Brewing carbon emission abnormity identification system and method

Abstract

The invention relates to the technical field of carbon monitoring, in particular to a brewing carbon emission abnormity identification system and method, and the system comprises an equipment identification management module, a multi-source sensor network module, a dynamic weighting accounting module, an abnormity detection engine module, a knowledge graph builder module, a GIS visual platform module and the like. According to the method, self-adaptive weight distribution can be carried out based on brewing equipment and a brewing process, accurate mapping of the equipment, the process and carbon emission is realized, so that a basis is provided for multi-source carbon emission anomaly identification, an anomaly detection engine module in which a threshold model and an isolated forest model are matched is adopted, and the threshold model carries out static threshold anomaly identification based on a baseline, so that the probability of abnormal identification is reduced. And the two models are combined to realize a dual-model collaborative anomaly detection architecture, and closed-loop feedback optimization is adopted in the method level to adjust the weight of the brewing equipment according to the early warning condition in actual use, so that the subsequent carbon emission anomaly identification and monitoring sensitivity is improved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, specifically a brewing carbon emission anomaly identification system and method. Background Technology

[0002] Against the backdrop of accelerated global efforts towards carbon neutrality, accurate identification and management of carbon emissions have become a core approach to addressing climate change. Traditional carbon emission monitoring methods reveal three significant shortcomings when dealing with such complex scenarios:

[0003] First, there is a lack of multi-source emission separation capabilities. Existing technologies struggle to accurately distinguish the carbon emission contributions from fermentation, distillation, and logistics transportation stages in the brewing process. Static accounting models neglect environmental interference factors such as the high temperature and humidity of the Tuojiang River in summer, leading to distorted carbon emission data and failing to support process optimization for emission reduction.

[0004] Secondly, the dynamic anomaly response mechanism is weak. Traditional threshold alarms rely on fixed baselines and cannot adapt to the cyclical fluctuations in brewing production, such as the surge in emissions during peak feeding periods. At the same time, frequent extreme weather events in the basin further amplify the risk of abnormal carbon emissions, and the existing system lacks the ability to intelligently identify nonlinear anomalies, such as sudden increases in CO2 caused by equipment failure, thus delaying the fault handling window.

[0005] Third, there is a lack of a collaborative watershed governance perspective. The current monitoring system operates on a single-enterprise basis and has not established a linkage analysis mechanism between emissions from winery clusters and the ecological carbon sequestration of the Tuojiang River. The lack of GIS spatial data makes it impossible to locate high-emission hotspots, and even more difficult to quantify the balance between industrial emission reduction and ecological carbon sequestration.

[0006] To address the aforementioned shortcomings, there is an urgent need to construct a carbon emission intelligent identification system that integrates dynamic weighted accounting, multi-source anomaly detection, and watershed spatial coordination, in order to break the predicament of unclear emission data, delayed anomaly response, and fragmented regional governance in the brewing industry. Summary of the Invention

[0007] The purpose of this invention is to provide a brewing carbon emission anomaly identification system and method to address the aforementioned problems.

[0008] The technical solution adopted in this invention is as follows: a brewing carbon emission anomaly identification system.

[0009] Applicable to brewing enterprises, characterized by including an equipment identification management module, a multi-source sensor network module, a dynamic weighted accounting module, an anomaly detection engine module, a knowledge graph builder module, a GIS visualization platform module, a multi-level early warning push module, and a carbon sink decision support module;

[0010] The equipment identification management module is used to identify the geographical coordinates and process parameters of brewing equipment within a brewing enterprise, and generate a unique associated industrial internet identifier.

[0011] The multi-source sensor network module is used to collect process data, energy consumption data, and environmental data of the identified brewing equipment.

[0012] The dynamic weighted accounting module calculates the carbon emission weight of the identified brewing equipment under the brewing process based on the process data, energy consumption data, and environmental data of the identified brewing equipment.

[0013] The anomaly detection engine module performs anomaly analysis based on the carbon emission weights of brewing equipment within the brewing enterprise obtained by the dynamic weighted accounting module.

