Legal compliance management system and operation method thereof

Abstract

The invention discloses a legal compliance management system and an operation method thereof. Some embodiments of the present disclosure relate to a server device comprising a processing module, a classification module, an analysis module, and a communication module. The processing module is configured to receive first data in real time from a peripheral device during a first period. The classification module is configured to classify the first data according to classification information. The analysis module is configured to analyze the classified first data and generate an analysis result, and generate an alert message in response to the analysis result exceeding a threshold. The communication module is configured to feed back the alert message to the peripheral device in real time during the first period.

Description

Technical Field

[0001] This disclosure relates to a legal compliance management system and its operating method, and more specifically, to an automated analysis environment security or information security management system and its operating method. Background Technology

[0002] Traditional workplace management (such as factories, laboratories, companies, department stores, restaurants, and banks) often relies on manual methods for areas like environmental safety, information security, and occupational safety, lacking systematic and automated management. Consequently, human negligence frequently leads to workplaces failing to meet regulations, or a failure to respond promptly to problems, resulting in risks such as fines, work stoppages, and even injuries or fatalities. Summary of the Invention

[0003] Some embodiments of this disclosure relate to a server apparatus, including a processing interface, a classification module, an analysis module, and a communication module. The processing interface is configured to receive first data from a peripheral device in real time during a first period. The classification module is configured to classify the first data according to different types. The analysis module is configured to analyze the classified first data and generate analysis results, and to generate an alert message in response to the analysis results exceeding a threshold. The communication module is configured to feed back the alert message to the peripheral device in real time during the first period.

[0004] Some embodiments of this disclosure relate to a method for operating a server device, which involves receiving first data from a peripheral device in real time during a first period via a processing interface; classifying the first data according to different types via a classification module; analyzing the classified first data and generating analysis results via an analysis module, and generating an alert message in response to the analysis results exceeding a threshold; and feeding back the alert message to the peripheral device in real time during the first period via a communication module. Attached Figure Description

[0005] Various aspects of the embodiments are discussed below with reference to the accompanying drawings, which are not drawn to scale and are merely illustrative and do not limit the scope of this disclosure. The element symbols used in the drawings and description are merely illustrative and do not limit the scope of this disclosure. Identical or similar elements are represented by the same element symbols, wherein:

[0006] Figure 1 The diagram shown is a schematic representation of a management system according to some embodiments of the present disclosure.

[0007] Figure 2 The diagram shown is an operation flowchart of the management system according to some embodiments of the present disclosure.

[0008] Figure 3 The diagram shown is an operation flowchart of the management system according to some embodiments of the present disclosure.

[0009] Figure 4 The diagram shown is an operation flowchart of the management system according to some embodiments of the present disclosure.

[0010] Figure 5 The diagram shown is an operation flowchart of the management system according to some embodiments of the present disclosure.

[0011] Figure 6 The diagram shown is an operation flowchart of the management system according to some embodiments of the present disclosure.

[0012] Figure 7 The diagram shown is a schematic representation of a management system according to some embodiments of the present disclosure.

[0013] Figure 8 The diagram shown is a schematic representation of a management system according to some embodiments of the present disclosure.

[0014] Figure 9 The diagram shown is an operation flowchart of the management system according to some embodiments of the present disclosure. Detailed Implementation

[0015] The wording and terminology used herein are exemplary only and are not intended to limit this disclosure. The singular or plural forms are exemplary only and are not intended to limit the system or method, its elements, components, or steps of this disclosure. The terms “comprising,” “including,” “having,” “containing,” “involving,” and other similar terms used herein cover the items, equivalents, and additional items listed thereafter. “or” and other similar terms may be construed as referring to any of the described items. Furthermore, “a” or “an” is used herein to describe the units, elements, and components described herein. This is done solely for convenience and to provide a general meaning regarding the scope of this disclosure. Therefore, unless it is clearly indicated otherwise, such description should be understood to include one, at least one, and the singular includes the plural.

[0016] Figure 1 The diagram shown is a schematic representation of a management system 1 according to some embodiments of the present disclosure. According to some embodiments of the present disclosure, the management system 1 may be a factory management system, a company management system, a bank management system, a laboratory management system, a hotel management system, an environmental safety management system, or a management system applied to other fields. The management system 1 includes a server device 10 and one or more peripheral devices 11, 12.

[0017] According to some embodiments of this disclosure, server device 10 may be a backend device, located at the supplier of management system 1, or provided through a third-party cloud platform. In some embodiments, server device 10 may be configured to calculate, update, and store data, and transmit data with peripheral devices 11 and 12 via network 13 (wired or wireless network). In some embodiments, peripheral devices 11 and 12 may be used in factories, laboratories, banks, companies, department stores, restaurants, or any other locations requiring environmental or information security management. Peripheral devices 11 and 12 are configured to collect and record relevant information, and process or store the information. Peripheral devices 11 and 12 may also be configured to transmit the collected or processed information to server device 10 via network 13 for storage, management, and / or processing by server device 10. Peripheral devices 11 and 12 may also obtain the required data from server device 10 via network 13.

[0018] According to some embodiments of this disclosure, the peripheral device 11 may include a processing module 111, a storage module 112, a retrieval module 113, an input module 114, and a communication module 115. In some embodiments, the peripheral device 11 may be or may include a mobile device, such as a mobile phone or a personal digital assistant (PDA). In some embodiments, the peripheral device 11 may be or may include a computer, such as a desktop computer, a server, a laptop computer, or a tablet computer.

[0019] In some embodiments, the processing module 111, storage module 112, retrieval module 113, input module 114, and communication module 115 may be integrated so that all their functions are implemented by a single electronic device. In other embodiments, the processing module 111, storage module 112, retrieval module 113, input module 114, and communication module 115 may be distributed among several devices, with multiple devices implementing all their functions.

[0020] Processing module 111 may be electrically connected to storage module 112, retrieval module 113, input module 114, and communication module 115, and configured to process (e.g., calculate) the received data. In some embodiments, processing module 111 may include a processor or processing device. For example, processing module 111 may include a central processing unit (CPU), a microcontroller unit (MCU), a graphics processing unit (GPU), etc.

[0021] Storage module 112 is electrically connected to processing module 111, retrieval module 113, input module 114, and communication module 115, and is configured to store data. In some embodiments, storage module 111 may include a storage device. For example, storage module 111 may include a hard disk, floppy disk, optical disk, USB flash drive, etc. In some embodiments, storage module 112 may also be replaced by cloud storage space.

[0022] The retrieval module 113 can be electrically connected to the processing module 111, the storage module 112, the input module 114, and the communication module 115, and can be configured to retrieve environmental information. In some embodiments, the retrieval module 113 can be configured to retrieve sound information (such as audio, decibel value, etc.), air information (such as particle content, concentration of specific substances, etc.), water content information (such as concentration of specific substances, etc.), vibration information (such as frequency, amplitude, etc.), light information (such as frequency, intensity, etc.), soil information (such as concentration of specific substances, etc.), electromagnetic wave signals (such as frequency, intensity, etc.), electrical signals (such as voltage, current, resistance, etc.), other aspects, or combinations of the above aspects. In some embodiments, the retrieval module 113 may include sensors, microelectromechanical systems (MEMS), etc.

