Mass monitoring and control terminal access environmental management platform operation method

Abstract

The application discloses an environmental protection management and control platform operation method for mass monitoring and monitoring terminal access, and relates to the technical field of data calculation. The method comprises the following steps: acquiring environmental monitoring data and determining the pollution intensity of different types of environmental monitoring data; based on the pollution intensity, the time difference between different types of environmental monitoring data at different time and the last time when pollution existed, and the proportion of pollution occurring at the same time every day in each point in the historical time, the historical state weight of each point at each time is calculated; based on the overall pollution level corresponding to the pollution intensity and the pollution intensity trend, the real-time trigger weight of each point corresponding to different types of environmental monitoring data is calculated; based on the historical state weight and the real-time trigger weight, the pollution weight of different points at the current time is calculated; based on the pollution weight, the real-time bandwidth allocation proportion of each monitoring terminal is determined. The application achieves the technical effect of reducing data loss caused by transmission quality.

Description

Technical Field

[0001] This invention relates to the field of data computing technology, specifically to an operation method for an environmental management and control platform with access to a massive number of monitoring and surveillance terminals. Background Technology

[0002] With the maturity of technologies such as the Internet of Things, cloud computing, and artificial intelligence, environmental management and control platforms are beginning to transform towards digitalization, intelligence, and integration. Large-scale and diverse monitoring and control equipment deployed in and around the plant area, such as air micro-stations, water quality monitoring stations, VOC (volatile organic compounds) / TSP (total suspended particulate matter) monitors, noise monitors, and high-definition video cameras, form the foundation for the platform to achieve full-area perception. The environmental management and control platform can correlate, complement, and combine data from different terminals and different types (such as sensor data, video data, and meteorological data) to ultimately achieve precise pollution control and collaborative governance.

[0003] However, managing the data from a massive number of monitoring terminals is a key challenge for the operation of environmental management and governance platforms. Due to the large number of terminals, data loss or corruption may occur if the data transmission quality of each terminal cannot be guaranteed. Summary of the Invention

[0004] To address the technical problem in related technologies where the large number of terminals makes it impossible to guarantee the data transmission quality of each terminal, potentially leading to data loss or corruption, this invention provides an operation method for an environmental management and control platform with access to a massive number of monitoring and surveillance terminals.

[0005] The specific technical solution adopted is as follows:

[0006] Obtain environmental monitoring data from different locations reported by various monitoring terminals in the environmental management and control platform, and determine the pollution intensity of different types of environmental monitoring data;

[0007] Based on pollution intensity, the time difference between different times and the previous time when pollution existed, and the proportion of pollution occurring at each location at the same time every day in historical time, the historical state weights of different locations at each time are calculated.

[0008] Based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, the real-time trigger weights of different types of environmental monitoring data at each location are calculated.

[0009] Based on historical state weights and real-time trigger weights, the pollution weights of different locations at the current moment are calculated.

[0010] Based on pollution weights, the real-time bandwidth allocation ratio for each monitoring terminal is determined, and the upload bandwidth of each monitoring terminal is dynamically allocated based on the real-time bandwidth allocation ratio to ensure stable transmission of environmental monitoring data.

[0011] In one possible implementation of this application, based on pollution intensity, the time difference between different times and the previous time when pollution existed, and the proportion of pollution occurring at each location at the same time each day in historical time periods, the historical state weights of different locations at each time are calculated, including:

[0012] Based on the pollution intensity and the time difference between different times and the previous time when pollution existed, the historical pollution discharge evaluation value of each point was calculated.

[0013] Based on the proportion of pollution occurring at each location at the same time each day in historical time periods, the repetition rate of pollution occurring at different locations at different times is calculated.

[0014] Based on the repetition rate and historical pollution discharge evaluation values, the historical state weights of different locations at various times are calculated.

[0015] In one possible implementation of this application, based on pollution intensity and the time difference between different times and the previous time when pollution existed, the historical pollution discharge evaluation value of each location is calculated, including:

[0016] Determine the first ratio between the time difference of various environmental monitoring data at different times and the previous time when pollution existed, and the pollution intensity;

[0017] Based on the total number of times corresponding to all environmental monitoring data, the first ratio is summed and averaged to calculate the historical pollution discharge evaluation value for each location.

[0018] In one possible implementation of this application, the repetition rate of pollution at different locations at different times is calculated based on the proportion of pollution occurring at each location at the same time each day in historical time periods, including:

[0019] For any given location, determine the first day of the environmental monitoring data transmitted at that location within the historical time period, as well as the maximum pollution intensity of various types of environmental monitoring data at that location at different times of the day.

[0020] Based on the maximum pollution intensity and the number of days in the first day, determine the proportion of pollution occurring at each location at the same time each day in the historical time period;

[0021] Based on the normalized value of the proportion, the repetition rate of pollution at different locations at different times is determined.

