A control optimization method, system, device and medium for environmental monitoring

Abstract

The present invention relates to the field of environmental monitoring technology, and in particular to a control optimization method, system, device and medium for environmental monitoring, comprising: dividing a unit monitoring volume into a single space, then calculating a data deviation value based on environmental data obtained at n time points and dividing the single space into a regular space and an edge space, calculating an environmental representative value based on the regular space data, calculating the difference characteristic value and edge frequency of each single space, and comprehensively processing the two to determine a spatial outlier, then dividing the single space into a stable area and a fluctuating area based on the spatial outlier, selecting a feature area based on labels of adjacent areas of the single space in a spatial simulation model and setting monitoring equipment; the present invention can achieve precise layout of monitoring equipment, focus on deploying monitoring equipment in fluctuating areas and key feature areas, and improve the monitoring capability and response speed to environmental changes.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a control optimization method, system, device and medium for environmental monitoring. Background Art

[0002] As environmental issues receive increasing attention, the importance of environmental monitoring becomes increasingly prominent. Accurate and efficient environmental monitoring can provide key data support for environmental assessments, pollution control, and other areas.

[0003] The prior art CN117744890A discloses a method for optimizing human settlement environment monitoring, which includes first comprehensively considering the static and dynamic indicators of air quality sensors in a first-class residential area, and quantifying the abnormality of the operating status of the air quality sensors by calculating the air transmission operation judgment coefficient, thereby improving the comprehensive grasp of the operating status of the air quality sensors; performing abnormality assessment on the air quality sensor data in the first-class residential area; and starting to monitor the operating status of the air quality sensors in the second-class residential area when the air quality is abnormal.

[0004] However, traditional environmental monitoring often adopts the method of evenly distributing monitoring equipment. This method does not fully consider the spatial differences of the monitoring area, resulting in excessive deployment of monitoring equipment in some areas with stable environmental characteristics, causing waste of resources. In key areas with drastic environmental changes, there may be insufficient monitoring equipment, and environmental data cannot be obtained in a timely and accurate manner. At the same time, when processing environmental data, it is difficult to accurately identify areas that require key monitoring, which affects the reliability and effectiveness of the monitoring results. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a control optimization method, system, device and medium for environmental monitoring.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A control optimization method for environmental monitoring, the method specifically comprising the following steps:

[0008] Step 1: Collect spatial distribution information of the target monitoring area, set up a spatial simulation model for the target monitoring area based on the spatial distribution information, and divide the spatial simulation model into several single spaces according to the unit monitoring volume;

[0009] Step 2: Obtain the environmental data of the single space at n time points. Based on the environmental data, calculate the data deviation value of each time point. At the same time, according to the data deviation value, divide the single space at each time point into regular space and edge space. Then, based on the environmental data corresponding to the regular space, calculate the environmental representative value of each time point.

[0010] Step 3: Calculate the difference characteristic value of each monomer space based on the environmental data of the monomer space corresponding to n time points and the environmental representative value of the corresponding time point. Then calculate the edge frequency of each monomer space based on the number of times the edge space appears in the monomer space within n time points. Combine the edge frequency and the difference characteristic value to determine the spatial outlier value of each monomer space.

[0011] Step 4: Based on the spatial outliers, the single space is divided into stable areas and fluctuating areas. The spatial simulation model is obtained again. According to the regional labels of the adjacent areas of the single space in the spatial simulation model, the characteristic area is finally selected. According to the spatial location of the characteristic area, the corresponding monitoring equipment is set in the target monitoring area.

[0012] As a further solution of the present invention, a method for dividing a single space includes:

[0013] Setting up a spatial simulation model for the target monitoring area according to the spatial distribution information, wherein the spatial distribution information refers to the geometric form of the target monitoring area, including shape, dimension and size;

[0014] A unit monitoring volume is set, and the corresponding space simulation model is divided into several single spaces according to the unit monitoring volume. Environmental monitoring equipment is set in each single space, where one environmental monitoring device collects real-time environmental data in one single space.