[0023] The input module 114 can be electrically connected to the processing module 111, the storage module 112, the retrieval module 113, and the communication module 115, and is configured to allow users to input data. In some embodiments, the input module 113 may include input devices such as a keyboard, mouse, stylus, touch screen, and microphone, so that users can input data in the form of text, images, or sound.

[0024] The communication module 115 is electrically connected to the processing module 111, storage module 112, retrieval module 113, and input module 114, and is configured to transmit or receive data. In some embodiments, the communication module 115 includes a wired communication device, such as a wire or optical fiber. In some embodiments, the communication module 115 includes a wireless communication device, such as a Wi-Fi module, a mobile network communication module, a Bluetooth module, a near-field communication module, etc.

[0025] In some embodiments, the peripheral device 11 may further include a power supply module (such as a battery), a display module (such as a monitor or camera), an output module (such as a speaker, printer, or warning light), etc.

[0026] According to some embodiments of this disclosure, the peripheral device 12 may include a processing module 121, a storage module 122, an input module 123, and a communication module 124. The processing module 121, storage module 122, input module 123, and communication module 124 have similar functions to the processing module 111, storage module 112, input module 114, and communication module 115 included in the peripheral device 11, respectively, and will not be described in detail here. Although Figure 1 Only two peripheral devices 11 and 12 are disclosed. It should be understood that the management system 1 may include N peripheral devices, where N is an integer greater than 1, depending on different needs. Furthermore, according to some embodiments of this disclosure, the types of modules included in the peripheral devices can be increased or decreased as needed, and are not necessarily... Figure 1 The limitations imposed by what is contained.

[0027] According to some embodiments of this disclosure, the server device 10 may include a processing module 101, a storage module 102, an input module 103, and a communication module 104. The processing module 101, storage module 102, input module 103, and communication module 104 have similar functions to the processing module 111, storage module 112, input module 114, and communication module 115 included in the peripheral device 11, respectively, and will not be described in detail here. Although Figure 1 Only one server device 10 is disclosed. It should be understood that the management system 1 may contain M server devices, where M is an integer greater than 1, depending on different needs.

[0028] According to some embodiments of this disclosure, peripheral devices 11 and 12 can transmit retrieved information or input data to server device 10 via network 13, where it can be processed or stored. Server device 10 can also transmit processed or stored data to peripheral devices 11 and 12 via network 13 for their use (e.g., display, management, storage). According to some embodiments of this disclosure, peripheral devices 11 and 12 can also store retrieved information or input data in their storage modules 112 and 122, or process it through their processing modules 111 and 121.

[0029] Figure 2 The diagram shown is a flowchart illustrating the operation of a management system according to certain embodiments of this disclosure. In some embodiments, Figure 2 The disclosed steps or some of the steps may be provided by, for example Figure 1 The system can be run by the management system 1 or other suitable management system.

[0030] Step S21 establishes a database based on the first parameter. In some embodiments, the first parameter may include relevant specification information. The first parameter can be obtained through... Figure 1A server device 10 is established. For example, the server device 10 may be configured to connect to a relevant website (such as an official website) to retrieve relevant information (such as regulations) from the website, and the relevant information may be stored in a storage module 102 via a processing module 101. In some embodiments, the relevant information may be input via an input module 103 of the server device 10. In some embodiments, the relevant information may be stored in the storage module 102 or a cloud storage device. In some embodiments, the relevant information may be transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12.

[0031] Step S22 involves generating inspection standards based on the first parameter. In some embodiments, the inspection items and standards to be met can be generated according to relevant regulations. For example, based on regulations concerning water pollution prevention and control measures and the management of testing and reporting, the required testing items for wastewater discharged by the factory are generated, along with relevant standards (e.g., the concentration of chemical oxygen demand (COD) should be less than 100 ppm). Similarly, based on regulations concerning air pollution prevention and control measures and the management of permits for the establishment and operation of stationary pollution sources and fuel use permits, the required testing items for exhaust gases emitted by the factory are generated, along with relevant standards (e.g., the concentration of nitrogen oxides (NOx) should be less than 100 ppm). Furthermore, based on regulations related to workers' rights, items that employees and employers must comply with are generated (e.g., management of weekly working hours for workers and training hours for workers in specific tasks). In some embodiments, the inspection standards can be presented in text, tables, graphics, sound, or other formats.

[0032] In some embodiments, the inspection criteria can be obtained through... Figure 1 The processing module 101 of the server device 10 generates the test standards. For example, the processing module 101 can retrieve keywords contained in the specifications (such as working hours, wastewater concentration, exhaust gas concentration, etc. specified in the specifications) and automatically generate test standards. In some embodiments, the test standards can be input through the input module 103 of the server device 10. In some embodiments, the test standards are stored in the storage module 102 or a cloud storage device. In some embodiments, the test standards are transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12.

[0033] Step S23 updates the database based on the second parameter. In some embodiments, the second parameter is an update of the first parameter (such as modification, deletion, or addition of content). For example, if relevant specification information changes, it can be updated through... Figure 1The server device 10 updates the database established in step S21. In some embodiments, the server device 10 may be configured to connect to a relevant website to retrieve update information from the website. In some embodiments, update information can be input through the input module 103 of the server device 10. In some embodiments, the update information is stored in the storage module 102 or a cloud storage device. In some embodiments, the update information is transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. In some embodiments, the server device 10 may also send a notification message to the peripheral devices 11 and 12, informing them that the information in the original database has been updated.

[0034] Step S24 involves updating the inspection criteria based on the second parameter. In some embodiments, the inspection items and standards that comply with the new specification can be updated according to the modifications. In some embodiments, the updated inspection criteria can be... Figure 1 The server device 10's processing module 101 generates the updated inspection criteria. For example, the processing module 101 can compare the original and modified regulations, retrieve their differences, and automatically generate updated inspection criteria based on the differences. In some embodiments, the inspection criteria can be updated via the server device 10's input module 103. In some embodiments, the updated inspection criteria are stored in the storage module 102 or a cloud storage device. In some embodiments, the updated inspection criteria are transmitted to peripheral devices 11 and 12 and stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. In some embodiments, the server device 10 can also send a notification message to the peripheral devices 11 and 12, informing them that the information in the original database has been updated.

[0035] Currently, factories (or laboratories, companies, department stores, restaurants, etc.) must consult professionals (such as in-house legal counsel or external lawyers) to check the relevant regulations they should follow. This is not only time-consuming and labor-intensive, but also makes it impossible to obtain real-time updates to the regulations. Therefore, non-compliance is often only discovered during audits, leading to liability for violations and potentially resulting in administrative fines, shutdowns, business closures, or even criminal charges. According to this disclosure... Figure 2 The steps outlined in the document allow factories to know in real time the regulations and inspection standards they must comply with, which can greatly improve management efficiency and avoid risks arising from violations.

[0036] Figure 3 The diagram shown is a flowchart illustrating the operation of a management system according to certain embodiments of this disclosure. In some embodiments, Figure 3 The disclosed steps can be as follows Figure 1 The management system 1 or other suitable management system is executed. In some embodiments, Figure 3 The steps described can be performed by factory personnel, external personnel (such as auditors), or jointly.