[0014] The knowledge graph builder module is used to associate brewing equipment status, brewing equipment process parameters, brewing equipment carbon emission values ​​and environmental factors, and generate root cause analysis chains;

[0015] The GIS visualization platform module is used to display the carbon emission intensity of brewing enterprises and to locate abnormal devices;

[0016] The multi-level early warning push module is based on the dynamic calculation value Y of real-time carbon emissions and historical baseline carbon emission data a from the anomaly detection engine module. o Early warnings are pushed out through multiple channels, specifically to enterprises and regulatory authorities;

[0017] The carbon sink decision support module is used to generate carbon emission reports for brewing enterprises and to generate brewing equipment optimization strategies and update energy efficiency parameters, which are then fed back to the equipment identification management module.

[0018] Optionally, the equipment identification management module obtains the GIS coordinates of brewing equipment based on the distribution data of brewing enterprises, generates equipment IDs through the SHA-256 hash algorithm, and binds the GIS coordinates with the equipment IDs.

[0019] Optionally, the multi-source sensor network module includes a CO2 concentration sensor, a temperature sensor, a pressure sensor, and an electricity meter;

[0020] The CO2 concentration sensor, temperature sensor, and pressure sensor are all installed inside the brewing fermentation tank to obtain fermentation temperature, CO2 concentration, and steam pressure.

[0021] The meter is connected to the power supply line of the brewing fermentation tank and is used to obtain the power consumption of the brewing fermentation tank;

[0022] The process data includes fermentation temperature and CO2 concentration;

[0023] The energy consumption data includes steam pressure and electricity consumption;

[0024] The environmental data includes temperature, humidity, and air pressure data, and is obtained through linkage with local weather stations.

[0025] Optionally, the dynamic weighted accounting module obtains the dynamic calculated value Y of real-time carbon emissions using the following formula:

[0026] ;

[0027] Where S represents carbon emission data from production equipment, which is composed of process data;

[0028] N represents carbon emission data from energy equipment, which is composed of energy consumption data;

[0029] u and h are the weighting coefficients for carbon emission data S from production equipment and carbon emission data N from energy equipment, respectively.

[0030] Optionally, the dynamic weighted accounting module also performs interference correction using the following formula:

[0031] ;

[0032] Where k1 and k2 are the watershed climate fitting coefficients, respectively;

[0033] ΔT is the temperature deviation value;

[0034] Humidity refers to ambient humidity.

[0035] This application also provides a method for identifying abnormal carbon emissions from brewing based on the above system, including the following steps:

[0036] S1. Equipment identification and data acquisition: The equipment identification management module generates brewing equipment IDs within the brewing enterprise and binds them to GIS coordinates. At the same time, the multi-source sensor network module collects process data, energy consumption data, and environmental data of the brewing equipment.

[0037] S2. Dynamic weighted accounting: The dynamic weighted accounting module assigns weights and corrects interference for the process data, energy consumption data and environmental data of the brewing equipment, and sends the accounting results to the anomaly detection engine module.

[0038] S3. Dual-mode anomaly detection: The anomaly detection engine module performs anomaly detection using both a threshold model and an isolated forest algorithm model.

[0039] S4. Knowledge graph tracing: The knowledge graph builder module associates data collected by the multi-source sensor network module to locate the cause of anomalies.

[0040] S5.GIS Spatial Visualization and Multi-level Early Warning Push: The GIS visualization platform module generates a carbon emission heat map of the brewing enterprise, and the multi-level early warning push module pushes early warnings based on the abnormal dual-mode detection results.

[0041] S6. Closed-loop feedback optimization: The carbon sink decision support module generates carbon emission reports for brewing enterprises and adjusts the weight of brewing equipment based on abnormal carbon emission data.

[0042] Optionally, in S3, during threshold model detection, when the dynamically calculated real-time carbon emission value Y obtained by the dynamic weighted accounting module is compared with the historical baseline carbon emission data a... o Based on the magnitude of the weights, anomalies are identified;

[0043] The isolated forest algorithm model uses time-series data of brewing equipment in a brewing enterprise collected over a year by a multi-source sensor network module, and is trained to identify nonlinear anomalies.

[0044] Optionally, in S5, the multi-level early warning push module includes at least two levels of push strategies, and the push methods include on-site audible and visual alarms and SMS notifications.

[0045] The beneficial effects of the present invention include at least one of the following;

[0046] 1. A brewing carbon emission anomaly identification system is provided, which includes an equipment identification management module, a multi-source sensor network module, a dynamic weighted accounting module, an anomaly detection engine module, a knowledge graph builder module, a GIS visualization platform module, a multi-level early warning push module, and a carbon sink decision support module. It can adaptively allocate weights based on brewing equipment and brewing process to achieve accurate mapping of equipment, process, and carbon emissions, thereby providing a basis for the identification of multi-source carbon emission anomalies.

