Workshop carbon management method and system based on Internet of Things

Abstract

The invention discloses a workshop carbon management method and system based on Internet of Things, relates to the technical field of carbon management, and is used for solving the problems of extensive carbon emission data acquisition and non-intuitive carbon emission analysis in the prior art. The workshop is divided into a plurality of sub-supervision areas, and an Internet of Things sensing network is established to carry out real-time supervision on each sub-supervision area; the method comprises the steps of obtaining a carbon emission data set of a sub-supervision region, constructing a three-dimensional structure model for a whole workshop, carrying out regional carbon emission analysis on the three-dimensional structure model based on the carbon emission data set of each sub-supervision region, establishing a carbon account of each sub-supervision region, recording a carbon emission concentration track in the sub-supervision region to the corresponding carbon account, and carrying out carbon emission analysis on the carbon emission data set of each sub-supervision region. According to the method and the system, all carbon accounts are subjected to data integration and visualization processing, a carbon emission management view of the whole workshop is obtained, substandard areas in the workshop are positioned based on the carbon emission management view, carbon emission optimization is executed, and refined, visualized and intelligent whole-process management of workshop carbon emission is realized.

Description

Technical Field

[0001] This invention relates to the field of carbon management technology, and specifically to a workshop carbon management method and system based on the Internet of Things. Background Technology

[0002] As the main unit of energy consumption and carbon emissions in manufacturing, the effectiveness of carbon management in workshops directly affects the success of enterprises' low-carbon transformation. However, existing workshop carbon management methods still have the following shortcomings: First, the methods for collecting carbon emission data are relatively crude, usually relying on monthly summary data at the plant or workshop level, which cannot achieve real-time monitoring at the equipment or process level, resulting in insufficient granularity of carbon management and difficulty in accurately locating high carbon emission sources. Secondly, existing carbon accounting methods cannot accurately link carbon emissions to specific production equipment, processes, and production orders, resulting in limited accuracy and reliability of the accounting results. Furthermore, it is difficult to grasp the spatiotemporal distribution characteristics of carbon emissions within the workshop, making carbon emission analysis unintuitive.

[0003] Therefore, there is an urgent need for a method and system that can achieve refined, real-time, and intelligent management of carbon emissions throughout the entire process of workshops in order to solve the aforementioned problems in existing technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a workshop carbon management method and system based on the Internet of Things to address the shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a workshop carbon management method based on the Internet of Things, comprising the following steps: Step S1: Divide the workshop into several sub-regulatory areas, build an Internet of Things (IoT) sensor network in each sub-regulatory area, and use the IoT sensor network to monitor the sub-regulatory areas in real time and obtain the carbon emission dataset within the sub-regulatory areas. Step S2: Construct a three-dimensional structural model for the entire workshop, perform regional carbon emission analysis on the three-dimensional structural model based on the carbon emission dataset of each sub-regulatory area under the workshop, establish a carbon account for each sub-regulatory area, and record the carbon emission concentration trajectory under the sub-regulatory area into the corresponding carbon account; Step S3: Integrate and visualize the carbon account data for each sub-regulatory area to obtain a carbon emission management view for the entire workshop. Based on the carbon emission management view, locate non-compliant areas within the workshop and perform carbon emission optimization.

[0006] Furthermore, the process of dividing the workshop into several sub-supervisory areas and building an IoT sensor network in each sub-supervisory area includes: The entire space area where the workshop is located is divided according to different dimensions, and then the space area corresponding to the workshop is divided into several sub-regulatory areas. The sub-regulatory areas are labeled, and based on the carbon emission type of the sub-regulatory areas, different types of sensors are deployed in each sub-regulatory area. A gateway device is deployed in each sub-regulatory area, and all sensors are connected to the gateway device. When any sensor requests a connection to the gateway device, the sensor's communication protocol is sent to the gateway device. The gateway device determines whether the current communication protocol can be recognized. Based on the determination result, it selects to allocate a listening port to the current sensor or compile and convert the communication protocol. When the sensor's communication protocol is successfully recognized by the gateway device, the sensor and the gateway device complete the connection. When all the sensors in a sub-supervised area are connected to the gateway device, the IoT sensor network of the corresponding sub-supervised area is completed. Repeat the requests from the sensors in each sub-supervised area to the gateway device to build several IoT sensor networks in the entire workshop space.