[0015] As a further solution of the present invention, the method for dividing the normal space and the edge space includes:

[0016] Obtain the environmental data collected at the same time and mark it as Hi, where i represents the position number of different monomer spaces, and i∈[1, I], indicating that there are I monomer spaces in the spatial simulation model;

[0017] Get all the environmental data Hi at this moment, and perform mean processing on all the environmental data to get the data mean Ha, using the formula Get the data deviation value Up;

[0018] Compare the data deviation value Up with the standard deviation value Ub. If Up≤Ub, all monomer spaces are marked as regular spaces. Otherwise, if Up>Ub, obtain The difference is calculated and the obtained difference is arranged in descending order to obtain a difference sequence. Then, according to the position order in the difference sequence, the environmental data corresponding to the difference at the first position is marked as abnormal data. Then, the abnormal data is removed and the data deviation value is calculated again for the remaining environmental data. The re-obtained data deviation value is compared with the standard deviation value again. If the data deviation value is less than or equal to the standard deviation value, the monomer space corresponding to the environmental data involved in the calculation is marked as a regular space. Otherwise, if the data deviation value is greater than the standard deviation value, the monomer space corresponding to the environmental data involved in the calculation is marked as a regular space. The difference is sorted in descending order to obtain a difference sequence again. Then, according to the position order in the difference sequence, the environmental data corresponding to the difference at the first position is marked as abnormal data, and the above processing method is repeated until the data deviation value is less than or equal to the standard deviation value;

[0019] Obtain all abnormal data and mark the individual space corresponding to the abnormal data as the edge space. At the same time, obtain the environmental data corresponding to the regular space, perform mean processing on all the environmental data corresponding to the regular space, and mark the obtained mean result as the representative value of the environment at this moment.

[0020] As a further solution of the present invention, a method for calculating the difference characteristic value includes:

[0021] Set the monomer space i as the target space i in turn, obtain the environmental data collected by the target space i at n time points, and mark them as Ej, j represents different time points, and j∈[1,n]. At the same time, obtain the environmental representative value corresponding to each time point j, and mark the environmental representative value as Tj. Then use the formula Get the difference eigenvalue Ci of target space i.

[0022] As a further embodiment of the present invention, a method for calculating spatial outliers includes:

[0023] Obtain the spatial labels of the single space i at n time points, where the spatial labels refer to the regular space and the edge space. Identify the number Nu of edge spaces of this single space i at different time points, and then use the formula Nu ÷ n = fs to obtain the edge frequency fs of this single space.

[0024] Get the difference eigenvalue Ci of this monomer space i, using the formula Get the spatial outlier Li of the monomer space i, and Both are proportional coefficients.

[0025] As a further solution of the present invention, the region labels include stable regions and fluctuating regions, and the method for distinguishing the region labels includes:

[0026] Obtain the spatial outlier values ​​Li of all monomer spaces, and compare the spatial outlier values ​​Li of the monomer space with the outlier limit threshold Ly in turn. If Li≤Ly, the corresponding monomer space is marked as a stable area. Conversely, if Li>Ly, the corresponding monomer space is marked as a fluctuating area.

[0027] As a further embodiment of the present invention, a method for selecting a characteristic region includes:

[0028] Arbitrarily select a monomer space corresponding to a stable region and mark it as the central space. Identify the monomer space adjacent to the central space and mark it as the boundary space. Then obtain the region labels corresponding to the boundary space. If there are m or more fluctuating regions in the boundary space, mark the corresponding central space as the feature region, and the value of m is set to 2.

[0029] Mark the monomer spaces corresponding to all stable regions as central spaces in turn, and process them according to the above method to select feature regions in the stable space;

[0030] Furthermore, the monomer spaces adjacent to the central space refer to the monomer spaces connected to the front end, rear end, upper end, lower end, left end and right end of the central space respectively;

[0031] Then, the position of the characteristic area in the spatial simulation interval is identified, and the corresponding position is identified in the corresponding target monitoring area. At the same time, the corresponding monitoring equipment is set at this position as the actual monitoring point of the target monitoring area.

[0032] A control optimization system for environmental monitoring, comprising:

[0033] The information acquisition module is used to collect environmental information and spatial distribution information of the target monitoring area and transmit it to the MCU main control module;

[0034] The data acquisition module is used to receive various types of sensor data in the target monitoring area and transmit the received sensor data to the MCU main control module;

[0035] The MCU main control module is used to receive environmental information and spatial distribution information, and optimize and analyze the target monitoring area based on the spatial distribution information. The MCU main control module includes a spatial processing unit, an environmental processing unit, an integrated analysis unit, and a spatial selection unit;