[0022] In one possible implementation of this application, based on the repetition rate and historical pollution discharge evaluation values, the historical state weights of different locations at various times are calculated, including:

[0023] The repetition rate of each location at all times is compared with a preset repetition rate threshold, and the times with repetition rates greater than the preset repetition rate threshold are marked as high-demand times.

[0024] Based on the difference between the repetition rate during high-demand periods and the preset repetition rate threshold, the adjustment coefficient for historical pollution discharge evaluation values ​​is determined.

[0025] Based on the normalized value of the product between the adjustment coefficient and the historical pollution discharge evaluation value, the historical state weights of different locations at each time point are calculated.

[0026] In one possible implementation of this application, based on the overall pollution level corresponding to the pollution intensity and the pollution intensity trend, the real-time trigger weights for different types of environmental monitoring data at each location are calculated, including:

[0027] Select all moments within a preset time period prior to the current moment as the trigger length;

[0028] For any given location, determine the first average value of the pollution intensity of various environmental monitoring data at that location;

[0029] Based on the difference between the first average value and the preset pollution threshold, the overall pollution level of different types of environmental monitoring data at each location is determined.

[0030] The pollution intensity trend value is calculated based on the difference between the pollution intensity at any time within the trigger length and the average pollution intensity of the time adjacent to the current time.

[0031] Based on the normalized value of the product between the overall pollution level and the pollution intensity trend value, the real-time trigger weights for different types of environmental monitoring data at each location are calculated.

[0032] In one possible implementation of this application, the pollution weight of different locations at the current moment is calculated based on historical state weights and real-time trigger weights, including:

[0033] For any given location, determine the real-time trigger weight of all locations within a preset range around the current location at the current moment, as well as the maximum real-time trigger weight of all types of environmental monitoring data at the current location.

[0034] The pollution weight of different locations at the current moment is calculated based on the product of historical state weights, real-time trigger weights, and the maximum weight.

[0035] In one possible implementation of this application, after calculating the real-time trigger weights for different types of environmental monitoring data at each location based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, the method further includes:

[0036] The real-time trigger weight is compared with the preset trigger threshold. If the real-time trigger weight is greater than the preset trigger threshold, the monitoring terminal is activated to take pictures of the points corresponding to the real-time trigger weight.

[0037] When the monitoring terminal detects emission behavior at any point, the real-time trigger weight of the current point is amplified to obtain the amplified real-time trigger weight.

[0038] Based on historical state weights and real-time trigger weights, the pollution weights of different locations at the current moment are calculated, including:

[0039] Based on the historical state weights and the real-time trigger weights after gain, the pollution weights of different points at the current moment are calculated.

[0040] In one possible implementation of this application, the real-time bandwidth allocation ratio for each monitoring terminal is determined based on pollution weight, including:

[0041] For any monitoring terminal, calculate the second average value of the pollution weight of all points collected by the current monitoring terminal, and use the second average value as the allocation evaluation value of the current monitoring terminal.

[0042] The real-time bandwidth allocation ratio of each monitoring terminal is calculated based on the ratio between the allocated evaluation value and the sum of the allocated evaluation values ​​of each monitoring terminal.

[0043] In one possible implementation of this application, after determining the real-time bandwidth allocation ratio of each monitoring terminal based on pollution weight, the method further includes:

[0044] If the pollution impact range of any point is determined to exceed the preset threshold, environmental governance equipment will be mobilized to carry out coordinated pollution control in order to promptly suppress the degree of pollution impact at each point.

[0045] The present invention has, but is not limited to, the following technical effects:

[0046] By acquiring environmental monitoring data from different locations reported by various monitoring terminals in the environmental management and control platform, and determining the pollution intensity of different types of environmental monitoring data, the historical state weights of different locations at each time are calculated based on pollution intensity, the time difference between different times and the previous time when pollution occurred, and the proportion of pollution occurring at each location at the same time each day in historical time. Based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, the real-time trigger weights for different types of environmental monitoring data at each location are calculated. Combining the historical state weights and real-time trigger weights, the pollution weights of different locations at the current time are calculated. Based on the pollution weights, the real-time bandwidth allocation ratio of each monitoring terminal is determined to ensure stable transmission of environmental monitoring data. This achieves intelligent allocation of upload bandwidth for monitoring terminals. Dynamic bandwidth allocation ensures that environmental monitoring data can be transmitted to the platform in a timely and stable manner when pollution occurs, enabling governance decisions and reducing data loss due to transmission quality issues. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the first embodiment of the environmental management and control platform operation method for accessing a large number of monitoring and surveillance terminals as described in this application.

[0048] Figure 2 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0050] This application provides an operation method for an environmental management and control platform with access to a large number of monitoring terminals. In the first embodiment of this application's operation method for an environmental management and control platform with access to a large number of monitoring terminals, refer to... Figure 1 The methods include:

[0051] Step S10: Obtain environmental monitoring data from different locations reported by various monitoring terminals in the environmental protection management and control platform, and determine the pollution intensity of different types of environmental monitoring data.

