Enterprise energy collection and carbon emission intelligent management system based on Internet of Things

Abstract

The invention relates to the technical field of carbon emission management, in particular to an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things, which comprises a data collection module, a server, a feature recognition analysis module, an energy consumption analysis module, a carbon emission analysis module, a carbon quota distribution module, a carbon quota transaction module and a cloud database. According to the method, the comprehensive carbon shadow value is calculated by monitoring the basic characteristics and the energy consumption information of the enterprise factory, meanwhile, the carbon emission detection points are arranged in the enterprise factory, the carbon emission data of each layer are collected, the carbon emission evaluation coefficient is calculated by combining the comprehensive carbon shadow value, and the carbon emission evaluation coefficient is compared with the carbon emission state grading table; the method determines the carbon emission state grade of the enterprise, distributes the carbon quota according to the carbon emission state grade, tracks the current carbon emission evaluation coefficient in real time, and automatically triggers the carbon quota transaction instruction when the current carbon emission evaluation coefficient exceeds the preset threshold value, thereby achieving the flexible management and optimization of the carbon quota, and improving the environmental protection enthusiasm and management benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management, and particularly to an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things. Background Technique

[0002] The intensification of global climate change has made the carbon emission issue one of the core topics of concern for countries around the world. To effectively control greenhouse gas emissions, it is necessary to carry out refined management of energy consumption and carbon emissions in order to achieve energy conservation and emission reduction, fulfill environmental protection responsibilities, and optimize the cost of carbon quotas; However, current carbon emission standards are usually based on unified industry standards and regulations, without fully considering the actual energy consumption situation and operation mode of enterprises. This "one-size-fits-all" approach may lead to the unreasonableness of the standards. The actual energy consumption and carbon emission levels of some enterprises may deviate significantly from the industry average level, and the unified standards may impose unfair restrictions on them, unable to fully motivate these enterprises to actively carry out energy conservation and emission reduction.

[0003] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that current carbon emission standards are usually based on unified industry standards and regulations, without fully considering the actual energy consumption situation and operation mode of enterprises, resulting in the unreasonableness of the standards, and to propose an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An enterprise energy collection and carbon emission intelligent management system based on the Internet of Things, including: A feature recognition and analysis module, used to monitor the basic feature status information of an enterprise factory, calculate and output a first feature value, a second feature value, and a third feature value, so as to obtain a first carbon footprint value; An energy consumption analysis module, used to monitor the energy consumption status information of an enterprise factory, calculate and output a first energy consumption value and a second energy consumption value, so as to obtain a second carbon footprint value; A carbon emission analysis module, used to arrange carbon emission detection points in an enterprise factory, collect the carbon emission values of each layer of detection points, and at the same time extract the first carbon footprint value and the second carbon footprint value for comprehensive analysis to obtain a comprehensive carbon footprint value, and calculate a carbon emission assessment coefficient therefrom; A carbon quota allocation module, used to perform comparative analysis according to the carbon emission assessment coefficient and the carbon emission status classification table, determine the carbon emission status level of the enterprise, and thus allocate corresponding carbon quotas; The carbon quota trading module is used to obtain the current carbon emission assessment coefficient of an enterprise in real time, calculate the difference from the carbon emission assessment coefficient to obtain the carbon emission difference. When the carbon emission difference exceeds the preset threshold, a carbon quota trading instruction is triggered and corresponding trading processing is carried out.

[0006] Further, the solution processes for the first eigenvalue, the second eigenvalue, and the third eigenvalue are as follows: Obtain the category attribute corresponding to the enterprise factory and perform matching analysis with the category attribute status table to obtain the first eigenvalue Jbt1; Obtain the location coordinates corresponding to the enterprise factory. With the location coordinates as the center, set a radius to construct a multi-level concentric circle monitoring area. In each monitoring area, extract the number of residents in the monitoring area for analysis to obtain the second eigenvalue Jbt2; Obtain the floor area, the number of equipment, and the number of employees in the scale status parameters corresponding to the enterprise factory for analysis to obtain the third eigenvalue Jbt3.

