Intelligent city environment supervision system

Abstract

The application discloses a smart city environment supervision system, and particularly relates to the technical field of environment supervision, wherein a conversion delay response module, an information loss response module, a noise interference response module and a precision loss response module are used to respectively acquire a conversion delay coefficient, a data conversion loss coefficient, a data noise interference coefficient and a data precision loss coefficient, so that conversion delay, information loss, noise interference and precision loss problems in data format conversion can be found in time, a data conversion hidden danger evaluation model is constructed, a data conversion hidden danger evaluation index is generated, format conversion hidden dangers in the data format conversion process are evaluated, the accuracy and timeliness of data can be maintained in the environment crisis emergency response process, decision-making errors and response delays caused by inaccurate or delayed data can be avoided, based on the hidden danger evaluation result, early warning and corresponding decision-making can be automatically made, and it is indicated whether the system needs to adjust the data processing flow or perform emergency repair.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically, to a smart city environmental monitoring system. Background Technology

[0002] With the acceleration of urbanization, environmental crises (such as large-scale air pollution and floods) have become major issues affecting urban sustainable development and the quality of life of residents. To effectively address these environmental crises, smart city environmental monitoring systems have emerged. These systems integrate real-time data from various sources (such as air quality monitoring, weather forecasts, traffic flow, and social media information), utilizing advanced data analytics and artificial intelligence technologies to provide real-time monitoring, early warning, and decision support for environmental changes, aiming to achieve cross-departmental collaboration, timely response, and precise governance. However, smart city environmental monitoring systems typically involve multiple heterogeneous data sources, including environmental sensors, meteorological satellites, and traffic monitoring equipment. These data sources use different formats and protocols, resulting in inconsistent data structures and different collection frequencies. The data format conversion and integration process is complex, and improper handling can lead to data loss or misinterpretation, affecting the accuracy of information. Especially in disaster response, real-time performance and accuracy are crucial. Therefore, if potential problems during data format conversion (such as conversion delays, data loss, and noise interference) are not detected in time, decision-makers may respond based on erroneous or incomplete data, delaying emergency actions or even missing critical intervention opportunities. This can lead to obstacles in cross-departmental collaboration and information sharing, resulting in resource waste or inadequate crisis response in some areas, thus exacerbating the consequences of disasters. Especially in the face of environmental crises, rapid response and efficient coordination are crucial to reducing disaster losses. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a smart city environmental monitoring system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A smart city environmental monitoring system includes a conversion delay response module, an information loss response module, a noise interference response module, an accuracy loss response module, a comprehensive evaluation module, and an early warning decision module.

[0006] The conversion delay response module is used to obtain data conversion delay information during the data format conversion process, and to obtain the data conversion delay coefficient based on the data conversion delay information;

[0007] The information loss response module is used to obtain data conversion loss information during the data format conversion process and to obtain the data conversion loss coefficient based on the data conversion loss information.

[0008] The noise interference response module is used to acquire data noise interference information during the data format conversion process and to obtain the data noise interference coefficient based on the data noise interference information.

[0009] The accuracy loss response module is used to obtain data accuracy loss information during the data format conversion process and to obtain the data accuracy loss coefficient based on the accuracy loss information.

[0010] The comprehensive evaluation module is used to construct a data conversion risk assessment model based on data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, generate a data conversion risk assessment index, and assess the format conversion risks during the data format conversion process.

[0011] The early warning decision module is used to make early warning decisions on potential format conversion risks of the current data format conversion based on the assessment results of the format conversion risks.

[0012] In a preferred embodiment, by acquiring data conversion delay information during the data format conversion process, analyzing the data conversion delay situation during the data format conversion process, and obtaining a data conversion delay coefficient, the degree of data conversion delay during the data format conversion process is measured.