[0036] The spatial processing unit sets a spatial simulation model for the target monitoring area based on the spatial distribution information, and divides the spatial simulation model into several single spaces according to the unit monitoring volume;

[0037] The environmental processing unit is used to obtain the environmental data of the single space at n time points, calculate the data deviation value of each time point based on the environmental data, and divide the single space at each time point into regular space and edge space according to the data deviation value. Then, based on the environmental data corresponding to the regular space, calculate the environmental representative value of each time point;

[0038] An integrated analysis unit is used to calculate the difference characteristic value of each monomer space based on the environmental data of the monomer space corresponding to n time points and the environmental representative value of the corresponding time points, and then calculate the edge frequency of each monomer space based on the number of times the edge space of the monomer space appears within n time points, and comprehensively process the edge frequency and the difference characteristic value to determine the spatial outlier value of each monomer space;

[0039] The spatial selection unit is used to divide the monomer space into stable areas and fluctuating areas according to the spatial outliers, obtain the spatial simulation model again, and finally select the characteristic area according to the area labels of the adjacent areas of the monomer space in the spatial simulation model. The monitoring equipment is set in the target monitoring area according to the spatial location of the characteristic area;

[0040] The encryption module is used to encrypt the collected plaintext data. The encryption algorithm uses the SM4 algorithm and the encryption mode uses the CBC cipher block chaining mode. The specific encryption processing methods include:

[0041] The data to be encrypted is grouped into 16 bytes per group, and any bytes less than 16 bytes are padded with 0s. The first group of data is then encrypted. An initial vector is required for encryption of the first group of data. After encryption, the ciphertext of the first group of data is obtained. Each group of data after the first group of data is encrypted by XORing it with the ciphertext of the previous group of data and then encrypting it until all the data is encrypted. XOR refers to a logical operation method in a computer. Finally, the encryption module returns each encrypted group of ciphertext data to the MCU main control module. After receiving the encrypted ciphertext data, the MCU main control module encapsulates it according to the protocol set with the remote data cloud platform and sends the sorted data to the wireless communication module.

[0042] The wireless communication module is responsible for communication between modules and realizes remote transmission of data. The wireless communication module includes 4G communication and NB-IoT communication.

[0043] A computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable by the processor, and when the processor executes the computer program, it implements the above-mentioned control optimization method for environmental monitoring.

[0044] A computer-readable storage medium stores a computer program, which implements the above-mentioned control optimization method for environmental monitoring when executed by a processor.

[0045] Compared with the existing technology, the advantages of the present invention are:

[0046] The present invention collects spatial distribution information of the target monitoring area and sets up a spatial simulation model, divides the unit monitoring volume into single spaces, then calculates the data deviation value based on the environmental data of n time points obtained, and divides the single space into regular space and edge space. At the same time, the environmental representative value is calculated according to the regular space data, and the spatial outlier is determined by calculating the difference characteristic value and edge frequency of each single space and comprehensively processing the two. The single space is then divided into stable area and fluctuating area according to the spatial outlier. The feature area is selected and monitoring equipment is set in combination with the labels of adjacent areas of the single space in the spatial simulation model. The present invention can realize the precise layout of monitoring equipment, focus on deploying monitoring equipment in fluctuating areas and key feature areas, improve the monitoring capability and response speed of environmental changes, and reduce the deployment of monitoring equipment in stable areas to avoid waste of resources, thereby achieving the purpose of optimizing monitoring resource allocation, improving monitoring efficiency and reducing monitoring costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the method of the present invention;

[0048] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0050] Example 1, refer to Figure 1 A control optimization method for environmental monitoring, the method specifically comprising the following steps:

[0051] Step 1: Acquire the target monitoring area and collect environmental information of the target monitoring area, wherein the target monitoring area refers to the area where the environmental monitoring is performed by this method, and the environmental information refers to the standard environmental parameters in the target monitoring environment, specifically including the standard temperature range, standard humidity range, and other relevant environmental parameter standard ranges;

[0052] Step 2: Obtain spatial distribution information of the target monitoring area and set up a spatial simulation model for the target monitoring area according to the spatial distribution information, wherein the spatial distribution information refers to the geometric form of the target monitoring area, including shape, dimension, and size;

[0053] A unit monitoring volume is set, and the corresponding spatial simulation model is divided into a number of single spaces according to the unit monitoring volume, and an environmental monitoring device is respectively set in each single space, wherein one environmental monitoring device collects real-time environmental data in one single space. Furthermore, the specific value of the unit monitoring volume is set by those skilled in the art according to the equipment parameters of the detection equipment. In this embodiment, the unit monitoring volume is set to 0.5m×0.5m×0.5m;

