A gas access security anti-cheating method and system

Abstract

The application relates to a gas household security check anti-cheating method and system, aiming to solve the problems of easy forgery, positioning simulation and single verification dimension of static two-dimensional codes in the prior art. The method comprises the following steps: generating an electronic dynamic two-dimensional code containing a dynamic encryption factor and a geographic position based on user historical data; when a security check personnel scans the code, the system performs real-time multiple verification on the validity of the two-dimensional code, the position proximity and the consistency of the encryption factor; after the verification, the security check is performed and the on-site photos are uploaded; whether the photos have repetition, blur or object missing abnormalities is analyzed through image recognition technology; finally, quantitative evaluation and state management are performed based on the results. Through the combination of dynamic encryption, multiple real-time verification and intelligent image analysis, a three-dimensional anti-cheating system is constructed, and the authenticity, reliability and management efficiency of the household security check are significantly improved.

Description

Technical Field

[0001] This application relates to the field of electrical data processing technology, specifically to a method and system for preventing cheating in gas inlet safety inspections. Background Technology

[0002] Gas safety is directly related to public safety and the safety of people's lives and property. In practice, in-home safety inspections that rely on manual operation face the challenge of effectively supervising the behavior of staff throughout the process. In existing technologies, the following methods are commonly used to standardize operations and prevent cheating: Use static QR codes or barcodes to bind user identities and addresses, which are then scanned and reported by security personnel; utilize the GPS or base station positioning function of mobile terminals to ensure that security personnel arrive at the designated work area; and require the uploading of on-site photos as evidence of work. The aforementioned anti-cheating measures have obvious limitations. Static QR codes are easy to copy, forge, or photograph in advance, and it is impossible to confirm the real-time nature and authenticity of the scanning action. Location information can be simulated or tampered with through technical means. Existing technologies mostly focus on single-dimensional verification and lack a multi-dimensional and collaborative verification mechanism for user historical behavior characteristics, real-time dynamic factors, and spatial information. This results in insufficient ability to prevent upgraded cheating behaviors and leaves hidden dangers for gas safety management. Therefore, how to construct a comprehensive anti-cheating solution that can deeply integrate user characteristics, real-time dynamic encryption and intelligent image recognition to achieve higher accuracy and stronger anti-counterfeiting capabilities for in-home security checks has become a technical problem to be solved in this field. Summary of the Invention

[0003] To address the problems existing in the prior art, this application aims to provide a method and system for preventing cheating in gas home safety inspections by introducing historical user gas consumption data, safety inspection data, and user behavior analysis to generate dynamic encryption factors. This enables the QR code to possess user behavior characteristics, achieving higher accuracy in identity verification and anti-cheating effects, and ensuring the authenticity and effectiveness of gas home safety inspections.

[0004] The gas inlet safety inspection anti-cheating method described in this application includes: S101. Receive and store the authorized real-name authentication information submitted by the user through the user terminal, complete the real-name authentication and establish a file; S102. The back-end management generates a dynamic encryption factor based on the user's gas usage history, security inspection history and behavior data, and generates an electronic dynamic QR code containing the factor, validity period and geographical location information, and sends it to the user terminal for display. S103. Security personnel use a terminal with positioning function to scan the QR code and simultaneously verify the validity of the QR code, the proximity of the terminal position to the position within the QR code, and the real-time consistency of the dynamic encryption factor. S104. After the verification is passed, an in-home safety inspection will be conducted, and on-site photos containing the specified gas facilities will be taken and uploaded. S105. Analyze the photograph for any abnormalities such as reuse, insufficient clarity, or missing specified objects through image recognition; S106. Based on the comprehensive verification and identification results, quantitatively evaluate and judge the security inspection operation, complete the recording, and implement the corresponding status management.

[0005] Preferably, the user real-name registration and dynamic encryption factor generation are achieved by the system generating a dynamic encryption factor K based on the user's historical gas consumption data, security inspection data, and user behavior analysis after the user completes real-name authentication ("person, ID card, and license integration").