[0013] The logic for obtaining the data conversion delay coefficient is as follows:

[0014] A timestamp mechanism is used to record the time points of each stage of data format conversion, including the start timestamp of data format conversion. and data format conversion end timestamp Calculate data conversion delay The expression is as follows ,in The expected data format conversion time; obtain the set of delayed data within the time window T. ,in This represents the data conversion delay for the i-th data type. , It is a positive integer;

[0015] Calculate the average delay of data conversion within the time window T. The expression is as follows ;

[0016] Calculate the transformed standard deviation of data within the time window T. The expression is as follows ;

[0017] Calculate the data conversion delay factor The expression is as follows ,in The preset scaling factor, representing the standard deviation of the data transformation, is used to reflect the significance of data transformation delay fluctuations. This indicates the maximum allowable delay value.

[0018] In a preferred embodiment, by acquiring data conversion loss information during the data format conversion process, analyzing the data conversion loss situation during the data format conversion process, and obtaining a data conversion loss coefficient, the degree of data conversion loss during the data format conversion process is measured.

[0019] The logic for obtaining the data transformation loss coefficient is as follows:

[0020] Within the time window T, obtain the original data volume of each data point before data format conversion. The amount of data after data format conversion for each data point Calculate the data conversion loss rate The expression is as follows ,in This represents the amount of data after the j-th data point is transformed. This represents the original data volume of the j-th data point. , For positive integers; calculate the scope of impact of data loss. ,in To determine the scope of the data loss impact, This represents the influence coefficient of the j-th data point on environmental monitoring and emergency decision-making;

[0021] Calculate the time-loss coefficient The expression is as follows ,in This represents the time-weighted function. , Indicates the current moment. This represents the time when the nth data point was lost. , It is a positive integer;

[0022] Calculate the data conversion loss coefficient The expression is as follows .

[0023] In a preferred embodiment, by acquiring data noise interference information during the data format conversion process, analyzing the data noise interference situation during the data format conversion process, and obtaining the data noise interference coefficient, the degree of data noise interference during the data format conversion process is measured.

[0024] The logic for obtaining the data noise interference coefficient is as follows:

[0025] Obtain the format-converted data and perform a wavelet transform on it, as follows: ,in This represents the wavelet coefficients of the data at scale a and location b after format conversion. For the complex conjugate of wavelet basis functions, For the converted data, calculate the energy of the wavelet coefficients at scale a and location b. The expression is as follows The energy at different scales is compared with preset scale thresholds and energy thresholds. If scale a is less than or equal to the scale threshold and the energy is greater than the energy threshold, the energy is accumulated to calculate the data noise interference coefficient. .

[0026] In a preferred embodiment, by acquiring data precision loss information during the data format conversion process, analyzing the data precision loss during the data format conversion process, and obtaining a data precision loss coefficient, the degree of data precision loss during the data format conversion process is measured.

[0027] The logic for obtaining the data precision loss coefficient is as follows:

[0028] Obtain the original dataset before format conversion and the dataset after format conversion ,in This represents the data point in the g-th original dataset. This represents a data point in the dataset after the g-th format conversion;

[0029] Calculate the entropy of the original dataset The expression is as follows ,in Data points In the original dataset The probability of it appearing in;

[0030] Calculate the entropy of the dataset after format conversion. The expression is as follows ,in Data points Dataset after format conversion The probability of it appearing in;

[0031] Calculate joint entropy The expression is as follows ,in It is the probability that each pair of data points appears in both datasets;

[0032] Computing mutual information The expression is as follows ;

[0033] Calculate the data accuracy loss coefficient The expression is as follows .

[0034] In a preferred embodiment, a data conversion risk assessment model is constructed based on the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, and a data conversion risk assessment index is generated. The model is based on the following formula: In the formula These represent the preset proportional coefficients for the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, respectively. All are greater than 0.

[0035] In a preferred embodiment, the data conversion risk assessment index is compared with a preset data conversion risk assessment index threshold to make an early warning decision on the conversion format risk of the current data format conversion, as follows:

[0036] If the data conversion risk assessment index is greater than or equal to the data conversion risk assessment index threshold, it indicates that there is a significant risk in the current data format conversion process, and the system should immediately trigger an early warning and generate an early warning signal; if the data conversion risk assessment index is less than or equal to the data conversion risk assessment index threshold, it indicates that the risk in the data format conversion process is within an acceptable range, and there is no need to generate an early warning signal.