[0047] 2. An anomaly detection engine module is adopted that combines a threshold model and an isolated forest model. The former is used for static threshold anomaly identification based on a baseline, while the latter is used to identify nonlinear anomalies. The combination of the two realizes a dual-model collaborative anomaly detection architecture.

[0048] 3. At the methodological level, closed-loop feedback optimization is adopted to adjust the weight of brewing equipment based on the early warning situation in actual use, thereby improving the sensitivity of subsequent carbon emission anomaly identification and monitoring. Attached Figure Description

[0049] Figure 1 A schematic diagram of a system for identifying abnormal carbon emissions during brewing.

[0050] Figure 2 Flowchart of a method for identifying abnormal carbon emissions in brewing;

[0051] Figure 3 This is a schematic diagram of the structure of an electronic device. Detailed Implementation

[0052] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0054] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.

[0055] like Figure 1 As shown, a brewing carbon emission anomaly identification system includes an equipment identification management module, a multi-source sensor network module, a dynamic weighted accounting module, an anomaly detection engine module, a knowledge graph builder module, a GIS visualization platform module, a multi-level early warning push module, and a carbon sink decision support module.

[0056] The equipment identification management module is used to identify the geographical coordinates and process parameters of brewing equipment within a brewing enterprise, and generate a unique associated industrial internet identifier.

[0057] The multi-source sensor network module is used to collect process data, energy consumption data, and environmental data of the identified brewing equipment.

[0058] The dynamic weighted accounting module calculates the carbon emission weight of the identified brewing equipment under the brewing process based on the process data, energy consumption data, and environmental data of the identified brewing equipment.

[0059] The anomaly detection engine module performs anomaly analysis based on the carbon emission weights of brewing equipment within the brewing enterprise obtained by the dynamic weighted accounting module.

[0060] The knowledge graph builder module is used to associate brewing equipment status, brewing equipment process parameters, brewing equipment carbon emission values ​​and environmental factors, and generate root cause analysis chains;

[0061] The GIS visualization platform module is used to display the carbon emission intensity of brewing enterprises and to locate abnormal devices;

[0062] The multi-level early warning push module is based on the dynamic calculation value Y of real-time carbon emissions and historical baseline carbon emission data a from the anomaly detection engine module.o Early warnings are pushed out through multiple channels, specifically to enterprises and regulatory authorities;

[0063] The carbon sink decision support module is used to generate carbon emission reports for brewing enterprises and to generate brewing equipment optimization strategies and update energy efficiency parameters, which are then fed back to the equipment identification management module.

[0064] The purpose of this design is to provide a brewing carbon emission anomaly identification system that includes an equipment identification management module, a multi-source sensor network module, a dynamic weighted accounting module, an anomaly detection engine module, a knowledge graph builder module, a GIS visualization platform module, a multi-level early warning push module, and a carbon sink decision support module. It can adaptively allocate weights based on brewing equipment and brewing processes to achieve accurate mapping between equipment, processes, and carbon emissions, thereby providing a basis for identifying multi-source carbon emission anomalies.

[0065] Meanwhile, in this embodiment, the equipment identification management module obtains the GIS coordinates of brewing equipment based on the distribution data of brewing enterprises, generates equipment IDs through the SHA-256 hash algorithm, and binds the GIS coordinates with the equipment IDs.

[0066] Furthermore, the multi-source sensor network module includes a CO2 concentration sensor, a temperature sensor, a pressure sensor, and an electricity meter;

[0067] The CO2 concentration sensor, temperature sensor, and pressure sensor are all installed inside the brewing fermentation tank to obtain fermentation temperature, CO2 concentration, and steam pressure.

[0068] The meter is connected to the power supply line of the brewing fermentation tank and is used to obtain the power consumption of the brewing fermentation tank;

[0069] The process data includes fermentation temperature and CO2 concentration;

[0070] The energy consumption data includes steam pressure and electricity consumption;

[0071] The environmental data includes temperature, humidity, and air pressure data, and is obtained through linkage with local weather stations.

