A G-Smart government energy and carbon digital monitoring system

Abstract

The present invention discloses a G‑Smart government energy carbon digital monitoring system, which relates to the technical field of energy carbon monitoring. It discloses a data acquisition module, a classification analysis module, and a learning scheduling module. The classification analysis module is set to classify and mark enterprises of different categories, so that the government can intuitively understand the energy carbon emission positioning of enterprises of different categories. The government can require enterprises of different categories to make corresponding rectifications according to the energy carbon emission positioning. The learning scheduling module is set to automatically arrange lagging enterprises in high-energy carbon enterprises to learn from suitable excellent enterprises, so that enterprises of this type can quickly discover their own problems, thereby solving the energy carbon loss problem accordingly.

Description

Technical Field

[0001] The invention relates to a G-Smart government energy-carbon digital monitoring system, belonging to the technical field of energy-carbon monitoring. Background Art

[0002] Energy conservation and low carbon are the current environmental protection trends of various enterprises. Low carbon means lower greenhouse gas emissions, and energy conservation mainly refers to reducing energy consumption as much as possible to produce the same quantity and quality of products as before. The government will monitor the energy and carbon loss of enterprises and take corresponding measures against enterprises with high energy and carbon loss after monitoring. However, the current monitoring system cannot directly let the government understand the energy and carbon emission positioning of enterprises in different categories, and it is also impossible to arrange learning between enterprises. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a G-Smart government energy and carbon digital monitoring system.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A G-Smart government energy carbon digital monitoring system, including a data acquisition module, a classification analysis module, and a learning scheduling module;

[0006] The data collection module is used to collect the monthly energy consumption and carbon emissions of the enterprise, and send the monthly energy consumption and carbon emissions to the server for storage;

[0007] The classification analysis module is used to classify and mark enterprises in different categories, specifically:

[0008] Step 1: Obtain the monthly energy-carbon value Hs of the enterprise in the current year, and mark the monthly energy-carbon value Hs of the enterprise as the actual energy-carbon value, set each actual energy-carbon value to correspond to a standard energy-carbon value, compare the actual energy-carbon value with the standard energy-carbon value, and when the actual energy-carbon value is greater than the standard energy-carbon value, mark the actual energy-carbon value as an over-standard energy-carbon value; when the actual energy-carbon value is less than the standard energy-carbon value, mark the actual energy-carbon value as a low-standard energy-carbon value, and obtain the excess value Wx and low value Db of the enterprise;

[0009] Step 2: Get the classification category to which the enterprise belongs. Enterprises are classified into mining, agriculture, forestry, animal husbandry, fishery, manufacturing, electricity, gas and water production and supply, etc. The excess values ​​Wx of enterprises in the same classification category are summed and averaged to obtain the classified excess value and marked as Zs. The low values ​​Db of enterprises in the same classification category are summed and averaged to obtain the classified low value and marked as Rk.

[0010] Step 3: Obtain the classified over-standard average interval Qa and the classified under-standard average interval Vj;

[0011] Step 4: Obtain the energy-carbon classification value Xe of the enterprise in this category, set the energy-carbon classification value threshold to Ki, when the absolute value of the energy-carbon classification value Xe ≥ the energy-carbon classification value threshold Ki and the energy-carbon classification value Xe is a positive number, mark the enterprise in this category as a high-energy carbon enterprise; when the absolute value of the energy-carbon classification value Xe ≥ the energy-carbon classification value threshold Ki and the energy-carbon classification value Xe is a negative number, mark the enterprise in this category as a low-energy carbon enterprise; when the absolute value of the energy-carbon classification value Xe < the energy-carbon classification value threshold Ki, mark the enterprise in this category as a general energy carbon enterprise, and display the marking result on the government terminal;

[0012] The learning scheduling module is used to arrange the lagging enterprises to the appropriate excellent enterprises for learning, specifically:

[0013] Step 1: Calculate the difference between the excess value Wx and the low value Db of each high-energy carbon enterprise to obtain the energy-carbon difference of the enterprise, set the energy-carbon difference threshold to Ys, and when the energy-carbon difference of the enterprise is ≥ the carbon difference threshold Ys and the energy-carbon difference is a positive number, mark the enterprise as a lagging enterprise for monitoring; when the energy-carbon difference of the enterprise is ≥ the carbon difference threshold Ys and the energy-carbon difference is a negative number, mark the enterprise as an excellent enterprise for monitoring; when the energy-carbon difference of the enterprise is < the carbon difference threshold Ys, mark the enterprise as an ordinary enterprise for monitoring;

[0014] Step 2: Get the position of the lagging enterprise, and use the current position as the center and the preset radius to draw a circle to get the learning range, mark the excellent enterprises within the learning range as pre-learning enterprises, get the absolute value of the energy-carbon difference of the pre-learning enterprise and mark it as Sy, get the excellent learning value of the pre-learning enterprise and mark it as Fr;

[0015] Step 3: Calculate the distance difference between the position of the monitored lagging enterprise and the position of the pre-learning enterprise to obtain the learning distance and mark it as Oc. Use the formula The learning selection value Zi of the pre-learning enterprise is obtained, where d1, d2, and d3 are all preset proportional coefficients, and the position of the pre-learning enterprise with the largest learning selection value Zi is sent to the office terminal of the leader of the monitoring lagging enterprise.

[0016] Furthermore, the excellent learning value Fr of the pre-learning enterprise is obtained by the following steps: the number of times the pre-learning enterprise is marked as an excellent monitoring enterprise in all years before the current time of the system is obtained, and marked as Qh; the energy-carbon difference of all pre-learning enterprises marked as excellent monitoring enterprises in all years before the current time of the system is summed up and averaged, and the excellent energy-carbon balance is obtained, and marked as Np; the formula is used The superior learning value Fr of the pre-learning enterprise is obtained, where c1 and c2 are preset proportional coefficients.

[0017] Furthermore, the monthly energy carbon value Hs of the enterprise is obtained by the following steps: mark the monthly energy consumption of the enterprise as Kz, mark the monthly carbon emission of the enterprise as Rc, and use the formula Obtain the monthly energy carbon value Hs of the enterprise, where a1 and a2 are preset proportional coefficients.

[0018] Furthermore, the excess value Wx of the enterprise is obtained by the following steps: the excess carbon value is calculated by difference between the excess carbon value and the standard carbon value to obtain the excess carbon difference, which is marked as Mj, and the excess carbon difference coefficient is set as Pi, using the formula The excess value Wx of the enterprise is obtained, where i=1, 2, ..., n, and n is the total number of months in which the actual energy-to-carbon value of the enterprise is marked as an excess energy-to-carbon value.

[0019] Furthermore, the low value Db of the enterprise is obtained by the following steps: the standard energy carbon value and the low standard energy carbon value are calculated to obtain the low energy carbon difference, which is marked as En, and the low energy carbon difference coefficient is set to Tg. The low value Db of the enterprise is obtained, where g=1, 2, ..., n, and n is the total number of months in which the actual energy-carbon value of the enterprise is marked as the low standard energy-carbon value.

[0020] Furthermore, the classified over-standard average interval Qa and the classified under-standard average interval Vj are obtained through the following steps: sort the months corresponding to the over-standard energy-carbon values ​​of the enterprises in the current year in order, calculate the difference between the months corresponding to two adjacent over-standard energy-carbon values ​​to obtain the over-standard energy-carbon monthly interval, sum up all the over-standard energy-carbon monthly intervals and take the average to obtain the over-standard energy-carbon average interval, sum up the over-standard energy-carbon average intervals of enterprises in the same category and take the average to obtain the classified over-standard average interval, and mark it as Qa, sort the months corresponding to the under-standard energy-carbon values ​​of the enterprises in the current year in order, calculate the difference between the months corresponding to two adjacent under-standard energy-carbon values ​​to obtain the under-standard energy-carbon monthly interval, sum up all the under-standard energy-carbon monthly intervals and take the average to obtain the under-standard energy-carbon average interval, sum up the under-standard energy-carbon average intervals of enterprises in the same category and take the average to obtain the classified under-standard average interval, and mark it as Vj.