[0037] The technical effects and advantages of this invention are as follows:

[0038] 1. This invention utilizes conversion delay response modules, information loss response modules, noise interference response modules, and accuracy loss response modules to acquire and calculate data conversion delay coefficients, data conversion loss coefficients, data noise interference coefficients, and data accuracy loss coefficients, promptly identifying conversion delays, information loss, noise interference, and accuracy loss issues during data format conversion. It constructs a data conversion hazard assessment model, generates a data conversion hazard assessment index, and evaluates format conversion hazards during the data format conversion process. This ensures that the system maintains data accuracy and timeliness during environmental crisis emergency responses, avoiding decision-making errors and response delays caused by inaccurate or delayed data. Based on the hazard assessment results, it can automatically issue early warnings and make corresponding decisions, indicating whether the system needs to adjust the data processing flow or perform emergency repairs. This timely and intelligent early warning mechanism effectively improves the system's emergency response capabilities, especially during environmental disasters, enabling rapid and accurate triggering of emergency response measures to minimize the impact of environmental crises on cities and residents. Attached Figure Description

[0039] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0040] Figure 1 This is a flowchart of the system according to an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example: The present invention provides, as follows Figure 1 The smart city environmental monitoring system shown includes a conversion delay response module, an information loss response module, a noise interference response module, an accuracy loss response module, a comprehensive evaluation module, and an early warning decision module.

[0043] The conversion delay response module is used to obtain data conversion delay information during the data format conversion process, and to obtain the data conversion delay coefficient based on the data conversion delay information;

[0044] The information loss response module is used to obtain data conversion loss information during the data format conversion process and to obtain the data conversion loss coefficient based on the data conversion loss information.

[0045] The noise interference response module is used to acquire data noise interference information during the data format conversion process and to obtain the data noise interference coefficient based on the data noise interference information.

[0046] The accuracy loss response module is used to obtain data accuracy loss information during the data format conversion process and to obtain the data accuracy loss coefficient based on the accuracy loss information.

[0047] The comprehensive evaluation module is used to construct a data conversion risk assessment model based on data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, generate a data conversion risk assessment index, and assess the format conversion risks during the data format conversion process.

[0048] The early warning decision module is used to make early warning decisions on the potential risks of current data format conversion based on the assessment results of the potential risks of format conversion.

[0049] The conversion delay response module is used to obtain data conversion delay information during the data format conversion process, and to obtain the data conversion delay coefficient based on the data conversion delay information;

[0050] The data conversion delay factor measures the impact of delays during data format conversion on the real-time performance of a system. This is particularly crucial in emergency responses to environmental crises, where data timeliness is paramount. Delayed data can prevent decision-makers from accessing the latest environmental information, leading to missed critical intervention opportunities, delayed emergency actions, and potentially rendering response measures ineffective or exacerbating the crisis. The potential impacts of data conversion delays are detailed below:

[0051] Missing critical intervention opportunities: In environmental crises such as air pollution or floods, monitoring data must reflect changing trends in real time. For example, if air quality monitoring data is delayed by minutes or hours, changes in the pollution sources may have already occurred, and response measures may no longer be able to be implemented in a timely manner. Data delays can cause policymakers to miss the optimal time for emergency evacuations or pollution control, thereby impacting public health and safety.

[0052] Impact on resource allocation and cross-departmental collaboration: Different departments (such as emergency management, transportation, and fire services) rely on real-time environmental data to develop response plans. Data conversion delays can cause time lags in information access for various departments, thereby affecting multi-departmental coordination and resource allocation. For example, delayed traffic flow data may lead to errors in evacuation route planning and missed optimal evacuation windows.

[0053] Misleading Decision-Making and Emergency Response: Emergency responses in environmental crises often rely on data-driven decision-making. Data delays can lead to decisions based on incomplete or outdated information. For example, allocating relief resources based on outdated weather data can result in an ineffective response or wasted resources.

[0054] Impact on Post-Disaster Assessment and Recovery Speed: Rapid post-disaster assessment is crucial for recovery and reconstruction after environmental disasters. Delays in data conversion reduce the timeliness of post-disaster assessments, further slowing the recovery process and increasing economic losses.