[0007] Further, the solution process for the first carbon shadow value is as follows: Extract the values of the first eigenvalue Jbt1, the second eigenvalue Jbt2, and the third eigenvalue Jbt3 for normalization processing. According to the formula: , obtain the first carbon shadow value TZZ, where e represents the natural constant, and γ1, γ2, and γ3 respectively represent the correction coefficients of the first eigenvalue, the second eigenvalue, and the third eigenvalue.

[0008] Further, the solution process for the first energy consumption value is as follows: Obtain the respective equipment corresponding to the enterprise factory, and at the same time extract the power, energy consumption value, and load rate in the basic parameters of each equipment for analysis to obtain the basic evaluation index of each equipment; Compare and analyze the basic evaluation index of each equipment with the preset reference comparison interval. When the basic evaluation index of a certain equipment is greater than the maximum value of the reference comparison interval, then the certain equipment is determined to be high energy consumption. When the basic evaluation index of a certain equipment is within the reference comparison interval, then the certain equipment is determined to be medium energy consumption. When the basic evaluation index of a certain equipment is less than the minimum value of the reference comparison interval, then the certain equipment is determined to be low energy consumption; Respectively count the number of equipment determined to be high energy consumption, medium energy consumption, and low energy consumption, and label them as Gnmd1, Gnmd2, and Gnmd3 respectively. According to the formula: , obtain the first energy consumption value Nyz1, where sbs represents the total number of all production equipment in the enterprise factory, and w1, w2, and w3 respectively represent the set weight coefficients, and w1 > w2 > w3.

[0009] Further, the solution process for the second energy consumption value is as follows: Obtain the operating status parameters of each device corresponding to the enterprise factory in each historical monitoring period. The operating status parameters include the operation value, operation efficiency value, and operation consumption value, and label them as Ysz i j 、Yxz i j and Ywz i j respectively. Here, i represents the number of each historical monitoring period, and i = 1, 2, 3…n1, where n1 represents the total number of the numbers of each historical monitoring period. j represents the number of each device corresponding to the enterprise factory, and j = 1, 2, 3…n2, where n2 represents the total number of the numbers of each device corresponding to the enterprise factory. According to the formula: obtain the second energy consumption value Nyz2. Among them, SBZ i represents the comprehensive operation value of the device in the i-th historical monitoring period, and w4, w5, and w6 respectively represent the set value-taking coefficients, and w4 > w5 > w6.

[0010] Furthermore, the solution process of the operating status parameters of the device is as follows: Obtain the total operating duration of each device corresponding to the enterprise factory in the historical monitoring period, and label it as the operation value; Obtain the actual output power of each device corresponding to the enterprise factory in the historical monitoring period. Take time as the abscissa and the actual output power at the corresponding time point as the ordinate, and thus establish a dynamic coordinate system of the actual output power. Plot the actual output power of each device corresponding to the enterprise factory in the historical monitoring period on the dynamic coordinate system of the actual output power, set a power reference line on the dynamic coordinate system of the actual output power, and label all the actual output powers of the points above the power reference line as abnormal points. Count the proportion of the abnormal points, and conduct a control matching analysis on the proportion of the abnormal points with the operation efficiency degree data table stored in the cloud database, and thus obtain the operation efficiency value of each device; Obtain the consumption value of each device corresponding to the enterprise factory in the historical monitoring period, and conduct a standard deviation analysis on it to obtain the operation consumption value of each device.

[0011] Furthermore, the solution process of the second carbon shadow value is as follows: Extract the numerical values of the first energy consumption value Nyz1 and the second energy consumption value Nyz2 for normalization processing. According to the formula: obtain the second carbon shadow value NHA. Among them, γ4 and γ5 respectively represent the correction coefficients of the first energy consumption value and the second energy consumption value.

[0012] Furthermore, the solution process of the comprehensive carbon shadow value is as follows: Extract the numerical values of the first carbon shadow value TZZ and the second carbon shadow value NHA for normalization processing. According to the formula: , the comprehensive carbon shadow value δ is obtained, where β1 and β2 respectively represent the set correction factors.

