CN117829557A - Atmospheric chamber gas monitoring site selection method and system based on multi-technology integration - Google Patents

Atmospheric chamber gas monitoring site selection method and system based on multi-technology integration Download PDF

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CN117829557A
CN117829557A CN202410246069.6A CN202410246069A CN117829557A CN 117829557 A CN117829557 A CN 117829557A CN 202410246069 A CN202410246069 A CN 202410246069A CN 117829557 A CN117829557 A CN 117829557A
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沈玉亮
杨关盈
张昊
卢燕宇
殷剑
周先锋
燕少威
陆斌
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Anhui Atmosphere Detection Technical Guarantee Center
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Abstract

The application relates to the technical field of gas monitoring, and provides an atmospheric chamber gas monitoring site selection method and system based on multi-technology integration, wherein the method comprises the following steps: acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; calculating a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address; estimating net greenhouse gas emissions for each candidate site based on the first emissions estimate, the second emissions estimate, and the change estimate for each candidate site; a final score for each candidate address is calculated based on the target class characteristic score and the net greenhouse gas emissions for each candidate address. The greenhouse gas monitoring station can completely cover all areas to be monitored.

Description

Atmospheric chamber gas monitoring site selection method and system based on multi-technology integration
Technical Field
The application relates to the technical field of gas monitoring, in particular to an atmospheric temperature chamber gas monitoring site selection method and system based on multi-technology integration.
Background
The existing atmospheric air chamber gas monitoring station site selection method based on multi-technology integration mainly comprises the step of selecting to set monitoring stations at different positions in a city or an area according to air flowability and city layout so as to cover the area as wide as possible. However, due to the large difference in the environments inside cities, a single site cannot fully reflect the greenhouse gas emission conditions of the whole city. Also, the monitoring site may not fully cover all areas due to factors such as the number of devices and cost.
Disclosure of Invention
The embodiment of the application provides an atmospheric air room gas monitoring station site selection method and system based on multi-technology integration, and aims to realize that all areas are completely covered and monitored by a greenhouse gas monitoring station.
In a first aspect, an embodiment of the present application provides a method for locating an atmospheric chamber gas monitoring station based on multi-technology integration, including:
acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
Calculating a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address;
estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the variation estimate for each of the candidate addresses;
calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
and determining a target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
In a second aspect, embodiments of the present application provide an atmospheric chamber gas monitoring site location system based on multi-technology integration, including:
the acquisition module is used for acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
the first computing module is used for computing the target class feature scores of the candidate addresses based on the first class feature data and the second class feature data of the candidate addresses;
An estimation module for estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the change estimate for each of the candidate addresses;
a second calculation module for calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
and the address determining module is used for determining the target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
In a third aspect, an embodiment of the present application provides an electronic device, where the electronic device includes a memory, a processor, and a determining machine program stored in the memory and capable of running on the processor, and when the processor executes the determining machine program, the processor implements the method for selecting an atmospheric chamber gas monitoring site based on multi-technology integration according to the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory determining machine-readable storage medium, the non-transitory determining machine-readable storage medium including a determining machine program, which when executed by a processor, implements the atmospheric chamber gas monitoring site location method based on multi-technology integration of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer product comprising a determining computer program that, when executed by a processor, implements the multi-technology fusion-based atmospheric chamber gas monitoring site location method of the first aspect.
According to the method and the device, the candidate addresses are comprehensively scored according to the plandability and constructability of the candidate addresses, the first estimated carbon dioxide gas discharge amount, the second estimated methane gas discharge amount and the estimated soil organic carbon change amount of the candidate addresses, and the target greenhouse gas monitoring site addresses in the candidate addresses are selected according to the final scoring score, so that the finally selected greenhouse gas monitoring sites can completely cover and monitor all areas.
