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

Abstract

The present invention relates to the field of carbon emission management technology, and specifically to an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things, including 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 present invention calculates a comprehensive carbon shadow value by monitoring the basic characteristics and energy consumption information of the enterprise factory. At the same time, carbon emission detection points are arranged in the enterprise factory to collect carbon emission data at each layer. The carbon emission assessment coefficient is calculated in combination with the comprehensive carbon shadow value, and is compared with the carbon emission status classification table to determine the carbon emission status level of the enterprise and allocate carbon quotas accordingly. The current carbon emission assessment coefficient is then tracked in real time, and a carbon quota trading instruction is automatically triggered when it exceeds a preset threshold, thereby realizing flexible management and optimization of carbon quotas and improving environmental protection enthusiasm and management efficiency.

Description

Technical Field

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

[0002] The intensification of global climate change has made carbon emissions a core issue of concern to all countries. To effectively control greenhouse gas emissions and promote green and low-carbon development, many countries and regions have formulated strict carbon emission policies and gradually implemented carbon quota trading mechanisms. As the main source of energy consumption and carbon emissions, enterprises, driven by both environmental protection policies and market mechanisms, need to conduct refined management of their energy consumption and carbon emissions to achieve energy conservation and emission reduction, fulfill environmental protection responsibilities, and optimize carbon quota costs.

[0003] However, current carbon emission standards are usually based on unified industry standards and regulations, without fully considering the actual energy consumption and operating models of enterprises. This "one-size-fits-all" approach may lead to irrational standards. The actual energy consumption and carbon emission levels of some enterprises may deviate significantly from the industry average, and unified standards may impose unfair restrictions on them, failing to fully incentivize these enterprises to actively engage in energy conservation and emission reduction.

[0004] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0005] The purpose of this invention is to solve the problem that the current carbon emission standards are usually based on unified industry standards and regulations, but do not fully consider the actual energy consumption and operating model of the enterprise, resulting in irrational standards, and propose an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The IoT-based enterprise energy collection and carbon emission intelligent management system includes:

[0008] The feature recognition and analysis module is used to monitor the basic feature status information of the enterprise factory, calculate and output the first feature value, the second feature value and the third feature value, and thus obtain the first carbon shadow value;

[0009] An energy consumption analysis module is used to monitor the energy consumption status information of the enterprise factory, calculate and output a first energy consumption value and a second energy consumption value, and thus obtain a second carbon shadow value;

[0010] The carbon emission analysis module is used to deploy carbon emission detection points in enterprise factories, collect carbon emission values ​​at each detection point, and then extract the first carbon shadow value and the second carbon shadow value for comprehensive analysis to obtain the comprehensive carbon shadow value, thereby calculating the carbon emission assessment coefficient;

[0011] The carbon quota allocation module is used to compare and analyze the carbon emission assessment coefficient with the carbon emission status classification table to determine the carbon emission status level of the enterprise and allocate the corresponding carbon quota;

[0012] The carbon quota trading module is used to obtain the company's current carbon emission assessment coefficient in real time, and calculate the difference with the carbon emission assessment coefficient to obtain the carbon emission difference. When the carbon emission difference exceeds the preset threshold, the carbon quota trading instruction is triggered and the corresponding transaction processing is carried out.

[0013] Furthermore, the process of solving the first eigenvalue, the second eigenvalue, and the third eigenvalue is as follows:

[0014] Obtain the category attribute corresponding to the enterprise factory, and match it with the category attribute state table to obtain the first eigenvalue Jbt1;

[0015] Obtain the location coordinates corresponding to the enterprise factory, and use the location coordinates as the center and set the 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;

[0016] The floor area, number of equipment, and number of employees corresponding to the scale status parameters of the enterprise factory are obtained for analysis to obtain the third eigenvalue Jbt3.

[0017] Furthermore, the process of solving the first carbon shadow value is as follows:

[0018] Extract the values ​​of the first eigenvalue Jbt1, the second eigenvalue Jbt2, and the third eigenvalue Jbt3 and perform normalization processing according to the formula: , and obtain the first carbon shadow value TZZ, where e represents a natural constant, and γ1, γ2, and γ3 represent correction coefficients of the first, second, and third eigenvalues, respectively.

[0019] Furthermore, the process of solving the first energy consumption value is as follows:

[0020] Obtain the equipment corresponding to the enterprise factory, extract the power, energy consumption and load rate of the basic parameters of each equipment for analysis, and obtain the basic evaluation index of each equipment;

[0021] 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.