[0052] As an example, the operation method of the environmental management and control platform accessed by the massive monitoring and control terminals can be applied to the operation device of the environmental management and control platform accessed by the massive monitoring and control terminals. The operation device of the environmental management and control platform accessed by the massive monitoring and control terminals belongs to the operation system of the environmental management and control platform accessed by the massive monitoring and control terminals. The operation system of the environmental management and control platform accessed by the massive monitoring and control terminals belongs to the operation equipment of the environmental management and control platform accessed by the massive monitoring and control terminals.

[0053] As an example, an environmental management and control platform is used to manage environmental monitoring data collected by its various monitoring terminals. These monitoring terminals can be air quality monitoring stations, VOCs, TSPs, cameras, etc. A monitoring network is constructed by combining the locations of each monitoring terminal, and the acquired environmental monitoring data includes, but is not limited to:

[0054] Air quality monitoring stations: monitor air quality data such as PM2.5, nitrogen dioxide, and sulfur dioxide concentrations;

[0055] VOC monitoring equipment: detects the concentration of volatile organic compounds in the air;

[0056] TSP monitoring equipment: detects atmospheric particulate matter in the air;

[0057] Cameras: AI algorithms are used to automatically identify and capture typical unorganized emission behaviors.

[0058] As an example, each monitoring terminal monitors multiple points within its coverage area. The method for determining each point can be: uniformly selecting points within the factory area and its surroundings. For example, for every 3m x 3m area, the center of that area is recorded as a point, thereby obtaining various environmental monitoring data for all points.

[0059] As an example, the environmental monitoring data collected by various monitoring terminals have different numerical ranges and standards. To facilitate subsequent analysis and calculation, the various types of environmental monitoring data are first quantified. Here, we first analyze environmental protection control, combining the collected data values ​​and the preset pollution thresholds for various types of environmental monitoring data to quantify them. The preset pollution thresholds are determined based on relevant standards or actual application needs within the factory area. For example, the 1-hour average concentration limit for nitrogen dioxide in the air is 200 μg / m³. 3 If the value is set as the preset pollution threshold for nitrogen dioxide concentration, the specific value can be adjusted according to actual needs.

[0060] As an example, taking the k-th type of data from any point collected by the monitoring terminal, the pollution intensity of the k-th type of environmental monitoring data at time t is... The calculation method is as follows:

[0061]

[0062] In the formula: This represents the monitoring value of the k-th type of environmental monitoring data at time t. This represents the preset pollution threshold for the k-th type of environmental monitoring data. This value is used as the denominator to limit the numerical range and avoid large differences in values ​​due to the difference in magnitude between different types of environmental monitoring data. Similarly, the pollution intensity of various types of environmental monitoring data at all times can be obtained. When the k-th type of environmental monitoring data at time t does not reach the preset pollution threshold, the pollution intensity value is negative, and it is positive when it exceeds the preset pollution threshold. The greater the pollution intensity, the more serious the pollution of a certain indicator at the corresponding time.

[0063] Step S20: Based on pollution intensity, the time difference between different times and the previous time when pollution existed, and the proportion of pollution occurring at each location at the same time every day in the historical time, calculate the historical state weight of each location at each time.

[0064] As an example, the purpose of this application embodiment is to achieve dynamic transmission based on the transmission characteristics of each monitoring terminal in the factory area and the network status at each time period, so as to avoid data loss or anomalies caused by transmission fluctuations, improve the data management quality of the platform, and improve the efficiency of network resource utilization. In order to avoid centralized transmission, it is necessary to dynamically allocate network bandwidth resources to avoid network congestion affecting data transmission quality. When performing analysis, a heat map is constructed based on the historical pollution status of each point, considering the severity and frequency of pollution at each point, to reflect the distribution of pollution status in location. The time difference between different times of various environmental monitoring data and the previous time when pollution occurred is used to reflect the frequency of pollution of each type of environmental monitoring data in the historical time period. The proportion of pollution at each point at the same time of each day in the historical time period is used to represent the proportion of the number of days with pollution in the historical days. For example, if the historical days are 3 days and the number of days with pollution is 1 day, then the proportion is 1 / 3.

[0065] As an example, historical state weights can be calculated based on the characteristics exhibited by all historical data and used as the basis for bandwidth allocation.

[0066] Step S20 further includes steps S21 to S23, including:

[0067] Step S21: Based on the pollution intensity and the time difference between different times and the previous time when pollution existed, calculate the historical pollution discharge evaluation value of each point.

[0068] As an example, historical pollution discharge assessment values ​​are used to reflect the overall pollution level shown by historical environmental monitoring data at each location. The larger the value, the more serious the overall pollution at that location. When implementing dynamic bandwidth allocation, priority should also be given to data transmission of polluting emissions, and high-precision monitoring should be carried out on the location and time period involved in the relevant waste emissions.