[0072] Meanwhile, the dynamic weighted accounting module obtains the dynamic calculated value Y of real-time carbon emissions using the following formula:

[0073] ;

[0074] Where S represents carbon emission data from production equipment, which is composed of process data;

[0075] N represents carbon emission data from energy equipment, which is composed of energy consumption data;

[0076] u and h are the weighting coefficients for carbon emission data S from production equipment and carbon emission data N from energy equipment, respectively.

[0077] Furthermore, the dynamic weighted accounting module also performs interference correction using the following formula:

[0078] ;

[0079] Where k1 and k2 are the watershed climate fitting coefficients, respectively;

[0080] ΔT is the temperature deviation value;

[0081] Humidity refers to ambient humidity.

[0082] The purpose of this design is to build a linkage of basic data layer by the equipment identification management module, multi-source sensor network module, and dynamic weighted accounting module. When identifying brewing equipment, such as fermentation tank ID 4a3b2c1d, it is bound to its internal temperature / CO2 sensor.

[0083] Meanwhile, in this embodiment, the analysis linkage part of the system is constructed by the anomaly detection engine module, the knowledge graph builder module, and the GIS visualization platform module. The anomaly detection engine module adopts an anomaly detection engine module that combines a threshold model and an isolated forest model. The former performs static threshold anomaly identification based on a baseline, while the latter is used to identify nonlinear anomalies. The combination of the two realizes a dual-model collaborative anomaly detection architecture. For example, in this embodiment, anomalies are determined based on Y < 0.08xa0.

[0084] In the knowledge graph builder module, the root cause chain can be generated based on the current state of the equipment, such as the opening of the fermentation tank valve, process parameters such as fermentation time, and environmental data such as humidity, based on the anomaly judgment results of the anomaly detection engine module. For example, rising CO2 causes the temperature control of the brewing fermentation tank to fail, resulting in the blockage of the cooling water valve and causing abnormal carbon emissions.

[0085] Furthermore, based on the root cause chain location data generated by the knowledge graph builder module, heatmap rendering is driven, using different colors such as red to mark high-emission areas of brewing enterprise clusters and green to mark carbon sink coverage areas of forests along the Tuojiang River.

[0086] Meanwhile, in this embodiment, the application layer linkage of the system is constructed by the multi-level early warning push module and the carbon sink decision support module. This linkage is used for the dynamic calculation value Y of real-time carbon emissions and the historical baseline carbon emission data a in the anomaly detection engine module. o Early warnings are pushed through multiple channels to enterprises and regulatory authorities. The carbon sink decision support module generates carbon emission reports for brewing enterprises and generates optimization strategies for brewing equipment and updates energy efficiency parameters, which are then fed back to the equipment identification management module.

[0087] like Figure 2 As shown, this embodiment also provides a method for identifying abnormal carbon emissions from brewing, which is based on the abnormal carbon emission identification system for brewing described in the above embodiment, and includes the following steps:

[0088] S1. Equipment identification and data acquisition: The equipment identification management module generates brewing equipment IDs within the brewing enterprise and binds them to GIS coordinates. At the same time, the multi-source sensor network module collects process data, energy consumption data, and environmental data of the brewing equipment.

[0089] S2. Dynamic weighted accounting: The dynamic weighted accounting module assigns weights and corrects interference for the process data, energy consumption data and environmental data of the brewing equipment, and sends the accounting results to the anomaly detection engine module.

[0090] S3. Dual-mode anomaly detection: The anomaly detection engine module performs anomaly detection using both a threshold model and an isolated forest algorithm model.

[0091] S4. Knowledge graph tracing: The knowledge graph builder module associates data collected by the multi-source sensor network module to locate the cause of anomalies.

[0092] S5.GIS Spatial Visualization and Multi-level Early Warning Push: The GIS visualization platform module generates a carbon emission heat map of the brewing enterprise, and the multi-level early warning push module pushes early warnings based on the abnormal dual-mode detection results.

[0093] S6. Closed-loop feedback optimization: The carbon sink decision support module generates carbon emission reports for brewing enterprises and adjusts the weight of brewing equipment based on abnormal carbon emission data.

[0094] Meanwhile, in this embodiment, in S3, during threshold model detection, when the dynamically calculated value Y of the real-time carbon emissions obtained by the dynamic weighted calculation module is compared with the historical baseline carbon emissions data a... o Based on the magnitude of the weights, anomalies are identified;

[0095] The isolated forest algorithm model uses time-series data of brewing equipment in a brewing enterprise collected over a year by a multi-source sensor network module, and is trained to identify nonlinear anomalies.