[0022] Count the number of devices that are judged to be high energy consumption, medium energy consumption, and low energy consumption, and mark them 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 production equipment in the enterprise factory, w1, w2 and w3 represent the set weight coefficients respectively, and w1>w2>w3.

[0023] Furthermore, the process of solving the second energy consumption value is as follows:

[0024] 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 respectively. 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 period, w4, w5 and w6 represent the set value coefficients respectively, and w4>w5>w6.

[0025] Furthermore, the process of solving the operating status parameters of the equipment is as follows:

[0026] 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;

[0027] Obtain the actual output power of each device in the enterprise factory during the historical monitoring period, with time as the horizontal axis and the actual output power at the corresponding time point as the vertical axis, and thus establish an actual output power dynamic coordinate system. Plot the actual output power of each device in the enterprise factory during the historical monitoring period on the actual output power dynamic coordinate system, set a power reference line on the actual output power dynamic coordinate system, and mark the actual output power of each point above the power reference line as an abnormal point. Count the proportion of points marked as abnormal points, compare and match the proportion of abnormal points with the operating efficiency data table stored in the cloud database, and thus obtain the operating efficiency value of each device;

[0028] 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 transportation consumption value of each device.

[0029] Furthermore, the solution process for the second carbon shadow value is as follows:

[0030] Extract the values ​​of the first energy consumption value Nyz1 and the second energy consumption value Nyz2 and perform normalization processing 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.

[0031] Furthermore, the process of solving the comprehensive carbon shadow value is as follows:

[0032] 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.

[0033] Furthermore, the process of solving the carbon emission assessment coefficient is as follows:

[0034] The first layer of detection points are evenly distributed along the discharge channel openings on key equipment in the enterprise factory;

[0035] Define a monitoring range around key equipment and evenly distribute the second-tier detection points within this range;

[0036] Evenly distribute third-level inspection points around the periphery of the enterprise's factory buildings;

[0037] Obtain the carbon emission values ​​of each layer of detection points corresponding to the enterprise factory during 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;

[0038] The specific solution of carbon emission value is as follows: multiply the carbon emission concentration and carbon emission amount by the corresponding weight coefficient and then add them together to obtain the carbon emission value;

[0039] According to the formula: , get the carbon emission assessment coefficient TXX;

[0040] Among them, Pts i1 g 、Pts i2 g and Pts i3 g represents the mean carbon emissions 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 mean values ​​of reference carbon emissions for the first, second and third layers respectively; λ1, λ2 and λ3 represent the weight coefficients of the degree of change of carbon emissions for the first, second and third layers respectively, and λ1>λ2>λ3.

[0041] Furthermore, the data collection module is used to collect basic characteristic status information, energy consumption status information and carbon emission status information corresponding to the enterprise factory.

[0042] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0043] 1. This invention calculates a comprehensive carbon shadow value by acquiring the company's characteristic status information and energy consumption status information. It also deploys multi-level carbon emission detection points in various areas of the company's factory to conduct comprehensive monitoring of the company's carbon emissions. Combined with the analysis of the comprehensive carbon shadow value, it calculates an accurate carbon emission assessment coefficient, thereby ensuring the integrity and accuracy of carbon emission data, providing a scientific basis for carbon emission analysis, and making carbon emission management no longer dependent on unified industry standards. Instead, it is based on the company's actual energy consumption, making it more targeted.

[0044] 2. The present invention reasonably allocates carbon quotas through matching analysis based on the carbon emission assessment coefficient and the carbon emission status classification table, and at the same time conducts real-time assessment of the current carbon emission status of the enterprise. When the carbon emissions of the enterprise exceed the carbon quota, the 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. Conversely, if the carbon emissions are lower than the carbon quota, the enterprise is supported to sell the remaining carbon quota to obtain carbon income, thereby realizing flexible management and dynamic optimization of carbon quotas, encouraging enterprises to control energy consumption and reduce carbon emissions in actual operations, so as to enhance the environmental protection enthusiasm of enterprises and the efficiency of quota management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0046] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] like Figure 1 As shown, the enterprise energy collection and carbon emission intelligent management system based on the Internet of Things includes: data collection module, server, feature recognition and analysis module, energy consumption analysis module, carbon emission analysis module, carbon quota allocation module, carbon quota trading module and cloud database;

[0049] 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 it to the characteristic recognition and analysis module, energy consumption analysis module and carbon emission analysis module respectively through the server;