[0069] As an example, for locations that are frequently or severely polluted, the more likely they are to be fixed discharge points, the more likely they are to become polluted again. Therefore, when pre-allocating the upload bandwidth for data from each monitoring terminal, locations that are frequently or severely polluted will have a higher priority.

[0070] Step S21 includes:

[0071] Determine the first ratio between the time difference of various environmental monitoring data at different times and the previous time when pollution existed, and the pollution intensity.

[0072] Based on the total number of times corresponding to all environmental monitoring data, the first ratio is summed and averaged to calculate the historical pollution discharge evaluation value for each location.

[0073] As an example, taking the historical pollution discharge assessment value of the i-th point as an example, the historical pollution discharge assessment value... The calculation method is as follows:

[0074]

[0075] in: This represents the total number of moments in all historical environmental monitoring data. This represents the pollution intensity of the k-th type of environmental monitoring data at the i-th location at time t. This represents the time when pollution was present between the t-th time and the k-th point in the environmental monitoring data of the i-th location (i.e., time t). The time difference between (the exact moments) is used as the denominator to reflect the frequency of historical pollution. This represents the ReLU function, and its expression is: avg() represents the average value operation. This represents the first ratio.

[0076] As an example, a pollution distribution heat map of the factory area is constructed based on the historical pollution discharge assessment values ​​of all points in and around the factory area. The overall pollution level of each point is visualized according to historical environmental monitoring data, which makes it easier to intuitively reflect the concentration of pollution discharge in terms of location characteristics. This is beneficial for initially identifying high-risk locations of environmental problems during environmental protection management and control, thereby improving management efficiency.

[0077] As an example, historical pollution discharge evaluation values ​​are parameters obtained based on the average conditions shown by historical environmental monitoring data of each location. However, in actual monitoring, the pollution characteristics of each location are different at different times, and the pollution discharge status of each location may show certain time patterns. For example, exhaust gas shows the characteristic of concentrated emissions at certain time points. Then, various data of exhaust gas emission-related locations will show a certain periodicity. Therefore, it is necessary to analyze the concentrated periods of pollution occurrence at each location to determine the pollution frequency periods of each location within a day. The periods of concentrated pollution occurrence are the periods with a high pollution repetition rate, that is, the periods with high upload bandwidth requirements of the relevant monitoring terminals at that location.

[0078] Step S22: Based on the proportion of pollution occurring at each location at the same time each day in historical time periods, calculate the repetition rate of pollution occurring at different locations at different times.

[0079] As an example, if a relatively high level of pollution at a certain location occurs continuously within a fixed time period, the pollution at that location may exhibit a certain periodicity. The repetition rate is used to represent the repetitive pattern in the timing characteristics of pollution occurrence at various locations.

[0080] Step S22 includes:

[0081] For any given location, determine the first day of the environmental monitoring data transmitted at that location within the historical time period, as well as the maximum pollution intensity of various types of environmental monitoring data at different times of the day at that location.

[0082] Based on the maximum pollution intensity and the number of days in the first day, the proportion of pollution occurring at each location at the same time each day in the historical time period is determined.

[0083] Based on the normalized value of the proportion, the repetition rate of pollution at different locations at different times is determined.

[0084] As an example, let's take the r-th moment of each day (where the r-th moment refers to the r-th moment within a 24-hour period) as an example. What is the repetition rate of the i-th point at the r-th moment? The calculation method is as follows:

[0085]

[0086] in: This represents the total number of days within a historical time period, that is, the first day. This represents the maximum pollution intensity of various data at the i-th location at the r-th time on day d. (x) denotes the sign function, where x is greater than 0 and takes the sign of 1, x is less than 0 and takes the sign of -1, and x is equal to 0 and takes the sign of 0. This represents the number of days since the i-th location was contaminated at time r, i.e., for any class (k classes) of data at time r on day d, the following applies: If this occurs, contamination occurs. Although the upload bandwidth is based on each monitoring terminal, in actual monitoring, contamination monitoring is not performed independently for each type of monitoring terminal. Therefore, the presence of contamination in any type of data at a given location is meaningful for all monitoring terminals involved at that location. Represents a linear normalization function. This represents the percentage of pollution occurring at each location at the same time each day in historical time. Similarly, the repetition rate of pollution occurring at each location at each time can be calculated.

[0087] Step S23: Based on the repetition rate and historical sewage discharge evaluation values, calculate the historical state weights of different locations at each time point.

[0088] Step S23 includes:

[0089] The repetition rate of each location at all times is compared with a preset repetition rate threshold, and the times corresponding to the repetition rates that are greater than the preset repetition rate threshold are marked as high-demand times.

[0090] As an example, the preset repetition rate threshold can be 0.3, 0.4, etc., and there is no specific limitation.