[0096] Furthermore, in S5, the multi-level early warning push module includes at least two levels of push strategies, and the push methods include on-site audible and visual alarms and SMS notifications.

[0097] In one embodiment of this application, the electronic device further includes a bus and a computer program stored in the memory and executable on the processor, such as a brewing carbon emission anomaly detection program.

[0098] Figure 3Only an electronic device with memory and processor is shown. Those skilled in the art will understand that the structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0099] Combination Figure 3 The memory in the electronic device stores a plurality of computer-readable instructions to implement a method for identifying anomalies in brewing carbon emissions, and the processor can execute the plurality of instructions to implement this method.

[0100] Specifically, the processor's implementation method for the above instructions can be found in the description of the relevant steps in the corresponding embodiment of the figure, and will not be repeated here.

[0101] Those skilled in the art will understand that the schematic diagram is merely an example of an electronic device and does not constitute a limitation on the electronic device. The electronic device may be a bus-type structure or a star-type structure. The electronic device may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, the electronic device may also include input / output devices, network access devices, etc.

[0102] It should be noted that electronic devices are merely examples. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0103] The memory includes at least one type of readable storage medium, which can be non-volatile or volatile. The readable storage medium includes flash memory, portable hard drives, multimedia cards, card-type memory (e.g., SD or DX memory), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the memory can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory can be an external storage device of the electronic device, such as a plug-in portable hard drive, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory can be used not only to store application software and various types of data installed on the electronic device, such as code for identifying abnormal carbon emissions, but also to temporarily store data that has been output or will be output.

[0104] In some embodiments, a processor can be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions. This includes combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor is the control unit of an electronic device, connecting various components of the device through various interfaces and lines. It performs various functions and processes data by running or executing programs or modules stored in the memory (e.g., executing a brewing carbon emission anomaly detection program) and accessing data stored in the memory.

[0105] The processor executes the operating system of the electronic device and various installed applications. The processor executes the applications to implement the steps in each of the above embodiments of the method for identifying abnormal carbon emissions in brewing, such as the steps shown in the figure.

[0106] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete this application. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in an electronic device. For example, the computer program may be divided into a receiving module, a preprocessing module, a projection module, and a determining module.

[0107] The integrated unit, implemented as a software functional module, can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the brewing carbon emission anomaly identification method described in the various embodiments of this application.

[0108] When modules / units integrated into an electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0109] This application provides a method for identifying abnormal carbon emissions from brewing, which can be applied to one or more electronic devices. An electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0110] Electronic devices can be any electronic product that allows human-computer interaction with a customer, such as personal computers, tablets, smartphones, personal digital assistants (PDAs), game consoles, interactive network television (IPTV), smart wearable devices, etc.

[0111] Electronic devices may also include network devices and / or client devices. The network devices include, but are not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0112] The networks in which electronic devices are located include, but are not limited to, the Internet, wide area networks, metropolitan area networks, local area networks, and virtual private networks (VPNs).

[0113] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, and other memory.

[0114] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0115] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus. The bus is configured to implement the connection and communication between the memory and at least one processor, etc.

[0116] This application also provides a computer-readable storage medium (not shown), which stores computer-readable instructions that are executed by a processor in an electronic device to implement a brewing carbon emission anomaly identification method as described in any of the above embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0118] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0120] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the specification may also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0121] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A brewing carbon emission anomaly identification system, applicable to brewing enterprises, characterized in that, It includes a device identification management module, a multi-source sensor network module, a dynamic weighted accounting module, an anomaly detection engine module, a knowledge graph builder module, a GIS visualization platform module, a multi-level early warning push module, and a carbon sink decision support module; The equipment identification management module is used to identify the geographical coordinates and process parameters of brewing equipment within a brewing enterprise, and generate a unique associated industrial internet identifier. The multi-source sensor network module is used to collect process data, energy consumption data, and environmental data of the identified brewing equipment. The dynamic weighted accounting module calculates the carbon emission weight of the identified brewing equipment under the brewing process based on the process data, energy consumption data, and environmental data of the identified brewing equipment. The anomaly detection engine module performs anomaly analysis based on the carbon emission weights of brewing equipment within the brewing enterprise obtained by the dynamic weighted accounting module. The knowledge graph builder module is used to associate brewing equipment status, brewing equipment process parameters, brewing equipment carbon emission values ​​and environmental factors, and generate root cause analysis chains; The GIS visualization platform module is used to display the carbon emission intensity of brewing enterprises and to locate abnormal devices; The multi-level early warning push module is based on the dynamic calculation value Y of real-time carbon emissions and historical baseline carbon emission data a from the anomaly detection engine module. o Early warnings are pushed out through multiple channels, specifically to enterprises and regulatory authorities; The carbon sink decision support module is used to generate carbon emission reports for brewing enterprises and to generate brewing equipment optimization strategies and update energy efficiency parameters, which are then fed back to the equipment identification management module.

