters at the current moment, is the initial preset value, is the rounding symbol, is the natural exponential function.

[0021] This technical solution dynamically adjusts the number of historical environmental parameters based on the accuracy of the current environmental parameters to more accurately calculate the current environmental parameter prediction error. When the reliability of the current environmental parameters is high, the interference of historical environmental parameters is reduced, highlighting the role of the current environmental parameters. When data reliability is low, historical environmental reference data is fully utilized to correct any potential deviations. This targeted adjustment makes the prediction error calculation more accurate and improves the accuracy of the prediction error.

[0022] Preferably, the method for collecting the environmental parameters of the park and their reference data in real time is:

[0023] An electrochemical sensor A is set up in the park, and several electrochemical sensors are arranged around A;

[0024] The environmental parameters collected by A at each moment are determined as the environmental parameters of the park at each moment, and the environmental parameters collected by several electrochemical sensors around A at the same moment are used as reference data for the environmental parameters of the park at each moment.

[0025] The above technical solution collects data from multiple sensors and performs data collection with sensor A as the core. Based on these reference data, the authenticity of the data collected by sensor A can be more accurately judged, and data that may contain errors can be identified.

[0026] Preferably, the method for monitoring and early warning of air pollution in the park based on the prediction results is:

[0027] The predicted value of the environmental parameter at the next moment of the current moment is compared with the previous value of the environmental parameter at the next moment of the current moment. The environmental parameters at each moment are constructed into a data set. is the default value;

[0028] Calculate the environmental parameters of the next moment based on the data set Value, if If the value is greater than the preset threshold, the environmental parameters at the next moment are abnormal data and an early warning is issued; If the value is not greater than the preset threshold, the environmental parameters at the next moment from the current moment are normal data and no warning is issued.

[0029] The present invention also provides a smart park management system, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of any one of the management methods.

[0030] The present invention has the following effects:

[0031] The present invention collects the environmental parameters and reference data of the park in real time, uses a specific formula to accurately evaluate the authenticity of the environmental parameters, effectively identifies interference data, and lays the foundation for accurate prediction. At the same time, the number of historical environmental parameters is dynamically adjusted according to the authenticity of the environmental parameters, and the prediction error is calculated, thereby improving the accuracy and flexibility of the prediction error calculation. Finally, the present invention combines The algorithm enables accurate prediction and early warning of the park's air quality, providing reliable decision-making support for the park's air quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0033] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0035] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0036] Reference Figure 1 The present invention provides a smart park management method, comprising steps S1 to S6:

[0037] S1: Real-time collection of the park's environmental parameters and reference data.

[0038] The environmental parameters and reference data referred to in the present invention are The specific method for collecting environmental parameters and reference data is to set up electrochemical sensors in the park. , and in Within 1 cm around the 4 electrochemical sensors, evenly arrange them and Collect environmental parameters synchronously at the same time, and at each moment, The collected environmental parameters are determined as the environmental parameters of the park at that moment, and the environmental parameters collected by the remaining four electrochemical sensors are used as reference data for the environmental parameters of the park at that moment.

[0039] This method builds a mutually verified data system. Since the environmental parameters at different locations in the park should have a certain correlation in the vicinity, this setting can use the data consistency of multiple sensors to verify the reliability of environmental parameters. When one of the sensors is disturbed and the environmental parameters are abnormal, the data of other sensors can assist in judging the abnormality, providing a multi-dimensional reference for subsequent data processing. For example, if If the difference between the collected environmental parameters and the environmental parameters collected by the other four reference sensors is too large, it can be suspected that The accuracy of environmental parameters.

[0040] All of the above sensors collect data every five minutes for 24 hours. To more accurately analyze the true level of environmental parameters, data collection can be performed one day in advance. The additional environmental parameters collected on this extra day are primarily used to assist in determining the reliability of other environmental parameters. By adding this additional day's worth of reference data, we can provide more information for the true level analysis.

[0041] In the current scenario, using traditional The prediction algorithm predicts the environmental parameters of the next moment from the current moment as follows:

[0042] S11: Determine historical trends. Specifically, the method is:

[0043] First Taking the current moment as an example, get the The current environmental parameters and the previous 100 environmental parameters, if these 100 environmental parameters are monotonically increasing, then the The historical trend of the environmental parameters at the current moment is an increasing trend. If these 100 environmental parameters show a monotonically decreasing trend, then the The historical trend of the environmental parameters at the current moment is a decaying type. Except for monotonically increasing and monotonically decreasing, the other cases are irregular. If the historical trend is an increasing type, then , if it is a decay type, then If the historical trend is irregular, then ,in, Both Tuning parameters of the prediction algorithm.

