Intelligent inspection system and method

Abstract

An intelligent inspection system, comprising a recognition module, an activity factor database, a learning module, a computation module, a transmission module, and a validation module. The recognition module recognizes a piece of activity data, searches, by means of the activity factor database, for an emission factor corresponding to the activity data, and transmits the emission factor to the computation module. The learning module updates the activity factor database by means of internet crawlers and machine learning. The computation module generates an inspection result by means of the activity data, the emission factor, and a global warming potential. The transmission module issues a verification request and transmits the inspection result generated by the computation module to the validation module for verification by a third-party verification agency. After the third-party verification agency completes the verification, the validation module locks the inspection result.

Description

Smart inspection system and method Technical Field

[0001] The present invention relates to an interrogation system, and in particular to an intelligent interrogation system and method. Background Art

[0002] Businesses must comply with numerous regulations and standards, and they must also strive to fulfill their social responsibilities by creating a sustainable, safe, fair, and healthy working environment for shareholders, employees, customers, suppliers, and society. To demonstrate that businesses meet certain regulatory requirements (e.g., environmental protection, fire protection, workplace safety, etc.) or standards (e.g., ISO quality systems), regular and periodic inspections are essential for modern business operations. However, these inspections are often tedious, time-consuming, and labor-intensive, requiring excessive human resources. Furthermore, the scope of inspections often fluctuates, making the inspection process and results difficult to record. This can lead to duplicate inspections due to unclear records.

[0003] Furthermore, ensuring the authenticity and accuracy of records and inspections is a significant challenge. In practice, the authenticity of inspections can be affected by various factors. Different employees may have different subjective judgments and operational capabilities, which can lead to subjective and inconsistent inspection results. The inspection process also carries the risk of human error, such as subjective bias by inspectors, inadvertent misrepresentation of records, or manipulation by the company. These factors can affect the authenticity of inspections and, in turn, the company's image and reputation in society and the market.

[0004] In view of this, developing an interrogation system that can significantly improve interrogation efficiency and avoid human errors has become an urgent goal in related fields.

[0005] Summary of the Invention

[0006] To address the issues of inefficiency and reliability in traditional inventory checks, the present invention provides an intelligent inventory check system comprising an identification module, an activity coefficient database, a learning module, a computing module, a transmission module, and a verification module. The identification module identifies activity data, extracts the type and quantity of the activity data, searches for an emission coefficient corresponding to the activity data from the activity coefficient database, and transmits the result to the computing module. The learning module updates the activity coefficient database through an internet crawler and machine learning. The computing module generates an inventory check result based on the activity data, the emission coefficient, and a global warming potential. The transmission module issues a verification request, transmitting the inventory check result generated by the computing module to the verification module for verification by a third-party verification agency. The verification module locks the inventory check result after the third-party verification agency completes the verification.

[0007] The intelligent inspection system further includes a reading module, which transmits the input activity data to the recognition module, wherein: the reading module includes a digital conversion unit, and when the activity data is paper or analog data, the digital conversion unit converts the activity data into digital images or text.

[0008] Among them, the smart inspection system further includes a publishing module, which will immediately publish the inspection results verified by the third-party verification agency online.

[0009] The learning module includes a crawler unit, a machine learning unit, and an optical character recognition unit. The crawler unit searches for and records data similar to the activity data on the Internet, and uses the optical character recognition unit and the machine learning unit to identify the text in the data image and analyze and compare the image or text of the data to obtain the emission coefficient or the global warming potential and record it in the activity coefficient database.

[0010] The activity coefficient database includes the emission coefficient and the global warming potential corresponding to the activity data. The emission coefficient can be an international emission coefficient, a national emission coefficient, a regional emission coefficient, a coefficient provided by a manufacturer, an empirical coefficient based on the same process or equipment, a measured coefficient, a mass-energy coefficient, or a mass balance coefficient.

[0011] The verification module verifies the login identity of a verifier of the third-party verification agency.

[0012] The learning module regularly updates the identification method of the activity data and updates the emission coefficient and the global warming potential of the activity coefficient database.

[0013] The transmission module electronically transmits the verification request using an encrypted transmission protocol.

[0014] The present invention also provides a smart interrogation method, which includes the smart interrogation system described above and comprises the following steps: reading the input activity data; the identification module identifying and assigning the activity data, wherein, when the activity data cannot be identified or assigned, the learning module learns to retrieve and search the Internet to obtain the identified or assigned data; the computing module generates and compiles the interrogation results; the transmission module sends the verification request to the verification module of the third-party verification organization; after the third-party verification organization confirms the verification request, the verification module authorizes the verification request and locks the interrogation results; and the interrogation results are immediately uploaded online.