[0037] Step S31 involves providing the item to be detected. In some embodiments, the item to be detected may be provided by, for example... Figure 2 Steps S22 or S24 are generated. In some embodiments, the item to be detected may also be generated in other ways. In some embodiments, the item to be detected may be generated by the processing modules 111 and 121 of peripheral devices 11 and 12. In some embodiments, the item to be detected may be stored in the storage modules 112 and 122 of peripheral devices 11 and 12. In some embodiments, the item to be detected may be generated by the processing module 101 of server device 10 and transmitted to peripheral devices 11 and 12. The item to be detected may be displayed (e.g., via a display module) or output (e.g., via a printer) by peripheral devices 11 and 12.

[0038] Step S32 involves performing the test on the item to be tested. In some embodiments, the user can test the item through the retrieval module 113 of the peripheral device 11. For example, the retrieval module 113 can measure the concentration of specific substances in wastewater or exhaust gas, the decibel level of machine operating sound, etc. In some embodiments, the user can also use other instruments or other methods to test the item. For example, the user can check the length of employees' working hours.

[0039] Step S33 involves recording the detection results. In some embodiments, the peripheral device 11 can automatically store the results detected in step S32 to the storage module 112, or it can transmit the detection results to the server device 10 via the network 13. In some embodiments, the user can also input the detection results into the peripheral device 11 via the input module 114.

[0040] Step S34 is to evaluate whether the test results comply with relevant regulations. In some embodiments, the peripheral device 11 can automatically compare the test results with standard values ​​(such as those obtained from...). Figure 2 The test results are compared with the inspection standards generated in step S22 or S24. If they meet the relevant regulations, the test item is displayed as completed (step S35); otherwise, a warning is generated for the failed test items, and a new test item is generated (step S36). Furthermore, for the new test items, the test can be repeated using steps S32-S34 until all test items meet the relevant regulations. In some embodiments, the warning generated in step S36 can be displayed on the management system 1 and / or sent to relevant personnel. In some embodiments, if the test results do not meet the relevant regulations, the server device 10 or peripheral device 11 can be configured to generate remedial measures for the non-compliant test content, allowing the user to re-perform the test according to the remedial measures to ensure the test results meet the relevant regulations.

[0041] Currently, factory (or laboratory, company, department store, restaurant, etc.) personnel often don't know which items to test if they want to check whether their behavior complies with relevant regulations. Hiring professionals (such as external lawyers or technicians) for testing is time-consuming and laborious, and results cannot be obtained in real time. Therefore, non-compliance is usually only discovered when auditing units conduct audits of factories, leading to administrative fines and even the risk of shutdown, closure, or criminal liability. According to this disclosure... Figure 3 The system can automatically generate the items to be tested and provide remedial measures for test contents that do not comply with relevant regulations, so that factory employees can conduct regular and real-time testing and management. This can greatly improve management efficiency and avoid risks caused by illegal activities.

[0042] Figure 4 The diagram shown is a flowchart illustrating the operation of a management system according to certain embodiments of this disclosure. In some embodiments, Figure 4 The disclosed steps can be as follows Figure 1 The system can be run by the management system 1 or other suitable management system.

[0043] Step S41 involves inputting certificate data. For example, factory certificates, technician certificates, or certificates of external personnel (suppliers, maintenance personnel, etc.) are input into the system. In some embodiments, the system can connect to the relevant certificate issuance website via server device 10 or peripheral devices 11, 12 to automatically retrieve data and store it in storage modules 102, 112, 122. In some embodiments, users can input certificate information via input modules 103, 114, 123.

[0044] Step S42 involves checking whether the certificate information complies with relevant regulations. For example, it checks whether the type of certificate held by the factory or employees is sufficient to perform current business operations. It also checks whether the certificate has expired or is about to expire. If the certificate information complies with relevant laws and regulations, the certificate is displayed as valid (step S43); otherwise, a warning is generated for non-compliant certificates (step S44), and relevant personnel are notified. In some embodiments, the server device 10 or peripheral device 11 can automatically compare the items and validity period contained in the certificate with the regulations currently in place to determine whether the certificate is valid.

[0045] In step S45, if the certificate information does not comply with relevant laws and regulations, in addition to issuing a warning, remedial measures are also taken based on the relevant regulations. For example, conditions for renewing the certificate are generated according to regulations (such as payment, training hours, etc.). After implementing the relevant remedial measures, step S42 is repeated until all certificates comply with the relevant regulations.

[0046] Currently, most licenses and permits for factories (or laboratories, companies, department stores, restaurants, etc.) are managed manually. Besides the risk of omissions, these licenses may also become invalid due to expiration or changes in regulations. According to this disclosure... Figure 4 The system can automatically manage licenses and provide remedies for invalid licenses, which can greatly improve management efficiency and avoid risks caused by illegal activities.

[0047] Figure 5 The diagram shown is a flowchart illustrating the operation of a management system according to certain embodiments of this disclosure. In some embodiments, Figure 5 The disclosed steps can be as follows Figure 1 The system can be run by the management system 1 or other suitable management system.

[0048] Step S51 involves detecting risk events. In some embodiments, risk events may include fire, power outage, mechanical failure, gas leakage, data anomaly, personal injury, etc. These risk events can be detected by peripheral devices 11, 12, or other devices.

[0049] When a risk event is detected, peripheral devices 11 and 12 are configured to generate an alarm signal and notify relevant personnel (step S52), and provide elimination steps according to the type of risk (step S53). In some embodiments, the alarm signal can be transmitted via light, sound, or message.

[0050] Peripheral devices 11 and 12 then detect whether the risk has been eliminated (step S54). If it has been eliminated, the warning signal is turned off and relevant personnel are notified (step S55); otherwise, the warning signal is continuously sent and relevant personnel are notified (step S56), and step S54 is repeated until it is confirmed that the risk has been eliminated.

[0051] Currently, although factories (or laboratories, companies, department stores, banks, restaurants, etc.) have established standard operating procedures (SOPs) for different risks, most employees are not necessarily familiar with these SOPs. When risks or accidents occur, they often cannot find the corresponding handling methods in a timely manner, leading to significant losses or casualties in the factory. According to this disclosure... Figure 5 The steps described herein will automatically generate corresponding standard operating procedures (SOPs) based on the type of risk or incident when the system detects it, and notify relevant personnel. Therefore, any employee can eliminate risks or incidents by following these SOPs, thus minimizing the harm caused by such risks or incidents.

[0052] Figure 6 The diagram shown is a flowchart illustrating the operation of a management system according to certain embodiments of this disclosure. In some embodiments, Figure 6 The disclosed steps can be as follows Figure 1The system can be run by the management system 1 or other suitable management system.

[0053] Step S61 involves establishing external personnel data. For example, data from external personnel such as suppliers and maintenance workers is input into the system. In some embodiments, the system can be connected to the relevant supplier or maintenance provider's system via server device 10 or peripheral devices 11, 12 to automatically retrieve data and store it in storage modules 102, 112, 122. In some embodiments, the data can be input by the user through input modules 103, 114, 123.