[0091] As an example, when the repetition rate of each point is greater than the preset repetition rate threshold, the time corresponding to that point is marked as a high-demand time. The demand time is the time when the probability of pollution at the corresponding point is relatively high. Therefore, at the high-demand time of the i-th point, the bandwidth pre-allocated to its related monitoring terminals needs to be increased to ensure that the data of possible pollution can be collected normally and uploaded to the environmental protection management and control platform in a timely manner.

[0092] The adjustment coefficient for historical pollution discharge evaluation values ​​is determined based on the difference between the repetition rate at high demand times and the preset repetition rate threshold.

[0093] Based on the normalized value of the product between the adjustment coefficient and the historical pollution discharge evaluation value, the historical state weights of different locations at each time point are calculated.

[0094] As an example, taking the i-th point at time r as an example, the historical state weights The calculation method can be:

[0095]

[0096] in: This represents the historical pollution discharge evaluation value of the i-th point. This represents the recurrence rate of contamination occurring at the i-th location at time r. This represents the preset repetition rate threshold, which is set here. This means that the weight is increased only during periods of high demand, while no changes are needed during periods of lower demand. This represents the adjustment coefficient. Similarly, the historical state weights of each location at each time point can be obtained. Thus, based on the characteristics exhibited by all historical environmental monitoring data, the historical state weights are obtained, which serve as the basic weights for bandwidth allocation.

[0097] Step S30: Based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, calculate the real-time trigger weight of different types of environmental monitoring data corresponding to each location.

[0098] As an example, the timing of pollution occurrences is often not fixed during actual monitoring. If bandwidth pre-allocation is based entirely on historical environmental monitoring data, it may be impossible to guarantee stable transmission when irregular pollution occurs temporarily. Therefore, it is also necessary to analyze the current pollution trend in real time and trigger bandwidth allocation in a timely manner according to the real-time pollution situation. Based on this, the real-time trigger weights for different types of environmental monitoring data at each location are calculated according to the overall pollution level and pollution intensity trend of each location.

[0099] As an example, real-time trigger weights are used to represent the changes in pollution levels over a short period of time before the current moment. This better reflects the real-time status of each location and facilitates timely adjustment of monitoring terminal bandwidth allocation based on the real-time pollution situation to cope with sudden pollution emissions.

[0100] Step S30 includes:

[0101] Select all times within a preset time period prior to the current time as the trigger length.

[0102] As an example, the preset time period can be 3 minutes, 4 minutes, etc., and there is no specific limitation. Taking a preset time period of 3 minutes as an example, all moments within the 3 minutes before the current moment are recorded as the trigger length, which is the time length of the trigger moment. Various environmental monitoring data within the trigger length are extracted for calculation to analyze the real-time pollution data upward trend.

[0103] For any given location, determine the first average value of the pollution intensity of various environmental monitoring data at that location.

[0104] Based on the difference between the first average value and the preset pollution threshold, the overall pollution level of different types of environmental monitoring data at each location is determined.

[0105] The pollution intensity trend value is calculated based on the difference between the pollution intensity at any time within the trigger length and the average pollution intensity of the time adjacent to the current time.

[0106] Based on the normalized value of the product between the overall pollution level and the pollution intensity trend value, the real-time trigger weights for different types of environmental monitoring data at each location are calculated.

[0107] As an example, taking the k-th type of environmental monitoring data at the i-th location as an example, the real-time trigger weight of the k-th type of environmental monitoring data at the i-th location... The calculation method is as follows:

[0108]

[0109] In the formula: This represents the average of all pollution intensities for the k-th type of data at the i-th location within the trigger length, which is also the first average. This represents the preset contamination threshold for the k-th type of data. Indicates the trigger length. This represents the contamination intensity of the k-th type of data at the i-th point at the l-th time of the trigger length. Let represent the average pollution intensity of the k-th type of data at the i-th location during the l-1 time intervals before the trigger length, denoted by . This indicates the overall contamination level of the current trigger length; the larger the value, the more severe the contamination. This value represents the trend of pollution intensity; a larger value indicates an upward trend.

[0110] After step S30, the following steps are also included:

[0111] The real-time trigger weight is compared with the preset trigger threshold. If the real-time trigger weight is greater than the preset trigger threshold, the monitoring terminal is activated to take pictures of the points corresponding to the real-time trigger weight.

[0112] When the monitoring terminal detects emission behavior at any point, the real-time trigger weight of the current point is amplified to obtain the amplified real-time trigger weight.

[0113] Based on historical state weights and real-time trigger weights, the pollution weights of different locations at the current moment are calculated, including:

[0114] Based on the historical state weights and the real-time trigger weights after gain, the pollution weights of different points at the current moment are calculated.

[0115] As an example, the preset trigger threshold can be 0.5, 0.6, etc., and there is no specific limitation.