[0044] The mean of these 100 environmental parameters is used as the The predicted value of the environmental parameters at the current moment , the maximum and minimum values ​​of these 100 environmental parameters are taken as The maximum predicted value of the environmental parameter at the current moment and the minimum predicted value , then and Constitute the The predicted range of environmental parameters at the current moment;

[0045] S12: The environmental parameters of the next moment from the current moment are predicted. Specifically, the following steps are included:

[0046] S121: Calculate the The preliminary predicted values ​​of the environmental parameters at the next moment from the current moment.

[0047]

[0048] In the formula, Indicates the The next moment from the current moment, For the The preliminary predicted value of the environmental parameters at the next moment from the current moment, For the The actual value of the environmental parameter at the current moment, is the preset error factor, For the The prediction error of the environmental parameters at the current moment.

[0049] Among them, The prediction error of the environmental parameters at the current moment The method to obtain is:

[0050]

[0051] In this formula, It is The predicted value of the environmental parameter at the current moment, For the The actual value of the environmental parameter at the current moment, is the preset error factor.

[0052] S122: Get the The predicted range of environmental parameters from the current moment to the next moment.

[0053] Specifically, according to The preliminary predicted values ​​of the environmental parameters at the next moment from the current moment , No. The influencing factors of the prediction range of the environmental parameters at the current moment and the preset prediction error range , get the The prediction range of the environmental parameters at the next moment from the current moment is:

[0054] ;

[0055] ;

[0056] In this formula It is The lower limit of the prediction range of the environmental parameters at the next moment from the current moment, It is The lower limit of the prediction range of the environmental parameters at the next moment from the current moment. and Constitute the The predicted range of environmental parameters from the current moment to the next moment, (Experience points).

[0057] S123: Adjust the The predicted range of environmental parameters from the current moment to the next moment.

[0058] In order to ensure the The final predicted value of the environmental parameter at the next moment can cover the The actual value of the environmental parameter at the current moment , make the following settings:

[0059] when Less than ,make equal ,when Greater than ,make equal ;

[0060] S124: According to The predicted range of environmental parameters at the next moment from the current moment is calculated. The final predicted value of the environmental parameter at the next moment from the current moment.

[0061] Specifically, the calculation formula is:

[0062] ;

[0063] In the formula, For the The final predicted value of the environmental parameter at the current moment.

[0064] S13: Repeat continuously to achieve real-time monitoring of environmental parameters.

[0065] Specifically, for the At the next moment after the current moment, the environmental parameters of the moment are predicted according to the same method as in step S121 to step S124. According to this method, real-time monitoring of the environmental parameters of the park can be achieved.

[0066] It can be seen that in the process of predicting the environmental parameters of the park, due to the traditional Prediction algorithms typically multiply the error between the predicted and actual values ​​of environmental parameters at the moment before the current moment by an error factor to determine the predicted error. This error is then used along with the actual value to predict the environmental parameters immediately following the current moment. This involves three moments: the current moment, the moment before the current moment, and the moment immediately following the current moment. The moment immediately following the current moment is the moment for which prediction is required, and the predicted value for the next moment is determined by the data at the current moment. Therefore, to prevent the current moment's data from being interference data, which could lead to inaccurate predicted values ​​for the next moment, the algorithm uses the error factor to calculate the predicted value for the next moment.

[0067] By deeply analyzing the changing characteristics of the environmental parameters at the current moment, the present invention can accurately determine the true degree of the environmental parameters at each current moment (the first moment immediately before each moment to be predicted), effectively identify and properly handle possible erroneous data. Moreover, based on the true degree of the environmental parameters at each current moment, the number of historical environmental parameters required to calculate the prediction error of the environmental parameters at the current moment is dynamically determined. Based on the calculated number, the historical data is selected to accurately calculate the prediction error of the environmental parameters at the current moment. In this way, when using When using the prediction algorithm, it can effectively avoid the interference of false data on the prediction results, effectively avoid error accumulation, improve the accuracy of the prediction results, and ensure the real-time monitoring and early warning capabilities of the park's air pollution situation.

[0068] S2: Evaluate the suspiciousness of environmental parameters at the current moment.