[0007] Furthermore, the process of selecting and assigning monitoring ports or compiling and converting communication protocols for the sensor based on the judgment results includes: The gateway device determines whether the current communication protocol can be recognized. If it can, the sensor is connected to the gateway device, and a listening port is allocated to the sensor for status monitoring. If not, the gateway device compiles the communication protocol of the sensor that initiated the request, converts the communication protocol into a communication protocol that the gateway device can recognize, and simultaneously saves the data of the entire compilation and conversion process to the storage area of ​​the gateway device. When the next sensor sends a request to the gateway device, if the sensor's communication protocol cannot be recognized, the gateway device retrieves the recognition records of the communication protocol in the past. If the same type of communication protocol exists in the recognition records, it is invoked to complete the communication connection between the next sensor and the gateway device. If it does not exist, the gateway device compiles the current sensor's communication protocol, converts it into a communication protocol that the gateway device can accept, and saves the compilation and conversion result to the gateway device.

[0008] Furthermore, the process of using IoT sensor networks to conduct real-time monitoring of sub-regulatory areas and obtain carbon emission datasets within those areas includes: Several spatial carbon monitoring points are set up within the area of ​​each sub-regulatory zone. All spatial carbon monitoring points within the area are covered by an Internet of Things sensor network to obtain the spatial coordinates of the spatial carbon monitoring points in the area. When a carbon emission event is detected in real time within the sub-regulatory area, carbon emission data of the current carbon emission event at different time series is obtained through different spatial carbon monitoring points. The carbon emission data of each time series is associated with spatial coordinates. Based on the temporal progression, all spatial carbon monitoring points of the carbon emission data are connected to obtain the carbon emission flow trajectory of the sub-regulatory area. The carbon emission data of several spatial carbon monitoring points corresponding to the carbon emission flow trajectory under a sub-regulatory area are aggregated to obtain the carbon emission dataset of the corresponding sub-regulatory area, and an identification certificate for the carbon emission dataset is created.

[0009] Furthermore, the process of constructing a three-dimensional structural model for the entire workshop includes: Assign a modeling node to each sub-supervisory area under the workshop, create a modeling operation space for the entire workshop, build a data sharing channel for communication and interconnection between all modeling nodes and the modeling operation space, select a sub-supervisory area in the workshop as the initial point for modeling, import the CAD drawings of the sub-supervisory area into the pre-configured 3D modeling software, and obtain the 3D area model of the sub-supervisory area. After parsing the 3D region model, the basic parameter information of the model is obtained and recorded in the form of data modeling text. The identification certificate of the sub-supervisory area is marked to the corresponding data modeling text. The data modeling text of the sub-supervisory area is shared to the modeling operation space through the data sharing channel for other modeling nodes to call. Take other sub-regulatory areas outside the sub-regulatory area corresponding to the initial modeling point as modeling derivative points, and determine whether the actual environment of the sub-regulatory area corresponding to each modeling derivative point is the same as that of the initial modeling point. If so, the modeling derivative point calls the data modeling text of the modeling initial point to replicate and restore the corresponding three-dimensional area model; if not, the three-dimensional area model is constructed based on the CAD drawing of the current sub-supervised area, and the corresponding basic model parameter information is parsed and the data modeling text is created and placed into the modeling operation space. Once the three-dimensional area models of all sub-supervisory areas under the entire workshop have been completed, all the three-dimensional area models are arranged based on the spatial connection relationship of the workshop to obtain the three-dimensional structural model of the entire workshop.

[0010] Furthermore, based on the carbon emission dataset of each sub-regulatory area under the workshop, regional carbon emission analysis is performed on the three-dimensional structural model to establish a carbon account for each sub-regulatory area. The process of recording the carbon emission concentration trajectory under the sub-regulatory area into the corresponding carbon account includes: The carbon emission dataset for each sub-regulatory area of ​​the workshop is set as several carbon emission data sources based on the spatial coordinates of each sub-regulatory area. The carbon emission data sources are then mapped to the same position in the three-dimensional structural model based on their own spatial coordinates. A corresponding real-time data stream carrier space is constructed for carbon emission data sources. The real-time data stream carrier space is used for regional carbon emission analysis. A carbon account is created for each sub-regulatory area. The identification certificate of the sub-regulatory area is used as the account certificate of the carbon account. The carbon account records the carbon emission concentration trajectory represented by the carbon emission intensity scalar field of several time periods of the sub-regulatory area.