[0013] Furthermore, the solution process of the carbon emission assessment coefficient is as follows: Evenly arrange the first-layer detection points along the emission channel openings on the key equipment in the enterprise factory; Define a monitoring range in the surrounding area of the key equipment, and evenly arrange the second-layer detection points within this range; Evenly arrange the third-layer detection points on the periphery of the enterprise factory building; Obtain the carbon emission values of the enterprise factory corresponding to each layer of detection points during each historical monitoring period, and calculate their average values to obtain the carbon emission average values of each layer, which are calibrated as Pts ic g , c represents the number of each layer, and c = 1, 2, 3, g represents several time points divided within the historical monitoring period, and g = 1, 2, 3…m, where m is the total number of all time points.

[0014] Among them, the specific solution of the carbon emission value is: the carbon emission value is obtained by multiplying the carbon emission concentration and the carbon emission amount by the corresponding weight coefficients respectively and then adding them together; According to the formula: , the carbon emission assessment coefficient TXX is obtained; Among them, Pts i1 g 、Pts i2 g and Pts i3 g respectively represent the carbon emission average values of the first layer, the second layer, and the third layer at the g-th time point during the i-th historical monitoring period, P 1 * 、P 2 * and P 3 * respectively represent the reference carbon emission average values of the first layer, the second layer, and the third layer, and λ1, λ2, and λ3 respectively represent the weight coefficients of the carbon emission change degrees of the first layer, the second layer, and the third layer, and λ1 > λ2 > λ3.

[0015] Furthermore, the data acquisition module is used to collect the basic characteristic status information, energy consumption status information, and carbon emission status information corresponding to the enterprise factory.

[0016] The technical solution provided by the present invention has the following beneficial effects compared with the known prior art: 1. In the present invention, by obtaining the characteristic status information and energy consumption status information of an enterprise, calculating the comprehensive carbon footprint value, and at the same time arranging multi-level carbon emission detection points in each area of the enterprise factory to comprehensively monitor the enterprise's carbon emissions, and combining the analysis of the comprehensive carbon footprint value, an accurate carbon emission assessment coefficient is calculated, thereby ensuring the integrity and accuracy of carbon emission data, providing a scientific basis for carbon emission analysis, making carbon emission management no longer rely on a unified industry standard, but being more targeted based on the actual energy consumption situation of the enterprise; 2. In the present invention, by means of the matching analysis between the carbon emission assessment coefficient and the carbon emission status grading table, carbon quotas are reasonably allocated. At the same time, the current carbon emission status of the enterprise is evaluated in real time. When the enterprise's carbon emissions exceed the carbon quota, a carbon quota trading instruction is automatically triggered so that the enterprise can purchase additional carbon quotas from the market to make up for the excess. On the contrary, if the carbon emissions are lower than the carbon quota, it supports the enterprise to sell the remaining carbon quotas to obtain carbon income, thereby realizing the flexible management and dynamic optimization of carbon quotas, encouraging enterprises to control energy consumption and reduce carbon emissions in actual operation, so as to enhance the enterprise's environmental protection enthusiasm and quota management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0020] As Figure 1 shown, the enterprise energy collection and carbon emission intelligent management system based on the Internet of Things includes: a data collection module, a server, a feature recognition and analysis module, an energy consumption analysis module, a carbon emission analysis module, a carbon quota allocation module, a carbon quota trading module, and a cloud database; The data acquisition module is used to collect the basic characteristic status information, energy consumption status information, and carbon emission status information corresponding to the enterprise factory, and send them to the characteristic recognition and analysis module, energy consumption analysis module, and carbon emission analysis module through the server respectively; It should be noted that the all-round data collection of the enterprise factory is realized through the Internet of Things technology. Among them, the basic characteristic status information includes category attributes, location coordinates, floor area, number of equipment, and number of employees. The energy consumption status information includes the basic parameters and operating status parameters of the equipment. Among them, the basic parameters include power, energy consumption value, and load rate. The operating status parameters include operation time value, operation efficiency value, and operation consumption value. The carbon emission status information includes carbon emission concentration and carbon emission amount; The cloud database is used to store the category attribute status table, operation efficiency degree data table, and carbon emission status classification table.