[0006] Where A is the gas consumption fluctuation coefficient, reflecting the user's gas consumption habits; B is the safety inspection compliance coefficient, reflecting the user's cooperation with safety inspections; C is the user behavior coefficient, reflecting abnormal user operations; α, β, γ are dynamically adjustable weighting coefficients, satisfying α+β+γ=1.

[0007] Preferably, in the multiple verification system of the electronic dynamic QR code, a dynamic encryption factor K is embedded in the QR code, and it includes validity period, timestamp, and geographical location information. When security personnel scan the code, the system simultaneously verifies it. QR code validity period and timestamp; The distance between the real-time location of the security personnel and the location within the QR code is ≤100 meters; The deviation between the K value in the QR code and the K′ value calculated by the system in real time is ≤0.05; The subsequent operations will only proceed after all three checks pass.

[0008] Preferably, the photo forgery identification and quantitative evaluation system analyzes uploaded photos using image recognition technology as follows: The similarity criterion for duplication is defined as a similarity of ≥90%; The sharpness score uses the Laplacian operator; a score below 60 indicates blurriness. Compliance assessment of key components, including whether gas stoves, pipes, valves, etc., are complete; Based on the comprehensive fraud risk index calculation, the above three types of indicators are used for quantitative assessment and warning.

[0009] Preferably, the system quantifies and scores the violations of security personnel based on the following dimensions: degree of photo falsification, degree of procedural violation, degree of hazard concealment, and degree of user complaints. The system then assigns a level of violation based on the scoring results, specifically from level one to level three, and sets a recovery time and recovery conditions, including completion of rectification, no new violations, and no complaints, for the purpose of standardized management of behavior.

[0010] The gas inlet safety inspection anti-cheating system described in this application includes: a user terminal module, a staff terminal module, and a back-end management module; The client-side module is deployed on the user's mobile terminal and is mainly used for: Users can complete the real-name authentication process of "person, certificate, and photo in one" by filling in personal information, uploading ID documents and real-time facial images; display users' basic information, historical security check records and gas usage; receive electronic dynamic QR codes containing information such as dynamic encryption factors, validity period and geographical location issued by the background, and show them to staff for scanning during security checks; Users can view current and historical security inspection reports to understand the details of the inspections and the status of any potential hazard rectification.

[0011] The staff-side module is deployed on the mobile terminals of security personnel and has functions such as positioning, scanning, taking photos, and data transmission, specifically including: The system acquires and reports staff location information in real time, with a positioning accuracy of ≤10 meters; it scans the electronic dynamic QR code presented by the user and parses the embedded dynamic encryption factor, timestamp, and geographical location information; it uploads the parsed data and the current location to the backend in real time, triggering triple verification of QR code validity, location proximity, and dynamic factor consistency; after successful verification, it provides standardized safety inspection process guidelines, supports taking on-site photos of key objects including gas facilities, and supports recording hazard information in text; it uploads the photos, inspection records, and location information to the backend management system in real time.

[0012] The backend management module is deployed on the server side and serves as the core processing and control platform of the system. It mainly includes the following functions: It stores a full range of data, including user real-name information, historical gas usage data, safety inspection records, dynamic encryption factors, QR code information, and photo files. Based on the user's gas consumption fluctuation coefficient, safety inspection compliance coefficient, and user behavior coefficient, combined with adjustable weights α, β, and γ, it calculates and updates the dynamic encryption factor K for each user in real time. It generates encrypted QR codes based on the dynamic encryption factor, validity period, and user location information, and manages their lifecycle. It receives scanned data uploaded by the APP, performs QR code validity period verification, geographical proximity judgment (distance ≤ 100 meters), and dynamic encryption factor consistency verification (|KK′| ≤ 0.05). It uses AI technology to analyze uploaded on-site photos, including duplicate similarity detection, clarity scoring, key object (gas stove, pipe, valve) identification, and calculates a comprehensive fraud risk index. It monitors the execution of the safety inspection process in real time, automatically generates safety inspection reports, and supports data query, statistical analysis, and export.