[0055] Data conversion latency coefficients provide a quantifiable latency metric for the system, enabling accurate assessment of latency in the conversion process for each data source and rapid identification of latency risks in real-time environments. This assessment helps identify bottlenecks that may affect system response speed, provides early warnings of potential format conversion latency issues, and prevents these problems from amplifying during environmental crises, thus impacting real-time decision-making and emergency response. It helps optimize the entire data processing workflow; the system can automatically adjust to reduce unnecessary format conversion steps, adopt more efficient data transmission and processing methods, and improve overall system response speed. In situations with complex data sources and inconsistent formats, timely assessment and optimization of latency avoids redundant processing steps and excessive data conversion, improving data flow efficiency and reducing the risk of potential data loss and misinterpretation. Assessing data conversion latency coefficients helps promote the effectiveness of cross-departmental coordination and information sharing. Since different departments rely on different data sources for decision-making, accumulated latency can lead to information lag, affecting resource scheduling and emergency response. By assessing the latency impact of each step, real-time data sharing can be achieved in multi-departmental collaboration, ensuring that all departments can obtain the latest and complete environmental data within the same timeframe, thereby improving the accuracy of decision-making and the synergy of responses. Especially in disaster situations, rapid data sharing and decision-making response are key to mitigating the impact of disasters and protecting public safety.

[0056] Therefore, by obtaining data conversion delay information during the data format conversion process, analyzing the data conversion delay situation, and obtaining the data conversion delay coefficient, the degree of data conversion delay during the data format conversion process can be measured.

[0057] The logic for obtaining the data conversion delay coefficient is as follows:

[0058] A timestamp mechanism is used to record the time points of each stage of data format conversion, including the start timestamp of data format conversion. and data format conversion end timestamp Calculate data conversion delay The expression is as follows ,in The expected data format conversion time; obtain the set of delayed data within the time window T. ,in This represents the data conversion delay for the i-th data type. , It is a positive integer;

[0059] It should be noted that the data type is determined based on the actual data type collected by the smart city environmental monitoring system, such as CSV, JSON, etc.

[0060] Calculate the average delay of data conversion within the time window T. The expression is as follows ;

[0061] Calculate the transformed standard deviation of data within the time window T. The expression is as follows ;

[0062] Calculate the data conversion delay factor The expression is as follows ,in The preset scaling factor, representing the standard deviation of the data transformation, is used to reflect the significance of data transformation delay fluctuations. Indicates the maximum allowable delay value;

[0063] It should be noted that the preset ratio of the data transformation standard deviation is set according to the actual situation. For example, the expert weighting method is adopted, that is, experts in relevant fields are invited to determine the preset ratio of the data transformation standard deviation through professional opinion surveys and comprehensive evaluations.

[0064] The information loss response module is used to obtain data conversion loss information during the data format conversion process and to obtain the data conversion loss coefficient based on the data conversion loss information.

[0065] The data conversion loss coefficient measures the severity of data loss during data format conversion. Data loss typically leads to the absence of critical information in a system, impacting the quality of environmental crisis response decisions and potentially causing missed intervention opportunities. Calculating the data conversion loss coefficient effectively identifies and quantifies the potential impact of lost data on the system, thereby optimizing data processing workflows and emergency response capabilities. By comprehensively considering the rate of data loss and its impact on decision-making, the data conversion loss coefficient not only reveals the severity of data loss but also helps the system assess the scope of the impact of lost data, enabling targeted measures to be taken. Specifically, the data conversion loss coefficient reflects the negative impact of lost data on environmental monitoring and disaster response decisions, ensuring that decision-makers can adjust data collection and conversion strategies in a timely manner to prevent decision-making errors or response delays due to the loss of critical information. A larger data conversion loss coefficient indicates a greater amount of data lost during data format conversion and a more severe impact on the system. This means the system may lose critical monitoring information or environmental change data, affecting timely response and decision-making in environmental crises, delaying emergency response measures, and potentially leading to incorrect decisions or wasted resources, and in severe cases, exacerbating the consequences of disasters. Conversely, a smaller data conversion loss coefficient indicates a lower degree of data loss, a relatively stable data conversion process in the system, less data loss, and a smaller impact on overall monitoring and emergency response. This means higher data integrity and accuracy, effectively supporting environmental monitoring, real-time early warning, and decision support, reducing potential risks caused by data loss, thereby ensuring more efficient cross-departmental collaboration and resource allocation, and improving the timeliness and accuracy of emergency response.