[0015] The step in which the transmission module sends the verification request to the verification module of the third-party verification agency and the verification module authorizes the verification request and locks the verification result after the third-party verification agency confirms the verification request further includes the following steps: the verification module confirms that the third-party verification agency has received the verification request; the verification module verifies the login identity of a verifier of the third-party verification agency; the verification module prompts the verifier to verify the verification result; and the verification module records a verification result. If the verification result indicates that the verification result has passed verification, the verification module locks the verification result so that it cannot be modified.

[0016] It can be seen from the above description that the present invention has the following characteristics:

[0017] 1. The intelligent inventory method and system of the present invention can automatically acquire and locate the data required for inventory through a learning module, and can be continuously updated and optimized, significantly reducing the cost and time required for users to organize and locate emission coefficients and activity data, and speeding up inventory.

[0018] 2. The intelligent inventory method and system of the present invention can aggregate and standardize various activity data, significantly improving inventory efficiency and saving costs. It can also avoid unnecessary human errors and improve the accuracy of inventory.

[0019] 3. The intelligent interrogation method and system of the present invention aggregates and assigns interrogation results generated by various activity data, which can be directly transmitted to a third-party organization for verification and audit. The identity of the verifier can also be confirmed, ensuring the authenticity of the interrogation results and preventing the possibility of data loss or tampering, thereby ensuring the authenticity and accountability of the interrogation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] FIG1 is a flow chart of steps of a preferred embodiment of the present invention;

[0021] FIG2 is a schematic diagram of a learning module system according to a preferred embodiment of the present invention; and

[0022] FIG3 is a flow chart of verification steps according to a preferred embodiment of the present invention.

[0023] Explanation of symbols: 42: Learning module 421: Crawler unit 422: Optical character recognition unit 423: Machine learning unit 424: Activity coefficient database S1-S9: Steps DETAILED DESCRIPTION

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing each embodiment. Obviously, the drawings described below are merely examples or embodiments of the present invention. Those skilled in the art can apply the present invention to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0025] As used herein and in the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list; a method or apparatus may also include additional steps or elements.

[0026] Flowcharts are used in this disclosure to illustrate the operations performed by systems according to embodiments of the present invention. It should be understood that the preceding and following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0027] Please refer to Figure 1, which is a flowchart of a preferred embodiment of a smart interrogation method provided by the present invention. The smart interrogation method includes the following steps:

[0028] Step S1: Inputting Activity Data. In step S1, a user or an external system inputs the activity data into a smart inventory system. The smart inventory system includes a reading module that reads the input activity data. This activity data is related to activities such as production, transportation, and energy usage. For example, this activity data can include a paper or electronic electricity bill for a factory, water consumption for a steam boiler, mobile fuel usage, a purchase order for raw materials, a screenshot of an air conditioner model, and so on.

[0029] Step S2: Read the activity data. In step S2, the reading module of the intelligent inspection system reads the input activity data. In step S2, if the activity data is paper or analog information, the intelligent inspection system converts the activity data into a digital image or text format through a digital conversion unit and stores it in a database of the intelligent inspection system.

[0030] Step S3: Identify the activity data. In step S3, the smart inventory system includes a recognition module that identifies and analyzes the type and quantity of the activity data through methods such as optical character recognition and image analysis. In one embodiment, the recognition module of the smart inventory system reads the electricity consumption and carbon emissions per unit of electricity from the electronic electricity bill. In another embodiment, the recognition module of the smart inventory system identifies the air conditioner model and rated power values ​​in the air conditioner model screenshot.

[0031] Step S41: Assign the activity data. In step S41, after the smart inventory system completes the identification of the activity data in step S3, it assigns the identified activity data to a classification group. Preferably, the smart inventory system includes an activity coefficient database 424, which organizes and records a plurality of emission coefficients and a plurality of global warming potentials (GWPs) corresponding to the activity data. The emission coefficient can be an international emission coefficient, a national emission coefficient, a regional emission coefficient, a coefficient provided by a manufacturer, an experience coefficient of the same process or the same equipment, a measured coefficient, a mass-energy coefficient, or a mass balance coefficient. Preferably, the emission coefficient is a measured coefficient, a mass-energy coefficient, or a mass balance coefficient, but the activity database 424 can still include coefficients from different sources for other coefficients, and weight or select them according to the accuracy of each emission coefficient. For example, if the activity data is a paper electricity bill, the smart inventory system may group the paper electricity bills from different months into electricity classification groups. The smart inventory system then calculates or finds the emission coefficient corresponding to the activity data based on the characteristics of each classification group. In one implementation, the activity data is multiple smart meter readings from different areas of the factory. These smart meter readings are all classified into the same classification group by the smart inventory system, and the emission coefficient corresponding to the classification group is loaded from the activity coefficient database 424.