[0021] The characteristic recognition and analysis module is used to monitor the basic characteristic status information corresponding to the enterprise factory, and thus analyze the basic characteristic status of the enterprise. The specific analysis process is as follows: By obtaining the category attributes corresponding to the enterprise factory and performing matching analysis with the category attribute status table stored in the cloud database, the first characteristic value Jbt1 is obtained. Among them, the type attribute status table includes Class A, Class B, Class C, and Class D, and each of the Class A, Class B, Class C, and Class D directories includes four sub-classifications, namely class a, class b, class c, and class d. And each sub-classification directory includes four third-level classifications, namely p1, p2, p3, and p4. At this time, if the category attribute corresponding to the enterprise factory belongs to p1 under class a under Class A, the first characteristic value is 12. If the category attribute corresponding to the enterprise factory belongs to p2 under class b under Class A, the first characteristic value is 10. If the category attribute corresponding to the enterprise factory belongs to p2 under class b under Class B, the first characteristic value is 9. If the category attribute corresponding to the enterprise factory does not belong to any class, the first characteristic value is 1; By obtaining the location coordinates corresponding to the enterprise factory, a multi-level concentric circle monitoring area is constructed with the location coordinates as the center and a radius is set, and it is marked as Q, and Q = 1, 2, 3, 4. When Q = 1, it represents the core area. When Q = 2, it represents the close area. When Q = 3, it represents the medium-distance area. When Q = 4, it represents the long-distance area. In each monitoring area, the number of residents in the monitoring area is extracted and marked as snm Q , according to the formula: , the second characteristic value Jbt2 is obtained, where szn represents the sum of the number of residents in each monitoring area, and a Q represents the weight coefficient of each concentric circle monitoring area, and a 1 > a 2 > a 3> a 4 The values are 1.0, 0.8, 0.5, and 0.3 respectively; By obtaining the scale status parameters corresponding to the enterprise factory, the scale status parameters include floor area, number of equipment, and number of employees, and they are respectively marked as zdm, sbs, and ygs. According to the formula: the third eigenvalue Jbt3 is obtained, where b1, b2, and b3 respectively represent the set weight coefficients, and b2 > b3 > b1; It should be noted that the floor area refers to the total area occupied by the buildings corresponding to the enterprise factory on the horizontal plane, which reflects the overall scale of the factory; the number of equipment refers to the total number of all production equipment in the enterprise factory; the number of employees refers to the number of employees working in the enterprise factory, which reflects the intensity of the factory's daily operation activities.

[0022] Extract the values of the first eigenvalue Jbt1, the second eigenvalue Jbt2, and the third eigenvalue Jbt3 for normalization processing. According to the formula: the first carbon footprint value TZZ is obtained, where e represents the natural constant, and γ1, γ2, and γ3 respectively represent the correction coefficients of the first eigenvalue, the second eigenvalue, and the third eigenvalue; Send the obtained first carbon footprint value to the carbon emission analysis module through the server.

[0023] The energy consumption analysis module is used to monitor the energy consumption status information corresponding to the enterprise factory, and thus analyze the energy consumption status of the enterprise. The specific analysis process is as follows: By obtaining the corresponding equipment of the enterprise factory and simultaneously extracting the basic parameters of each equipment, the basic parameters include power, energy consumption value, and load rate, and they are respectively calibrated as T 功率 、T 能耗 and T 负载 According to the formula: the basic evaluation index Tgngs of each equipment is obtained, where k1, k2, and k3 respectively represent the set weight coefficient values, and k1 > k2 > k3; It should be noted that the power refers to the rated power of the equipment, that is, the maximum power output required by the equipment. The energy consumption value refers to the energy consumption data of the equipment during actual operation. The load rate refers to the ratio of the actual load to the rated load of the equipment.