[0054] When external personnel wish to enter the factory, peripheral devices 11 and 12 are configured to generate a standard entry procedure based on the external personnel's attributes (step S62). For example, based on the external personnel's work items, relevant entry permissions and corresponding steps are generated. In some embodiments, the generated standard entry procedure may be displayed (e.g., via a display module) or output (e.g., via a printer) by the peripheral devices.

[0055] Step S63 involves recording all actions of external personnel and checking whether they comply with standard procedures. In some embodiments, the behavior of external personnel can be recorded using image retrieval devices (such as cameras or video cameras) and sensing devices, and the processing modules 111 and 121 of peripheral devices 11 and 12 can determine whether they conform to the entry standard procedures (such as the path taken, the steps performed, etc.). In some embodiments, the behavior of external personnel can be recorded by personnel inside the plant, and the processing modules 111 and 121 of peripheral devices 11 and 12 can determine whether they conform to the entry standard procedures.

[0056] If it is determined that the external personnel are following the standard procedures, then the relevant records are stored (step S62). For example, the recorded data is stored in the storage modules 112 and 122 of the peripheral devices 11 and 12. If it is determined that the external personnel are not following the standard procedures, then an alarm signal is generated and the relevant personnel are notified (step S65), and step S63 is repeated until it is confirmed that the external personnel have followed the standard procedures.

[0057] Currently, factories (or laboratories, companies, department stores, banks, restaurants, etc.) typically manage the entry of external personnel manually, which is not only time-consuming and labor-intensive, but also difficult to record and document. According to this disclosure... Figure 6 The system can automatically generate standard entry procedures for external personnel with different purposes, and track and record them automatically, which can greatly improve management efficiency and reduce labor costs.

[0058] In some embodiments, Figure 2-6The methods or steps described herein can be executed by a joint resource-sharing program having multiple program instructions (code). A joint resource-sharing program is a computer program product that can be stored in a non-transitory computer-readable storage medium. When the program instructions are loaded into an electronic device ( Figure 1 When the server device 10, peripheral devices 11, 12, etc. are installed, the computer program executes as follows: Figure 2-6 The methods or steps described herein.

[0059] In some embodiments, if a factory (or laboratory, company, department store, bank, restaurant, or any other location requiring environmental safety management) has multiple factory sites (or branches, stores, etc.), then each factory site may have one or more peripheral devices 11, 12 for such purposes. Figure 2-6 The steps described herein can integrate data from each factory site, and the integrated data can be sent to all factory sites, a designated factory site (such as the corporate headquarters), and / or server device 10. The integrated data allows viewing individual information for all factory sites (such as non-compliant items at each factory site) or aggregated information for all factory sites (such as the number of invalid licenses for all factory sites).

[0060] In some embodiments, if a factory (or laboratory, company, department store, corporation, restaurant, or any other location requiring environmental safety management) has multiple factory sites (or branches, sub-branches, etc.), one factory site can use peripheral devices 11 and 12 as its internal server, connecting to server device 10 via network 13 to transmit data. Other factory sites use similar electronic devices (such as computers, tablets, mobile phones, etc.) to connect to the internal server via an internal network to transmit data. Information from other factory sites is integrated through the internal server and transmitted to all factory sites, a designated factory site (such as the corporate headquarters), and / or server device 10. The integrated data allows viewing individual information for all factory sites (such as non-compliant items at each site) or the aggregated information for all factory sites (such as the number of invalid licenses for all sites). Because information from other factory sites is transmitted from the internal network to the internal server and not directly to server device 10 via network 13, it offers high security.

[0061] Figure 7 The diagram shown is a schematic representation of a management system 2 according to a partial embodiment of this disclosure. Similar to... Figure 1The management system 1 and management system 2 shown can be factory management systems, company management systems, bank management systems, laboratory management systems, hotel management systems, environmental safety management systems, medical management systems, vehicle driving management systems, or management systems applied to other fields. Management system 2 includes server device 20 and one or more peripheral devices 21 and 22. Figure 1 The steps or operations performed by the management system 1 shown can also be performed by the management system 2 in a similar manner.

[0062] According to some embodiments of this disclosure, server device 20 is a backend device, which may be located at the supplier of management system 2 or provided through a third-party cloud platform. In some embodiments, server device 20 may be configured to calculate, update, and store data, and connect to peripheral devices 21 and 22 via network 23 (wired or wireless network) to transmit data, etc.

[0063] Peripheral devices 21 and 22 are Internet of Things (IoT) devices. Peripheral devices 21 and 22 can be applied to the example environment of the aforementioned management system 2, or any other environment requiring environmental or information security management. Peripheral devices 21 and 22 can operate and acquire client data (e.g., raw data) via a graphical user interface. The graphical user interface is displayed, for example, on the operation panel, dashboard, metering panel, or other similar display of peripheral devices 21 and 22.

[0064] Peripheral devices 21 and 22 are configured to sense environmental data in their surrounding area, record relevant information, and process or store the information. Peripheral devices 21 and 22 are configured to transmit the sensed or processed information to server device 20 in real time via network 23 for storage, classification, and / or analysis. Server device 20 feeds back the analyzed data to peripheral devices 21 and 22 in real time via network 23.

[0065] According to some embodiments of this disclosure, the peripheral device 21 includes a sensing module 211, a storage module 212, a processing module 213, and a communication module 214. The sensing module 211, storage module 212, processing module 213, and communication module 214 are electrically coupled to each other. The peripheral device 21 collects or senses client data from the surrounding environment, user input, or user usage patterns, and exchanges the data with the server device 20 through the application and network 23 therein. In some embodiments, the peripheral device 21 is a factory management device. The peripheral device 21 may be a vehicle. The peripheral device 21 includes, for example, a surveillance camera, a surveillance television, a mobile device, or sports equipment. The peripheral device 21 may also include, for example, a mobile phone or a PDA. The peripheral device 21 may be or may include a computer, such as a desktop computer, a server, a laptop computer, or a tablet computer.

[0066] In some embodiments, the sensing module 211, storage module 212, processing module 213, and communication module 214 are integrated so that all their functions are implemented by a single electronic device. In other embodiments, the sensing module 211, storage module 212, processing module 213, and communication module 214 are distributed across several devices, with all their functions implemented by multiple devices.

[0067] The sensing module 211 is configured to sense and retrieve environmental information about the peripheral device 21 itself and its surroundings. The environmental information may be or may include raw data from the client. The raw data may include text, numerical values, images, video, or audio.