Drawings
For a clearer description of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for locating atmospheric chamber gas monitoring stations based on multi-technology integration provided in an embodiment of the present application;
FIG. 2 is a schematic structural diagram of an atmospheric chamber gas monitoring site selection system based on multi-technology integration provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Optionally, the site selection method for the atmospheric chamber gas monitoring site based on multi-technology integration provided by the embodiment of the application uses a site selection system as an execution subject for illustration. Referring to fig. 1, fig. 1 is a schematic flow chart of a method for locating an atmospheric air chamber gas monitoring station based on multi-technology integration according to an embodiment of the present application. The embodiment of the application provides an atmospheric chamber gas monitoring station site selection method based on multi-technology integration, which comprises steps 101 to 105, and specifically comprises the following steps:
Step 101, acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address;
optionally, the site selection system acquires first-class feature data and second-class feature data of each candidate address, wherein the first-class feature data represents the plandability of the candidate address, and the second-class feature data represents the constructability of the candidate address. The programmability refers to the feasibility of the candidate address being programmed as a greenhouse gas monitoring site. When the site selection of the greenhouse gas monitoring site is carried out, the factors such as site position, interference of the surrounding environment of the site and the like are comprehensively considered, meanwhile, the factors such as whether the size of the site can accommodate a certain amount of electronic equipment, whether the load bearing of the site meets the use requirement and the like are considered, and the feasibility of planning the candidate address as the greenhouse gas monitoring site is comprehensively evaluated according to each factor. The first type of feature data comprises one or more planning feature data, wherein the number of the planning feature data is not limited, each planning feature data respectively represents scores of factors affecting the planability of the candidate address, and the planning feature data forms the first type of feature data to quantitatively represent the planability of the candidate address. For example, the first type of feature data of one candidate address includes four planning feature data, which respectively represent the advantages and disadvantages of the candidate address position, the interference strength around the candidate address, the size of the candidate address and the bearing of the candidate address. It should be noted that the factors affecting the candidate address planability are not limited to the above-described influencing factors.
Constructable refers to the feasibility of the candidate address being constructed as a greenhouse gas monitoring site. When building the greenhouse gas monitoring site, different addresses have different construction cost and construction difficulty, meanwhile disputes possibly occur due to noise and other disturbance in the construction process, and the feasibility of the candidate addresses to be built into the greenhouse gas monitoring site is comprehensively evaluated based on influence factors when the greenhouse gas monitoring site is built. The second type of feature data comprises one or more pieces of construction feature data, wherein the number of the construction feature data is not limited, each piece of construction feature data respectively represents scores of factors affecting the constructability of the candidate address, the plurality of pieces of construction feature data form the second type of feature data, and the constructability of the candidate address is quantitatively represented. For example, the second type of feature data of one candidate address includes three construction feature data, which respectively represent construction cost of the candidate address constructed as a greenhouse gas monitoring site, construction difficulty of the candidate address constructed as the greenhouse gas monitoring site, and whether the candidate address can perform the co-construction of the business hall and the greenhouse gas monitoring site.
It should be noted that the factors affecting the constructability of the candidate address are not limited to the above-described influencing factors. In addition, the number of the planning feature data contained in the first type of feature data and the number of the planning feature data contained in the second type of feature data can be the same or different, and can be increased or decreased according to actual use requirements.
Further, a first estimated carbon dioxide gas emission amount, a second estimated methane gas emission amount and a soil organic carbon change amount of each candidate address are obtained, specifically:
for a first estimated amount of carbon dioxide gas to be emitted for each candidate site:
the site selection system acquires a first estimation model of carbon dioxide gas of each candidate address, and the expression of the first estimation model is as follows:
wherein,the concentration value of the carbon dioxide gas at the current time for each candidate address,a total concentration value of carbon dioxide gas for each candidate address history;
further, the site selection system takes the concentration value of the carbon dioxide gas of each candidate address at the first moment as the concentration value of the carbon dioxide gas at the current moment, and inputs the concentration value of the carbon dioxide gas of each candidate address at the first moment into a first estimation model to obtain an estimated amount of carbon dioxide gas emission of each candidate address at the first moment;
further, the site selection system takes the concentration value of the carbon dioxide gas of each candidate address at the second moment as the concentration value of the carbon dioxide gas at the current moment, and inputs the concentration value of the carbon dioxide gas of each candidate address at the second moment into the first estimation model to obtain the estimated amount of the carbon dioxide gas of each candidate address at the second moment;
Further, the site selection system calculates a first estimated carbon dioxide gas emission amount of each candidate address according to the estimated carbon dioxide gas emission amount of each candidate address at the first moment and the estimated carbon dioxide gas emission amount of each candidate address at the second moment, and a calculation formula of the first estimated carbon dioxide gas emission amount is as follows:
wherein S is a first emission estimate,an estimated amount of carbon dioxide gas emissions at a first time for each candidate site,an estimate of carbon dioxide gas emissions at a second time is made for each candidate site.