[0024] Compare and analyze the basic evaluation index of each equipment with the preset reference comparison interval. When the basic evaluation index of a certain equipment is greater than the maximum value of the reference comparison interval, then the certain equipment is determined to be high energy consumption. When the basic evaluation index of a certain equipment is within the reference comparison interval, then the certain equipment is determined to be medium energy consumption. When the basic evaluation index of a certain equipment is less than the minimum value of the reference comparison interval, then the certain equipment is determined to be low energy consumption; Count the number of devices determined to be high energy consumption, medium energy consumption, and low energy consumption respectively, and label them as Gnmd1, Gnmd2, and Gnmd3 respectively. According to the formula: , obtain the first energy consumption value Nyz1, where w1, w2, and w3 respectively represent the set weight coefficients, and w1 > w2 > w3; By obtaining the operating state parameters of each device corresponding to the enterprise factory in each historical monitoring time period, the operating state parameters include operation time value, operation efficiency value, and operation consumption value, and label them as Ysz i j 、Yxz i j and Ywz i j , i represents the number of each historical monitoring time period, and i = 1, 2, 3…n1, n1 represents the total number of the numbers of each historical monitoring time period, j represents the number of each device corresponding to the enterprise factory, and j = 1, 2, 3…n2, n2 represents the total number of the numbers of each device corresponding to the enterprise factory. According to the formula: , obtain the second energy consumption value Nyz2, where SBZ i represents the comprehensive operation value of the device in the i-th historical monitoring time period, and w4, w5, and w6 respectively represent the set value-taking coefficients, and w4 > w5 > w6.

[0025] It should be noted that the solution process of the operating state parameters of the device is as follows: Obtain the total operating duration of each device corresponding to the enterprise factory in the historical monitoring time period, and label it as the operation time value; Obtain the actual output power of each device corresponding to the enterprise factory in the historical monitoring time period. Take the historical monitoring time period as the abscissa and the actual output power at the corresponding time point as the ordinate, and thus establish an actual output power dynamic coordinate system. Plot the actual output power of each device corresponding to the enterprise factory in the historical monitoring time period on the actual output power dynamic coordinate system, and set a power reference line on the actual output power dynamic coordinate system. Analyze the positional relationship between the actual output power of each point on the actual output power dynamic coordinate system and the set power reference line, and label the actual output power of each point above the power reference line as an abnormal point. Count the proportion of the abnormal points, and compare and match the proportion of the abnormal points with the operation efficiency degree data table stored in the cloud database to obtain the operation efficiency value of each device, and each proportion of the abnormal points obtained corresponds to an operation efficiency value; Obtain the consumption value (i.e., the total amount of electricity, water, and natural gas consumed) of each device corresponding to the enterprise factory in the historical monitoring time period, and calculate its standard deviation. According to the standard deviation formula: , the transportation consumption values of each device are obtained, where g represents several time points divided within the historical monitoring time period, and g = 1, 2, 3…m, f g represents the consumption value at the g-th time data point, f represents the average value of all data, that is, the average value of the consumption values within the historical monitoring time period, and m is the total number of all time points; Extract the numerical values of the first energy consumption value Nyz1 and the second energy consumption value Nyz2 for normalization processing, according to the formula: , to obtain the second carbon shadow value NHA, where γ4 and γ5 respectively represent the correction coefficients of the first energy consumption value and the second energy consumption value; Send the obtained second carbon shadow value to the carbon emission analysis module through the server.