[0066] Therefore, by obtaining data conversion loss information during the data format conversion process, the data conversion loss situation during the data format conversion process can be analyzed, and the data conversion loss coefficient can be obtained to measure the degree of data conversion loss during the data format conversion process.

[0067] The logic for obtaining the data transformation loss coefficient is as follows:

[0068] Within the time window T, obtain the original data volume of each data point before data format conversion. The amount of data after data format conversion for each data point Calculate the data conversion loss rate The expression is as follows ,in This represents the amount of data after the j-th data point is transformed. This represents the original data volume of the j-th data point. , For positive integers; calculate the scope of impact of data loss. ,in To determine the scope of the data loss impact, This represents the influence coefficient of the j-th data point on environmental monitoring and emergency decision-making;

[0069] It should be noted that the impact coefficient is used to measure the importance of data points to environmental monitoring and emergency decision-making. The value range is [0,1]. The larger the value, the more important the data point is to the decision. It can be set by the importance of the data source.

[0070] Calculate the time-loss coefficient The expression is as follows ,in This represents the time-weighted function. , Indicates the current moment. This represents the time when the nth data point was lost. , It is a positive integer;

[0071] Calculate the data conversion loss coefficient The expression is as follows ;

[0072] It should be noted that before calculating the data transformation loss coefficient, it is necessary to ensure that the data transformation loss ratio, the scope of data loss impact, and the loss timeliness coefficient have all been normalized. Commonly used normalization methods include Min-Max normalization and Z-Score standardization. The settings can be made according to the actual situation. For example, the expert empowerment method can be adopted, which involves inviting experts in relevant fields to determine the preset ratio coefficients of each indicator through professional opinion surveys and comprehensive evaluations.

[0073] The noise interference response module is used to acquire data noise interference information during the data format conversion process and to obtain the data noise interference coefficient based on the data noise interference information.

[0074] The data noise interference coefficient measures the impact of external interference (such as electromagnetic interference and environmental noise) on data quality and reliability during data format conversion. During conversion, data may be distorted, inaccurate, or lost due to external interference, affecting the accuracy of system decisions. Especially in smart city environmental monitoring systems involving real-time monitoring and emergency response, data precision and accuracy are crucial; therefore, the noise interference coefficient becomes an important parameter for evaluating the stability and effectiveness of the data conversion process. The data noise interference coefficient reflects the impact of external environmental factors (such as electromagnetic interference and environmental noise) on the data format conversion process. A high noise interference coefficient indicates that the data may be subject to significant distortion or error during conversion, leading to a decrease in data quality and consequently affecting the accuracy of system decisions.

[0075] Therefore, by acquiring data noise interference information during the data format conversion process, analyzing the data noise interference situation during the data format conversion process, and obtaining the data noise interference coefficient, the degree of data noise interference during the data format conversion process can be measured.

[0076] The logic for obtaining the data noise interference coefficient is as follows:

[0077] Obtain the format-converted data and perform a wavelet transform on it, as follows: ,in This represents the wavelet coefficients of the data at scale a and location b after format conversion. For the complex conjugate of wavelet basis functions, For the converted data, calculate the energy of the wavelet coefficients at scale a and location b. The expression is as follows The energy at different scales is compared with preset scale thresholds and energy thresholds. If scale a is less than or equal to the scale threshold and the energy is greater than the energy threshold, the energy is accumulated to calculate the data noise interference coefficient. ;

[0078] In wavelet transform, the scale controls the degree of scaling of the wavelet basis function. A smaller scale means a higher temporal (or spatial) resolution of the wavelet function, i.e., a stronger local analysis capability. Energy represents the degree of local variation of the signal at that scale.