[0021] Furthermore, the energy-carbon classification value Xe of the enterprise in this category is obtained by the following steps: obtain the total number of months in which the energy-carbon value of the enterprise in the same classification category exceeds the standard in the current year, and mark it as Lt; obtain the total number of months in which the energy-carbon value of the enterprise in the same classification category exceeds the standard in the current year, and mark it as As; and use the formula The energy-carbon classification value Xe of the enterprise in this category is obtained, where b1, b2, b3, b4, b5, and b6 are all preset proportional coefficients.

[0022] The beneficial effects of the present invention are:

[0023] 1. Set up a classification analysis module to classify and mark enterprises in different categories, so that the government can intuitively understand the energy and carbon emission positioning of enterprises in different categories. The government can require enterprises in different categories to make corresponding rectifications based on the energy and carbon emission positioning;

[0024] 2. Setting up a learning scheduling module can automatically arrange lagging enterprises in high-energy carbon enterprises to learn from appropriate excellent enterprises, so that this type of enterprise can quickly discover its own problems and solve the problem of energy carbon loss accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 It is a flowchart of the classification analysis module of the present invention;

[0027] Figure 2 It is a flowchart of the learning scheduling module of the present invention;

[0028] Figure 3 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] Embodiment 1:

[0031] Reference Figure 1 , a G-Smart government energy carbon digital monitoring system, including a data collection module and a classification analysis module;

[0032] The data collection module is used to collect the monthly energy consumption and carbon emissions of the enterprise, and send the monthly energy consumption and carbon emissions to the server for storage;

[0033] The classification analysis module is used to classify and mark enterprises in different categories, specifically:

[0034] Step 1: Obtain the monthly energy carbon value Hs of the enterprise for the current year. The monthly energy carbon value Hs of the enterprise is obtained through the following steps: Mark the monthly energy consumption of the enterprise as Kz, mark the monthly carbon emissions of the enterprise as Rc, and use the formula to obtain the monthly energy carbon value Hs of the enterprise, where a1 and a2 are both preset proportionality coefficients, the value of a1 is 0.65, the value of a2 is 0.85, and mark the monthly energy carbon value Hs of the enterprise as the actual energy carbon value. Set that each actual energy carbon value corresponds to a standard energy carbon value. Compare the actual energy carbon value with the standard energy carbon value. When the actual energy carbon value is greater than the standard energy carbon value, mark this actual energy carbon value as an excessive energy carbon value. Calculate the difference between the excessive energy carbon value and the standard energy carbon value to obtain the excessive energy carbon difference and mark it as Mj. When the actual energy carbon value is less than the standard energy carbon value, mark this actual energy carbon value as a substandard energy carbon value. Calculate the difference between the standard energy carbon value and the substandard energy carbon value to obtain the low energy carbon difference and mark it as En. Set the excessive energy carbon difference coefficient as Pi, and use the formula to obtain the excess value Wx of the enterprise, where i = 1, 2, …, n, and n is the total number of months when the actual energy carbon value of the enterprise is marked as an excessive energy carbon value; Pi, i = 1, 2, 3, …, n; P1 < P2 < P3 < … < Pn. Set that each excessive energy carbon difference coefficient corresponds to a range of excessive energy carbon differences, including (0, M1], (M1, M2], …, (Mj - 1, Mj]. When Mj ∈ (0, M1], the corresponding excessive energy carbon difference coefficient is P1; Set the low energy carbon difference coefficient as Tg, and use the formula to obtain the low value Db of the enterprise, where g = 1, 2, …, n, and n is the total number of months when the actual energy carbon value of the enterprise is marked as a substandard energy carbon value; Tg, g = 1, 2, 3, …, n; T1 < T2 < T3 < … < Tn. Set that each low energy carbon difference coefficient corresponds to a range of low energy carbon differences, including (0, E1], (E1, E2], …, (En - 1, En]. When En ∈ (0, E1], the corresponding low energy carbon difference coefficient is T1;