[0026] The carbon emission analysis module is used to monitor the carbon emission status information corresponding to the enterprise factory, and thus analyze the carbon emission status of the enterprise. The specific analysis process is as follows: On the key devices in the enterprise factory (such as chimneys, exhaust channels, etc.), the first layer of detection points are evenly arranged along the emission channel outlet. Among them, these detection points are directly installed on the device emission outlet, and are used to monitor and record the instantaneous emission concentration of the device in real time, and capture the most original carbon emission data; A monitoring range is delimited in the surrounding area of the key device, that is, the designated range around the device emission outlet. Within this range, the second layer of detection points are evenly arranged. Among them, the monitoring range expands centered on the device to ensure coverage of the main emission diffusion area of the device, and is strictly controlled within the boundary of the factory building. The detection points of this layer are used to capture the change of carbon concentration in the air around the device, provide auxiliary data to calibrate the values of the first layer of detection points, and help judge the diffusion trend of the device emission; The third layer of detection points are evenly arranged on the periphery of the enterprise factory building. These detection points are distributed on the boundary around the building and are mainly used to monitor the carbon concentration of the overall emission of the factory. Among them, the layout density of the third layer of detection points is lower than the first two layers; Obtain the carbon emission values of each layer of detection points corresponding to the enterprise factory in each historical monitoring time period, and calculate the average value of the carbon emission values of each layer of detection points corresponding to the enterprise factory in each historical monitoring time period to obtain the carbon emission average value of each layer corresponding to the enterprise factory in each historical monitoring time period, and calibrate it as Pts ic g , c represents the number of each layer corresponding to the enterprise factory, and c = 1, 2, 3. When c = 1, it represents the first layer. When c = 2, it represents the second layer. When c = 3, it represents the third layer. Among them, the specific solution of the carbon emission value is: the carbon emission value is obtained by multiplying the carbon emission concentration and the carbon emission amount by the corresponding weight coefficients and then adding them; Extract the numerical values of the first carbon shadow value TZZ and the second carbon shadow value NHA for normalization, according to the formula: , to obtain the comprehensive carbon shadow value δ, where β1 and β2 respectively represent the set correction factors.

[0027] According to the formula: , to obtain the carbon emission assessment coefficient TXX, where Pts i1 g represents the average carbon emission at the g-th time point of the first layer in the i-th historical monitoring period, and Pts i2 g represents the average carbon emission at the g-th time point of the second layer in the i-th historical monitoring period, and Pts i3 g represents the average carbon emission at the g-th time point of the third layer in the i-th historical monitoring period, and P 1 * 、P 2 * and P 3 * respectively represent the reference average carbon emissions of the first layer, the second layer and the third layer, and λ1, λ2 and λ3 respectively represent the weight coefficients of the carbon emission change degrees of the first layer, the second layer and the third layer, and λ1 > λ2 > λ3; Send the obtained carbon emission assessment coefficient to the carbon quota allocation module through the server.

[0028] The carbon quota allocation module is used to receive the carbon emission assessment coefficient, and conduct carbon quota allocation analysis for the enterprise accordingly. The specific analysis process is as follows: Retrieve the carbon emission assessment coefficient of the enterprise, and conduct a comparison and matching analysis between the carbon emission assessment coefficient of the enterprise and the carbon emission status grading table stored in the cloud database, so as to obtain the carbon emission status level of the enterprise. Among them, each carbon emission assessment coefficient of the enterprise has a corresponding carbon emission status level, and each carbon emission status level corresponds to a specific carbon quota, so as to obtain the corresponding carbon quota of the enterprise; The carbon quota trading module is used to obtain the current carbon emission assessment coefficient of the enterprise in real time within a period of time, calculate the difference between the current carbon emission assessment coefficient and the carbon emission assessment coefficient to obtain the carbon emission difference, and compare and analyze the carbon emission difference with the preset carbon emission difference threshold. When the carbon emission difference is greater than the preset carbon emission difference threshold, a carbon quota abnormal signal is generated. Otherwise, a carbon quota normal signal is generated. According to the generated carbon quota abnormal signal, a carbon quota trading instruction is triggered. According to the triggered carbon quota trading instruction, carbon quota trading processing is carried out. The specific processing is as follows: Retrieve the carbon emission difference. If the carbon emission difference is positive (that is, it means that the enterprise's carbon emission exceeds the carbon quota), the purchase process is started, and the enterprise needs to purchase additional carbon quotas from the quota market to make up for the excess part; If the carbon emission difference is negative (i.e., it indicates that the enterprise's carbon emissions are lower than its carbon quota), the selling process will be initiated, and the enterprise can sell the remaining carbon quota to other enterprises in the quota market to obtain carbon proceeds.