[0079] Therefore, a smaller scale with higher energy usually indicates stronger local variations in the signal at that scale, which may be noise or rapidly changing signal components. Specifically:

[0080] Smaller scales mean a higher degree of temporal or spatial localization of the wavelet function, enabling it to capture more subtle signal changes, especially rapidly changing signal components. Smaller scales correspond to the high-frequency components of the signal.

[0081] Higher energy indicates more drastic changes in the signal at that scale, typically noise or high-frequency components. High energy suggests significant fluctuations or irregularities at that scale.

[0082] The accuracy loss response module is used to obtain data accuracy loss information during the data format conversion process and to obtain the data accuracy loss coefficient based on the accuracy loss information.

[0083] The data precision loss coefficient measures the degree of data precision loss during data format conversion due to conversion operations, data scaling, or other factors. Precision loss can affect the accuracy of data analysis and decision-making, especially in environmental monitoring and emergency response systems, where it can impact the timely perception of environmental changes and the effectiveness of decision-making. The data precision loss coefficient reflects potential data quality problems caused by precision degradation during data format conversion. Assessing this coefficient helps the system identify potential risks early, particularly in environmental crisis emergency response. By quantifying precision loss and assessing its potential impact, the system can more accurately judge the risks of inaccurate data or flawed decision-making during the conversion process, thereby providing more reliable information support in environmental monitoring, disaster response, and resource allocation. A high data precision loss coefficient indicates severe precision loss during data conversion, requiring the system to detect and respond promptly to avoid erroneous decisions due to precision loss. This is particularly important for cross-departmental collaboration, ensuring consistent and accurate data across departments, reducing communication barriers caused by information errors, and promoting more timely and effective emergency response and pollution control measures.

[0084] Therefore, by obtaining information on data precision loss during the data format conversion process, analyzing the data precision loss situation during the data format conversion process, and obtaining the data precision loss coefficient, the degree of data precision loss during the data format conversion process can be measured.

[0085] The logic for obtaining the data precision loss coefficient is as follows:

[0086] Obtain the original dataset before format conversion and the dataset after format conversion ,in This represents the data point in the g-th original dataset. This represents a data point in the dataset after the g-th format conversion;

[0087] Calculate the entropy of the original dataset The expression is as follows ,in Data points In the original dataset The probability of it appearing in;

[0088] Calculate the entropy of the dataset after format conversion. The expression is as follows ,in Data points Dataset after format conversion The probability of it appearing in;

[0089] Calculate joint entropy The expression is as follows ,in It is the probability that each pair of data points appears in both datasets;

[0090] Computing mutual information The expression is as follows ;

[0091] Calculate the data accuracy loss coefficient The expression is as follows ;

[0092] The comprehensive evaluation module is used to construct a data conversion risk assessment model based on data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, generate a data conversion risk assessment index, and assess the format conversion risks during the data format conversion process.

[0093] A data conversion risk assessment model is constructed based on the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, generating a data conversion risk assessment index. The model is based on the following formula: In the formula These represent the preset proportional coefficients for the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, respectively. All are greater than 0;

[0094] It should be noted that before constructing the data conversion risk assessment model, it is necessary to ensure that the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient have all been normalized. Commonly used normalization methods include Min-Max normalization and Z-Score standardization. The settings can be made according to the actual situation. For example, the expert empowerment method can be adopted, which involves inviting experts in relevant fields to determine the preset ratio coefficients of each indicator through professional opinion surveys and comprehensive evaluations.

[0095] As can be seen from the above calculation expressions, the larger the data conversion delay coefficient, the larger the data conversion loss coefficient, the larger the data noise interference coefficient, and the larger the data precision loss coefficient, the larger the data conversion risk assessment index. This means that the system has significant data conversion problems, which may lead to data distortion, delay, loss, and other phenomena, affecting the quality of information sharing and the accuracy of decision-making. Conversely, the smaller the data conversion delay coefficient, the smaller the data conversion loss coefficient, the smaller the data noise interference coefficient, and the smaller the data precision loss coefficient, the smaller the data conversion risk assessment index. This represents the superiority of the system's data quality, indicating that the system has a strong ability to respond to environmental crises, responds efficiently, and utilizes resources optimally. This plays a crucial role in ensuring the sustainable development and safety management of smart cities.