[0068] In some embodiments, environmental information includes resource usage information (e.g., the power consumption, water consumption, or oil consumption of the peripheral device 21 itself, or the power consumption, water consumption, or oil consumption of other devices near the peripheral device 21), temperature information (e.g., the operating temperature of the peripheral device 21 itself, or the operating temperature of other devices near the peripheral device 21), liquid element content information (e.g., the content or concentration of a specific element in the working tank of the peripheral device 21, or the content or concentration of a specific element in the liquid in the environment near the peripheral device 21), personnel identification information (e.g., user facial recognition, voice recognition), and sound information (the operating sound of the peripheral device 21 itself). The sensing module 211 can include the following information: frequency and decibel value (or the operating audio frequency and decibel value of other devices near peripheral device 21); air information (such as the content of particulate matter in the air of the surrounding environment, the concentration of specific substances, etc.); vibration information (such as the operating frequency and amplitude of peripheral device 21 itself, or the operating frequency and amplitude of other devices near peripheral device 21); light information (such as frequency and intensity, etc.); soil information (such as the concentration of specific substances, etc.); electromagnetic wave signals (such as the operating frequency and intensity of peripheral device 21 itself, or the operating frequency and intensity of other devices near peripheral device 21, etc.); electrical signals (such as voltage, current, resistance, etc.); and other aspects or combinations thereof. In some embodiments, the sensing module 211 includes sensors, MEMS, smart hooks, etc.

[0069] In some embodiments, the sensing module 211 is configured to retrieve data manually entered by the user. The sensing module 211 may also be configured to retrieve desired data via network 23.

[0070] Storage module 212 may be configured to store data retrieved by sensing module 211. In some embodiments, storage module 212 may include a storage device or memory device. For example, storage module 212 may include a hard disk, floppy disk, optical disk, USB flash drive, etc. In some embodiments, storage module 212 may also be replaced by cloud storage space.

[0071] Processing module 213 may be configured to process (e.g., calculate) the data retrieved by sensing module 211. In some embodiments, processing module 213 may include a processor or processing device. For example, processing module 213 may include a central processing unit (CPU), a microcontroller (MCU), a graphics processing unit (GPU), etc.

[0072] The communication module 214 can be configured to transmit or receive data. In some embodiments, the communication module 214 includes a wired communication device, such as a wire or optical fiber. In some embodiments, the communication module 214 includes a wireless communication device, such as a Wi-Fi module, a mobile network communication module, a Bluetooth module, a near-field communication module, etc.

[0073] In some embodiments, the peripheral device 21 may further include a power supply module (such as a battery), a display module (such as a monitor or camera), an input module (such as a keyboard, mouse, stylus, touch screen, microphone, etc., for users to input data in the form of text, images, video or audio), and an output module (such as a speaker, printer, or warning light).

[0074] According to some embodiments of this disclosure, the peripheral device 22 includes a sensing module 221, a storage module 222, a processing module 223, and a communication module 224. Similar to the peripheral device 21, the peripheral device 22 has similar functions, which will not be described in detail here.

[0075] According to some embodiments of this disclosure, the server device 20 includes a processing module 201, a storage module 202, a classification module 203, an analysis module 204, and a communication module 205, wherein the processing module 201, storage module 202, classification module 203, analysis module 204, and communication module 205 are electrically coupled to each other. Although Figure 7 Only one server device 20 is disclosed, but it should be understood that the management system 2 may contain M server devices, where M is an integer greater than 1, depending on different needs.

[0076] Processing module 201 includes input interfaces, output interfaces, a processor, and processing devices. For example, processing module 223 includes a central processing unit (CPU), a microcontroller (MCU), a graphics processing unit (GPU), etc.

[0077] Storage module 202 includes a storage device or memory device. For example, storage module 222 includes a hard disk, floppy disk, optical disk, USB flash drive, etc. In some embodiments, storage module 222 may also be replaced by cloud storage space.

[0078] The classification module 203 includes memory, a processor, a processing device, and a classifier model, which can be configured to perform general and fine-grained classification. The analysis module 204 includes memory, a processor, a processing device, and an artificial intelligence (AI) analysis model, which includes a neural network-based image processing engine, a speech processing engine, a feature extraction engine, and a natural language processing (NLP) engine. The image processing engine of the AI ​​analysis model in analysis module 204 may include an image processing engine and a video processing engine. The communication module 205 includes a wired or wireless communication device.

[0079] According to some embodiments of this disclosure, peripheral devices 21 and 22 transmit the retrieved information or input data to server device 20 in real time via network 23, where it is stored and analyzed. Server device 20 may also feed back the processed and analyzed data to peripheral devices 21 and 22 in real time via network 23 for their use (e.g., display, management, storage, etc.).

[0080] For example, the processing module 201 of the server device 20 is configured to receive first data from the peripheral device 21 in real time during a first period and store the first data in the storage module 202. The storage module 202 may be configured to store the first data. The storage module 202 is configured to store classification information for the detected content. The storage module 202 is configured to store classification information associated with the environmental information of the peripheral device 21. In some embodiments, the classification information may be a general classification, which may include classification types such as text, numbers, images, video, audio, etc. The classification information may be a fine classification, which may include combinations of aspects corresponding to the environmental information of the peripheral device 21, which will not be elaborated here. The fine classification may include detection criteria (e.g., conforming and non-conforming criteria), error messages, and limiting conditions corresponding to the environmental information of the peripheral device 21. Limiting conditions may include time conditions, location conditions, event conditions, number of people conditions, power conditions, item composition conditions, air composition conditions, or liquid composition conditions, and combinations thereof. The storage module 202 may be configured to store thresholds associated with the environmental information of the peripheral device 21.

[0081] The classification module 203 of the server device 20 is configured to retrieve first data from the storage module 202 and classify the first data according to different types in the classification information. The classification module 203 is configured to perform a general classification of the first data based on the general classification settings of the classification information. The classification module 203 is configured to perform a general classification of the first data based on a combination of the general classifications of the classification information. The classification module 203 is configured to perform a detailed classification of the first data based on the detailed classification settings of the classification information. The classification module 203 is configured to perform a detailed classification of the first data based on a combination of the detailed classifications of the classification information. The analysis module 204 is configured to analyze the classified first data based on a threshold stored in the storage module 202 and generate an analysis result. The analysis module 204 is configured to generate an alert message in response to the analysis result exceeding the threshold.

[0082] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a smart meter. The smart meter can be used to collect resource usage information of the peripheral device 21 during a first period (e.g., the power, water, or fuel consumption of the peripheral device 21 itself, or the power, water, or fuel consumption of other devices near the peripheral device 21). The processing module 213 is configured to process the resource usage information retrieved by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 can be configured to transmit the first data to the processing module 201 of the server device 20 in real time via the network 23 during the first period.

[0083] The processing module 201 of the server device 20 is configured to receive first data in real time during the first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into text and numerical values ​​according to different types in the classification information.

[0084] Analysis module 204 is configured to analyze classified first data based on a first threshold stored in storage module 202 and generate analysis results. If the first data exceeds the first threshold, the analysis results indicate that the resource usage information of peripheral device 21 exceeds the limit, and analysis module 204 is configured to generate an alert message. Communication module 205 is configured to feed back the alert message to peripheral device 21 in real time during the first period. Accordingly, peripheral device 21 can receive the alert message in real time, thereby prompting the user to know that resource usage information (e.g., electricity consumption, water consumption, or fuel consumption) exceeds the limit. In this case, the user can check peripheral device 21 to confirm whether a component in peripheral device 21 is faulty or damaged, causing a surge in electricity, water, or fuel consumption.