[0069] This step aims to analyze the numerical values ​​of the park's environmental parameters to determine the degree of suspicion of the environmental parameters at the current moment. The greater the degree of suspicion of the environmental parameters at the current moment, the less true the environmental parameters at the current moment are, and the more likely they are to affect the accuracy of the predicted results of the environmental parameters at the next moment.

[0070] Because unrealistic environmental parameters often present prominent and abnormal numerical performance, the suspicious degree of the environmental parameters at the current moment is analyzed in order to determine the degree to which the environmental parameters at the current moment are affected by noise.

[0071] Specifically, when analyzing the degree of suspicion of the current environmental parameter, the following rules are followed: the greater the difference between the current environmental parameter and its surrounding environmental parameters, the greater the degree of suspicion of the current environmental parameter. The greater the difference between the predicted value and the actual value of the current environmental parameter, the greater the degree of suspicion of the current environmental parameter. The more uniform the numerical values ​​of the current environmental parameter and the surrounding environmental parameters, the greater the degree of suspicion of the current environmental parameter.

[0072] In order to implement the above logic, this step sets a local data segment for each current moment's environmental parameter to analyze the suspiciousness level of each current moment's environmental parameter.

[0073] In one embodiment, the The environmental parameters at the current moment, and before the current moment The environmental parameters at each moment constitute the A local data segment of the environmental parameters at the current moment, It is the preset number, and the experience value is 100.

[0074] This step is mainly to evaluate and screen the quality of environmental parameters during the prediction process, find out the environmental parameter data that may be greatly affected by noise, and then more reasonably optimize the calculation of prediction errors in the subsequent prediction process to improve the accuracy of the prediction.

[0075] In one embodiment, the suspiciousness level of the environmental parameters at the current moment satisfies the following relationship:

[0076]

[0077] In the formula, For the The suspiciousness of the environmental parameters at the current moment, and Respectively The predicted and actual values ​​of the environmental parameters at the current moment, For the The average value of the local data segment of the environmental parameters at the current moment (the average value of the actual values ​​of all environmental parameters in the local data segment), For the The local data segment of the environmental parameters at the current moment is The variance of all environmental parameters except the current environmental parameters, is the absolute value symbol, It is a normalization operation.

[0078] In this formula, Quantified the The difference between the actual value of the environmental parameter at the current moment and the average value of its local data segment, the larger the value, the greater the difference between the actual value of the environmental parameter at the current moment and the average value of its local data segment. The more suspicious the environmental parameters are at the current moment. Quantified the The difference between the actual value and the predicted value of the environmental parameter at the current moment, the larger the value, the The greater the difference between the actual value of the environmental parameter at the current moment and the average value of its local data segment, the higher the credibility. The greater the possibility that the environmental parameters at the current moment will have unrealistic numerical changes, the The more suspicious the environmental parameters at the current moment are. The variance was quantified by The local data of the environmental parameters at the current moment are The numerical difference of the other environmental parameters except the current environmental parameters. The smaller the value, the more uniform the numerical performance of the other environmental parameters. The greater the difference between the actual value of the environmental parameter at the current moment and the predicted value, the more likely it is that it is an unrealistic numerical influence (rather than a real change in the environmental parameter). The greater the possibility that the environmental parameters at the current moment have unrealistic numerical changes, the higher the credibility. The more suspicious the environmental parameters are at the current moment.

[0079] Based on a precise assessment of the degree of doubt in environmental parameters, prediction error calculations can be more rationally optimized during subsequent forecasting. For highly suspicious environmental parameters, appropriate measures can be taken to mitigate their negative impact on forecast results; while reliable environmental parameters can be better utilized in forecasting. This effectively improves forecast accuracy, reduces prediction errors caused by data quality issues, and enhances the reliability and stability of the entire forecast model.

[0080] S3: Optimize the suspiciousness of the environmental parameters at the current moment based on the similarity between the environmental parameters at the current moment and the reference data to obtain the true degree of the environmental parameters at the current moment.

[0081] Through the previous steps, the suspicious degree of each environmental parameter at the current moment is obtained. This indicator is based on the electrochemical sensor of the park. The collected environmental parameters are analyzed, but it is not possible to distinguish between unreal environmental parameters and abnormally large environmental parameters actually generated in the park. That is, it only analyzes the suspicious degree of the environmental parameters at each current moment, and cannot rule out the influence of the abnormally large environmental parameters actually generated in the park.