For a second estimated emission of methane gas for each candidate address:
the site selection system acquires a second estimation model of methane gas of each candidate address; the expression of the second estimation model is:
wherein,for the concentration value of methane gas at the current moment of each candidate address,a total concentration value of methane gas for each candidate address history;
further, the site selection system takes the concentration value of the methane gas at the first moment of each candidate address as the concentration value of the methane gas at the current moment, and inputs the concentration value of the methane gas into the second estimation model to obtain the estimated methane gas emission quantity at the first moment of each candidate address;
Further, the site selection system takes the concentration value of the methane gas of each candidate address at the second moment as the concentration value of the methane gas at the current moment, and inputs the concentration value of the methane gas into a second estimation model to obtain the estimated methane gas emission quantity of each candidate address at the second moment;
further, the site selection system calculates a second estimated emission amount of the methane gas at each candidate address according to the estimated emission amount of the methane gas at the first moment and the estimated emission amount of the methane gas at the second moment at each candidate address; the second emission estimator is calculated as:
for the second estimated amount of emissions,for each candidate address an estimated methane gas emission at the first time,an estimate of methane gas emissions at a second time for each candidate site.
Estimates of soil organic carbon change for each candidate address were obtained:
the site selection system acquires a third estimation model of soil organic carbon of each candidate address; the expression of the third estimation model is:
wherein,the concentration value of the organic carbon in the soil at the current moment is used as each candidate address,for the total concentration value of soil organic carbon of each candidate address history,is a preset weight;
further, the site selection system takes the concentration value of the soil organic carbon of each candidate address at the first moment as the concentration value of the soil organic carbon at the current moment, and inputs the concentration value of the soil organic carbon of each candidate address into a third estimation model to obtain the variation quantity of the soil organic carbon of each candidate address at the first moment;
Further, the site selection system takes the concentration value of the soil organic carbon of each candidate address at the second moment as the concentration value of the soil organic carbon at the current moment, and inputs the concentration value of the soil organic carbon of each candidate address at the second moment into a third estimation model to obtain the variation quantity of the soil organic carbon of each candidate address at the second moment;
further, the site selection system calculates an estimated change amount of the soil organic carbon of each candidate address according to the change amount of the soil organic carbon of each candidate address at the first moment and the change amount of the soil organic carbon of each candidate address at the second moment; the calculation formula of the change estimator is:
wherein,for the purpose of the variation estimation quantity,for the amount of change of the organic carbon in the soil at the first moment of each candidate address,the amount of change of the organic carbon in the soil at the second moment is used as each candidate address.
102, calculating a target class feature score of each candidate address based on first class feature data and second class feature data of each candidate address;
optionally, the site selection system calculates the target class feature score of each candidate address according to the first class feature data and the second class feature data of each candidate address, specifically:
acquiring a first type characteristic index corresponding to the first type characteristic data and a second type characteristic index corresponding to the second type characteristic data of each candidate address;
Determining a first class feature score for each of the candidate addresses based on the first class feature data and the first class feature index for each of the candidate addresses;
determining a second class feature score for each of the candidate addresses based on the second class feature data and the second class feature index for each of the candidate addresses;
a target class feature score for each of the candidate addresses is calculated based on the first class feature score and the second class feature score for each of the candidate addresses.
The first type of feature index is the influence degree of the first type of feature on the candidate address, the second type of feature index is the influence degree of the second type of feature on the candidate address, the first type of feature is a type of feature representing the address planability and comprises one or more planning features, the planning feature data is data corresponding to the planning features, the second type of feature is a type of feature representing the address constructability and comprises one or more construction features, and the construction feature data is data corresponding to the construction features. The manner of determining the first class of characteristic indexes and the second class of characteristic indexes is not limited. The candidate addresses in different areas have different requirements, and the relative importance degree of each influence factor on the candidate address selection can be adjusted by setting the first type characteristic index and the second type characteristic index.
In an alternative embodiment, the first and second class of characteristic indices are determined by:
establishing a judgment matrix based on a preset scale method;
and calculating a first type of characteristic index based on the judgment matrix.