2. The brewing carbon emission anomaly identification system according to claim 1, characterized in that, The equipment identification management module obtains the GIS coordinates of brewing equipment based on the distribution data of brewing enterprises, generates equipment IDs through the SHA-256 hash algorithm, and binds the GIS coordinates with the equipment IDs.

3. The brewing carbon emission anomaly identification system according to claim 1, characterized in that, The multi-source sensor network module includes a CO2 concentration sensor, a temperature sensor, a pressure sensor, and an electricity meter; The CO2 concentration sensor, temperature sensor, and pressure sensor are all installed inside the brewing fermentation tank to obtain fermentation temperature, CO2 concentration, and steam pressure. The meter is connected to the power supply line of the brewing fermentation tank and is used to obtain the power consumption of the brewing fermentation tank; The process data includes fermentation temperature and CO2 concentration; The energy consumption data includes steam pressure and electricity consumption; The environmental data includes temperature, humidity, and air pressure data, and is obtained through linkage with local weather stations.

4. The brewing carbon emission anomaly identification system according to claim 1, characterized in that, The dynamic weighted calculation module obtains the dynamic calculated value Y of real-time carbon emissions using the following formula: ； Where S represents carbon emission data from production equipment, which is composed of process data; N represents carbon emission data from energy equipment, which is composed of energy consumption data; u and h are the weighting coefficients for carbon emission data S from production equipment and carbon emission data N from energy equipment, respectively.

5. The brewing carbon emission anomaly identification system according to claim 4, characterized in that, The dynamic weighted calculation module also performs interference correction using the following formula: ； Where k1 and k2 are the watershed climate fitting coefficients, respectively; ΔT is the temperature deviation value; Humidity refers to ambient humidity.

6. A method for identifying anomalies in brewing carbon emissions, implemented based on the brewing carbon emission anomaly identification system described in claim 5, characterized in that, Includes the following steps: S1. Equipment identification and data acquisition: The equipment identification management module generates brewing equipment IDs within the brewing enterprise and binds them to GIS coordinates. At the same time, the multi-source sensor network module collects process data, energy consumption data, and environmental data of the brewing equipment. S2. Dynamic weighted accounting: The dynamic weighted accounting module assigns weights and corrects interference for the process data, energy consumption data and environmental data of the brewing equipment, and sends the accounting results to the anomaly detection engine module. S3. Dual-mode anomaly detection: The anomaly detection engine module performs anomaly detection using both a threshold model and an isolated forest algorithm model. S4. Knowledge graph tracing: The knowledge graph builder module associates data collected by the multi-source sensor network module to locate the cause of anomalies. S5.GIS Spatial Visualization and Multi-level Early Warning Push: The GIS visualization platform module generates a carbon emission heat map of the brewing enterprise, and the multi-level early warning push module pushes early warnings based on the abnormal dual-mode detection results. S6. Closed-loop feedback optimization: The carbon sink decision support module generates carbon emission reports for brewing enterprises and adjusts the weight of brewing equipment based on abnormal carbon emission data.

7. The method for identifying abnormal carbon emissions from brewing according to claim 6, characterized in that, In step S3, during threshold model detection, when the dynamically calculated real-time carbon emission value Y obtained by the dynamic weighted calculation module differs from the historical baseline carbon emission data a... o Based on the magnitude of the weights, anomalies are identified; The isolated forest algorithm model uses time-series data of brewing equipment in a brewing enterprise collected over a year by a multi-source sensor network module, and is trained to identify nonlinear anomalies.

8. The method for identifying abnormal carbon emissions from brewing according to claim 6, characterized in that, In S5, the multi-level early warning push module includes at least two levels of push strategy, and the push methods include on-site audible and visual alarms and SMS notifications.

Images