[0029] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. Enterprise energy collection and carbon emission intelligent management system based on the Internet of Things, including: The feature recognition and analysis module is used to monitor the category attributes, location coordinates, and scale status parameters of the enterprise factory to obtain the first carbon shadow value; The energy consumption analysis module is used to monitor the energy consumption status information of the enterprise factory equipment, including power, energy consumption value and load rate, thereby respectively determining the equipment with different energy consumption to calculate the first energy consumption value, and calculating the second energy consumption value according to the operation status parameter of the equipment, and determining the second carbon shadow value accordingly. The operation status parameter is constructed based on the operation efficiency value, operation time value and operation consumption value, and is characterized by: The operation efficiency value uses the historical monitoring time period as the horizontal axis and the actual output power of the equipment as the vertical axis. A dynamic coordinate system of actual output power is established to construct a power reference line. The actual output power of the abnormal point is determined based on the power reference line, and the operation efficiency value of each device is obtained by combining the proportion of the abnormal point. The carbon emission analysis module is used to arrange carbon emission detection points in combination with the equipment area, collect the carbon emission values ​​of the detection points on each layer, and then combine the first carbon shadow value and the second carbon shadow value to obtain a comprehensive carbon shadow value to determine the carbon emission assessment coefficient; The carbon quota allocation module is used to determine the carbon emission status level of the enterprise based on the carbon emission assessment coefficient and allocate the corresponding carbon quota; The carbon quota trading module is used to determine whether to trigger a carbon quota trading instruction based on the carbon emission assessment coefficient and to perform corresponding transaction processing.

2. According to the IoT-based enterprise energy collection and carbon emission intelligent management system of claim 1, it is characterized in that: The solution process for the first carbon shadow value is as follows: Obtain the category attribute corresponding to the enterprise factory, and match it with the category attribute state table to obtain the first feature value Jbt1; Obtain the location coordinates corresponding to the enterprise factory, take the location coordinates as the center, set the radius to build a multi-level concentric circle monitoring area, and in each monitoring area, extract the number of residents in the monitoring area for analysis to obtain the second eigenvalue Jbt2; Obtain the floor space, number of equipment and number of employees in the scale state parameters corresponding to the enterprise factory for analysis to obtain the third eigenvalue Jbt3; The values ​​of the first eigenvalue Jbt1, the second eigenvalue Jbt2 and the third eigenvalue Jbt3 are extracted and normalized according to the formula: , and the first carbon shadow value TZZ is obtained, where e represents a natural constant, and γ1, γ2 and γ3 represent correction coefficients of the first eigenvalue, the second eigenvalue and the third eigenvalue, respectively.

3. According to the IoT-based enterprise energy collection and carbon emission intelligent management system of claim 1, it is characterized in that: The solution process for the first energy consumption value is as follows: Obtain the equipment corresponding to the enterprise factory, extract the power, energy consumption value and load rate of the basic parameters of each equipment for analysis, and obtain the basic evaluation index of each equipment; Compare and analyze the basic evaluation index of each device with the preset reference comparison interval. When the basic evaluation index of a device is greater than the maximum value of the reference comparison interval, the device is judged as high energy consumption. When the basic evaluation index of a device is within the reference comparison interval, the device is judged as medium energy consumption. When the basic evaluation index of a device is less than the minimum value of the reference comparison interval, the device is judged as low energy consumption. The number of devices that are judged to be high energy consumption, medium energy consumption and low energy consumption are counted respectively, and they are marked as Gnmd1, Gnmd2 and Gnmd3 respectively, according to the formula: , and obtain the first energy consumption value Nyz1, where sbs represents the total number of all equipment used for production in the enterprise factory, w1, w2 and w3 represent the set weight coefficients respectively, and w1>w2>w3.

4. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The solution process for the second energy consumption value is as follows: Obtain the operating status parameters of each device in the enterprise factory during each historical monitoring period. The operating status parameters include operation time value, operation efficiency value and operation consumption value, and mark them as Ysz i j 、Yxz i j and Ywz i j , i represents the number of each historical monitoring time period, and i=1, 2, 3…n1, n1 represents the total number of numbers of each historical monitoring time period, j represents the number of each device corresponding to the enterprise factory, and j=1, 2, 3…n2, n2 represents the total number of each device corresponding to the enterprise factory, according to the formula: , get the second energy consumption value Nyz2, where SBZ i It represents the comprehensive operating value of the equipment in the i-th historical monitoring time period, w4, w5 and w6 represent the set value coefficients respectively, and w4>w5>w6.

5. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 4 is characterized in that: The process of solving the operating status parameters is as follows: Obtain the total operating time of each device in the enterprise factory during the historical monitoring period and calibrate it as the operating time value; The actual output power of each device corresponding to the enterprise factory during the historical monitoring period is obtained, with the historical monitoring period as the horizontal coordinate and the actual output power at the corresponding time point as the vertical coordinate, and a dynamic coordinate system of actual output power is established thereby, and the actual output power of each device corresponding to the enterprise factory during the historical monitoring period is plotted on the dynamic coordinate system of actual output power, and a power reference line is set on the dynamic coordinate system of actual output power, and the positional relationship between the actual output power of each point on the dynamic coordinate system of actual output power and the set power reference line is analyzed, and the actual output power of each point above the power reference line is marked as an abnormal point, and the proportion of the points marked as abnormal points is counted, and the proportion of the abnormal points is compared and matched with the operation efficiency degree data table stored in the cloud database for analysis, thereby obtaining the operation efficiency value of each device, and the proportion value of each abnormal point obtained corresponds to an operation efficiency value; Obtain the consumption value of each device in the enterprise factory during the historical monitoring period, and perform standard deviation analysis on it to obtain the operation and consumption value of each device.

6. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The solution process for the second carbon shadow value is as follows: The values ​​of the first energy consumption value Nyz1 and the second energy consumption value Nyz2 are extracted and normalized according to the formula: , and obtain the second carbon shadow value NHA, where γ4 and γ5 represent the correction coefficients of the first energy consumption value and the second energy consumption value respectively.

7. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The process of solving the comprehensive carbon shadow value is as follows: Extract the values ​​of the first carbon shadow value TZZ and the second carbon shadow value NHA for normalization according to the formula: , and the comprehensive carbon shadow value δ is obtained, where β1 and β2 represent the set correction factors respectively.

8. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The process of solving the carbon emission assessment coefficient is as follows: The first layer of detection points are evenly distributed along the discharge channel openings on key equipment in the enterprise factory; Define a monitoring range around key equipment and evenly distribute the second-layer detection points within this range; Evenly distribute the third-level inspection points around the periphery of the enterprise's factory buildings; Obtain the carbon emission values ​​of each layer of detection points corresponding to the enterprise factory in each historical monitoring period, calculate the average value, obtain the average carbon emission value of each layer, and calibrate it as Pts ic g , c represents the number of each layer, and c=1, 2, 3, g represents a number of time points divided in the historical monitoring period, and g=1, 2, 3…m, m is the total number of all time points; The specific solution of carbon emission value is as follows: the carbon emission value is obtained by multiplying the carbon emission concentration and carbon emission amount by the corresponding weight coefficient and then adding them together; According to the formula: , get the carbon emission assessment coefficient TXX; Among them, Pts i1 g , Pts i2 g and Pts i3 g represents the mean carbon emission of the 1st, 2nd and 3rd layers at the gth time point in the i-th historical monitoring period, respectively. * 、P2 * and P3 * They represent the reference carbon emission means of the 1st, 2nd and 3rd layers respectively, λ1, λ2 and λ3 represent the weight coefficients of the carbon emission change degree of the 1st, 2nd and 3rd layers respectively, and λ1>λ2>λ3.

9. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 1 is characterized in that: The specific process of determining whether to trigger a carbon quota trading instruction is as follows: The current carbon emission assessment coefficient of the enterprise within a period of time is obtained in real time, the difference between the current carbon emission assessment coefficient and the carbon emission assessment coefficient is calculated to obtain the carbon emission difference, and the carbon emission difference is compared and analyzed with the preset carbon emission difference threshold. When the carbon emission difference is greater than the preset carbon emission difference threshold, a carbon quota abnormal signal is generated, and a carbon quota trading instruction is triggered based on the generated carbon quota abnormal signal. Otherwise, a carbon quota normal signal is generated.

10. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 1 is characterized in that: It includes a data collection module for collecting basic characteristic status information, energy consumption status information and carbon emission status information corresponding to the enterprise factory.

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