Wherein, the preset scale method has 9 grades: level 1 represents that the two elements are of equal importance compared; level 2 indicates that one element is slightly more important than the other element than two elements; level 5 indicates that one element is more important than the other element than two elements; level 7 indicates that one element is more important than the other element than two elements; level 9 indicates that one element is extremely important compared to the other element; level 2 represents intermediate values between level 1 and level 3, and 4, 6, 8 are equally available and will not be described in detail herein. Taking the determination of the first class of characteristic indexes as an example, the determination of the second class of characteristic indexes is the same as the following:
the first type of feature data of a certain candidate address comprises three planning feature data, wherein the first planning feature data is address unit price grading, the second planning feature data is address area grading, and the third planning feature data is address bearing grading, wherein the candidate address is high in budget during construction, the address unit price is not particularly seen, the size and bearing capacity of the candidate address are more seen, the address area grading is 5 grades compared with the address unit price grading, the address bearing grading is 5 grades compared with the address unit price grading, the address area grading is 1 grade compared with the address bearing grading, and the constructed judgment matrix is as follows:
Wherein,to determine the elements of the ith row and jth column of the matrix, the level of the ith planning feature data relative to the jth planning feature data is represented, for example,=5 means that the level of the second planning feature data compared to the first planning feature data is 5, that is to say the level of the address area score compared to the address unit price score is 5, and conversely,=1/5 means that the importance of the first planning feature data compared to the second planning feature data is 1/5, that is to say the importance of the address unit price score is 1/5 of the importance of the address area;
normalizing the judgment matrix, namely dividing each item in the judgment matrix by the sum of each item in the column of the item to obtain a new normalized matrix:
wherein n represents the number of elements in a column of the matrixI.e., the number of rows of the matrix,representing elements corresponding to aij in the normalized new matrix;
taking the average value of each row in the new matrix to obtain a first type of characteristic index:
where m represents the number of elements in a row of the matrix, i.e. the number of columns of the matrix,representing normalized new matrix and in matrixThe corresponding element(s),a value representing an ith feature index corresponding to the ith planning feature data; the combination of values of the individual characteristic indices constitutes a first type of characteristic index.
In an alternative embodiment, the first class of feature data comprises m planning feature data, the first class of feature indices comprises m planning feature indices corresponding to the planning feature data, and the first class of feature scores are calculated using the following model:
wherein,a first class of feature scores is represented,representing the ith planning feature data,representing an ith planning feature index;
the second class of feature data comprises n construction feature data, the second class of feature indexes comprises n construction feature indexes corresponding to the construction feature data, and the second class of feature scores are calculated by using the following model:
wherein,representing a second type of feature score,representing the data of the j-th construction characteristic,representing the j-th construction characteristic index.
In another alternative embodiment, the first type of feature data includes m planning feature data, the first type of feature index includes m planning feature indices corresponding to the planning feature data, and the first type of feature score is calculated using the following model:
wherein,a first class of feature scores is represented,representing the ith planning feature data,represents the standard value of the ith planning feature data,representing an ith planning feature index;
the second class of feature data comprises n construction feature data, the second class of feature indexes comprises n construction feature indexes corresponding to the construction feature data, and the second class of feature scores are calculated by using the following model:
Wherein,representing a second type of feature score,representing the data of the j-th construction characteristic,represents the standard value of the ith construction characteristic data,representing a j-th construction characteristic index;
here, the standard value of the i-th planning feature data is index data of the i-th planning feature of the candidate address. If the ith planning characteristic data of the candidate address is larger than the index data, the ith planning characteristic data of the candidate address meets the index, and the more the index data is exceeded, the better the performance is; if the ith planning characteristic data of the candidate address is smaller than the index data, the ith planning characteristic data of the candidate address does not meet the index, and the larger the difference between the ith planning characteristic data and the index data is, the worse the performance is. The standard value of the planning feature data can be adjusted according to a specific application scenario, for example, if a certain candidate address presets more devices, the standard value of the address area of the candidate address is larger, for example, 120 square meters, the standard value of the address area score is larger, for example, 9 minutes, and another candidate address presets less devices, for example, the standard value of the address area of the candidate address is smaller, for example, 80 square meters, and the standard value of the address area score is smaller, for example, 6 minutes. The standard value of the j-th construction feature data is the same as the standard value principle of the i-th planning feature data.
In another alternative embodiment, the first type of feature data includes m planning feature data, the first type of feature index includes m planning feature indices corresponding to the planning feature data, and the first type of feature score is calculated using the following model:
wherein,a first class of feature scores is represented,representing the ith planning feature data,representing the minimum value of the ith planning feature data,representing the maximum value of the ith planning feature data,representing an ith planning feature index;
the second class of feature data comprises n construction feature data, the second class of feature indexes comprises n construction feature indexes corresponding to the construction feature data, and the second class of feature scores are calculated by using the following model:
wherein,representing a second type of feature score,representing the data of the j-th construction characteristic,representing the minimum value of the j-th construction feature data,represents the j-th construction siteThe maximum value of the characterization data is calculated,representing a j-th construction characteristic index;
here, the minimum value of the i-th planning feature data represents the lowest index data of the i-th planning feature data, and the maximum value of the i-th planning feature data represents the highest index data of the i-th planning feature data. For example, the preset address area of a certain candidate address is not less than 80 square meters and not more than 120 square meters, if the address area score corresponding to the address area of 80 square meters is 6 points and the address area score corresponding to the address area of 120 square meters is 9 points, the minimum value of the address area score is 6 points and the maximum value of the address area score is 9 points. The standard value of the j-th construction feature data is the same as the standard value principle of the i-th planning feature data, and is not described herein.