[0082] The difference between false environmental parameters and abnormally high environmental parameters actually generated by the park in this solution is that when abnormally high environmental parameters actually occur in the park, both electrochemical sensor A and the four surrounding electrochemical sensors will detect these abnormally high environmental parameters. However, false environmental parameters do not exhibit this characteristic and will only appear in the environmental parameters collected by sensor A.

[0083] Therefore, in this step, the similarity between the environmental parameters collected by electrochemical sensor A and the environmental parameters collected by the four surrounding electrochemical sensors will be analyzed, and the suspicious degree of the environmental parameters at each current moment will be optimized to obtain the true degree of the environmental parameters at each current moment.

[0084] When analyzing the authenticity of each current moment's environmental parameter, if the similarity between each current moment's environmental parameter and the reference data is weaker, it can be said that the authenticity of the current moment's environmental parameter is lower, and vice versa.

[0085] However, when analyzing the similarity between each current moment's environmental parameter and the reference data, there may be a situation where both the current moment's environmental parameter and the reference data are subject to signal interference during transmission. In this case, the higher the similarity between the current moment's environmental parameter and the reference data, the more likely it is to be identified as the true environmental parameter, affecting the accuracy of subsequent data predictions. Therefore, this solution analyzes the degree of suspicion of each current moment's environmental parameter, and then analyzes the similarity between each current moment's environmental parameter and the reference data. This optimizes the degree of suspicion of the current moment's environmental parameter and determines the true degree of the current moment's environmental parameter, effectively solving this problem.

[0086] In one embodiment, the calculation formula for the authenticity of the environmental parameters at the current moment is:

[0087]

[0088] In this formula, For the The authenticity of the environmental parameters at the current moment, For the The suspicious degree of the environmental parameters at the current moment is used to reflect the extent to which the environmental parameters are affected by noise. is the total number of reference data of environmental parameters at any moment, and Respectively The current environmental parameter and the Reference data, For the The variance of all reference data of the environmental parameters at the current moment, is a hyperparameter, and the empirical value is , which exists to prevent A case of 0 occurs.

[0089] In this formula, Reflects the The suspicious degree of the environmental parameters at the current moment. The smaller the value, the more suspicious the The less likely the environmental parameters at the current moment are to be affected by signal interference and produce false data, the The greater the authenticity of the environmental parameters at the current moment.

[0090] In this formula, Reflects the The current environmental parameters and The smaller the value, the greater the difference between the reference data. The greater the similarity between the environmental parameters at the current moment and the reference data, the The greater the authenticity of the environmental parameters at the current moment. Reflects the The numerical difference of all reference data of the environmental parameters at the current moment. The smaller the value, the The smaller the numerical difference of all reference data of the environmental parameters at the current moment, the The smaller the possibility of false environmental parameters appearing in all reference data of the environmental parameters at the current moment, the The stronger the reliability of all reference data of the environmental parameters at the current moment, the greater the reference value, which can further explain the The environmental parameters at the current moment and their The smaller the difference between the reference data, the higher the credibility. The greater the similarity between the environmental parameters at the current moment and the reference data, the higher the credibility. The greater the authenticity of the environmental parameters at the current moment.

[0091] In this formula, Comprehensively quantified the The similarity between the environmental parameters at the current moment and the reference data. The larger the value, the more The less likely the environmental parameters at the current moment are to be affected by signal interference and produce false data, the The greater the authenticity of the environmental parameters at the current moment.

[0092] This formula can effectively optimize the degree of suspicion of the environmental parameters at the current moment, thereby accurately obtaining the true degree of the environmental parameters at the current moment, which helps to improve the accuracy of subsequent data predictions and also solves the problem of misjudgment caused by signal interference during transmission.

[0093] S4: Select historical environmental data according to the authenticity of the environmental parameters at the current moment to calculate the prediction error of the environmental parameters at the current moment.

[0094] The previous analysis steps have yielded the true degree of each current moment's environmental parameter. This step uses this indicator to determine the number of historical environmental parameters used to calculate the prediction error for each current moment's environmental parameter, and further calculates the prediction error for each current moment's environmental parameter.