[0011] Furthermore, the carbon accounts of each sub-regulatory area are integrated and visualized to obtain a carbon emission management view for the entire workshop. Based on this view, non-compliant areas within the workshop are located, and carbon emission optimization is performed. This process includes: Establish a data integration engine to integrate carbon account-related data from all sub-regulatory regions; A data dashboard view is created for each sub-regulatory region using visualization technology. The data dashboard view is used to record key carbon emission indicators in the corresponding carbon account of each sub-regulatory region. The carbon account data after data integration is mapped to the three-dimensional structural model of the workshop, the carbon emission intensity scalar field of each sub-regulatory area is rendered into a three-dimensional heat map, and the three-dimensional heat maps of all sub-regulatory areas are integrated to generate a carbon emission management view of the entire workshop. Set the regional dynamic carbon emission threshold for each of the sub-regulatory areas corresponding to the carbon emission management view and the three-dimensional heat map. When the carbon emission concentration of a certain sub-regulatory area exceeds the regional dynamic carbon emission threshold, the corresponding sub-regulatory area is marked as a non-compliant area. Retrieve historical carbon account data for non-compliant areas, and select matching optimization schemes from a pre-set optimization strategy library based on the equipment type, production process characteristics, and carbon emission patterns of the non-compliant areas. Adjust equipment operating parameters or production process flow to perform carbon emission optimization.

[0012] The present invention also provides an Internet of Things-based workshop carbon management system, the system comprising: The workshop data acquisition module is used to divide the workshop into several sub-monitoring areas, build an Internet of Things (IoT) sensor network in each sub-monitoring area, and use the IoT sensor network to monitor the sub-monitoring area in real time and obtain the carbon emission dataset within the sub-monitoring area. The workshop carbon emission analysis module is used to build a three-dimensional structural model for the entire workshop. Based on the carbon emission dataset of each sub-regulatory area under the workshop, it performs regional carbon emission analysis on the three-dimensional structural model, establishes a carbon account for each sub-regulatory area, and records the carbon emission concentration trajectory under the sub-regulatory area into the corresponding carbon account. The workshop carbon emission management module is used to integrate and visualize the carbon accounts of each sub-regulatory area to obtain a carbon emission management view of the entire workshop. Based on the carbon emission management view, it locates non-compliant areas within the workshop and performs carbon emission optimization.

[0013] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention achieves refined monitoring of carbon emissions in the workshop by dividing the workshop into several sub-monitoring areas and building an Internet of Things (IoT) sensor network. By deploying an IoT sensor network in each sub-monitoring area, it is possible to collect accurate carbon emission data at the equipment and process levels, thus solving the problems of coarse data collection and insufficient management granularity in the prior art.

[0014] 2. This invention achieves spatiotemporal visualization analysis and traceability of carbon emissions by constructing a three-dimensional structural model of the workshop and establishing carbon accounts for sub-monitoring areas. By using the three-dimensional structural model to bind abstract carbon emission data with the specific spatial location and production equipment of the workshop, and recording the carbon emission concentration trajectory, it is possible to intuitively grasp the spatiotemporal distribution characteristics of carbon emissions, thus solving the problem of unintuitive carbon emission analysis in the prior art. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 As shown in the figure, the workshop carbon management method based on the Internet of Things described in this embodiment includes the following steps: Step S1: Divide the workshop into several sub-regulatory areas, build an Internet of Things (IoT) sensor network in each sub-regulatory area, and use the IoT sensor network to monitor the sub-regulatory areas in real time and obtain the carbon emission dataset within the sub-regulatory areas. Step S2: Construct a three-dimensional structural model for the entire workshop, perform regional carbon emission analysis on the three-dimensional structural model based on the carbon emission dataset of each sub-regulatory area under the workshop, establish a carbon account for each sub-regulatory area, and record the carbon emission concentration trajectory under the sub-regulatory area into the corresponding carbon account; Step S3: Integrate and visualize the carbon account data for each sub-regulatory area to obtain a carbon emission management view for the entire workshop. Based on the carbon emission management view, locate non-compliant areas within the workshop and perform carbon emission optimization.