[0035] Step 2: Obtain the classification category to which the enterprise belongs. Enterprises are classified into mining, agriculture, forestry, animal husbandry, fishery, manufacturing, production and supply of electricity, gas and water, etc., and can also be further subdivided. Sum up and take the average of the excess values Wx of the enterprises in the same classification category to obtain the classified excess value and mark it as Zs. Sum up and take the average of the low values Db of the enterprises in the same classification category to obtain the classified low value and mark it as Rk;

[0036] Step 3: Sort the months corresponding to the excessive energy-carbon values ​​of the enterprise in the current year in order, calculate the difference between the months corresponding to two adjacent excessive energy-carbon values ​​to obtain the excessive energy-carbon monthly interval, sum all the excessive energy-carbon monthly intervals and take the average to obtain the excessive energy-carbon average interval, sum and average the excessive energy-carbon average intervals of enterprises in the same classification, obtain the classified excessive average interval, and mark it as Qa, sort the months corresponding to the low-standard energy-carbon values ​​of the enterprise in the current year in order, calculate the difference between the months corresponding to two adjacent low-standard energy-carbon values ​​to obtain the low-standard energy-carbon monthly interval, sum all the low-standard energy-carbon monthly intervals and take the average to obtain the low-standard energy-carbon average interval, sum and average the low-standard energy-carbon average intervals of enterprises in the same classification, obtain the classified low-standard average interval, and mark it as Vj;

[0037] Step 4: Obtain the total number of months in which the energy-carbon value of enterprises in the same classification category in the current year exceeds the standard, and mark it as Lt; obtain the total number of months in which the energy-carbon value of enterprises in the same classification category in the current year is below the standard, and mark it as As. Use the formula Obtain the energy-carbon classification value Xe of the enterprise in this category, where b1, b2, b3, b4, b5, and b6 are all preset proportional coefficients, b1 is 0.25, b2 is 0.28, b3 is 0.23, b4 is 0.24, b5 is 0.18, and b6 is 0.16. Set the energy-carbon classification value threshold to Ki. When the absolute value of the energy-carbon classification value Xe ≥ the energy-carbon classification value threshold Ki and the energy-carbon classification value Xe is a positive number When the absolute value of the energy-carbon classification value Xe is ≥ the energy-carbon classification value threshold Ki and the energy-carbon classification value Xe is negative, the enterprise in this category is marked as a low-energy carbon enterprise. When the absolute value of the energy-carbon classification value Xe is < the energy-carbon classification value threshold Ki, the enterprise in this category is marked as a general energy-carbon enterprise, and the marking result is displayed on the government terminal (energy-carbon loss of high-energy carbon enterprises> energy-carbon loss of general energy-carbon enterprises> energy-carbon loss of low-energy carbon enterprises). The energy-carbon classification value threshold is 25. When the energy-carbon classification value of a mining enterprise is -28, the mining enterprise is marked as a low-energy carbon enterprise. When the energy-carbon classification value of an animal husbandry enterprise is 26, the animal husbandry enterprise is marked as a high-energy carbon enterprise. When the energy-carbon classification value of a fishery enterprise is 18, the fishery enterprise is marked as a general energy-carbon enterprise.

[0038] Embodiment 2

[0039] Reference Figure 2-Figure 3 On the basis of Example 1, a learning scheduling module is further included, and the learning scheduling module is used to arrange the monitored lagging enterprises to the appropriate monitored excellent enterprises for learning, specifically:

[0040] Step 1: Calculate the difference between the excess value Wx and the low value Db of each high-energy carbon enterprise to obtain the energy-carbon difference of the enterprise, set the energy-carbon difference threshold to Ys, when the energy-carbon difference of the enterprise ≥ the carbon difference threshold Ys and the energy-carbon difference is a positive number, mark the enterprise as a lagging enterprise for monitoring; when the energy-carbon difference of the enterprise ≥ the carbon difference threshold Ys and the energy-carbon difference is a negative number, mark the enterprise as an excellent enterprise for monitoring; when the energy-carbon difference of the enterprise < the carbon difference threshold Ys, mark the enterprise as an ordinary enterprise for monitoring; the energy-carbon difference threshold is 10, when the energy-carbon difference of enterprise a is 12, mark enterprise a as a lagging enterprise for monitoring; when the energy-carbon difference of enterprise b is -11, mark enterprise b as an excellent enterprise for monitoring; when the energy-carbon difference of enterprise c is -6, mark enterprise c as an ordinary enterprise for monitoring;

[0041] Step 2: Get the position of the lagging monitored enterprise, and use the current position as the center and the preset radius to draw a circle to get the learning range, mark the monitored excellent enterprises within the learning range as pre-learning enterprises, get the absolute value of the energy-carbon difference of the pre-learning enterprise, and mark it as Sy, get the excellent learning value of the pre-learning enterprise and mark it as Fr. The excellent learning value Fr of the pre-learning enterprise is obtained by the following steps: get the number of times the pre-learning enterprise is marked as an excellent monitored enterprise in all years before the current time of the system, and mark it as Qh, sum and average the energy-carbon difference of all pre-learning enterprises marked as excellent monitored enterprises in all years before the current time of the system, get the excellent energy-carbon adjustment, and mark it as Np, and use the formula The superior learning value Fr of the pre-learning enterprise is obtained, where c1 and c2 are both preset proportional coefficients, the value of c1 is 0.55, and the value of c2 is 0.5;

[0042] Step 3: Calculate the distance difference between the position of the monitored lagging enterprise and the position of the pre-learning enterprise to obtain the learning distance and mark it as Oc. Use the formula The learning selection value Zi of the pre-learning enterprise is obtained, where d1, d2, and d3 are all preset proportional coefficients, the value of d1 is 0.46, the value of d2 is 0.48, and the value of d3 is 0.4. The position of the pre-learning enterprise with the largest learning selection value Zi is sent to the office terminal of the leader of the monitoring lagging enterprise.

[0043] Working principle:

[0044] A classification analysis module can be set up to classify and label enterprises in different categories, allowing the government to intuitively understand the energy and carbon emission positioning of enterprises in different categories. The government can require enterprises in different categories to make corresponding rectifications based on the energy and carbon emission positioning. A learning scheduling module can be set up to automatically arrange lagging enterprises in high-energy carbon enterprises to learn from appropriate excellent enterprises, so that this type of enterprise can quickly discover its own problems and solve the energy and carbon loss problem accordingly.

[0045] The above is only a preferred embodiment of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