[0054] Step 3: Obtain environmental data collected at the same time and mark it as Hi, where i represents the location number of different monomer spaces, and i∈[1, I], indicating that there are I monomer spaces in the spatial simulation model;

[0055] Based on the environmental data collected at the same time, the environmental representative value in the target monitoring area at that time is calculated. The specific calculation method of the environmental representative value includes:

[0056] Get all the environmental data Hi at this moment, and perform mean processing on all the environmental data to get the data mean Ha, using the formula Get the data deviation value Up;

[0057] Compare the data deviation value Up with the standard deviation value Ub. If Up≤Ub, all monomer spaces are marked as regular spaces. Otherwise, if Up>Ub, obtain The difference is calculated and the obtained difference is arranged in descending order to obtain a difference sequence. Then, according to the position order in the difference sequence, the environmental data corresponding to the difference at the first position is marked as abnormal data. Then, the abnormal data is removed and the data deviation value is calculated again for the remaining environmental data. The re-obtained data deviation value is compared with the standard deviation value again. If the data deviation value is less than or equal to the standard deviation value, the monomer space corresponding to the environmental data involved in the calculation is marked as a regular space. Otherwise, if the data deviation value is greater than the standard deviation value, the monomer space corresponding to the environmental data involved in the calculation is marked as a regular space. The difference is sorted in descending order to obtain a difference sequence again. Then, according to the position order in the difference sequence, the environmental data corresponding to the difference at the first position is marked as abnormal data, and the above processing method is repeated until the data deviation value is less than or equal to the standard deviation value. Furthermore, the standard deviation value is a threshold value, and its specific value is obtained by those skilled in the art after big data calculation;

[0058] Then, all abnormal data are obtained, and the single space corresponding to the abnormal data is marked as the edge space. At the same time, the environmental data corresponding to the normal space is obtained, and all the environmental data corresponding to the normal space are averaged. The obtained average result is marked as the representative value of the environment at this moment;

[0059] According to the above method, environmental data of n time periods are collected, and the environmental representative value corresponding to each time period is calculated respectively. The specific value of n is set by those skilled in the art based on big data experience. In this embodiment, the value of n is 100;

[0060] Step 4: Randomly select a single space i and mark it as target space i. Taking target space i as an example, obtain the environmental data collected at n times for this target space i, and combine it with the environmental representative value for analysis to determine the difference characteristic value of the target space. The specific method for determining the difference characteristic value includes:

[0061] Obtain the environmental data collected at n time points in the target space and mark them as Ej, where j represents different time points and j∈[1,n]. At the same time, obtain the environmental representative value corresponding to each time point j and mark the environmental representative value as Tj. Then use the formula Get the difference eigenvalue Ci of target space i;

[0062] All monomer spaces are taken as target spaces in turn, and processed according to the above method to obtain the difference eigenvalue Ci of each monomer space;

[0063] Step 5: Obtain the difference characteristic value of each monomer space and the corresponding spatial label at n time points, and calculate the corresponding spatial outlier value of the monitoring equipment in each monomer space. The specific method for calculating the spatial outlier value includes:

[0064] Randomly select a single space i and take this single space i as an example. Obtain the spatial labels of this single space i at n time points, where the spatial labels refer to the normal space and the edge space. Then identify the number Nu of edge spaces of this single space i at different time points, and then use the formula Nu ÷ n = fs to obtain the edge frequency fs of this single space.

[0065] Get the difference eigenvalue Ci of this monomer space i, using the formula Get the spatial outlier Li of the monomer space i, and are proportional coefficients, and their specific values ​​are obtained by those skilled in the art after calculation based on big data. The value is set to 0.65, The value is set to 0.35;

[0066] Step 6: Based on the spatial outliers, the corresponding monomer space is selected in the spatial simulation model and marked as a characteristic area. The specific method for determining the characteristic area includes:

[0067] Obtain the spatial outlier values ​​Li of all monomer spaces, and compare the spatial outlier values ​​Li of the monomer spaces with the outlier limit threshold Ly in turn. If Li≤Ly, the corresponding monomer space is marked as a stable region. Conversely, if Li>Ly, the corresponding monomer space is marked as a fluctuating region. The specific value of the outlier limit threshold Ly is obtained by those skilled in the art after big data calculation.