[0020] It should be further explained that, in the specific implementation process, the workshop is divided into several sub-supervisory areas, and the process of building an IoT sensor network in each sub-supervisory area includes: The entire spatial area where the workshop is located is divided based on different dimensions, and then the spatial area corresponding to the workshop is divided into several sub-supervisory areas. These sub-supervisory areas are labeled and denoted as i, where i = 1, 2, 3, ..., n, and n is a natural number greater than 0. Furthermore, different dimensions specifically include dividing the entire space of the workshop based on functional units. For example, the raw material pretreatment area, machining area, welding area, spraying area, assembly area and storage area in the workshop are all independent functional units. When the corresponding production process is executed in each functional unit, the corresponding carbon emission sources are similar. The dimensions include dividing the spatial area based on the process steps. On a certain production line in the workshop, the processes with significantly different carbon emission characteristics are divided into different areas. For example, on the welding production line in the workshop, it is divided into spot welding station, laser welding station and grinding station according to the process steps, and each is divided into an independent sub-monitoring area to facilitate the tracking of high-energy-consuming carbon emission processes under each process step. The dimensions also include dividing the space based on equipment clusters, grouping equipment clusters with high power density and similar operating characteristics into one area. For example, the central air conditioning system, air compressor station, and large stamping equipment in the workshop are designated as independent key monitoring areas. It should be noted that the division of sub-monitoring areas within the workshop is not a simple physical spatial division, but a multi-dimensional intelligent division based on carbon source characteristics, process flow, and equipment cluster management. This is more targeted and facilitates accurate tracking of carbon emission processes in different spatial areas. Based on the regional carbon emission types of different sub-regulatory areas, several different types of sensors are deployed in each sub-regulatory area, and a gateway device is deployed in each sub-regulatory area. All sensors are connected to the gateway device in the corresponding sub-regulatory area. When any sensor requests a connection to the gateway device, the sensor's communication protocol is sent to the gateway device. The gateway device determines whether the current communication protocol can be recognized. If so, the sensor is directly connected to the gateway device, and a listening port is allocated to the sensor on the gateway device. The listening port is used to monitor the sensor's status. If not, the gateway device will compile the communication protocol corresponding to the sensor that initiated the request, and then convert the communication protocol into a communication protocol that the current gateway device can recognize, and simultaneously save the data of the entire compilation and conversion process to the storage area of ​​the gateway device; When the next sensor sends a request to the gateway device, if the communication protocol corresponding to the sensor cannot be identified, the gateway device will retrieve the identification record of the communication protocol in the past identification. If the current sensor sending the request has the same type of communication protocol in the identification record, the gateway device will retrieve all the data that has been compiled and converted in the storage area to convert the current communication protocol into a communication protocol that the gateway device can directly recognize, thus completing the communication connection between the sensor and the gateway device. If the communication protocol corresponding to the current sensor type is not found in the identification record, the gateway device will compile the current sensor's communication protocol, convert it into a communication protocol that the gateway device can accept, and save the compilation and conversion result to the gateway device. When all sensors in a sub-supervised area are connected to the gateway device, the corresponding IoT sensor network for that sub-supervised area is completed. Network bandwidth is allocated to the IoT sensor network to maintain its operation. The requests from sensors in each sub-supervised area to the gateway device are repeated, thereby completing the construction of several IoT sensor networks corresponding to the entire space area of ​​the workshop.

[0021] It should be further explained that, in the specific implementation process, the process of using the IoT sensor network to conduct real-time monitoring of the sub-regulatory area and obtain the carbon emission dataset within the sub-regulatory area includes: Several spatial carbon monitoring points are set up in the corresponding area space of each sub-regulatory area. All spatial carbon monitoring points in the area space are covered by the Internet of Things sensor network corresponding to each sub-regulatory area to obtain the spatial coordinates of each spatial carbon monitoring point in the area space. When a carbon emission event is detected in real time within a sub-regulatory area, the carbon emission data of the current carbon emission event at different time series is obtained through different spatial carbon monitoring points within the area. The carbon emission data of each time series is associated with the spatial coordinates of the spatial carbon monitoring points. Based on the time series progression, the spatial carbon monitoring points corresponding to all the carbon emission data are connected in sequence to construct the carbon emission flow trajectory under the current sub-regulatory area. The carbon emission data of several spatial carbon monitoring points corresponding to the carbon emission flow trajectory under a sub-regulatory area are summarized to obtain the carbon emission dataset of the corresponding sub-regulatory area. The sub-regulatory area number, the spatial coordinates of all spatial carbon monitoring points under the sub-regulatory area and the corresponding time sequence are used as the identification credentials of the corresponding carbon emission dataset.

[0022] The carbon emission related data includes direct carbon emission accounting and indirect process carbon emission characterization. The direct carbon emission amount generated by the direct carbon emission accounting is obtained through the carbon emission accounting formula: direct carbon emission amount = ∑(energy consumption of various types in each sub-supervisory area in the workshop × real-time / standard carbon emission factor of the corresponding type of energy in the sub-supervisory area). The indirect process carbon emission characterization is used to obtain the carbon emission corresponding to process emissions that are difficult to measure directly. It is collected through the concentration-flux correlation method or the material balance method. Specifically, it is to monitor the cumulative change of environmental concentration in the sub-monitoring area and estimate the carbon emission of the process in reverse by combining the regional ventilation volume model.