[0013] The gas in-home safety inspection anti-cheating method and system described in this application has the advantage of effectively solving the technical problem that static identity certificates are easily forged or misused. By comprehensively considering the user's gas usage behavior characteristics, safety inspection history and operation records to generate dynamic encryption factors and embedding QR codes, each electronic certificate has timeliness, user uniqueness and behavior correlation, which improves the anti-counterfeiting strength from the source and overcomes the security defects of static QR codes that can be copied and obtained in advance. When security personnel scan the code, the system simultaneously performs triple verification of validity period, geographical proximity, and dynamic factor consistency, realizing multi-dimensional real-time cross-verification. This effectively identifies and blocks cheating behaviors implemented through common technical means such as simulated positioning, time tampering, and credential replay, ensuring the "real person, on-site, and real-time" operation requirements. By using image recognition technology to automatically analyze and quantify the repeatability, clarity, and completeness of key equipment in uploaded photos, the traditional review method that relies on subjective human judgment is transformed into an objective recognition based on algorithms. This significantly improves the ability to detect and warn about typical cheating behaviors such as using historical photos, taking blurry pictures, and deliberately omitting key parts. Based on multi-source information such as photo recognition results, process compliance, completeness of hazard reporting, and user complaints, the system quantifies and classifies the operations of security personnel, and links them with corresponding status management and recovery conditions to coordinate and standardize front-line operational behavior from both institutional and technical perspectives. Attached Figure Description

[0014] Figure 1 This application describes a method for preventing cheating during gas inlet safety inspections. Figure 1 ; Figure 2 This application describes a method for preventing cheating during gas inlet safety inspections. Figure 2 ; Figure 3 This application describes a method for preventing cheating during gas inlet safety inspections. Figure 3 . Detailed Implementation

[0016] like Figures 1-3 As shown in the figure, this application describes a method and system for preventing cheating in gas inlet safety inspections.

[0017] like Figures 1-3 As shown, when a gas company uses the gas in-home safety inspection anti-cheating method and system of this invention to conduct in-home safety inspections, it works as follows: Step 1: User registration and authentication: Residential users register on the gas company's user terminal mini-program, fill in personal information, and complete the "person, certificate, and ID card three-in-one" real-name authentication through mobile phone number and ID card. The authentication information is then uploaded to the back-end management system. The second step involves calculating the dynamic encryption factor by retrieving the user's gas consumption data from the past 12 months through the system. Calculated ; Two out of the last three security checks were compliant, so B = 2 / 3 ≈ 0.67; Since there have been no abnormal operations in the past 6 months, D=0, therefore C=1; Substitute into the formula ; Step 3: Generate an electronic dynamic QR code: After the user completes the authentication, the system generates an electronic dynamic QR code based on the dynamic encryption factor K=0.865, which includes the validity period (15 minutes), timestamp, latitude and longitude and encryption factor information, and displays it on the user's mini program; Step 4: Security personnel wearing uniforms with the company's logo carry mobile phones and open the staff app. Upon arriving at the user's residential area, the app automatically records the user's location information with an accuracy of 5 meters. Step 5: Security personnel present their work identification to the user, who then displays an electronic dynamic QR code on the mini-program. The security personnel scan the QR code using the app, and the system calculates the user's current encryption factor in real time. Verification results Furthermore, if the distance between the staff and the user is 50 meters (≤100 meters), the QR code is valid, and the verification is successful, security checks are permitted. Step 6: Safety inspectors will inspect the gas facilities in the user's home according to the prescribed inspection criteria, take photos to record the relevant information (including the gas meter number and door number), and upload the photos to the back-end management system via the APP. Step 7: The backend management system uses AI recognition technology to analyze the uploaded photos, determines that the photos are clear and contain key identifiers, have no duplicate or similar issues, and do not trigger alarms. Step 8: If a safety hazard is found, the safety inspector will inform the user in writing and provide guidance on rectification. At the same time, the hazard and rectification suggestions will be recorded in the APP, and the back-end management system will track and record the rectification. Step 9: After the security check is completed, the system automatically generates a security check report, which includes the encryption factor verification results. Users can view the report through the mini-program, and the backend management system archives the security check report.