[0046] In the description of the present invention, it should be understood that the terms "upper", "lower", "left", "right", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, and a specific direction structure and operation, and therefore, cannot be understood as a limitation on the present invention. In addition, "first" and "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A G-Smart government energy carbon digital monitoring system, characterized in that: Including data collection module, classification analysis module, and learning scheduling module; The data collection module is used to collect the monthly energy consumption and carbon emissions of the enterprise, and send the monthly energy consumption and carbon emissions to the server for storage; The classification analysis module is used to classify and mark enterprises in different categories, specifically: Step 1: Obtain the monthly energy-carbon value Hs of the enterprise in the current year, and mark the monthly energy-carbon value Hs of the enterprise as the actual energy-carbon value, set each actual energy-carbon value to correspond to a standard energy-carbon value, compare the actual energy-carbon value with the standard energy-carbon value, and when the actual energy-carbon value is greater than the standard energy-carbon value, mark the actual energy-carbon value as an over-standard energy-carbon value; when the actual energy-carbon value is less than the standard energy-carbon value, mark the actual energy-carbon value as a low-standard energy-carbon value, and obtain the excess value Wx and low value Db of the enterprise; The excess value Wx of the enterprise is obtained by calculating the difference between the excess energy-carbon value and the standard energy-carbon value to obtain the excess energy-carbon difference, thereby calculating the excess value Wx; The low value Db is obtained by calculating the difference between the standard energy carbon value and the low standard energy carbon value to obtain the low energy carbon difference, thereby calculating the low value Db; Step 2: Obtain the classification category to which the enterprise belongs, sum and average the excess values ​​Wx of enterprises in the same classification category, obtain the classified excess value and mark it as Zs, sum and average the undervalues ​​Db of enterprises in the same classification category, obtain the classified undervalue and mark it as Rk; Step 3: Sort the months corresponding to the excessive energy-carbon values ​​of the enterprise in the current year in order, calculate the difference between the months corresponding to two adjacent excessive energy-carbon values ​​to obtain the excessive energy-carbon monthly interval, sum all the excessive energy-carbon monthly intervals and take the average to obtain the excessive energy-carbon average interval, sum and average the excessive energy-carbon average intervals of enterprises in the same classification, obtain the classified excessive average interval, and mark it as Qa, sort the months corresponding to the low-standard energy-carbon values ​​of the enterprise in the current year in order, calculate the difference between the months corresponding to two adjacent low-standard energy-carbon values ​​to obtain the low-standard energy-carbon monthly interval, sum all the low-standard energy-carbon monthly intervals and take the average to obtain the low-standard energy-carbon average interval, sum and average the low-standard energy-carbon average intervals of enterprises in the same classification, obtain the classified low-standard average interval, and mark it as Vj; Step 4: Obtain the energy-carbon classification value Xe of the enterprise in this category, set the energy-carbon classification value threshold to Ki, when the absolute value of the energy-carbon classification value Xe ≥ the energy-carbon classification value threshold Ki and the energy-carbon classification value Xe is a positive number, mark the enterprise in this category as a high-energy carbon enterprise; when the absolute value of the energy-carbon classification value Xe ≥ the energy-carbon classification value threshold Ki and the energy-carbon classification value Xe is a negative number, mark the enterprise in this category as a low-energy carbon enterprise; when the absolute value of the energy-carbon classification value Xe < the energy-carbon classification value threshold Ki, mark the enterprise in this category as a general energy carbon enterprise, and display the marking result on the government terminal; The learning scheduling module is used to arrange the lagging enterprises to the appropriate excellent enterprises for learning, specifically: Step 1: Calculate the difference between the excess value Wx and the low value Db of each enterprise in high-energy carbon enterprises to obtain the energy-carbon difference of the enterprise. Set the energy-carbon difference threshold as Ys. When the energy-carbon difference of the enterprise ≥ the carbon difference threshold Ys and the energy-carbon difference is positive, mark the enterprise as a monitored backward enterprise. When the energy-carbon difference of the enterprise ≥ the carbon difference threshold Ys and the energy-carbon difference is negative, mark the enterprise as a monitored excellent enterprise. When the energy-carbon difference of the enterprise < the carbon difference threshold Ys, mark the enterprise as a monitored ordinary enterprise; Step 2: Obtain the location of the monitored backward enterprise, draw a circle with the current location as the center and a preset radius to obtain the learning range. Mark the monitored excellent enterprises whose locations are within the learning range as pre-learning enterprises. Obtain the absolute value of the energy-carbon difference of the pre-learning enterprises and mark it as Sy. Obtain the excellent learning value of the pre-learning enterprises and mark it as Fr; Step 3: Calculate the distance difference between the location of the monitored backward enterprise and the location of the pre-learning enterprise to obtain the learning distance and mark it as Oc. Use the formula to obtain the learning selection value Zi of the pre-learning enterprise, where d1, d2, and d3 are all preset proportionality coefficients. Send the location of the pre-learning enterprise with the largest learning selection value Zi to the office terminal of the leader of the monitored backward enterprise.

2. According to claim 1, a G-Smart government energy carbon digital monitoring system is characterized in that: The excellent learning value Fr of the pre-learning enterprise is obtained by the following steps: obtain the number of times the pre-learning enterprise is marked as an excellent monitoring enterprise in all years before the current time of the system, and mark it as Qh; sum and average the energy and carbon content differences of all pre-learning enterprises marked as excellent monitoring enterprises in all years before the current time of the system, obtain the excellent energy and carbon content adjustment, and mark it as Np; use the formula The superior learning value Fr of the pre-learning enterprise is obtained, where c1 and c2 are both preset proportional coefficients.