[0068] Obtain the spatial simulation model again, and mark the corresponding region labels of the monomer spaces in the spatial simulation model according to the locations of the monomer spaces. At this time, each monomer space in the spatial simulation model displays the corresponding region label, where the region labels refer to stable regions and fluctuating regions;

[0069] Arbitrarily select a single space corresponding to a stable region and mark it as the central space. Identify the single spaces adjacent to the central space and mark them as the boundary spaces. Then obtain the region labels corresponding to the boundary spaces. If there are m or more fluctuating regions in the boundary space, mark the corresponding central space as the feature region, where m is the threshold. In this embodiment, the value of m is set to 2.

[0070] Mark the monomer spaces corresponding to all stable regions as central spaces in turn, and process them according to the above method to select feature regions in the stable space;

[0071] It should be further explained that the individual spaces adjacent to the central space refer to the individual spaces connected to the front end, rear end, upper end, lower end, left end and right end of the central space respectively;

[0072] Then, the position of the characteristic area in the spatial simulation interval is identified, and the corresponding position is identified in the corresponding target monitoring area. At the same time, the corresponding monitoring equipment is set at this position as the actual monitoring point of the target monitoring area.

[0073] Example 2: A control optimization system for environmental monitoring, referring to Figure 2 , including: information acquisition module, data acquisition module, MCU main control module, encryption module and wireless communication module;

[0074] Among them, the information acquisition module is used to collect environmental information and spatial distribution information of the target monitoring area and transmit it to the MCU main control module;

[0075] The data acquisition module is used to receive various types of sensor data in the target monitoring area and transmit the received sensor data to the MCU main control module, including digital signals, analog signals, RS485 signals, RS232 signals, RS422 signals and CAN signals;

[0076] The MCU main control module is used to receive environmental information and spatial distribution information, and optimize and analyze the target monitoring area based on the spatial distribution information. The MCU main control module includes a spatial processing unit, an environmental processing unit, an integrated analysis unit, and a spatial selection unit.

[0077] The spatial processing unit sets up a spatial simulation model for the target monitoring area based on the spatial distribution information, and divides the spatial simulation model into several single spaces according to the unit monitoring volume;

[0078] The environmental processing unit is used to obtain the environmental data of the single space at n time points, calculate the data deviation value of each time point based on the environmental data, and divide the single space at each time point into regular space and edge space according to the data deviation value. Then, based on the environmental data corresponding to the regular space, calculate the environmental representative value of each time point;

[0079] The integrated analysis unit is used to calculate the difference characteristic value of each monomer space based on the environmental data of the monomer space corresponding to n time points and the environmental representative value of the corresponding time points, and then calculate the edge frequency of each monomer space based on the number of times the edge space of the monomer space appears within n time points, and comprehensively process the edge frequency and the difference characteristic value to determine the spatial outlier value of each monomer space;

[0080] The spatial selection unit is used to divide the monomer space into stable areas and fluctuating areas according to the spatial outliers, obtain the spatial simulation model again, and finally select the characteristic area according to the area labels of the adjacent areas of the monomer space in the spatial simulation model. The monitoring equipment is set in the target monitoring area according to the spatial location of the characteristic area.

[0081] The encryption module is used to encrypt the collected plaintext data. The encryption algorithm uses the SM4 algorithm and the encryption mode uses the CBC cipher block chaining mode. The specific encryption processing methods include:

[0082] The data to be encrypted is grouped into 16 bytes per group, and any bytes less than 16 bytes are padded with 0s. The first group of data is then encrypted. An initial vector is required for encryption of the first group of data. After encryption, the ciphertext of the first group of data is obtained. Each group of data after the first group of data is encrypted by XORing it with the ciphertext of the previous group of data and then encrypting it until all the data is encrypted. XOR refers to a logical operation method in a computer. Finally, the encryption module returns each encrypted group of ciphertext data to the MCU main control module. After receiving the encrypted ciphertext data, the MCU main control module encapsulates it according to the protocol set with the remote data cloud platform and sends the sorted data to the wireless communication module.

[0083] The wireless communication module is responsible for communication between modules and realizes remote transmission of data. The wireless communication module includes 4G communication and NB-IoT communication.