[0095] In one embodiment, the number of historical environmental parameters required to calculate the prediction error of the current environmental parameter satisfies the following relationship:

[0096]

[0097] In this formula, To calculate the The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, For the The authenticity of the environmental parameters at the current moment, is the initial preset value, that is, the number of historical environmental parameters required to calculate the prediction error of environmental parameters at any time. The experience value is , For rounding up, even if the real degree of a certain environmental parameter at the current moment is particularly large, at least one historical environmental parameter is required to calculate its predicted value. is the natural exponential function.

[0098] From this formula, it can be seen that when determining the number of historical environmental parameters based on which the prediction error of the environmental parameters at each current moment is calculated, the smaller the authenticity of the environmental parameters at each current moment, the lower the authenticity of the environmental parameters at the current moment, and the more likely they are to be untrue environmental parameters. Therefore, more historical environmental parameter data is needed when calculating the prediction error of the environmental parameters at the current moment, so as to eliminate and smooth out the environmental parameters at the current moment as much as possible, thereby obtaining a more accurate prediction error of the environmental parameters at the current moment.

[0099] Further, in the The current environmental parameters are selected before Historical environmental parameters are used for subsequent calculation of The prediction error of the environmental parameters at the current moment.

[0100] In one embodiment, the calculation formula for the prediction error of the environmental parameters at the current moment is:

[0101]

[0102] In this formula, For the The prediction error of the environmental parameters at the current moment, To calculate the The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, and In the calculation of The prediction error of the environmental parameters at the current moment is required The predicted and actual values ​​of historical environmental parameters, is a uniformly preset error factor, , this value is an experience value.

[0103] This formula is obtained by The predicted value of historical environmental parameters With actual value The absolute value of the difference is summed and averaged, which can effectively measure the degree of deviation between the predicted value and the actual value. The accuracy of the current environmental parameters is dynamically determined, so that when faced with environmental parameters of different realities, the amount of historical data used to calculate the prediction error is more reasonable. Secondly, multiply by a uniform preset error factor , the overall prediction error can be appropriately adjusted to adapt to the error evaluation criteria in specific scenarios.

[0104] Overall, this formula comprehensively considers the dynamic selection of historical environmental parameters and the unified preset error factors, and can accurately reflect the error between the predicted value and the actual value of the environmental parameters at the current moment, providing a reliable quantitative indicator for subsequent analysis, decision-making and model optimization based on prediction errors.

[0105] S5: Pass The prediction algorithm predicts the environmental parameters at the next moment based on the prediction error and actual value of the environmental parameters at the current moment.

[0106] The present invention is mainly to In the process of predicting the environmental parameters of the next moment from the current moment, the method of obtaining the prediction error of each environmental parameter at the current moment is optimized to improve the accuracy of the prediction. The rest of the steps are the same as the traditional The prediction process of the prediction algorithm remains the same.

[0107] Specifically, the first The prediction error of the environmental parameters at the current moment , then calculate the The preliminary predicted values ​​of the environmental parameters at the next moment from the current moment:

[0108]

[0109] In the formula, For the The preliminary predicted values ​​of the environmental parameters at the next moment from the current moment, For the The actual value of the environmental parameter at the current moment, is the preset error factor, For the The prediction error of the environmental parameters at the current moment.

[0110] Then, the same method as in step S122 to step S124 is used to obtain the first The final predicted value at the current moment.

[0111] In summary, the core of the present invention is to improve the traditional The prediction algorithm has been improved, particularly in terms of obtaining the prediction error for each current environmental parameter. This optimization makes the entire prediction algorithm more adaptable and accurate. In practical applications, environmental parameters at different times may be affected by a variety of complex factors. Accurately obtaining the prediction error is crucial to improving the reliability of the overall prediction. This solution addresses this shortcoming of traditional prediction algorithms.

[0112] S6: Monitor and warn of air pollution in the park based on the prediction results to achieve air quality management in the park.

[0113] In one embodiment, the present invention adopts a more detailed and comprehensive method to assess the air pollution situation in the park.

[0114] First, for each current moment, the predicted environmental parameter values ​​for the next moment and the environmental parameters for the M moments before the next moment are integrated to construct a specific data set, with M set to 100 (an empirical value). Then, based on this constructed data set, the Z-Score (standard score) value of the environmental parameter for the next moment is further calculated. The Z-Score value is a standardized statistic that clearly indicates the degree of deviation between a data point and the mean of the data set. Next, based on the calculated Z-Score value, a judgment is made:

[0115] If the Z-Score value of the next moment's environmental parameter is greater than the pre-set threshold, it means that the next moment's environmental parameter has deviated significantly from the historical data, which is very likely to indicate abnormal air pollution conditions. At this time, the system will issue an early warning signal. Conversely, if the Z-Score value of the next moment's environmental parameter is not greater than the pre-set threshold, it means that the next moment's environmental parameter is within a reasonable range of historical data fluctuations. In other words, the next moment's environmental parameter is normal data, and the system will not issue an early warning, indicating that the current park's air pollution situation is relatively stable.