[0085] The sensing module 211 of the peripheral device 21 is used to collect resource usage information of the peripheral device 21 during a second period, and the processing module 213 is configured to process the resource usage information retrieved by the sensing module 211 into second data, wherein the second period is after the first period. Similarly, the processing module 201 of the server device 20 is configured to receive the second data in real time during the second period and store the second data in the storage module 202. The classification module 203 is configured to classify the second data into text and numerical values ​​according to different types in the classification information. The analysis module 204 is configured to analyze the classified second data based on a second threshold and the first data stored in the storage module 202 and generate an analysis result. If the difference between the sum of the second data and the sum of the first data exceeds the second threshold, the analysis result indicates that the resource usage information of the peripheral device 21 during the second period exceeds a specific upper limit of the resource usage information during the first period, and the analysis module 204 is configured to generate an alert message. The communication module 205 is configured to feed back the alert message to the peripheral device 21 in real time during the second period. Accordingly, peripheral device 21 can receive alert messages in real time, thereby informing the user that resource usage information (such as electricity consumption, water consumption, or oil consumption) during the second cycle exceeds a specific upper limit of resource usage information during the first cycle. In this case, the user can monitor the use of peripheral device 21 during the second cycle to confirm whether the use of peripheral device 21 during the second cycle is as intended.

[0086] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a temperature control dashboard and a camera. The camera can detect the temperature control dashboard of the peripheral device 21 itself or other nearby devices. The processing module 213 can be configured to process the temperature information retrieved by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 can be configured to transmit the first data to the processing module 201 of the server device 20 via the network 23 in real time during a first period.

[0087] The processing module 201 of the server device 20 is configured to receive first data in real time during the first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into images and videos based on different types in the classification information using the image processing engine and video processing engine of the AI ​​analysis model.

[0088] Analysis module 204 is configured to apply multiple images or videos of the first data to the image processing engine of the AI ​​analysis model, thereby determining multiple regions of interest within each image. These multiple regions of interest can be specific temperature gauges on a temperature control dashboard. The image processing engine of the AI ​​analysis model of analysis module 204 is configured to determine the D of the nth region of interest in the mth image.mn Descriptors, each associated with a display value in each image. Display values ​​can correspond to the chromaticity and luminance of pixels (which include red, green, blue, and white pixels, etc.). The image processing engine of the AI ​​analysis model in analysis module 204 is configured to be based on the D... mn The peripheral device 21 is determined to be operating normally or abnormally by comparing the descriptor with the threshold stored in the storage module 202. If the D mn If the descriptor exceeds the threshold, the analysis result indicates that the operating temperature of peripheral device 21 exceeds the upper limit, and analysis module 204 is configured to generate an alarm message. Communication module 205 is configured to feed back the alarm message to peripheral device 21 in real time during the first cycle. Accordingly, peripheral device 21 can receive the alarm message in real time, thereby prompting the user to know that temperature information (such as the operating temperature of peripheral device 21 itself, or the operating temperature of other devices near peripheral device 21) has exceeded the upper limit. In this case, the user can check peripheral device 21 to confirm whether a component in peripheral device 21 is faulty or damaged, or whether a program problem is causing the temperature increase.

[0089] The sensing module 211 of the peripheral device 21 is used to collect temperature information of the peripheral device 21 during a second cycle, and the processing module 213 is configured to process the temperature information retrieved by the sensing module 211 into second data, wherein the second cycle occurs after the first cycle. Similarly, the processing module 201 of the server device 20 is configured to receive the second data in real time during the second cycle and store the second data in the storage module 202. Similarly, the image processing engine of the AI ​​analysis model of the analysis module 204 is configured to analyze the second data to determine whether the peripheral device 21 is operating normally or abnormally.

[0090] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a sensor disposed in the working tank of the peripheral device 21 for detecting liquid element content information in the working tank (such as the content or concentration of a specific element in the working tank of the peripheral device 21, or the content or concentration of a specific element in the liquid in the vicinity of the peripheral device 21). The specific element is, for example, chlorine, sulfur, or any other element to be detected by the peripheral device 21. The processing module 213 is configured to process the liquid element content information retrieved by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 is configured to transmit the first data to the processing module 201 of the server device 20 in real time via the network 23 during a first period.

[0091] The processing module 201 of the server device 20 is configured to receive first data in real time during a first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into text and numerical values ​​according to different types in the classification information. The classification module 203 is also configured to classify the first data into multiple first datasets according to the constraints stored in the storage module 202.

[0092] The analysis module 204 is configured to determine whether the content of a first element (e.g., chlorine) in the peripheral device 21 is normal by comparing a first set of detection standards stored in the storage module 202 with a first dataset from a plurality of first datasets, thereby generating a first analysis result. The analysis module 204 is also configured to determine whether the content of a second element (e.g., sulfur) in the peripheral device 21 is normal by comparing a second set of detection standards stored in the storage module 202 with a second dataset from a plurality of first datasets, thereby generating a second analysis result.

[0093] If the first analysis result exceeds the first set of detection standards, the analysis result indicates that the content of the first element in peripheral device 21 exceeds the upper limit, and analysis module 204 is configured to generate a first warning message. Similarly, if the second analysis result exceeds the second set of detection standards, the analysis result indicates that the content of the second element in peripheral device 21 exceeds the upper limit, and analysis module 204 is configured to generate a second warning message. Communication module 205 is configured to feed back the first and second warning messages to peripheral device 21 in real time during the first cycle. Accordingly, peripheral device 21 can receive the first and second warning messages in real time, thereby prompting the user to know that the liquid element content information (e.g., chlorine, sulfur) in the working tank exceeds the upper limit. In this case, the user can test peripheral device 21 to confirm whether a component in peripheral device 21 is faulty or damaged, causing the increase in chlorine or sulfur content.

[0094] Similarly, peripheral device 21 and server device 20 can repeat operations during a second cycle following the first cycle.

[0095] According to some embodiments of this disclosure, the sensing module 211 of the peripheral device 21 includes a camera and a decibel meter. The camera and decibel meter can detect images and sounds of users or other personnel from the peripheral device 21 itself or other nearby devices. The processing module 213 can be configured to process the personnel identification information or sound information retrieved by the sensing module 211 into first data (i.e., raw data from the peripheral device 21). The communication module 214 of the peripheral device 21 can be configured to transmit the first data to the processing module 201 of the server device 20 in real time via the network 23 during a first period.

[0096] The processing module 201 of the server device 20 is configured to receive first data in real time during a first period and store the first data in the storage module 202. The classification module 203 is configured to classify the first data into images, videos, and audio based on different types in the classification information.

[0097] Analysis module 204 is configured to apply multiple images or videos of the first data to the image processing engine of the AI ​​analysis model to determine multiple regions of interest (e.g., faces or ID cards) within each image. The image processing engine of the AI ​​analysis model in analysis module 204 is configured to determine the D region of interest for the nth image in the mth image. mn Descriptors, each associated with a displayed value in each image. The image processing engine of the AI ​​analysis model in analysis module 204 is configured to be based on the D... mn The user of the peripheral device 21 is determined to match their identity by comparing the descriptor with a set of detection criteria stored in the storage module 202. The image processing engine of the AI ​​analysis model in the analysis module 204 is configured to be based on the D mn The descriptor is compared with a set of detection criteria stored in storage module 202 to determine whether the equipment worn by the user of peripheral device 21 meets the detection criteria. If analysis module 204 determines that the user identity does not match or the equipment does not meet the detection criteria, then analysis module 204 is configured to generate an alert message. Communication module 205 is configured to feed back the alert message to peripheral device 21 in real time during the first cycle. Accordingly, peripheral device 21 can receive the alert message in real time, thereby alerting the user.