[0023] It should be noted that the real-time / standard carbon emission factor for each type of energy is determined based on the specific scenario. For electricity, the consumption data comes from smart meters, and the carbon emission factor can be dynamically accessed from the actual factor published by the power grid or the regional average value can be used. For natural gas, the consumption data comes from flow meters, and its fixed carbon emission factor is used.

[0024] It should be further explained that, in the specific implementation process, the process of constructing a three-dimensional structural model for the entire workshop includes: Assign a modeling node to each sub-supervisory area under the workshop, create a modeling operation space for the entire workshop, and build a data sharing channel for communication and interconnection between all modeling nodes and the modeling operation space; Select a sub-supervisory area in the workshop as the initial point for modeling. By importing the CAD drawings corresponding to the sub-supervisory area into the pre-configured 3D modeling software, a 3D area model is constructed that is the same in spatial position as the actual environment of the sub-supervisory area and the corresponding production line, production line equipment and workshop pipelines in the actual environment. After parsing the 3D region model, the basic parameter information of the model is obtained and recorded in the form of data modeling text. The basic parameter information of the model is used to characterize all modeling process information of the current sub-supervisory region in the actual environment. After recompiling the basic parameter information of the model, the reconstruction of the corresponding 3D region model is completed. The identification certificate of the sub-supervision area is marked to the corresponding data modeling text. The data modeling text of the current sub-supervision area is shared to the modeling operation space through the data sharing channel between its own modeling node and the modeling operation space for other modeling nodes to call. Other sub-regulatory areas outside the sub-regulatory area corresponding to the initial modeling point are used as modeling derivative points. It is then determined whether the actual environment of the sub-regulatory area corresponding to each modeling derivative point is the same as that of the initial modeling point. If so, the modeling derivative point calls the data modeling text of the initial modeling point to replicate and restore the corresponding 3D area model. If not, the CAD drawing of the sub-regulatory area corresponding to the current modeling derivative point is obtained to construct the corresponding 3D area model. The basic parameter information of the current 3D area model is parsed and obtained, and the data modeling text is created and placed into the modeling operation space.

[0025] Once the corresponding three-dimensional area models of all sub-supervisory areas under the entire workshop have been constructed, all the three-dimensional area models are arranged based on the spatial connection relationship of the workshop to obtain the three-dimensional structural model of the entire workshop. The three-dimensional structural model is used to characterize the actual structural situation of the space in which the workshop is located.

[0026] It should be further explained that, in the specific implementation process, the process of performing regional carbon emission analysis on the 3D structural model based on the carbon emission dataset of each sub-monitoring area under the workshop, establishing a carbon account for each sub-monitoring area, and recording the carbon emission concentration trajectory under the sub-monitoring area into the corresponding carbon account includes: The carbon emission dataset for each sub-regulatory area of ​​the workshop is set as several carbon emission data sources based on the spatial coordinates of each sub-regulatory area. The carbon emission data sources are then mapped to the same position in the three-dimensional structural model based on their own spatial coordinates. A real-time data stream carrier space is constructed for carbon emission data sources based on a preset time period. The real-time data stream carrier space is used to perform regional carbon emission analysis corresponding to the carbon emission data source. The real-time data stream carrier spaces of all carbon emission data sources under the same sub-regulatory area are integrated to obtain the regional carbon emission analysis under the corresponding sub-regulatory area. By analyzing regional carbon emissions, a scalar field of carbon emission intensity corresponding to several time periods is obtained for each sub-regulatory area. A carbon account is created for each sub-regulatory area, and the identification certificate of the sub-regulatory area is used as the account certificate of the carbon account. The carbon account is used to record the carbon emission concentration trajectory represented by the carbon emission intensity scalar field of the corresponding sub-regulatory area for several time periods. A database is created, and the account certificate of each carbon account is entered into the database. When data related to a certain carbon account is needed, the database is matched based on the account certificate.