3. According to claim 2, a G-Smart government energy carbon digital monitoring system is characterized in that: The monthly energy carbon value Hs of an enterprise is obtained by the following steps: mark the monthly energy consumption of the enterprise as Kz, mark the monthly carbon emission of the enterprise as Rc, and use the formula Obtain the monthly energy carbon value Hs of the enterprise, where a1 and a2 are preset proportional coefficients.

4. According to claim 3, a G-Smart government energy carbon digital monitoring system is characterized in that: The excess value Wx of the enterprise is obtained by the following steps: Calculate the difference between the excess energy carbon value and the standard energy carbon value to obtain the excess energy carbon difference, mark it as Mj, set the excess energy carbon difference coefficient as Pi, and use the formula Obtain the excess value Wx of the enterprise, i=1, 2, ..., n, n is the total number of months that the actual energy-carbon value of the enterprise is marked as an excess energy-carbon value; Pi, i = 1, 2, 3,..., n; P1 < P2 < P3 <... < Pn. Set a range of super energy-carbon differences corresponding to each super energy-carbon difference coefficient, including (0, M1], (M1, M2],..., (Mj - 1, Mj]. When Mj ∈ (0, M1], the corresponding super energy-carbon difference coefficient is P1.

5. A G-Smart government energy carbon digital monitoring system according to claim 4, characterized in that: The low value Db of the enterprise is obtained by the following steps: calculate the difference between the standard energy carbon value and the low standard energy carbon value, obtain the low energy carbon difference, and mark it as En, set the low energy carbon difference coefficient as Tg, and use the formula Obtain the low value Db of the enterprise, g=1, 2, ..., n, n is the total number of months that the actual energy-carbon value of the enterprise is marked as the low standard energy-carbon value; Tg, g = 1, 2, 3,..., n; T1 < T2 < T3 <... < Tn. Set a range of low energy-carbon differences corresponding to each low energy-carbon difference coefficient, including (0, E1], (E1, E2],..., (En - 1, En]. When En ∈ (0, E1], the corresponding low energy-carbon difference coefficient is T1.

6. A G-Smart government energy carbon digital monitoring system according to claim 5, characterized in that: The classified over-standard average interval Qa and the classified under-standard average interval Vj are obtained through the following steps: Sort the months corresponding to the over-standard energy-carbon values of enterprises in the current year in sequence. Calculate the difference between the months corresponding to two adjacent over-standard energy-carbon values to obtain the over-standard energy-carbon monthly interval. Sum up all the over-standard energy-carbon monthly intervals and take the average to obtain the over-standard energy-carbon average interval. Sum up the over-standard energy-carbon average intervals of enterprises in the same classification category and take the average to obtain the classified over-standard average interval and mark it as Qa. Sort the months corresponding to the under-standard energy-carbon values of enterprises in the current year in sequence. Calculate the difference between the months corresponding to two adjacent under-standard energy-carbon values to obtain the under-standard energy-carbon monthly interval. Sum up all the under-standard energy-carbon monthly intervals and take the average to obtain the under-standard energy-carbon average interval. Sum up the under-standard energy-carbon average intervals of enterprises in the same classification category and take the average to obtain the classified under-standard average interval and mark it as Vj.

7. A G-Smart government energy carbon digital monitoring system according to claim 6, characterized in that: The energy-carbon classification value Xe of the enterprise in this category is obtained by the following steps: obtain the total number of months in which the energy-carbon value of the enterprise in the same classification category exceeds the standard in the current year, and mark it as Lt; obtain the total number of months in which the energy-carbon value of the enterprise in the same classification category exceeds the standard in the current year, and mark it as As; and use the formula The energy-carbon classification value Xe of the enterprise in this category is obtained, where b1, b2, b3, b4, b5, and b6 are all preset proportional coefficients.

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