[0032] Step S42: Learning. Referring to Figure 2 , when the intelligent inventory system is unable to identify the activity data or the emission coefficient corresponding to the activity data in step S3, or when the intelligent inventory system receives an update instruction or performs a periodic update, the intelligent inventory system updates the activity data identification method and the emission coefficient data in the activity coefficient database 424 through a learning module 42. In a preferred embodiment, the learning module 42 uses a crawler unit 421 to search and record data similar to the activity data on the internet, uses an optical character recognition unit 422 to identify text that may be present in related images, and uses a machine learning unit 423 to analyze and compare the images or text, thereby updating the activity data identification method and the emission coefficient data in the activity coefficient database 424.

[0033] For example, the learning module 42 uses the crawler unit 421 to search and record documents, forms, or images in the same or similar format as the input paper electricity bill on the internet. The optical character recognition unit 422 and the machine learning unit 423 then recognize and aggregate the information collected by the crawler unit 421 to confirm the data format of the paper electricity bill and facilitate the reading and recognition of future paper electricity bills of the same format in steps S2 and S3. Furthermore, the crawler unit 421 will also visit the website of the power company that provides the electricity and search for emission coefficient information related to the electricity on the power company's website. The aggregated information will be added to or updated in the activity coefficient database 424.

[0034] In another preferred embodiment, the learning module 42 retrieves and records the models of multiple air conditioners in the factory and the corresponding emission coefficient table information on the Internet through the crawler unit 421, and recognizes and summarizes them through the optical character recognition unit 422 or the machine learning unit 423, and then adds or updates the activity coefficient database 424.

[0035] Step S5: Generate an inventory result. In step S5, the intelligent inventory system includes a computing module. After identifying the activity data in step S41 and obtaining the emission coefficient data corresponding to the activity data, and then obtaining the global warming potential, the computing module calculates the emissions using the obtained parameters and generates the inventory result. The inventory result includes at least a carbon dioxide emissions equivalent result.

[0036] Step S6: Summarize the inventory results. In step S6, the computing module of the intelligent inventory system summarizes the inventory results and forms an inventory result table. The inventory result table includes detailed information of each inventory result and emission data.

[0037] Step S7: Issue a verification request. In step S7, the smart inventory system includes a transmission module, which issues the verification request to a third-party verification agency. Preferably, the transmission module electronically transmits the verification request using an encrypted transmission protocol. The third-party verification agency is an agency with carbon verification capabilities, which can be a company's inventory organization, an external legal person's inventory organization, or a government agency's inventory organization. Preferably, the third-party verification agency is an ISO 14065 & ISO / IEC 17029 confirmation and verification agency, a carbon emission verification agency approved by the China National Accreditation Administration (CNCA), accredited by the China National Accreditation Service for Conformity Assessment (CNAS), approved by the China National Accreditation Administration, or registered with the National Development and Reform Commission for certified voluntary emission reductions (CCER), and transmits the compiled inventory results to the third-party verification agency.

[0038] Step S8: Authorize the verification request and lock the query results. In step S8, when the third-party verification agency authorizes the verification request, the query result table is locked so that the query results cannot be modified. Please refer to Figure 3. In a preferred embodiment, step S8 can be further divided into the following steps:

[0039] Step S81: Receive Verification Request In step S81, the verification module of the smart inspection system confirms that the third-party verification agency has received the verification request.

[0040] Step S82: Verify login identity. In step S82, the verification module of the smart inspection system verifies the login identity of the third-party verification agency, and determines that the verification has relevant qualifications and that the identity conditions meet the specifications.

[0041] Step S83: Item Verification: In step S83, the verification module prompts the verifier to verify each item of the query result table.

[0042] Step S84: Record Verification Results. In step S83, the verifier records the verification results in the intelligent interrogation system. If the verification result form passes the verification, the verification request is authorized, and the verification module locks the interrogation result form, preventing the interrogation results from being modified. This ensures that the interrogation results and related documents cannot be tampered with after verification.

[0043] Step S9: Immediately upload the inspection results. In step S9, the first publishing module of the intelligent inspection system will update the inspection results that have been verified online immediately, so that relevant personnel can immediately view the inspection results.

[0044] It can be seen from the above description that the present invention achieves the following effects:

[0045] 1. The intelligent inventory method and system of the present invention can automatically acquire and locate the data required for inventory through a learning module, and can be continuously updated and optimized, significantly reducing the cost and time required for users to organize and locate emission coefficients and activity data, and speeding up inventory.