[0027] The analysis of carbon emissions in the region is carried out to construct a carbon emission intensity scalar field for several time periods and obtain the carbon emission concentration trajectory of each sub-regulatory area as follows: a spatial interpolation algorithm is used to construct several carbon emission intensity scalar fields corresponding to a sub-regulatory area in the real-time data stream carrier space, the interpolation reference point is determined, the spatial coordinates of each carbon emission data source are used as the reference point, a continuous period duration is set after the timestamp corresponding to the reference point, the carbon emission intensity value monitored under the continuous period duration is used as the reference value, and based on the spatial distribution of the reference point and the reference value, a continuously distributed carbon emission intensity scalar field in the three-dimensional space where the sub-regulatory area is located is obtained. According to the preset time period, the carbon emission intensity scalar field corresponding to each sub-regulatory area is periodically updated to form a time-continuous scalar field sequence. Based on the scalar field sequence of carbon emission intensity scalar field, multi-dimensional features are extracted for the carbon emission intensity scalar field of each time period. The relevant dimensional data of multi-dimensional feature extraction include carbon emission peak and its spatial coordinates, centroid coordinates of carbon emission intensity distribution, spatial range of carbon emission hotspots exceeding preset thresholds, and gradient change characteristics of carbon emission intensity distribution. The extracted trajectory feature parameters are arranged and combined in chronological order to form a structured carbon emission concentration trajectory data sequence. Based on the carbon emission concentration trajectory data sequence, the spatiotemporal evolution process of carbon emissions is reproduced in a three-dimensional structural model, showing the diffusion path and intensity change of carbon emission concentration.

[0028] It should be further explained that, in the specific implementation process, the carbon accounts of each sub-regulatory area undergo data integration and visualization to obtain a carbon emission management view for the entire workshop. Based on this view, the process of locating non-compliant areas within the workshop and implementing carbon emission optimization includes: Establish a data integration engine to integrate carbon account-related data from all sub-regulatory regions into the data integration engine through a data sharing channel. The data integration engine synchronizes carbon account-related data from different sub-regulatory regions based on a time-series alignment algorithm. A data dashboard view is created for each sub-regulatory region using visualization technology. The data dashboard view is used to record the key carbon emission indicators in the corresponding carbon account of each sub-regulatory region. The key carbon emission indicators include, but are not limited to, the total carbon emission in the region, carbon emission intensity, peak carbon emission concentration and their corresponding occurrence time. After the carbon account data is integrated, it is mapped to the three-dimensional structural model of the workshop. The gradient coloring algorithm is used to render the carbon emission intensity scalar field corresponding to each sub-regulatory area into a three-dimensional heat map. Warm colors are used to represent high carbon emission intensity areas and cool colors are used to represent low carbon emission intensity areas. The three-dimensional heat maps of all sub-regulatory areas are integrated to generate the carbon emission management view corresponding to the entire workshop. Set the regional dynamic carbon emission thresholds for each of the sub-regulatory areas corresponding to the carbon emission management view and several 3D heat maps. The regional dynamic carbon emission thresholds include the regional absolute value threshold and the regional relative change rate threshold. When the carbon emission concentration in a certain sub-monitoring area exceeds the regional dynamic carbon emission threshold, the corresponding sub-monitoring area in the workshop is marked as a non-compliant area. For the identified non-compliant area, the historical carbon account data of the area is retrieved, and the process of the carbon emission anomaly is reproduced by the carbon emission concentration trajectory. Based on the equipment type, production process characteristics and carbon emission pattern of the non-compliant area, a matching optimization scheme is selected from the pre-set optimization strategy library, and the optimization scheme is distributed to the corresponding sub-monitoring area. The equipment operating parameters or production process are adjusted to implement carbon emission optimization.

[0029] It should be noted that when the carbon emission concentration of a certain sub-regulatory area exceeds the absolute value threshold of the area more than the preset critical number of times, the carbon emission of the corresponding sub-regulatory area is determined to be non-compliant. A monitoring interval is set, and when the change value of the carbon emission concentration of a certain sub-regulatory area exceeds the relative change rate threshold of the area before and after the monitoring interval, the carbon emission of the corresponding sub-regulatory area is also determined to be non-compliant. If at least one of the above two conditions is met, the corresponding sub-regulatory area can be determined to be a non-compliant area.

[0030] Example 2, please refer to Figure 2 As shown, the present invention also provides an Internet of Things-based workshop carbon management system, which includes: The workshop data acquisition module is used to divide the workshop into several sub-monitoring areas, build an Internet of Things (IoT) sensor network in each sub-monitoring area, and use the IoT sensor network to monitor the sub-monitoring area in real time and obtain the carbon emission dataset within the sub-monitoring area. The workshop carbon emission analysis module is used to build a three-dimensional structural model for the entire workshop. Based on the carbon emission dataset of each sub-regulatory area under the workshop, it performs regional carbon emission analysis on the three-dimensional structural model, establishes a carbon account for each sub-regulatory area, and records the carbon emission concentration trajectory under the sub-regulatory area into the corresponding carbon account. The workshop carbon emission management module is used to integrate and visualize the carbon accounts of each sub-regulatory area to obtain a carbon emission management view of the entire workshop. Based on the carbon emission management view, it locates non-compliant areas within the workshop and performs carbon emission optimization.