[0098] Analysis module 204 is configured to apply multiple audio samples of the first data to the speech processing engine of an AI analysis model to determine multiple segments of interest (e.g., user voice) within each audio sample. It determines whether the multiple segments of interest match a set of detection criteria stored in storage module 202. If analysis module 204 determines that multiple segments of interest do not match the set of detection criteria, it is configured to generate an alert message. Communication module 205 is configured to feed back the alert message to peripheral device 21 in real time during the first period. Accordingly, peripheral device 21 can receive the alert message in real time, thereby alerting the user.

[0099] Analysis module 204 is configured to determine whether multiple sound segments of interest exceed thresholds based on thresholds stored in storage module 202. If analysis module 204 determines that multiple sound segments of interest exceed thresholds, then analysis module 204 is configured to generate an alert message. Communication module 205 is configured to feed back the alert message to peripheral device 21 in real time during the first cycle. In this case, the user can inspect peripheral device 21 to confirm whether any component in peripheral device 21 is malfunctioning or damaged, causing an increase in operating audio or decibel levels.

[0100] Similarly, peripheral device 21 and server device 20 can repeat operations during a second cycle following the first cycle.

[0101] Figure 8 The diagram shown is a schematic diagram of a management system 2' according to a partial embodiment of the present disclosure. Figure 8 The management system 2' is similar to Figure 7 The management system 2, in addition to the server device 20' of the management system 2', also includes a generator module 206 and a link generation module 207. The generator module 206 and the link generation module 207 are electrically coupled to the processing module 201, the storage module 202, the classification module 203, the analysis module 204, and the communication module 205.

[0102] Generator module 206 is configured to generate identifiers based on classified first data during a first period and to generate integrated data based on the identifiers. The identifiers are associated with the first data. The integrated data may include the first data and analysis results. Link generation module 207 is configured to convert the integrated data into URL links and link the URL links to peripheral device 21. The integrated data and the URL links are stored in storage module 202 as traceability data to facilitate user tracing and access to historical information and analysis results.

[0103] In another embodiment, the traceability function can be used to provide early warning of whether the peripheral device 21 needs to replace the sensing module 211. Under normal use, the aging of the sensing module 211's sensitivity will affect the sensing data. Accordingly, the usage data of the sensing module 211 can be compared with the traceability data to determine whether the sensing module 211 needs to be replaced. The usage data of the sensing module 211 includes information such as its purchase time, the new product lifespan of the module 211, and the start date of use of the sensing module 211. After purchasing the sensing module 211, the usage data of the sensing module 211 is stored in the storage module 202. In addition, after the sensing module 211 is replaced (i.e., the old sensing module 211 is replaced with a new sensing module 211), the replacement date of the sensing module 211 is stored in the storage module 202. Based on the start date of use and the replacement date of the sensing module 211, the actual lifespan of the sensing module 211 can be determined (i.e., the replacement date of the sensing module 211 minus the start date of use).

[0104] After the sensing module 211 begins use, it generates sensing data. During its use (which may include several periods), the generator module 206 and the link generation module 207 store traceable data in the storage module 202, thereby determining the actual service life of the sensor. If the actual service life is one year, then after the sensing module 211 has been used for 11 months, the server device 20' checks whether the values ​​of the sensing data of the sensing module 211 during the 9th to 11th months are abnormally high, thereby confirming whether the degree of failure of the sensing module 211 is too high. Through this confirmation, it is possible to predict whether the sensing module 211 has aged, and when the degree of aging is too high and is about to affect the sensitivity and accuracy, the sensing module 211 can be replaced in real time to avoid affecting the accuracy of the sensing data, thereby saving costs.

[0105] In some embodiments, if a factory (or laboratory, company, department store, corporation, restaurant, or any other location requiring environmental safety management) has multiple factory sites (or branches, sub-branches, etc.), then one factory site can use peripheral devices 21 and 22 as its internal server, connecting to server device 20' via network 23 to transmit data. Other factory sites use similar electronic devices (such as computers, tablets, mobile phones, etc.) to connect to the internal server via an internal network to transmit data. Information from other factory sites is integrated through the internal server and transmitted to all factory sites, a designated factory site (such as the corporate headquarters), and / or server device 20'. In some embodiments, without using URL links converted by link generation module 207, only the integrated data generated by generator module 206 can be used to view individual information for all factory sites, as well as the sum of information for all factory sites. Since information from other factory sites is transmitted to the internal server via the internal network and not directly to server device 20' via network 23, it offers higher security.

[0106] Figure 9 The diagram shown is a flowchart illustrating the operation of a management system according to certain embodiments of this disclosure. In some embodiments, Figure 9 The disclosed method or part of the operation can be performed by, for example Figure 7 Management system 2 or Figure 8 The management system 2' is executed.

[0107] Operation S71 involves the processing module 201 of the server device 20 of the management system 2 receiving first data in real time from peripheral devices 21 and 22 during the first cycle. The first data can be stored in the storage module 202.

[0108] Operation S72 involves the classification module 203 classifying the first data based on any one or a combination of the classification information, detection criteria, error messages, and limiting conditions stored in the storage module 202.

[0109] Operation S73 involves the analysis module 204 analyzing the classified first data and generating analysis results, and generating an alert message in response to the analysis results exceeding a threshold.

[0110] Operation S74 is performed via communication module 205 to feed back warning messages to peripheral devices 21 and 22 in real time during the first cycle.

[0111] The server device 20 of the management system 2 can repeatedly operate relative to the peripheral devices 21 and 22 from S71 to S74 during the second cycle.

[0112] While the technical content and features of this disclosure are as described above, those skilled in the art can make many variations and modifications without departing from the teachings and disclosure of this disclosure. Therefore, the scope of this disclosure is not limited to the disclosed embodiments but includes other variations and modifications without departing from this disclosure, as covered by the appended claims.