[0116] As an example, when the real-time trigger weight is greater than the preset trigger threshold, the monitoring terminal is activated to capture images. The real-time trigger weight is based on the gain of the captured images to avoid missing the current emission behavior when periodically sampling frames. The gain processing method can be:

[0117] For example, when emission behavior is detected, the real-time trigger weight is increased. , here The gain coefficient is set to 0.2, and the value can be adjusted according to specific circumstances. If the real-time trigger weight after gain exceeds 1, then the maximum value of 1 is used. This represents the real-time trigger weight of the k-th type of environmental monitoring data at the i-th location.

[0118] Step S40: Based on the historical state weights and real-time trigger weights, calculate the pollution weights of different locations at the current moment.

[0119] As an example, the historical state weight is calculated based on the historical pollution state of each location, while the real-time trigger weight is obtained by analyzing the real-time pollution trend of each location. Combining these two weights, a comprehensive pollution weight for different locations at the current moment is calculated. Since the various emission behaviors that occur in the factory area often do not affect a single location, but have a certain tendency to spread, such as dust and exhaust gas emissions, when a certain location shows an upward trend in pollution, other locations around that location may also be affected. The pollution weight of each location at the current moment is obtained by combining the diffusion of pollution.

[0120] Step S40 includes:

[0121] For any given location, determine the real-time trigger weight of all locations within a preset range around the current location at the current moment, as well as the maximum real-time trigger weight of all types of environmental monitoring data at the current location.

[0122] The pollution weight of different locations at the current moment is calculated based on the product of historical state weights, real-time trigger weights, and the maximum weight.

[0123] As an example, by combining the pollution increase trend of surrounding points with the historical state weights and real-time trigger weights of each point at the current moment, the pollution weight of each point at the current moment is obtained. Here, the pollution weight of the i-th point at the current moment is... The calculation method is as follows:

[0124]

[0125] In the formula: This represents the historical state weight of the i-th point at time r. This represents the real-time trigger weights of all surrounding points of the i-th point at the current moment. The maximum value of the real-time trigger weight of all types of environmental monitoring data at the i-th point is also the maximum weight. Similarly, the pollution weight of each point at the current moment can be calculated. This pollution weight represents the pollution situation at the i-th point at the current moment. The larger the value, the higher the priority of the upload bandwidth required by the related monitoring terminal.

[0126] As an example, another way to calculate pollution weights is to dynamically combine the historical state weights and real-time trigger weights of each point, and let the neural network model automatically learn the optimal fusion strategy to achieve adaptive calculation of pollution weights.

[0127] Specifically, a two-channel parallel and adaptive gating architecture is set up in the neural network model. A bidirectional LSTM network is used to output historical state weights, and a feedforward network + attention mechanism is used to output real-time trigger weights. The outputs of the two channels are concatenated and the neural network outputs two weight coefficients with a sum of 1. This dynamically determines which channel is trusted more at the current moment. For example, when the situation is calm, the channel corresponding to the historical state weight is trusted more, and when pollution is triggered, the channel corresponding to the real-time trigger weight is trusted more. Then, the two weights are weighted and fused through the output layer and passed through a fully connected network + Sigmoid function to output the final pollution weight with a value range of [0,1].

[0128] Step S50: Based on pollution weight, determine the real-time bandwidth allocation ratio of each monitoring terminal, and dynamically allocate the upload bandwidth of each monitoring terminal based on the real-time bandwidth allocation ratio to ensure stable transmission of environmental monitoring data.

[0129] Step S50 includes:

[0130] For any given monitoring terminal, calculate the second average value of the pollution weights of all points collected by the current monitoring terminal, and use the second average value as the allocation evaluation value for the current monitoring terminal.

[0131] The real-time bandwidth allocation ratio of each monitoring terminal is calculated based on the ratio between the allocated evaluation value and the sum of the allocated evaluation values ​​of each monitoring terminal.

[0132] As an example, since different types of monitoring terminals have different monitoring ranges, for example, the k-th type of data at two certain points is collected by the same terminal, while the (k+1)-th type of data is collected by different terminals. Therefore, after obtaining the pollution weight of all points, the bandwidth allocation of each monitoring terminal is realized based on the average pollution weight of the points covered by each monitoring terminal.

[0133] Taking the m-th monitoring terminal as an example, the allocation evaluation value of the m-th monitoring terminal ,in Let represent the average pollution weight of all points collected by the m-th monitoring terminal, which is also the second average value. Then, the real-time bandwidth allocation ratio of the m-th monitoring terminal is... ,in This represents the sum of the allocation evaluation values ​​for all monitoring terminals. After determining the real-time bandwidth allocation ratio for each monitoring terminal, the upload bandwidth for each terminal is dynamically allocated based on this ratio to ensure stable data transmission.

[0134] After step S50, the following is also included:

[0135] If the pollution impact range of any point is determined to exceed the preset threshold, environmental governance equipment will be mobilized to carry out coordinated pollution control in order to promptly suppress the degree of pollution impact at each point.