[0096] The early warning decision module is used to make early warning decisions on the potential risks of current data format conversion based on the assessment results of the potential risks of format conversion.

[0097] The data conversion risk assessment index is compared with the preset data conversion risk assessment index threshold to make early warning decisions on the conversion format risks of the current data format conversion, as follows:

[0098] If the data conversion hazard assessment index is greater than or equal to the data conversion hazard assessment index threshold, it indicates that there is a significant hazard in the current data format conversion process, and the system should immediately trigger an early warning and generate an early warning signal. In this case, quality problems in the data conversion process may lead to distortion or delay of the basic data on which critical decisions depend, affecting the effectiveness of the entire emergency response process. If the data conversion hazard assessment index is less than or equal to the data conversion hazard assessment index threshold, it indicates that the hazard in the data format conversion process is within an acceptable range, and there is no need to generate an early warning signal. The current data format conversion process does not have significant delays, data loss, noise interference, or accuracy loss problems, and data processing and conversion can continue according to the predetermined process to support subsequent environmental monitoring and decision analysis.

[0099] This invention utilizes conversion delay response modules, information loss response modules, noise interference response modules, and accuracy loss response modules to acquire and calculate data conversion delay coefficients, data conversion loss coefficients, data noise interference coefficients, and data accuracy loss coefficients, promptly identifying conversion delays, information loss, noise interference, and accuracy loss issues during data format conversion. It constructs a data conversion hazard assessment model, generates a data conversion hazard assessment index, and evaluates format conversion hazards during the data format conversion process. This ensures that the system maintains data accuracy and timeliness during environmental crisis emergency responses, avoiding decision-making errors and response delays caused by inaccurate or delayed data. Based on the hazard assessment results, it can automatically issue early warnings and make corresponding decisions, indicating whether the system needs to adjust data processing procedures or perform emergency repairs. This timely and intelligent early warning mechanism effectively improves the system's emergency response capabilities, especially during environmental disasters, enabling rapid and accurate triggering of emergency response measures to minimize the impact of environmental crises on cities and residents.

[0100] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0101] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0102] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0103] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart city environmental monitoring system, characterized in that: It includes a conversion delay response module, an information loss response module, a noise interference response module, an accuracy loss response module, a comprehensive evaluation module, and an early warning decision module; The conversion delay response module is used to obtain data conversion delay information during the data format conversion process, and to obtain the data conversion delay coefficient based on the data conversion delay information; The information loss response module is used to obtain data conversion loss information during the data format conversion process and to obtain the data conversion loss coefficient based on the data conversion loss information. The noise interference response module is used to acquire data noise interference information during the data format conversion process and to obtain the data noise interference coefficient based on the data noise interference information. The accuracy loss response module is used to obtain data accuracy loss information during the data format conversion process and to obtain the data accuracy loss coefficient based on the accuracy loss information. The comprehensive evaluation module is used to construct a data conversion risk assessment model based on data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, generate a data conversion risk assessment index, and assess the format conversion risks during the data format conversion process. The early warning decision module is used to make early warning decisions on potential format conversion risks of the current data format conversion based on the assessment results of the format conversion risks.

2. The smart city environmental monitoring system according to claim 1, characterized in that: By acquiring data conversion delay information during the data format conversion process, the data conversion delay situation is analyzed, and a data conversion delay coefficient is obtained to measure the degree of data conversion delay in the data format conversion process. The logic for obtaining the data conversion delay coefficient is as follows: A timestamp mechanism is used to record the time points of each stage of data format conversion, including the start timestamp of data format conversion. and data format conversion end timestamp Calculate data conversion delay The expression is as follows ,in The expected data format conversion time; obtain the set of delayed data within the time window T. ,in This represents the data conversion delay for the i-th data type. , It is a positive integer; Calculate the average delay of data conversion within the time window T. The expression is as follows ; Calculate the transformed standard deviation of data within the time window T. The expression is as follows ; Calculate the data conversion delay factor The expression is as follows ,in The preset scaling factor, representing the standard deviation of the data transformation, is used to reflect the significance of data transformation delay fluctuations. This indicates the maximum allowable delay value.