[0050] It should be noted that the Internet of Things technology enables comprehensive data collection of enterprise factories, among which basic feature status information includes category attributes, location coordinates, floor space, number of equipment and number of employees; energy consumption status information includes basic parameters and operating status parameters of equipment, among which basic parameters include power, energy consumption value and load rate; operating status parameters include operating time value, operating efficiency value and operating consumption value; carbon emission status information includes carbon emission concentration and carbon emission amount;

[0051] The cloud database is used to store the category attribute status table, the operation efficiency data table, and the carbon emission status classification table;

[0052] The feature recognition and analysis module is used to monitor the basic feature status information corresponding to the enterprise factory, thereby analyzing the basic feature status of the enterprise. The specific analysis process is as follows:

[0053] By obtaining the category attribute corresponding to the enterprise factory and matching it with the category attribute status table stored in the cloud database for analysis, the first eigenvalue Jbt1 is obtained, wherein the type attribute status table includes Class A, Class B, Class C and Class D, and the Class A, Class B, Class C and Class D directories respectively include four sub-categories Class A, Class B, Class C and Class D, and each sub-category directory includes four third-level categories p1, p2, p3 and p4 respectively. At this time, if the category attribute corresponding to the enterprise factory belongs to Class p1 under Class a directory under Class A directory, then the first eigenvalue is 12; if the category attribute corresponding to the enterprise factory belongs to Class p2 under Class b directory under Class A directory, then the first eigenvalue is 10; if the category attribute corresponding to the enterprise factory belongs to Class p2 under Class b directory under Class B directory, then the first eigenvalue is 9; if the category attribute corresponding to the enterprise factory does not belong to any category, then the first eigenvalue is 1;

[0054] By obtaining the location coordinates corresponding to the enterprise factory, taking the location coordinates as the center and setting the radius, a multi-level concentric circle monitoring area is constructed and marked as Q, and Q=1, 2, 3, 4. When Q=1, it represents the core area, when Q=2, it represents the close distance area, when Q=3, it represents the medium distance area, and 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: , and obtain the second eigenvalue Jbt2, where szn represents the sum of the number of residents in each monitoring area, a Q represents the weight coefficient of each concentric circle monitoring area, and a1>a2>a3>a4, with values ​​of 1.0, 0.8, 0.5 and 0.3 respectively;

[0055] By obtaining the scale status parameters corresponding to the enterprise factory, the scale status parameters include the floor area, the number of equipment and the number of employees, and marking them as zdm, sbs and ygs respectively, according to the formula: , obtaining the third eigenvalue Jbt3, where b1, b2, and b3 represent the set weight coefficients respectively, and b2>b3>b1;

[0056] It should be noted that the floor area refers to the total area occupied by the corresponding buildings of the enterprise factory on the horizontal plane, reflecting the overall scale of the factory; the number of equipment refers to the total number of all equipment used for production in the enterprise factory; the number of employees refers to the number of employees working in the enterprise factory, reflecting the intensity of the factory's daily operations;

[0057] Extract the values ​​of the first eigenvalue Jbt1, the second eigenvalue Jbt2, and the third eigenvalue Jbt3 and perform normalization processing according to the formula: , obtaining the first carbon shadow value TZZ, where e represents a natural constant, and γ1, γ2, and γ3 represent correction coefficients for the first, second, and third eigenvalues, respectively;

[0058] The obtained first carbon shadow value is sent to the carbon emission analysis module through the server.

[0059] The energy consumption analysis module is used to monitor the energy consumption status information corresponding to the enterprise factory, thereby analyzing the energy consumption status of the enterprise. The specific analysis process is as follows:

[0060] By obtaining the corresponding equipment of the enterprise factory, the basic parameters of each equipment are extracted, including power, energy consumption value and load rate, and they are calibrated as T 功率 、T 能耗 and T 负载 , according to the formula: , obtain the basic evaluation index Tgngs of each device, where k1, k2 and k3 represent the set weight coefficients respectively, and k1>k2>k3;

[0061] It should be noted that power refers to the rated power of the equipment, that is, the maximum power output required by the equipment, energy consumption value refers to the energy consumption data of the equipment during actual operation, and load rate refers to the ratio of the actual load of the equipment to the rated load;

[0062] 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.