[0084] In a third embodiment, the present application discloses a computer device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, the steps of a control optimization method for environmental monitoring as described above are implemented.

[0085] In real-time example 4, the present application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the control optimization method for environmental monitoring as described above.

[0086] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A control optimization method for environmental monitoring, characterized in that: The method specifically comprises the following steps: Step 1: Collect spatial distribution information of the target monitoring area, set up a spatial simulation model for the target monitoring area based on the spatial distribution information, and divide the spatial simulation model into several single spaces according to the unit monitoring volume; Step 2: Obtain the environmental data of the single space at n time points, obtain the environmental data collected at the same time, and mark it as Hi, where i represents the position number of different single spaces, and i∈[1,I], indicating that there are I single spaces in the spatial simulation model; Get all the environmental data Hi at this moment, and perform mean processing on all the environmental data to get the data mean Ha, using the formula Obtain the data deviation value Up. At the same time, based on the data deviation value, divide the monomer space at each time point into regular space and edge space. Then obtain the environmental data corresponding to the regular space, average all the environmental data corresponding to the regular space, and mark the obtained average result as the representative value of the environment at this moment. Step 3: According to the environmental data of the monomer space corresponding to n time points and the environmental representative value of the corresponding time point, the monomer space i is set as the target space i in turn, and the environmental data collected by the target space i at n time points are obtained and marked as Ej, j represents a different time point, and j∈[1,n]. At the same time, the environmental representative value corresponding to each time point j is obtained and marked as Tj. Then, the formula is used Get the difference eigenvalue Ci of target space i, then calculate the edge frequency of each monomer space according to the number of times the edge space appears in the monomer space within n time points, and then get the difference eigenvalue Ci of this monomer space i, using the formula Get the spatial outlier value Li of the monomer space i, and then determine the spatial outlier value of each monomer space, and are all proportional coefficients, fs is the edge frequency; Step 4: Based on the spatial outliers, the single space is divided into stable areas and fluctuating areas. The spatial simulation model is obtained again. According to the regional labels of the adjacent areas of the single space in the spatial simulation model, the characteristic area is finally selected. According to the spatial location of the characteristic area, the corresponding monitoring equipment is set in the target monitoring area.

2. A control optimization method for environmental monitoring according to claim 1, characterized in that: The methods for dividing a single space include: Setting up a spatial simulation model for the target monitoring area according to the spatial distribution information, wherein the spatial distribution information refers to the geometric form of the target monitoring area, including shape, dimension and size; A unit monitoring volume is set, and the corresponding space simulation model is divided into several single spaces according to the unit monitoring volume. Environmental monitoring equipment is set in each single space, where one environmental monitoring device collects real-time environmental data in one single space.

3. The control optimization method for environmental monitoring according to claim 1, characterized in that: Methods for dividing regular space and edge space include: Compare the data deviation value Up with the standard deviation value Ub. If Up≤Ub, all monomer spaces are marked as regular spaces. Otherwise, if Up>Ub, obtain The difference is calculated and the obtained difference is arranged in descending order to obtain a difference sequence. Then, according to the position order in the difference sequence, the environmental data corresponding to the difference at the first position is marked as abnormal data. Then, the abnormal data is removed and the data deviation value is calculated again for the remaining environmental data. The re-obtained data deviation value is compared with the standard deviation value again. If the data deviation value is less than or equal to the standard deviation value, the monomer space corresponding to the environmental data involved in the calculation is marked as a regular space. Otherwise, if the data deviation value is greater than the standard deviation value, the monomer space corresponding to the environmental data involved in the calculation is marked as a regular space. Difference, and arrange the obtained differences in descending order to obtain a difference sequence again, and then mark the environmental data corresponding to the difference at the first position as abnormal data according to the position order in the difference sequence, and repeat the above processing method until the data deviation value is less than or equal to the standard deviation value; Obtain all abnormal data and mark the individual space corresponding to the abnormal data as the edge space. At the same time, obtain the environmental data corresponding to the regular space, perform mean processing on all the environmental data corresponding to the regular space, and mark the obtained mean result as the representative value of the environment at this moment.

4. The control optimization method for environmental monitoring according to claim 1, characterized in that: The calculation method of edge frequency includes: Obtain the spatial labels of the single space i at n time points, where the spatial labels refer to the regular space and the edge space. Identify the number Nu of edge spaces of this single space i at different time points, and then use the formula Nu ÷ n = fs to obtain the edge frequency fs of this single space.