[0031] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for plant carbon management based on Internet of Things, characterized in that, The method comprises the following steps: Step S1: divide the workshop into several sub-supervision areas, build an Internet of Things sensing network under each sub-supervision area, live supervision of the sub-supervision area by the Internet of Things sensing network, and obtain the carbon emission dataset in the sub-supervision area; Step S2: build a three-dimensional structure model for the entire workshop, perform regional carbon emission analysis on the three-dimensional structure model based on the carbon emission dataset of each sub-supervision area under the workshop, establish the carbon account of each sub-supervision area, and record the carbon emission concentration trajectory under the sub-supervision area into the corresponding carbon account; Step S3: integrate and visually process the carbon account of each sub-supervision area, obtain the carbon emission management view corresponding to the entire workshop, locate the substandard area in the workshop based on the carbon emission management view, and perform carbon emission optimization.

2. The method of claim 1, wherein, The process of dividing the workshop into several sub-supervision areas and building an Internet of Things sensing network under each sub-supervision area comprises: Divide the entire space area where the workshop is located based on different dimensions, thereby dividing the space area corresponding to the workshop into several sub-supervision areas, label the several sub-supervision areas, deploy different types of sensors under each sub-supervision area based on the regional carbon emission type of the sub-supervision area, deploy a gateway device under each sub-supervision area, and connect all the sensors to the gateway device; When any sensor requests connection to the gateway device, send the communication protocol of the sensor to the gateway device, judge whether the current communication protocol can be identified by the gateway device, select to assign a listening port to the current sensor or compile and convert the communication protocol based on the judgment result, and complete the connection between the sensor and the gateway device when the communication protocol of the sensor is successfully identified by the gateway device; When all the sensors in a sub-supervision area are connected to the gateway device, the Internet of Things sensing network of the corresponding sub-supervision area is built, and the request of the sensor in each sub-supervision area to the gateway device is repeated, thereby building several Internet of Things sensing networks for the entire space area of the workshop.

3. The method of claim 2, wherein, The process of selecting to assign a listening port to the sensor or compile and convert the communication protocol based on the judgment result comprises: Judge whether the current communication protocol can be identified by the gateway device, if yes, connect the sensor to the gateway device, assign a listening port to the sensor for state listening, if not, compile the communication protocol of the sensor initiating the request by the gateway device, convert the communication protocol into a communication protocol that can be recognized by the current gateway device, and save the data of the entire compilation and conversion process to the storage area of the gateway device; When the next sensor initiates a request to the gateway device, if the communication protocol of the sensor cannot be identified, call the identification record of the communication protocol in the past identification by the gateway device, if there is a communication protocol of the same type in the identification record, call it, complete the communication connection between the next sensor and the gateway device, if not, compile the communication protocol of the current sensor by the gateway device, convert it into a communication protocol accepted by the gateway device, and save the result of the compilation and conversion to the gateway device.

4. The method of claim 3, wherein, The process of real-time supervision of the sub-supervision area by the Internet of Things sensing network and obtaining the carbon emission dataset in the sub-supervision area includes: A plurality of spatial carbon monitoring points are set in the regional space of each sub-supervision area, all spatial carbon monitoring points in the regional space are covered through the Internet of Things sensing network, and the spatial coordinates of the spatial carbon monitoring points in the regional space are obtained; When a carbon emission event is supervised in real time in the sub-supervision area, the point carbon emission data of the current carbon emission event at different time sequences are obtained through different spatial carbon monitoring points, each time sequence of point carbon emission data is associated with the spatial coordinates, the spatial carbon monitoring points of all point carbon emission data are connected based on the time sequence advancing order to obtain the carbon emission flow trajectory of the sub-supervision area; The point carbon emission related data of the plurality of spatial carbon monitoring points corresponding to the carbon emission flow trajectory under one sub-supervision area are summarized to obtain the carbon emission dataset of the corresponding sub-supervision area, and an identification certificate of the carbon emission dataset is created.