[0046] 2. The intelligent inventory method and system of the present invention can aggregate and standardize various activity data, significantly improving inventory efficiency and saving costs. It can also avoid unnecessary human errors and improve the accuracy of inventory.

[0047] 3. The intelligent interrogation method and system of the present invention aggregates and assigns interrogation results generated by various activity data, which can be directly transmitted to a third-party organization for verification and audit. The identity of the verifier can also be confirmed, ensuring the authenticity of the interrogation results and preventing the possibility of data loss or tampering, thereby ensuring the authenticity and accountability of the interrogation data.

[0048] It should be noted that, based on the explanations and elaborations of the above description, those skilled in the art may also make changes and modifications to the above embodiments. Therefore, the present disclosure is not limited to the specific embodiments disclosed and described above, and equivalent modifications and variations of the present disclosure are also within the scope of protection of the claims of the present disclosure. Furthermore, although certain specific terms are used in this description, these terms are for convenience only and do not constitute any limitation on the invention.

Claims

1. A smart interrogation system, characterized in that: The system comprises an identification module, an activity coefficient database, a learning module, a calculation module, a transmission module, and a verification module, wherein: The identification module identifies an activity data, extracts the type and quantity of the activity data, searches for an emission coefficient corresponding to the activity data through the activity coefficient database, and transmits the result to the calculation module; The learning module updates the activity coefficient database through Internet crawlers and machine learning; The calculation module generates an inventory result using the activity data, the emission factor, and a global warming potential; and The transmission module issues a verification request to transmit the interrogation result generated by the calculation module to the verification module for verification by a third-party verification agency. The verification module locks the interrogation result after the third-party verification agency completes the verification.

2. The intelligent interrogation system according to claim 1, characterized in that: The intelligent interrogation system further includes a reading module, which transmits the input activity data to the recognition module, wherein: The reading module includes a digital conversion unit. When the activity data is paper or analog data, the digital conversion unit converts the activity data into digital images or text.

3. The intelligent interrogation system according to claim 1, characterized in that: The smart inspection system further includes a publishing module, which will immediately publish the inspection results verified by the third-party verification agency online.

4. The intelligent interrogation system according to claim 2, characterized in that: The learning module includes a crawler unit, a machine learning unit, and an optical character recognition unit. The crawler unit searches for and records data similar to the activity data on the Internet, and uses the optical character recognition unit and the machine learning unit to recognize text in the data image and analyze and compare the image or text of the data to obtain the emission coefficient or the global warming potential and record it in the activity coefficient database.

5. The intelligent interrogation system according to claim 2, characterized in that: The activity coefficient database includes the emission coefficient and the global warming potential corresponding to the activity data. The emission coefficient can be an international emission coefficient, a national emission coefficient, a regional emission coefficient, a coefficient provided by a manufacturer, an empirical coefficient based on the same process or equipment, a measured coefficient, a mass-energy coefficient, or a mass balance coefficient.

6. The intelligent interrogation system according to claim 4, characterized in that: The verification module verifies the login identity of a verifier of the third-party verification agency.

7. The intelligent interrogation system according to claim 1, characterized in that: The learning module regularly updates the identification method of the activity data and updates the emission coefficient and the global warming potential of the activity coefficient database.

8. The intelligent interrogation system according to claim 1, characterized in that: The transmission module electronically transmits the verification request using an encrypted transmission protocol.

9. A smart interrogation method, characterized in that: The method comprises the intelligent interrogation system according to any one of claims 1 to 7, and includes the following steps: Read the input data of this activity; The identification module identifies and assigns the activity data, wherein when the activity data cannot be identified or assigned, the learning module learns to retrieve and search the Internet to obtain the identified or assigned data; The computing module generates and summarizes the inspection result; The transmission module sends the verification request to the verification module of the third-party verification agency. After confirmation by the third-party verification agency, the verification module authorizes the verification request and locks the verification result; and The results of the investigation will be posted online immediately.

10. The intelligent interrogation method according to claim 9, wherein: The step of the transmission module sending the verification request to the verification module of the third-party verification agency, and the verification module authorizing the verification request and locking the verification result after confirmation by the third-party verification agency, further includes the following steps: The verification module confirms that the third-party verification agency has received the verification request; The verification module verifies the login identity of a verifier of the third-party verification agency; The verification module prompts the verifier to verify the interrogation result; as well as The verification module records a verification result of the verification. If the verification result indicates that the verification result passes the verification, the verification module locks the verification result so that it cannot be modified.

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