It should be noted that, the model for calculating the first type feature score and the second type feature score is not limited to the model described above, and the model for calculating the first type feature score and the model for calculating the second type feature score are the same, that is, the first type feature score and the second type feature score are calculated by using the same model.
In an alternative embodiment, for each candidate address, adding the first class feature score and the second class feature score to obtain a target class feature score; in another alternative embodiment, for each candidate address, the target class feature score is calculated by weighting its first class feature score and second class feature score.
In another alternative embodiment, the target class feature scores are calculated using the following model:
wherein the method comprises the steps ofThe object class feature is scored,a first class of feature scores is represented,a pass value representing a first class of feature scores,a satisfaction value representing a first class of feature scores,representing a second type of feature score,a pass value representing a second class of feature scores,a satisfaction value representing a second class of feature scores; here, the pass value of the first class feature score is a lower standard limit of the first class feature score, and the satisfaction value of the first class feature score is an upper standard limit of the first class feature score. For example, the upper standard limit of the first class feature score of a candidate address is 10 points, that is, the satisfaction value of the first class feature score is 10 points; the lower standard limit of the first class feature scores is 6 points, namely the pass value of the first class feature scores is 6 points. The sum and satisfaction values of the second class feature scores are the same as those of the first class feature scores.
Optionally, in another embodiment, the target class feature score = first class feature score γ1+second class feature score γ2; where γ1 represents the weight of the first class feature score and γ2 represents the weight of the second class feature score.
Step 103, estimating the net emission of greenhouse gases from each candidate address based on the first emission estimator, the second emission estimator and the variation estimator of each candidate address;
optionally, the site selection system estimates the net greenhouse gas emission amount of each candidate address according to the first emission estimated amount, the second emission estimated amount and the variation estimated amount of each candidate address, and the specific calculation formula of the net greenhouse gas emission amount of each candidate address is as follows:
wherein,for the net emission of greenhouse gases,for the first estimated amount of emissions,for the second estimated amount of emissions,an estimate of the change is made.
104, calculating the final score of each candidate address based on the target class feature score and the net emission of greenhouse gases of each candidate address;
optionally, the site selection system calculates the final score of each candidate address according to the target class feature score and the net emission of greenhouse gases of each candidate address, and the calculation formula of the final score of each candidate address is as follows:
Wherein,for the final score to be a score,the object class feature is scored,for the net emission of greenhouse gases,is a preset correction coefficient.
Step 105, determining a target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
Optionally, the site locating system determines a final score with the largest value in the final scores of the candidate addresses, and determines the candidate address corresponding to the final score with the largest value as the target greenhouse gas monitoring site address.
According to the method and the device, the candidate addresses are comprehensively scored according to the plandability and constructability of the candidate addresses, the first estimated carbon dioxide gas discharge amount, the second estimated methane gas discharge amount and the estimated soil organic carbon change amount of the candidate addresses, and the target greenhouse gas monitoring site addresses in the candidate addresses are selected according to the final scoring score, so that the finally selected greenhouse gas monitoring sites can completely cover and monitor all areas.
The following describes the multi-technology integration-based atmospheric chamber gas monitoring site location system provided by the embodiment of the application, and the multi-technology integration-based atmospheric chamber gas monitoring site location system described below and the multi-technology integration-based atmospheric chamber gas monitoring site location method described above can be referred to correspondingly. Referring to fig. 2, fig. 2 is a schematic structural diagram of an atmospheric chamber gas monitoring site selection system based on multi-technology integration provided in an embodiment of the present application, where the atmospheric chamber gas monitoring site selection system based on multi-technology integration provided in an embodiment of the present application includes:
An acquisition module 201, configured to acquire first type feature data, second type feature data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas, and an estimated change amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
a first calculation module 202, configured to calculate a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address;
an estimation module 203 for estimating net greenhouse gas emissions for each candidate site based on the first emissions estimate, the second emissions estimate, and the change estimate for each candidate site;
a second calculation module 204 for calculating a final score for each of the candidate addresses based on the target class feature scores and the net greenhouse gas emissions for each of the candidate addresses;
an address determination module 205 for determining a target greenhouse gas monitoring site address in each of the candidate addresses based on a final scoring score for each of the candidate addresses.