[0063] Count the number of devices that are judged to be high energy consumption, medium energy consumption, and low energy consumption, and mark them as Gnmd1, Gnmd2, and Gnmd3 respectively, according to the formula: , obtaining a first energy consumption value Nyz1, wherein w1, w2 and w3 represent set weight coefficients respectively, and w1>w2>w3;

[0064] By obtaining 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 calibrating 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 represents the comprehensive operating value of the equipment during the i-th historical monitoring period, w4, w5 and w6 represent the set value coefficients, and w4>w5>w6;

[0065] It should be noted that the process of solving the equipment's operating status parameters is as follows:

[0066] 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;

[0067] Obtain the actual output power of each device corresponding to the enterprise factory during the historical monitoring period, with the historical monitoring period as the horizontal coordinate and the actual output power at the corresponding time point as the vertical coordinate, and thereby establish an actual output power dynamic coordinate system, and plot the actual output power of each device corresponding to the enterprise factory during the historical monitoring 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 mark the actual output power of each point above the power reference line as an abnormal point, and count the proportion of the points marked as abnormal points, and compare and match the proportion of the abnormal points with the operating efficiency data table stored in the cloud database to obtain the operation efficiency value of each device, and the proportion value of each abnormal point obtained corresponds to an operation efficiency value;

[0068] Obtain the consumption value (i.e., the total amount of electricity, water, and natural gas consumed) of each device in the enterprise factory during the historical monitoring period, and calculate the standard deviation based on the standard deviation formula: , get the transportation consumption value of each device, where g represents several time points divided in the historical monitoring 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 consumption values ​​within the historical monitoring period, and m is the total number of all time points;

[0069] Extract the values ​​of the first energy consumption value Nyz1 and the second energy consumption value Nyz2 and perform normalization processing according to the formula: , obtaining the second carbon shadow value NHA, wherein γ4 and γ5 represent the correction coefficients of the first energy consumption value and the second energy consumption value respectively;

[0070] The obtained second carbon shadow value is sent to the carbon emission analysis module through the server.

[0071] The carbon emission analysis module is used to monitor the carbon emission status information corresponding to the enterprise's factories, thereby analyzing the enterprise's carbon emission status. The specific analysis process is as follows:

[0072] On key equipment within the factory (such as chimneys, exhaust ducts, etc.), the first layer of detection points are evenly distributed along the emission channel openings. These detection points are directly installed on the equipment emission outlets to monitor and record the instantaneous emission concentration of the equipment in real time, capturing the most original carbon emission data;

[0073] A monitoring range is defined around key equipment, i.e., a designated area around the equipment's emission outlet. A second layer of monitoring points is evenly distributed within this range. The monitoring range extends from the equipment itself, ensuring coverage of the equipment's primary emission diffusion area and remaining strictly within the boundaries of the factory building. This layer of monitoring points is used to capture changes in carbon concentration in the air surrounding the equipment, providing auxiliary data to calibrate the values ​​of the first layer of monitoring points and help determine the diffusion trend of equipment emissions.

[0074] A third layer of monitoring points is evenly distributed around the perimeter of the factory building. These monitoring points are distributed around the building boundary and are primarily used to monitor the overall carbon concentration of the factory's emissions. The density of monitoring points on the third layer is lower than that on the first two layers.

[0075] Obtain the carbon emission values ​​of each detection point on each layer of the enterprise factory during each historical monitoring period, and calculate the average of the carbon emission values ​​of each detection point on each layer of the enterprise factory during each historical monitoring period to obtain the average carbon emission value of each layer of the enterprise factory during each historical monitoring period, and mark it as Pts ic g , c represents the number of each layer of the enterprise factory, and c=1, 2, 3. When c=1, it represents the first layer, when c=2, it represents the second layer, and when c=3, it represents the third layer. The specific solution of the carbon emission value is: the carbon emission concentration and carbon emission amount are multiplied by the corresponding weight coefficient and then added to obtain the carbon emission value;

[0076] Extract the values ​​of the first carbon shadow value TZZ and the second carbon shadow value NHA for normalization according to the formula: , the comprehensive carbon shadow value δ is obtained, where β1 and β2 represent the set correction factors respectively;

[0077] According to the formula: , and obtain the carbon emission assessment coefficient TXX, where Pts i1 g Pts represents the average carbon emission value at the g-th time point in the first layer during the i-th historical monitoring period.i2 g Pts represents the average carbon emission value at the g-th time point in the second layer during the i-th historical monitoring period. i3 g represents the average carbon emission value at the g-th time point in the 3rd layer during the i-th historical monitoring period, P1 * 、P2 * and P3 * represents the reference carbon emission mean of the first, second and third layers respectively, λ1, λ2 and λ3 represent the weight coefficients of the carbon emission change degree of the first, second and third layers respectively, and λ1>λ2>λ3;

[0078] The obtained carbon emission assessment coefficient is sent to the carbon quota allocation module through the server.