5. The control optimization method for environmental monitoring according to claim 1, characterized in that: Regional labels include stable areas and fluctuating areas. The methods for distinguishing regional labels include: Obtain the spatial outlier values ​​Li of all monomer spaces, and compare the spatial outlier values ​​Li of the monomer space with the outlier limit threshold Ly in turn. If Li≤Ly, the corresponding monomer space is marked as a stable area. Conversely, if Li>Ly, the corresponding monomer space is marked as a fluctuating area.

6. A control optimization method for environmental monitoring according to claim 5, characterized in that: Methods for selecting feature areas include: Arbitrarily select a monomer space corresponding to a stable region and mark it as the central space. Identify the monomer space adjacent to the central space and mark it as the boundary space. Then obtain the region labels corresponding to the boundary space. If there are m or more fluctuating regions in the boundary space, mark the corresponding central space as the feature region, and the value of m is set to 2. Mark the monomer spaces corresponding to all stable regions as central spaces in turn, and process them according to the above method to select feature regions in the stable space; The monomer spaces adjacent to the central space refer to the monomer spaces connected to the front end, rear end, upper end, lower end, left end and right end of the central space respectively; Then, the position of the characteristic area in the spatial simulation interval is identified, and the corresponding position is identified in the corresponding target monitoring area. At the same time, the corresponding monitoring equipment is set at this position as the actual monitoring point of the target monitoring area.

7. A control optimization system for environmental monitoring, the control optimization system adopts the control optimization method for environmental monitoring according to any one of claims 1 to 6, characterized in that: include: The information acquisition module is used to collect environmental information and spatial distribution information of the target monitoring area and transmit it to the MCU main control module; The data acquisition module is used to receive various types of sensor data in the target monitoring area and transmit the received sensor data to the MCU main control module; The MCU main control module is used to receive environmental information and spatial distribution information, and optimize and analyze the target monitoring area based on the spatial distribution information. The MCU main control module includes a spatial processing unit, an environmental processing unit, an integrated analysis unit, and a spatial selection unit; The spatial processing unit sets a spatial simulation model for the target monitoring area based on the spatial distribution information, and divides the spatial simulation model into several single spaces according to the unit monitoring volume; The environmental processing unit is used to obtain the environmental data of the single space at n time points, calculate the data deviation value of each time point based on the environmental data, and divide the single space at each time point into regular space and edge space according to the data deviation value. Then, based on the environmental data corresponding to the regular space, calculate the environmental representative value of each time point; An integrated analysis unit is used to calculate the difference characteristic value of each monomer space based on the environmental data of the monomer space corresponding to n time points and the environmental representative value of the corresponding time points, and then calculate the edge frequency of each monomer space based on the number of times the edge space of the monomer space appears within n time points, and comprehensively process the edge frequency and the difference characteristic value to determine the spatial outlier value of each monomer space; The spatial selection unit is used to divide the monomer space into stable areas and fluctuating areas according to the spatial outliers, obtain the spatial simulation model again, and finally select the characteristic area according to the area labels of the adjacent areas of the monomer space in the spatial simulation model. The monitoring equipment is set in the target monitoring area according to the spatial location of the characteristic area; The encryption module is used to encrypt the collected plaintext data. The encryption algorithm uses the SM4 algorithm and the encryption mode uses the CBC cipher block chaining mode. The specific encryption processing methods include: The data to be encrypted is grouped into 16 bytes per group, and any bytes less than 16 bytes are padded with 0s. The first group of data is then encrypted. An initial vector is required for encryption of the first group of data. After encryption, the ciphertext of the first group of data is obtained. Each group of data after the first group of data is encrypted by XORing it with the ciphertext of the previous group of data and then encrypting it until all the data is encrypted. XOR refers to a logical operation method in a computer. Finally, the encryption module returns each encrypted group of ciphertext data to the MCU main control module. After receiving the encrypted ciphertext data, the MCU main control module encapsulates it according to the protocol set with the remote data cloud platform and sends the sorted data to the wireless communication module. The wireless communication module is responsible for communication between modules and realizes remote transmission of data. The wireless communication module includes 4G communication and NB-IoT communication.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable by the processor, wherein when the processor executes the computer program, a control optimization method for environmental monitoring as claimed in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the control optimization method for environmental monitoring according to any one of claims 1 to 6 is implemented.

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