[0018] like Figures 1-3 As shown, the gas inlet safety inspection anti-cheating method described in this application includes the following steps: like Figures 1-3 As shown, S101: Receive and store the authorized real-name authentication information submitted by the user through the user terminal, complete the real-name authentication and establish a file.

[0019] Furthermore, in step S101, the user registers and fills in information on a dedicated mini-program, and performs real-name authentication of "person, certificate, and photo in one" using a mobile phone number and ID card. After the authentication is successful, the relevant information is entered into the system to establish the user's real-name profile.

[0020] like Figures 1-3 As shown in S102, the back-end management generates a dynamic encryption factor based on the user's gas usage history, security inspection history, and behavior data, and generates an electronic dynamic QR code containing the factor, validity period, and geographical location information, and sends it to the user terminal for display.

[0021] Furthermore, in step S102, the system generates a dynamic encryption factor based on historical user gas consumption data, historical security inspection data, and user behavior analysis, using a fluctuation coefficient, as shown in the following formula:

[0022] Where: k is the dynamic encryption factor (range 0.8-1.2, used for QR code encryption); A is the gas consumption fluctuation coefficient. Q max This is the highest gas consumption in nearly 12 months, Q min This is the lowest gas consumption in nearly 12 months, Q avg This represents the average gas consumption over the past 12 months. B represents the security inspection compliance coefficient. N 合规 For the last 3 compliance security checks, N 总 This refers to the number of tests that should have been conducted in the last three periods. C represents the user behavior coefficient. D represents the number of abnormal user operations in the past 6 months, such as overdue unresolved issues and unauthorized repair requests. These are the weighting coefficients. ,default value It can be dynamically adjusted according to user type; α is the weight of the gas consumption fluctuation coefficient: the default value is 0.3. Gas consumption is an important basic data for users in the process of using gas. Its fluctuation can reflect users' gas consumption habits to a certain extent. The default value of 0.3 is set because although gas consumption fluctuation can reflect user behavior characteristics, its direct impact on preventing cheating in gas entry safety inspections is slightly weaker than that of safety inspection compliance. For example, users' gas consumption will fluctuate normally in different seasons. Such fluctuations are reasonable and predictable, so there is no need to assign too high a weight. β is the safety inspection compliance coefficient weight: the default value is 0.5. The safety inspection compliance is directly related to the quality and effectiveness of the gas home safety inspection. It is a key indicator for judging whether users cooperate with the safety inspection and whether the safety inspection is carried out in a standardized manner. It is given the highest default weight of 0.5 to highlight the core position of safety inspection compliance in preventing cheating. If a user fails to comply with the safety inspection multiple times, it means that there is a high risk or lack of cooperation in the safety inspection process. This needs to be highlighted in the dynamic encryption factor to strengthen the anti-cheating control of the safety inspection process for such users. γ is the weight of the user behavior coefficient: the default value is 0.2. Abnormal user behavior, such as overdue unresolved hazards and unauthorized repair requests, reflects the user's level of attention and cooperation with gas safety. The default value of 0.2 is set because although these behaviors will affect gas safety management, their frequency and direct correlation are relatively lower than those of safety inspection compliance. However, for users with multiple abnormal behaviors, this coefficient can still play a certain regulatory role in the dynamic encryption factor, reminding safety inspectors to pay more attention. The values ​​of α, β, and γ are not fixed and can be dynamically adjusted according to the user type. The specific adjustment criteria are as follows: The user type is industrial users: Industrial users typically consume more gas, and their gas-using equipment and scenarios are more complex. Fluctuations in gas consumption may have a significant impact on production safety. At the same time, their safety inspection and compliance requirements are also more stringent. Therefore, the weights of α and β can be appropriately increased, for example, adjusted to α=0.4, β=0.5, γ=0.1. The user type is elderly residents: elderly residents may have some difficulty operating smartphones and cooperating with security checks, and may have relatively more abnormal operating behaviors, but their gas consumption fluctuates less. In this case, the weight of γ can be appropriately increased and the weight of α can be decreased, for example, adjusted to α=0.2, β=0.5, γ=0.3. For commercial users, such as restaurants and hotels, gas usage is frequent and safety inspections are crucial for their operational safety. Gas consumption fluctuations are also related to their business conditions. Therefore, a high weight can be maintained for β, and α and γ can be adjusted appropriately, for example, α=0.35, β=0.5, γ=0.15.