[0079] The carbon quota allocation module is used to receive the carbon emission assessment coefficient and perform carbon quota allocation analysis on the enterprise. The specific analysis process is as follows:

[0080] Retrieve the enterprise's carbon emission assessment coefficient and compare and match the enterprise's carbon emission assessment coefficient with the carbon emission status grading table stored in the cloud database to obtain the enterprise's carbon emission status level. 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, thereby obtaining the enterprise's corresponding carbon quota;

[0081] 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. Based on the generated carbon quota abnormal signal, a carbon quota trading instruction is triggered. Based on the triggered carbon quota trading instruction, carbon quota trading processing is carried out. The specific processing is as follows:

[0082] The carbon emission difference is retrieved. If the carbon emission difference is positive (i.e., the company's carbon emissions exceed its carbon quota), the purchase process is initiated and the company needs to purchase additional carbon quotas from the quota market to make up for the excess;

[0083] If the carbon emission difference is negative (that is, the company's carbon emissions are lower than its carbon quota), the sales process will be initiated and the company can sell the remaining carbon quota to other companies in the quota market to obtain carbon benefits.

[0084] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only 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 enterprise factory equipment, including power, energy consumption value and load rate, thereby identifying equipment with different energy consumption to calculate a first energy consumption value, and calculating a second energy consumption value based on the operating status parameters of the equipment, and then determining a second carbon shadow value based on the second energy consumption value. The operating status parameters are constructed by combining the operation efficiency value, operation time value and operation consumption value. The following features are featured: The operational efficiency value uses the historical monitoring period as the horizontal axis and the actual output power of the equipment as the vertical axis. A dynamic coordinate system for actual output power is established, and a power reference line is constructed. The actual output power of abnormal points is determined based on the power reference line, and the operational efficiency value of each device is calculated based on the proportion of abnormal points. 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 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. Count the number of devices that are judged to be high energy consumption, medium energy consumption, and low energy consumption, and mark 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, w1, w2 and w3 represent the set weight coefficients, and w1>w2>w3; The carbon emission analysis module is used to arrange carbon emission detection points according to 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 perform corresponding transaction processing.

2. 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 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 eigenvalue Jbt1; Obtain the location coordinates corresponding to the enterprise factory, and use the location coordinates as the center and set the 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 space, number of equipment, and number of employees corresponding to the scale state parameters of the enterprise factory and analyze them to obtain the third eigenvalue Jbt3; Extract the values ​​of the first eigenvalue Jbt1, the second eigenvalue Jbt2, and the third eigenvalue Jbt3 and perform normalization processing according to the formula: , and obtain the first carbon shadow value TZZ, where e represents a natural constant, and γ1, γ2, and γ3 represent correction coefficients of the first, second, and third eigenvalues, respectively.

3. 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 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 respectively. 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 period, w4, w5 and w6 represent the set value coefficients respectively, and w4>w5>w6.

4. The enterprise energy collection and carbon emission intelligent management system based on the Internet of Things according to claim 3 is characterized in that: The process of solving the operating state 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; Obtain the actual output power of each device corresponding to the enterprise factory during the historical monitoring period, with the historical monitoring period as the horizontal coordinate and the actual output power at the corresponding time point as the vertical coordinate, and thereby establish an actual output power dynamic coordinate system, and plot the actual output power of each device corresponding to the enterprise factory during the historical monitoring 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 mark the actual output power of each point above the power reference line as an abnormal point, and count the proportion of the points marked as abnormal points, and compare and match the proportion of the abnormal points with the operating efficiency data table stored in the cloud database to obtain 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 transportation consumption value of each device.

5. 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: Extract the values ​​of the first energy consumption value Nyz1 and the second energy consumption value Nyz2 and perform normalization processing 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.

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 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.

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 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-tier detection points within this range; Evenly distribute 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 during 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: multiply the carbon emission concentration and carbon emission amount by the corresponding weight coefficient and then add them together to obtain the carbon emission value; 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 emissions 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 mean values ​​of reference carbon emissions for the first, second and third layers respectively; λ1, λ2 and λ3 represent the weight coefficients of the degree of change of carbon emissions for the first, second and third layers respectively, and λ1>λ2>λ3.

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 specific process of determining whether a carbon quota trading instruction is triggered 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. Based on the generated carbon quota abnormal signal, a carbon quota trading instruction is triggered. Otherwise, a carbon quota normal signal is generated.

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: It includes a data acquisition module for collecting basic characteristic status information, energy consumption status information and carbon emission status information corresponding to the enterprise factory.

Images