According to the method and the device, the candidate addresses are comprehensively scored according to the plandability and constructability of the candidate addresses, the first estimated carbon dioxide gas discharge amount, the second estimated methane gas discharge amount and the estimated soil organic carbon change amount of the candidate addresses, and the target greenhouse gas monitoring site addresses in the candidate addresses are selected according to the final scoring score, so that the finally selected greenhouse gas monitoring sites can completely cover and monitor all areas.
In one embodiment, the first computing module 202 is further to:
acquiring a first type characteristic index corresponding to the first type characteristic data and a second type characteristic index corresponding to the second type characteristic data of each candidate address;
determining a first class feature score for each of the candidate addresses based on the first class feature data and the first class feature index for each of the candidate addresses;
determining a second class feature score for each of the candidate addresses based on the second class feature data and the second class feature index for each of the candidate addresses;
a target class feature score for each of the candidate addresses is calculated based on the first class feature score and the second class feature score for each of the candidate addresses.
In one embodiment, the acquisition module 201 is further configured to:
acquiring a first estimation model of carbon dioxide gas of each candidate address; the expression of the first estimation model is:
wherein,the concentration value of the carbon dioxide gas at the current time for each candidate address,a total concentration value of carbon dioxide gas for each candidate address history;
inputting the concentration value of the carbon dioxide gas at the first moment of each candidate address into the first estimation model based on the concentration value of the carbon dioxide gas at the current moment of each candidate address to obtain an estimated amount of carbon dioxide gas emission at the first moment of each candidate address;
Inputting the concentration value of the carbon dioxide gas at the second moment of each candidate address into the first estimation model based on the concentration value of the carbon dioxide gas at the current moment of each candidate address to obtain an estimated amount of carbon dioxide gas emission at the second moment of each candidate address;
calculating a first estimated amount of carbon dioxide gas for each candidate site based on the estimated amount of carbon dioxide gas at the first time and the estimated amount of carbon dioxide gas at the second time; the first emission estimator is calculated as:
wherein S is a first emission estimate,an estimated amount of carbon dioxide gas emissions at a first time for each candidate site,an estimate of carbon dioxide gas emissions at a second time is made for each candidate site.
In one embodiment, the acquisition module 201 is further configured to:
obtaining a second estimation model of methane gas of each candidate address; the expression of the second estimation model is:
wherein,for the concentration value of methane gas at the current moment of each candidate address,a total concentration value of methane gas for each candidate address history;
based on the concentration value of the methane gas of each candidate address at the first moment as the concentration value of the methane gas at the current moment, inputting the concentration value of the methane gas of each candidate address at the first moment into a second estimation model to obtain an estimated methane gas emission amount of each candidate address at the first moment;
Based on the concentration value of the methane gas of each candidate address at the second moment as the concentration value of the methane gas at the current moment, inputting the concentration value of the methane gas of each candidate address at the second moment into a second estimation model to obtain an estimated methane gas emission amount of each candidate address at the second moment;
calculating a second estimated methane gas emission for each candidate site based on the estimated methane gas emission for each candidate site at the first time and the estimated methane gas emission for each candidate site at the second time; the second emission estimator is calculated as:
for the second estimated amount of emissions,for each candidate address an estimated methane gas emission at the first time,an estimate of methane gas emissions at a second time for each candidate site.
In one embodiment, the acquisition module 201 is further configured to:
acquiring a third estimation model of soil organic carbon of each candidate address; the expression of the third estimation model is:
wherein,the concentration value of the organic carbon in the soil at the current moment is used as each candidate address,for the total concentration value of soil organic carbon of each candidate address history,is a preset weight;
based on the concentration value of the soil organic carbon of each candidate address at the first moment as the concentration value of the soil organic carbon at the current moment, inputting the concentration value of the soil organic carbon of each candidate address at the first moment into a third estimation model to obtain the variation of the soil organic carbon of each candidate address at the first moment;
Based on the concentration value of the soil organic carbon of each candidate address at the second moment as the concentration value of the soil organic carbon at the current moment, inputting the concentration value of the soil organic carbon of each candidate address at the second moment into a third estimation model to obtain the variation of the soil organic carbon of each candidate address at the second moment;
calculating an estimated amount of change of the organic carbon in the soil of each candidate address based on the amount of change of the organic carbon in the soil of each candidate address at the first moment and the amount of change of the organic carbon in the soil of each candidate address at the second moment; the calculation formula of the change estimator is:
wherein,for the purpose of the variation estimation quantity,for the amount of change of the organic carbon in the soil at the first moment of each candidate address,the amount of change of the organic carbon in the soil at the second moment is used as each candidate address.