3. The smart city environmental monitoring system according to claim 1, characterized in that: By acquiring data conversion loss information during the data format conversion process, the data conversion loss situation is analyzed, and a data conversion loss coefficient is obtained to measure the degree of data conversion loss during the data format conversion process. The logic for obtaining the data transformation loss coefficient is as follows: Within the time window T, obtain the original data volume of each data point before data format conversion. The amount of data after data format conversion for each data point Calculate the data conversion loss rate The expression is as follows ,in This represents the amount of data after the j-th data point is transformed. This represents the original data volume of the j-th data point. , For positive integers; calculate the scope of impact of data loss. ,in To determine the scope of the data loss impact, This represents the influence coefficient of the j-th data point on environmental monitoring and emergency decision-making; Calculate the time-loss coefficient The expression is as follows ,in This represents the time-weighted function. , Indicates the current moment. This represents the time when the nth data point was lost. , It is a positive integer; Calculate the data conversion loss coefficient The expression is as follows .

4. The smart city environmental monitoring system according to claim 1, characterized in that: By acquiring data noise interference information during the data format conversion process, the data noise interference situation during the data format conversion process is analyzed, and the data noise interference coefficient is obtained to measure the degree of data noise interference during the data format conversion process. The logic for obtaining the data noise interference coefficient is as follows: Obtain the format-converted data and perform a wavelet transform on it, as follows: ,in This represents the wavelet coefficients of the data at scale a and location b after format conversion. For the complex conjugate of wavelet basis functions, For the converted data, calculate the energy of the wavelet coefficients at scale a and location b. The expression is as follows ; The energy at different scales is compared with a preset scale threshold and an energy threshold. If scale a is less than or equal to the scale threshold and the energy is greater than the energy threshold, the energy is accumulated to calculate the data noise interference coefficient. .

5. The smart city environmental monitoring system according to claim 1, characterized in that: By acquiring information on data precision loss during the data format conversion process, we analyze the data precision loss during the data format conversion process and obtain a data precision loss coefficient to measure the degree of data precision loss during the data format conversion process. The logic for obtaining the data precision loss coefficient is as follows: Obtain the original dataset before format conversion and the dataset after format conversion ,in This represents the data point in the g-th original dataset. This represents a data point in the dataset after the g-th format conversion; Calculate the entropy of the original dataset The expression is as follows ,in Data points In the original dataset The probability of it appearing in; Calculate the entropy of the dataset after format conversion. The expression is as follows ,in Data points Dataset after format conversion The probability of it appearing in; Calculate joint entropy The expression is as follows ,in It is the probability that each pair of data points appears in both datasets; Computing mutual information The expression is as follows ; Calculate the data accuracy loss coefficient The expression is as follows .

6. The smart city environmental monitoring system according to claim 1, characterized in that: A data conversion risk assessment model is constructed based on the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, generating a data conversion risk assessment index. The model is based on the following formula: In the formula These represent the preset proportional coefficients for the data conversion delay coefficient, data conversion loss coefficient, data noise interference coefficient, and data accuracy loss coefficient, respectively. All are greater than 0.

7. A smart city environmental monitoring system according to claim 6, characterized in that: The data conversion risk assessment index is compared with the preset data conversion risk assessment index threshold to make early warning decisions on the conversion format risks of the current data format conversion, as follows: If the data conversion risk assessment index is greater than or equal to the data conversion risk assessment index threshold, it indicates that there is a significant risk in the current data format conversion process, and the system should immediately trigger an early warning and generate an early warning signal; if the data conversion risk assessment index is less than or equal to the data conversion risk assessment index threshold, it indicates that the risk in the data format conversion process is within an acceptable range, and there is no need to generate an early warning signal.

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