5. The method of claim 4, wherein, The process of constructing a three-dimensional structure model for the entire workshop includes: Each sub-supervision area under the workshop is assigned a modeling node, a modeling operation space is created for the entire workshop, a data sharing channel for communication and interconnection of all modeling nodes and the modeling operation space is built, one sub-supervision area in the workshop is selected as a modeling initial point, the CAD drawing of the sub-supervision area is imported into the pre-configured three-dimensional modeling software to obtain a three-dimensional regional model of the sub-supervision area; After analyzing the three-dimensional regional model, the model basic parameter information is obtained, and the model basic parameter information is recorded in the form of data modeling text, the identification certificate of the sub-supervision area is labeled to the corresponding data modeling text, and the data modeling text of the sub-supervision area is shared to the modeling operation space through the data sharing channel for calling by other modeling nodes; The other sub-supervision areas except the sub-supervision area corresponding to the modeling initial point are taken as modeling derivative points, and it is judged whether the actual environment of each modeling derivative point corresponding to the sub-supervision area is the same as that of the modeling initial point; If yes, the data modeling text of the modeling initial point is called by the modeling derivative point to reproduce the corresponding three-dimensional regional model, and if no, a three-dimensional regional model is constructed based on the CAD drawing of the current sub-supervision area, the corresponding model basic parameter information is analyzed to create a data modeling text and put it into the modeling operation space; After the construction of the three-dimensional regional model of all sub-supervision areas in the entire workshop is completed, all three-dimensional regional models are arranged based on the spatial connection relationship of the workshop to obtain a three-dimensional structure model of the entire workshop.

6. The method of claim 5, wherein, The process of analyzing the regional carbon emission of the three-dimensional structure model based on the carbon emission dataset of each sub-supervision area under the workshop and establishing the carbon account of each sub-supervision area and recording the carbon concentration trajectory under the sub-supervision area in the corresponding carbon account includes: The carbon emission dataset of each sub-supervision area corresponding to the workshop is set as a plurality of carbon emission data sources based on the spatial coordinates of the respective sub-supervision area corresponding regional space, and the carbon emission data sources are mapped to the same position of the three-dimensional structure model based on their spatial coordinates; A real-time data stream carrier space is constructed for a carbon emission data source, the real-time data stream carrier space is used for regional carbon emission analysis, a carbon account is created for each sub-supervision region, an identification certificate of the sub-supervision region is used as an account certificate of the carbon account, and the carbon account records carbon emission concentration trajectories of carbon emission intensity scalar fields of a plurality of time sequence segments of the sub-supervision region.

7. The method of claim 6, wherein, The carbon account of each sub-supervision region is subjected to data integration and visual processing, a carbon emission management view corresponding to the entire workshop is obtained, a non-compliance region in the workshop is located based on the carbon emission management view, and a carbon emission optimization process is performed, including: A data integration engine is established to integrate carbon account related data of all sub-supervision regions; A data dashboard view is established for each sub-supervision region through a visual processing technology, the data dashboard view is used to record carbon emission key indicators in the carbon account corresponding to each sub-supervision region; Carbon account related data after data integration is mapped to a three-dimensional structure model of the workshop, the carbon emission intensity scalar field of each sub-supervision region is rendered into a three-dimensional heat map, and the three-dimensional heat maps of all sub-supervision regions are integrated to generate a carbon emission management view of the entire workshop; The carbon emission management view is set with a regional dynamic carbon emission threshold of each sub-supervision region where the three-dimensional heat map is located, when the carbon emission concentration of a sub-supervision region exceeds the regional dynamic carbon emission threshold, the corresponding sub-supervision region is marked as a non-compliance region; The carbon account historical data of the non-compliance region is called, a matching optimization scheme is selected from a preset optimization strategy library based on the device type, production process characteristics and carbon emission mode of the non-compliance region, and a device operation parameter or a production process flow is adjusted to perform carbon emission optimization.

8. An Internet of Things based plant carbon management system for implementing the plant carbon management method of any one of claims 1 to 7, characterized in that, The system comprises: A workshop data acquisition module is used to divide the workshop into a plurality of sub-supervision regions, build an Internet of Things sensing network in each sub-supervision region, live supervise the sub-supervision region by the Internet of Things sensing network, and obtain a carbon emission data set in the sub-supervision region; A workshop carbon emission analysis module is used to construct a three-dimensional structure model for the entire workshop, perform regional carbon emission analysis on the three-dimensional structure model based on the carbon emission data set of each sub-supervision region under the workshop, establish a carbon account of each sub-supervision region, and record carbon emission concentration trajectories under the sub-supervision region into the corresponding carbon account; A workshop carbon emission management module is used to integrate and visually process the carbon account of each sub-supervision region, obtain a carbon emission management view corresponding to the entire workshop, locate a non-compliance region in the workshop based on the carbon emission management view, and perform carbon emission optimization.

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