The specific embodiments of the atmospheric chamber gas monitoring site selection system based on the multi-technology integration are basically the same as the embodiments of the atmospheric chamber gas monitoring site selection method based on the multi-technology integration, and are not described in detail herein.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 310, communication interface (Communication Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320, memory 330 accomplish communication with each other through communication bus 340. The processor 310 may invoke a deterministic computer program in the memory 330 to perform steps of an atmospheric chamber gas monitoring site location method based on a multi-technology fusion, for example, including:
Acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
calculating a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address;
estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the variation estimate for each of the candidate addresses;
calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
and determining a target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a deterministic machine-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a determiner device (which may be a personal determiner, a server, a network device, etc.) to perform all or part of the steps of the method described in the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present application further provide a non-transitory determining machine-readable storage medium, where the non-transitory determining machine-readable storage medium includes a determining machine program, where the determining machine program may be stored on the non-transitory determining machine-readable storage medium, and when the determining machine program is executed by a processor, the determining machine may perform the steps of the atmospheric temperature chamber gas monitoring site location method based on the multi-technology fusion provided in the foregoing embodiments, for example, including:
acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
calculating a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address;
estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the variation estimate for each of the candidate addresses;
calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
And determining a target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
In yet another aspect, embodiments of the present application further provide a computer product including a determining computer program, where the determining computer program is capable of executing the steps of the method for locating an atmospheric chamber gas monitoring site based on multi-technology fusion provided in the foregoing embodiments, where the determining computer program is capable of being stored on the computer product, and when the determining computer program is executed by a processor, the determining computer program includes:
acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
calculating a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address;
estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the variation estimate for each of the candidate addresses;
Calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
and determining a target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the above technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a determiner-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., comprising several instructions for causing a determiner device (which may be a personal determiner, a server, a network device, etc.) to perform the embodiments or the methods described by some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (8)

1. An atmospheric chamber gas monitoring station site selection method based on multi-technology integration is characterized by comprising the following steps:
acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
calculating a target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address;
Estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the variation estimate for each of the candidate addresses;
calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
and determining a target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
2. The method for locating an atmospheric chamber gas monitoring station based on a multi-technology integration according to claim 1, wherein the calculating the target class feature score of each candidate address based on the first class feature data and the second class feature data of each candidate address comprises:
acquiring a first type characteristic index corresponding to the first type characteristic data and a second type characteristic index corresponding to the second type characteristic data of each candidate address;
determining a first class feature score for each of the candidate addresses based on the first class feature data and the first class feature index for each of the candidate addresses;
determining a second class feature score for each of the candidate addresses based on the second class feature data and the second class feature index for each of the candidate addresses;
A target class feature score for each of the candidate addresses is calculated based on the first class feature score and the second class feature score for each of the candidate addresses.
3. The method of claim 1, wherein obtaining a first estimated amount of carbon dioxide emissions for each candidate site comprises:
acquiring a first estimation model of carbon dioxide gas of each candidate address; the expression of the first estimation model is:
；
wherein,for the concentration value of carbon dioxide gas at the current moment of each candidate address, +.>A total concentration value of carbon dioxide gas for each candidate address history;
inputting the concentration value of the carbon dioxide gas at the first moment of each candidate address into the first estimation model based on the concentration value of the carbon dioxide gas at the current moment of each candidate address to obtain an estimated amount of carbon dioxide gas emission at the first moment of each candidate address;
inputting the concentration value of the carbon dioxide gas at the second moment of each candidate address into the first estimation model based on the concentration value of the carbon dioxide gas at the current moment of each candidate address to obtain an estimated amount of carbon dioxide gas emission at the second moment of each candidate address;
Calculating a first estimated amount of carbon dioxide gas for each candidate site based on the estimated amount of carbon dioxide gas at the first time and the estimated amount of carbon dioxide gas at the second time; the first emission estimator is calculated as:
；
wherein S is a first emission estimate,for each candidate addressAn estimated amount of carbon dioxide gas emissions at a first time,an estimate of carbon dioxide gas emissions at a second time is made for each candidate site.