[0113] Symbol Explanation

[0114] 1: Management System

[0115] 2: Management System

[0116] 2': Management System

[0117] 10: Server equipment

[0118] 11: Peripheral devices

[0119] 12: Peripheral devices

[0120] 13: Internet

[0121] 20: Server device

[0122] 20': Server device

[0123] 21: Peripheral devices

[0124] 22: Peripheral devices

[0125] 23: Internet

[0126] 101: Processing Module

[0127] 102: Storage Module

[0128] 103: Input Module

[0129] 104: Communication Module

[0130] 111: Processing Module

[0131] 112: Storage Module

[0132] 113: Search Module

[0133] 114: Input Module

[0134] 115: Communication Module

[0135] 121: Processing Module

[0136] 122: Storage Module

[0137] 123: Input Module

[0138] 124: Communication Module

[0139] 201: Processing Module

[0140] 202: Storage Module

[0141] 203: Classification Module

[0142] 204: Analysis Module

[0143] 205: Communication Module

[0144] 206: Generator Module

[0145] 207: Link Generation Module

[0146] 211: Sensing Module

[0147] 212: Storage Module

[0148] 213: Processing Module

[0149] 214: Communication Module

[0150] 221: Sensing Module

[0151] 222: Storage Module

[0152] 223: Processing Module

[0153] 224: Communication Module

[0154] S21: Steps

[0155] S22: Steps

[0156] S23: Steps

[0157] S24: Steps

[0158] S31: Steps

[0159] S32: Steps

[0160] S33: Steps

[0161] S34: Steps

[0162] S35: Steps

[0163] S36: Steps

[0164] S41: Steps

[0165] S42: Steps

[0166] S43: Steps

[0167] S44: Steps

[0168] S45: Steps

[0169] S51: Steps

[0170] S52: Steps

[0171] S53: Steps

[0172] S54: Steps

[0173] S55: Steps

[0174] S56: Steps

[0175] S61: Steps

[0176] S62: Steps

[0177] S63: Steps

[0178] S64: Steps

[0179] S65: Steps

[0180] S66: Steps

[0181] S71: Steps

[0182] S72: Steps

[0183] S73: Steps

[0184] S74: Steps.

Claims

1. A server device comprising: The processing module is configured to receive first data from a peripheral device in real time during the first cycle; The classification module is configured to classify the first data according to the classification information; An analysis module is configured to analyze the classified first data based on a threshold and generate analysis results, and to generate an alert message in response to the analysis results exceeding the threshold; A communication module is configured to send the warning message back to the peripheral device in real time during the first period.

2. The server apparatus of claim 1, further comprising a storage module configured to store the first data. The storage module is further configured to store the classification information, the threshold, the first set of detection criteria, the second set of detection criteria, and multiple limiting conditions. The classification module is configured to classify the first data into multiple first datasets based on the multiple constraints.

3. The server apparatus of claim 2, wherein the classification module is configured to classify a plurality of first datasets into a plurality of first data subsets according to the first set of detection criteria.

4. The server apparatus of claim 1, wherein the first data includes the plurality of images and the plurality of images of the first data are associated with the peripheral device, and the analysis module is configured to: The multiple images are applied to an image processing model to determine multiple regions of interest within each image; Determine the D of the nth region of interest in the mth image. mn Descriptors, each descriptor is associated with a display value in each image; as well as Based on the image processing model described above, D mn The peripheral device is determined to be operating normally or abnormally by comparing the descriptor with the threshold.

5. The server apparatus of claim 1, further comprising a generator module configured to generate an identifier based on the classified first data and to generate integrated data based on the identifier, wherein the identifier is associated with the first data and the integrated data includes the first data and the analysis result.

6. The server apparatus of claim 5, further comprising a link generation module configured to convert the integrated data into URL links and link the URL links to the peripheral device.

7. The server apparatus of claim 2, wherein the plurality of limiting conditions includes time conditions, location conditions, event conditions, number of people conditions, power conditions, article composition conditions, air composition conditions, or liquid composition conditions.

8. The server apparatus according to claim 2, wherein The multiple constraints include time constraints and liquid composition constraints, wherein the classification module is configured to classify the first data into the multiple first datasets based on the time constraints and the liquid composition constraints. The analysis module is configured to determine whether the content of the first element in the peripheral device is normal based on the comparison of the first set of detection standards with the first dataset in the plurality of first datasets, and to determine whether the content of the second element in the peripheral device is normal based on the comparison of the second set of detection standards with the second dataset in the plurality of first datasets.

9. The server apparatus according to claim 2, wherein: The processing module is configured to receive second data from the peripheral device during a second cycle, which follows the first cycle. The classification module is configured to classify the second data according to the classification information and to classify the second data into multiple second datasets according to the power conditions among the multiple constraints; The analysis module is configured to compare the plurality of first datasets and the plurality of second datasets and generate the analysis results and the alert message in response to the difference between the sum of the plurality of second datasets and the sum of the plurality of first datasets exceeding the threshold. and The communication module is configured to send the warning message back to the peripheral device in real time during the second period.

10. The server apparatus of claim 1, wherein the first data is retrieved by the sensing module of the peripheral device.

11. A method of operating a server device, comprising: The processing module receives the first data from the peripheral device in real time during the first cycle; The first data is classified according to the classification information using the classification module; The analysis module analyzes the classified first data and generates analysis results, and generates an alert message in response to the analysis results exceeding a threshold. as well as The warning message is fed back to the peripheral device in real time during the first cycle via a communication module.

12. The method of claim 11, wherein the first data received by the processing module is stored in the storage module. The storage module is configured to store the classification information, the threshold, the first set of detection criteria, the second set of detection criteria, and multiple limiting conditions. The classification module is configured to classify the first data into multiple first datasets based on the multiple constraints.

13. The method of claim 12, wherein the classification module is configured to classify a plurality of first datasets into a plurality of first data subsets according to the first set of detection criteria.

14. The method of claim 11, wherein the first data comprises the plurality of images and the plurality of images of the first data are associated with the peripheral device, and the method further comprises performing the following operations through the analysis module: The multiple images are applied to an image processing model to determine multiple regions of interest within each image; Determine the D of the j-th region of interest in the i-th image. ij Descriptors, each descriptor being associated with a display value in each image; and Based on the D via the computational model ij The peripheral device is determined to be operating normally or abnormally by comparing the descriptor with the threshold.

15. The method of claim 11, further comprising: A generator module generates identifiers based on the classified first data, and generates integrated data based on the identifiers, wherein the identifiers are associated with the first data, and the integrated data includes the first data and the analysis results. The integrated data is transmitted to the peripheral device via the communication module.

16. The method of claim 15, further comprising: The integrated data is converted into URL links via a link generation module; Associate the URL link with the first data stored in the storage module; and Link the URL to the peripheral device.

17. The method of claim 12, wherein the plurality of limiting conditions includes time conditions, location conditions, event conditions, number of people conditions, power conditions, article composition conditions, air composition conditions, or liquid composition conditions.

18. The method of claim 12, wherein the plurality of limiting conditions includes time conditions and liquid composition conditions, wherein the method further comprises performing the following operations through the classification module: Based on the time conditions and the liquid composition conditions, the first data is classified into the plurality of first datasets; The content of the first element in the peripheral device is determined to be normal by comparing the first set of detection standards with the first dataset in the plurality of first datasets; The content of the second element in the peripheral device is determined to be normal by comparing the second set of detection standards with the second dataset in the plurality of first datasets.

19. The method of claim 12, further comprising: The processing module receives second data from the peripheral device during a second cycle, which follows the first cycle. The classification module classifies the second data according to the classification information and further classifies the second data into multiple second datasets according to the power conditions among the multiple constraints. The analysis module compares the plurality of first datasets and the plurality of second datasets and generates the analysis result, and generates the warning message in response to the difference between the sum of the plurality of second datasets and the sum of the plurality of first datasets exceeding the threshold. as well as The warning message is fed back to the peripheral device in real time during the second cycle via the communication module.

20. The method of claim 11, wherein the first data is retrieved by the sensing module of the peripheral device.

Images