[0116] Through such a rigorous and scientific monitoring and early warning mechanism, we can make full use of the prediction results, timely capture abnormal changes in the park's air pollution, and provide strong support for the park's air quality management.

[0117] In addition, the present invention also provides a smart park management system, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of the management method.

[0118] In the description of this specification, "a plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly defined.

[0119] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

Claims

1. A smart park management method, characterized in that: include: Real-time collection of park environmental parameters and reference data; The environmental parameters and reference data are Data; take each moment as the current moment and evaluate the authenticity of the environmental parameters at the current moment: , For the The authenticity of the environmental parameters at the current moment, For the The suspicious degree of the environmental parameters at the current moment is used to reflect the extent to which the environmental parameters are affected by noise. is the total number of reference data of environmental parameters at any moment, and Respectively The current environmental parameter and the Reference data, For the The variance of all reference data of the environmental parameters at the current moment, is a hyperparameter, is the natural exponential function, is the absolute value symbol; Determining the number of historical environmental parameters required for calculating the prediction error of the environmental parameter at the current moment according to the authenticity of the environmental parameter at the current moment, and selecting historical environmental parameters according to the required number of historical environmental parameters to calculate the prediction error of the environmental parameter at the current moment; pass The prediction algorithm predicts the environmental parameters at the next moment based on the prediction error and actual value of the environmental parameters at the current moment, and monitors and warns of air pollution in the park based on the prediction results to achieve air quality management in the park.

2. The smart park management method according to claim 1, characterized in that: The calculation formula for the prediction error of the environmental parameters at the current moment is: , where For the The prediction error of the environmental parameters at the current moment, To calculate the The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, and In the calculation of The prediction error of the environmental parameters at the current moment is required The predicted and actual values ​​of historical environmental parameters, is a uniformly preset error factor.

3. The smart park management method according to claim 1, characterized in that: The suspicious degree of the environmental parameters at the current moment satisfies the following relationship: , where For the The suspiciousness of the environmental parameters at the current moment, and Respectively The predicted and actual values ​​of the environmental parameters at the current moment, For the The average value of the local data segment of the environmental parameters at the current moment, For the The local data segment of the environmental parameters at the current moment is The variance of all environmental parameters except the current environmental parameters, is the absolute value symbol, It is a normalization operation.

4. The smart park management method according to claim 3, characterized in that: The local data segment of the environmental parameters is determined in the following way: The environmental parameters at the current moment, and before the current moment The environmental parameters at each moment constitute the A local data segment of the environmental parameters at the current moment, The preset quantity.

5. The smart park management method according to claim 1, characterized in that: The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters satisfies the following relationship: , To calculate the The number of historical environmental parameters required to calculate the prediction error of the current environmental parameters, For the The authenticity of the environmental parameters at the current moment, is the initial preset value, is the rounding symbol, is the natural exponential function.

6. The smart park management method according to claim 1, characterized in that: The method for real-time collection of the park's environmental parameters and their reference data is: Installing electrochemical sensors in the park , and in Several electrochemical sensors are arranged around the periphery; Will The environmental parameters collected at each moment are determined as the environmental parameters of the park at each moment. The environmental parameters collected by several electrochemical sensors around the park serve as reference data for the environmental parameters of the park at each moment.

7. The smart park management method according to claim 1, characterized in that: The method for monitoring and early warning of air pollution in the park based on the prediction results is as follows: The predicted value of the environmental parameter at the next moment of the current moment is compared with the previous value of the environmental parameter at the next moment of the current moment. The environmental parameters at each moment are constructed into a data set. is the default value; Calculate the environmental parameters of the next moment based on the data set Value, if If the value is greater than the preset threshold, the environmental parameters at the next moment are abnormal data and an early warning is issued; If the value is not greater than the preset threshold, the environmental parameters at the next moment from the current moment are normal data and no warning is issued.

8. A smart park management system, characterized in that: The management system includes a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of the management method according to any one of claims 1 to 7.

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