4. The method of multi-technology fusion based atmospheric chamber gas monitoring site location of claim 1, wherein obtaining a second estimated emissions of methane gas for each candidate site comprises:
obtaining a second estimation model of methane gas of each candidate address; the expression of the second estimation model is:
；
wherein,for the concentration value of methane gas at the current moment of each candidate address, +.>A total concentration value of methane gas for each candidate address history;
based on the concentration value of the methane gas of each candidate address at the first moment as the concentration value of the methane gas at the current moment, inputting the concentration value of the methane gas of each candidate address at the first moment into a second estimation model to obtain an estimated methane gas emission amount of each candidate address at the first moment;
Based on the concentration value of the methane gas of each candidate address at the second moment as the concentration value of the methane gas at the current moment, inputting the concentration value of the methane gas of each candidate address at the second moment into a second estimation model to obtain an estimated methane gas emission amount of each candidate address at the second moment;
calculating a second estimated methane gas emission for each candidate site based on the estimated methane gas emission for each candidate site at the first time and the estimated methane gas emission for each candidate site at the second time; the second emission estimator is calculated as:
；
for the second emission estimate,/->For each candidate address, the estimated methane gas emission at the first moment,/for each candidate address>An estimate of methane gas emissions at a second time for each candidate site.
5. The atmospheric chamber gas monitoring site selection method based on multi-technology integration according to claim 1, wherein obtaining the estimated amount of change of soil organic carbon for each candidate address comprises:
acquiring a third estimation model of soil organic carbon of each candidate address; the expression of the third estimation model is:
；
wherein,for the concentration value of soil organic carbon of each candidate address at the current moment,/for the soil organic carbon concentration value>For the total concentration value of soil organic carbon of each candidate address history,/ >Is a preset weight;
based on the concentration value of the soil organic carbon of each candidate address at the first moment as the concentration value of the soil organic carbon at the current moment, inputting the concentration value of the soil organic carbon of each candidate address at the first moment into a third estimation model to obtain the variation of the soil organic carbon of each candidate address at the first moment;
based on the concentration value of the soil organic carbon of each candidate address at the second moment as the concentration value of the soil organic carbon at the current moment, inputting the concentration value of the soil organic carbon of each candidate address at the second moment into a third estimation model to obtain the variation of the soil organic carbon of each candidate address at the second moment;
calculating an estimated amount of change of the organic carbon in the soil of each candidate address based on the amount of change of the organic carbon in the soil of each candidate address at the first moment and the amount of change of the organic carbon in the soil of each candidate address at the second moment; the calculation formula of the change estimator is:
；
wherein,for the change estimate +.>For the change of soil organic carbon of each candidate address at the first moment, +.>The amount of change of the organic carbon in the soil at the second moment is used as each candidate address.
6. The method for locating an atmospheric greenhouse gas monitoring station based on multi-technology integration according to claim 1, wherein the calculation formula of the net emission of greenhouse gas of each candidate address is as follows:
；
wherein,clean for greenhouse gases Discharge amount (I)>For the first emission estimate,/->For the second emission estimate,/->An estimate of the change is made.
7. The atmospheric chamber gas monitoring site location method based on multi-technology integration according to claim 1, wherein a calculation formula of a final score of each candidate address is:
；
wherein,for the final score, ++>Scoring the target class characteristics->For net emission of greenhouse gases->Is a preset correction coefficient.
8. An atmospheric chamber gas monitoring station site selection system based on multi-technology integration is characterized by comprising:
the acquisition module is used for acquiring first type characteristic data, second type characteristic data, a first estimated emission amount of carbon dioxide gas, a second estimated emission amount of methane gas and a change estimated amount of organic carbon in soil of each candidate address; the first type of characteristic data represents the planability of the candidate address, and the second type of characteristic data represents the constructability of the candidate address;
the first computing module is used for computing the target class feature scores of the candidate addresses based on the first class feature data and the second class feature data of the candidate addresses;
an estimation module for estimating net greenhouse gas emissions for each of the candidate addresses based on the first emissions estimate, the second emissions estimate, and the change estimate for each of the candidate addresses;
A second calculation module for calculating a final score for each candidate address based on the target class feature score and the net greenhouse gas emissions for each candidate address;
and the address determining module is used for determining the target greenhouse gas monitoring site address in each candidate address based on the final score of each candidate address.
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