[0136] As an example, the platform continuously receives and processes data from the intelligent transmission layer, assesses the level and impact range of pollution events in real time, and controls spraying, fog cannons and other treatment equipment to work together when the pollution impact is significant or the pollution impact range of any point exceeds the preset threshold, so as to suppress the pollution in a timely manner and update the monitoring data in the database in real time. The treatment effect is evaluated through subsequent monitoring data and the strategy is optimized.

[0137] This application provides an operation method for an environmental management and control platform with massive monitoring terminal access. It acquires environmental monitoring data from different locations reported by various monitoring terminals within the platform, determines the pollution intensity of different types of environmental monitoring data, and calculates the historical state weights of different locations at different times based on pollution intensity, the time difference between different times and the previous time of pollution, and the proportion of pollution occurring at the same time each day in historical timeframes. Based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, it calculates the real-time trigger weights for different types of environmental monitoring data at each location. Combining the historical state weights and real-time trigger weights, it calculates the pollution weights of different locations at the current time. Based on the pollution weights, it determines the real-time bandwidth allocation ratio for each monitoring terminal to ensure stable transmission of environmental monitoring data. This achieves intelligent allocation of upload bandwidth for monitoring terminals. Dynamic bandwidth allocation ensures that environmental monitoring data can be transmitted to the platform promptly and stably when pollution occurs, enabling governance decisions and reducing data loss due to transmission quality issues.

[0138] Reference Figure 2 , Figure 2 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0139] like Figure 2As shown, the environmental management and control platform operation equipment connected to this massive monitoring terminal may include: a processor 1001, a memory 1003, and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1003.

[0140] Optionally, the environmental management and control platform operation equipment connected to this massive monitoring and control terminal may also include user interfaces, network interfaces, cameras, RF (Radio Frequency) circuits, sensors, WiFi modules, etc. User interfaces may include displays and input sub-modules such as keyboards; optional user interfaces may also include standard wired and wireless interfaces. Network interfaces may include standard wired and wireless interfaces (such as Wi-Fi interfaces).

[0141] Those skilled in the art will understand that Figure 2 The structure of the environmental management and control platform operation equipment shown in the figure does not constitute a limitation on the environmental management and control platform operation equipment connected to the massive monitoring and control terminals. It may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0142] like Figure 2 As shown, the memory 1003, serving as a storage medium, may include an operating system, a network communication module, and an operating program for the environmental management and control platform accessible by a large number of monitoring terminals. The operating system is a program that manages and controls the hardware and software resources of the environmental management and control platform operating equipment accessible by the large number of monitoring terminals, supporting the operation of the environmental management and control platform operating program and other software and / or programs. The network communication module is used to enable communication between the various components within the memory 1003, as well as communication with other hardware and software in the environmental management and control platform operating system accessible by the large number of monitoring terminals.

[0143] exist Figure 2 In the environmental management and control platform operation equipment connected to the massive monitoring and control terminals shown, the processor 1001 is used to execute the environmental management and control platform operation program connected to the massive monitoring and control terminals stored in the memory 1003 to implement the steps of the environmental management and control platform operation method connected to the massive monitoring and control terminals described above.

[0144] The specific implementation method of the environmental protection management and control platform operation equipment accessed by the massive monitoring and control terminals in this application is basically the same as the above-mentioned embodiments of the environmental protection management and control platform operation method accessed by the massive monitoring and control terminals, and will not be repeated here.

[0145] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0146] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0148] The above are merely preferred embodiments of this application and do not limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

[0149] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for operating an environmental management and control platform with access to a massive number of monitoring and surveillance terminals, characterized in that, The method includes: Obtain environmental monitoring data from different locations reported by various monitoring terminals in the environmental management and control platform, and determine the pollution intensity of different types of environmental monitoring data; Based on the pollution intensity, the time difference between different times and the previous time when pollution existed, and the proportion of pollution occurring at each location at the same time each day in historical time periods, the historical state weights of different locations at each time are calculated, specifically including: Based on the pollution intensity and the time difference between different times and the previous time when pollution existed, the historical pollution discharge evaluation value of each point is calculated. Based on the proportion of pollution occurring at each location at the same time each day in historical time periods, the repetition rate of pollution occurring at different locations at different times is calculated. Based on the repetition rate and the historical pollution discharge evaluation value, the historical state weights at different locations at various times are calculated. The calculation method is as follows: ； in: This represents the historical pollution discharge evaluation value of the i-th point. This represents the recurrence rate of contamination occurring at the i-th location at time r. This indicates the preset repetition rate threshold; Based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, the real-time trigger weights for different types of environmental monitoring data corresponding to each of the aforementioned locations are calculated, specifically including: Select all moments within a preset time period prior to the current moment as the trigger length; For any given location, determine the first average value of the pollution intensity of various environmental monitoring data at that location; Based on the difference between the first average value and the preset pollution threshold, the overall pollution level of different types of environmental monitoring data corresponding to each of the points is determined; The pollution intensity trend value is calculated based on the difference between the pollution intensity at any time within the trigger length and the average pollution intensity before the current time. Based on the normalized value of the product between the overall pollution level and the pollution intensity trend value, the real-time trigger weights for different types of environmental monitoring data corresponding to each of the aforementioned locations are calculated. The calculation method is as follows: ； In the formula: This represents the first average value of all pollution intensities for the k-th type of data at the i-th location within the trigger length. This represents the preset contamination threshold for the k-th type of data. Indicates the trigger length. This represents the contamination intensity of the k-th type of data at the i-th point at the l-th time of the trigger length. This represents the average pollution intensity of the k-th type of data at the i-th location during the (l-1)-th time intervals before the trigger length. This indicates the overall contamination level of the current trigger length. Indicates the trend value of pollution intensity; Based on the historical state weights and real-time trigger weights, the pollution weights of different locations at the current moment are calculated. The pollution weight The calculation method is as follows: ； in, This represents the historical state weight of the i-th point at time r. This represents the real-time trigger weights of all surrounding points of the i-th point at the current moment. The maximum value of the real-time trigger weights for all types of environmental monitoring data at the i-th location; Based on the pollution weight, the real-time bandwidth allocation ratio of each monitoring terminal is determined, and the upload bandwidth of each monitoring terminal is dynamically allocated based on the real-time bandwidth allocation ratio, so as to ensure stable transmission of the environmental monitoring data.

2. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, Based on the pollution intensity and the time difference between different times and the previous time when pollution existed, the historical pollution discharge evaluation value of each location is calculated, including: Determine a first ratio between the time difference of various environmental monitoring data at different times and the previous time when pollution existed, and the pollution intensity; Based on the total number of times corresponding to all environmental monitoring data, the first ratio is summed and averaged to calculate the historical pollution discharge evaluation value for each location.

3. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, The repetition rate of pollution at different locations at different times is calculated based on the proportion of pollution occurring at each location at the same time each day in historical time periods, including: For any given location, determine the first day of the environmental monitoring data transmitted at that location within the historical time period, as well as the maximum pollution intensity of various types of environmental monitoring data at that location at different times of the day. Based on the maximum pollution intensity and the first day, determine the percentage of pollution occurring at each location at the same time each day in the historical time period; Based on the normalized value of the aforementioned proportion, the repetition rate of pollution at different locations at various times is determined.

4. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, The calculation of historical state weights at different locations at various times, based on the repetition rate and the historical pollution discharge evaluation value, includes: The repetition rate of all times at each of the aforementioned points is compared with a preset repetition rate threshold, and the times corresponding to the repetition rates that are greater than the preset repetition rate threshold are marked as high-demand times. Based on the difference between the repetition rate at the high-demand moment and the preset repetition rate threshold, the adjustment coefficient of the historical sewage discharge evaluation value is determined. Based on the normalized value of the product between the adjustment coefficient and the historical pollution discharge evaluation value, the historical state weights of different locations at each time point are calculated.

5. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, The pollution weight of different locations at the current moment is calculated based on the historical state weight and the real-time trigger weight, including: For any given location, determine the real-time trigger weight of all locations within a preset range around the current location at the current moment, as well as the maximum real-time trigger weight of all types of environmental monitoring data at the current location. The pollution weight of different locations at the current moment is calculated based on the product of the historical state weight, the real-time trigger weight, and the maximum weight.

6. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, After calculating the real-time trigger weights for different types of environmental monitoring data at each location based on the overall pollution level and pollution intensity trend corresponding to the pollution intensity, the method further includes: The real-time trigger weight is compared with the preset trigger threshold. If the real-time trigger weight is greater than the preset trigger threshold, the monitoring terminal is activated to take pictures of the points corresponding to the real-time trigger weight. When the monitoring terminal detects emission behavior at any point, the real-time trigger weight of the current point is amplified to obtain the amplified real-time trigger weight. The pollution weight of different locations at the current moment is calculated based on the historical state weight and the real-time trigger weight, including: Based on the historical state weights and the real-time trigger weights after gain, the pollution weights of different locations at the current moment are calculated.

7. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, The step of determining the real-time bandwidth allocation ratio for each monitoring terminal based on the pollution weight includes: For any monitoring terminal, calculate the second average value of the pollution weight of all points collected by the current monitoring terminal, and use the second average value as the allocation evaluation value of the current monitoring terminal; The real-time bandwidth allocation ratio of each monitoring terminal is calculated based on the ratio between the allocation evaluation value and the sum of the allocation evaluation values ​​of each monitoring terminal.

8. The operation method of the environmental protection management and control platform with massive monitoring and control terminal access as described in claim 1, characterized in that, After determining the real-time bandwidth allocation ratio for each monitoring terminal based on the pollution weight, the method further includes: If the pollution impact range of any point is determined to exceed the preset threshold, environmental governance equipment will be activated for coordinated pollution control to promptly suppress the pollution impact of each point.

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