[0023] like Figures 1-3 As shown in S103, security personnel use a terminal with positioning function to scan the QR code, and simultaneously verify the validity of the QR code, the proximity of the terminal position to the position within the QR code, and the real-time consistency of the dynamic encryption factor.

[0024] Furthermore, in step S103, the system embeds the dynamic encryption factor K into the electronic dynamic QR code, so that the QR code contains information such as validity period, time, latitude and longitude, user ID, and K value. The staff's APP has a positioning function, and when scanning the QR code, the system verifies it simultaneously. The QR code's validity period, timestamp, and the match between the current time; The distance between the staff member's location and the latitude and longitude of the QR code (≤100 meters). The K value in the QR code and the system's real-time calculation Value deviation ; Only after all three checks are passed can the subsequent security check process proceed.

[0025] like Figures 1-3 As shown in S104, after the verification is passed, an in-home safety inspection is performed, and on-site photos containing the specified gas facilities are taken and uploaded. S105. Analyze the photograph through image recognition to determine if there are any anomalies such as repeated use, insufficient clarity, or missing specified objects.

[0026] Furthermore, in steps S104-S105, historical gas volume data, historical security inspection data, and user behavior analysis are combined with weighting coefficients α, β, and γ to form a multi-dimensional and comprehensive anti-fraud mechanism. α starts with the basic gas usage characteristics of users, β focuses on the compliance of the security inspection work itself, and γ pays attention to the degree of cooperation of users with security inspection-related behaviors. The three are aimed at different types of fraud risk points. By dynamically adjusting the weight of the three, the system can flexibly strengthen the prevention and control of the corresponding dimensions according to the fraud risk characteristics of different user types. For industrial users, increasing the weights of α and β can help prevent fraudulent activities caused by complex gas consumption or high safety inspection requirements; for elderly residential users, increasing the weight of γ can better address the risk of fraudulent activities that may arise due to users' unfamiliarity with the operation. In one embodiment, the system employs AI-powered object recognition technology to prevent photo spoofing. It analyzes photos taken during security checks and determines whether they are duplicates (≥90% similarity), blurry, have a clarity score <60, or are not photographed as required (including gas stoves, gas cylinders, gas pipelines, valves, and alarms). The system will then trigger an AI-based alert to prevent photo spoofing. The specific implementation is as follows: In the algorithm for determining duplicate similarity:

[0027] in, The pixel area of ​​the overlapping region between the two photos. These represent the total pixel area of ​​the photo to be tested and the historical photos, respectively. When the S value is ≥90%, it is determined to be a duplicate similar photo. This formula can effectively identify the fraudulent behavior of staff reusing historical photos to impersonate previous security inspection photos by calculating the overlap between two photos. If the staff does not actually conduct a home security inspection and directly reuses a gas stove photo taken last month, the system will trigger a duplicate similarity alarm when the S value reaches 90% or more by calculating the overlap. In the sharpness scoring algorithm: Photo sharpness rating The gradient value is calculated using the Laplacian operator, and the formula is as follows:

[0028] in, Let P be the Laplacian operator value at pixel (x, y) of the image, M and L be the width and height of the image in pixels, respectively, and N = M × L be the total number of pixels. The value range is 0-100 points. If a photo is judged to be blurry, the Laplacian operator can reflect the edge changes of the image. The clearer the edge, the larger the gradient value and the higher the score. If staff deliberately take blurry photos to cover up the fact that no security check was actually carried out, the system will issue a blur alarm if the clarity score is lower than 60 points after calculating the clarity score. In the object compliance determination algorithm: Let the set of key objects required to be photographed during security checks be... (like For gas stoves, (For example, gas cylinders, etc.) The actual set of objects identified in the photo is: Object compliance index The calculation formula is:

[0029] Here, card() is the cardinality (number of elements) of the set. O represents the set of objects actually identified in the photo, O represents the set of key objects to be photographed, including gas stoves, pipes, and valves, and Co represents the object compliance index (%). when If the required objects are not photographed, for example, if the requirement is to photograph the gas stove (o1), gas pipe (o2), and valve (o3), but only the gas stove is detected in the photo, then... The system determines that the shooting is not done as required and issues an alarm. This formula quantifies the completion of the shooting of key objects to prevent staff from omitting key parts to falsify the footage. In the comprehensive fraud risk index algorithm: The overall risk index R for forgery is used to assess the overall risk of forgery of a photograph. The formula is as follows:

[0030] Where S is the repetition similarity index (%). Rate the clarity (in points). The object compliance index, R, ranges from 0 to 1. When R > 0.5, the system determines it as a high-risk fake photo and triggers a comprehensive alarm. This formula integrates the risk weights of three types of indicators. The weight of repetition similarity (0.4) is the highest because reusing photos is the most common means of forgery. Clarity and object compliance each account for 0.3, taking into account different forgery scenarios. Through comprehensive calculation, it can more comprehensively identify photo forgery behavior and improve the accuracy of anti-cheating.

[0031] like Figures 1-3 As shown in step S106, the security check operation is quantitatively evaluated and judged based on the comprehensive verification and identification results, and the record is completed and the corresponding status management is implemented.

[0032] Furthermore, in step S106, the security inspection process specifications introduce a quantitative algorithm to classify the fraudulent and irregular behaviors of security personnel, and specify the time required for different levels of violations to return to normal. User complaints are also taken into consideration, as detailed below: First, quantitative indicators for violations Let v be the number of a single violation committed by a security inspector, and let its quantified value be V. q It consists of the following four dimensions: Photo forgery level P i Based on the comprehensive fraud risk index R, P is determined. f =Rc×100, with a value range of 0-100; Degree of process violation P v The percentage of items not performed according to the standard procedure out of the total number of procedures. The value range is 0-100; Hidden danger level H c The quantitative values ​​corresponding to the levels of concealed safety hazards are as follows: 30 for general hazards, 60 for major hazards, and 100 for serious hazards. User complaint level C p The score is determined based on the severity of the user complaint and the verification results: 20 for general complaints (verified as minor issues), 50 for more serious complaints (verified as more serious issues), and 80 for serious complaints (verified as serious issues); 0 for complaints that are found to be unsubstantiated. Single violation quantification value V q The calculation formula is as follows:

[0033] In the algorithm for determining the level of violation: Based on the single violation quantification value V q The violations are divided into three levels: Level 1 violation (minor):

[0034] Level 2 violation (more serious):

[0035] Level 3 violation (serious):

[0036] If security personnel violate regulations multiple times within a certain period, the total violation index V will be increased. t The calculation formula is:

[0037] Among them, V qi Let be the quantified value of the i-th violation, and n be the number of violations within the period. This is a cumulative coefficient for multiple violations, reflecting the principle of stricter punishment for repeat offenders; Overall violation level determination: Level 1: V t <200; Level 2: 200≤V t <400; Level 3: V t ≥400; The algorithm for calculating the recovery time to normal includes: The duration T for restoring normal status for different levels of violations. r The calculation formula is as follows: Recovery time for a single violation: Level 1 violation: Heaven, among them The coefficient of variation within the same level, ranging from 0 to 1, therefore T r1The range is 30-60 days; Level 2 violation: sky, The value range is 0-1, T r2 The range is 90-180 days; Level 3 violation: sky, The value range is 0-1, T r3 The range is 365-730 days; Duration of recovery after multiple violations: Total violation recovery time T rt The sum of the duration of each violation and subsequent recovery. ,in , where is the weight of the i-th violation, reflecting the dominant role of serious violations in the total duration; Criteria for restoring normal function: The violation recovery period has expired, meaning the actual number of days D ≥ T. r (or T) rt ); No new violations occurred during the recovery period, meaning no new violation quantification value V was added. q新 =0; Completion rate of rectification of potential hazards caused by previous violations: C r =100%, ; The user complaint rate was 0 during the recovery period. ; For example, in a single violation by a security inspector, the degree of photo falsification is P. f =60, Process violation level P v =50, Hidden Danger Level H c =30, User complaint level C q =50; but This constitutes a Level 1 violation, and its recovery time is [not specified]. sky; If the person does not commit any new violations within these 59 days, all previous potential hazards are rectified, and there are no user complaints, then the system will return to normal after 59 days. By incorporating user complaints as a factor, the performance of safety inspectors can be more comprehensively reflected, further constraining their standardized operations and improving the quality of gas home safety inspections and user satisfaction.

[0038] The gas inlet safety inspection anti-cheating system described in this application includes: a user terminal module, a staff terminal module, and a back-end management module; The client-side module is deployed on the user's mobile terminal and is mainly used for: Users can complete the real-name authentication process of "person, certificate, and photo in one" by filling in personal information, uploading ID documents and real-time facial images; display users' basic information, historical security check records and gas usage; receive electronic dynamic QR codes containing information such as dynamic encryption factors, validity period and geographical location issued by the background, and show them to staff for scanning during security checks; Users can view current and historical security inspection reports to understand the details of the inspections and the status of any potential hazard rectification.

[0039] The staff-side module is deployed on the mobile terminals of security personnel and has functions such as positioning, QR code scanning, photo taking, and data transmission, specifically including: The system acquires and reports staff location information in real time, with a positioning accuracy of ≤10 meters; it scans the electronic dynamic QR code presented by the user and parses the embedded dynamic encryption factor, timestamp, geographical location, and other information; it uploads the parsed data and the current location to the backend in real time, triggering triple verification of QR code validity, location proximity, and dynamic factor consistency; after successful verification, it provides standardized safety inspection process guidelines, supports taking on-site photos of key objects including gas facilities, and supports recording hazard information in text; it uploads the photos, inspection records, location information, etc. to the backend management system in real time.

[0040] The backend management module is deployed on the server side and serves as the core processing and control platform of the system. It mainly includes the following functions: It stores a full range of data, including user real-name information, historical gas usage data, safety inspection records, dynamic encryption factors, QR code information, and photo files. Based on the user's gas consumption fluctuation coefficient, safety inspection compliance coefficient, and user behavior coefficient, combined with adjustable weights α, β, and γ, it calculates and updates the dynamic encryption factor K for each user in real time. It generates encrypted QR codes based on the dynamic encryption factor, validity period, user location, and other information, and manages their lifecycle. It receives scanned data uploaded by the APP, performs QR code validity period verification, geographical proximity judgment (distance ≤ 100 meters), and dynamic encryption factor consistency verification (|KK′| ≤ 0.05). It uses AI technology to analyze uploaded on-site photos, including duplicate similarity detection, clarity scoring, key object (gas stove, pipe, valve) identification, and calculates a comprehensive fraud risk index. It monitors the execution of the safety inspection process in real time, automatically generates safety inspection reports, and supports data query, statistical analysis, and export.

[0041] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this application.

Claims

1. A method for preventing cheating in gas pipeline safety inspections, characterized in that, include: S101. Receive and store the authorized real-name authentication information submitted by the user through the user terminal, complete the real-name authentication and establish a file; S102. The back-end management generates a dynamic encryption factor based on the user's gas usage history, security inspection history and behavior data, and generates an electronic dynamic QR code containing the factor, validity period and geographical location information, and sends it to the user terminal for display. S103. Security personnel use a terminal with positioning function to scan the QR code and simultaneously verify the validity of the QR code, the proximity of the terminal position to the position within the QR code, and the real-time consistency of the dynamic encryption factor. S104. After the verification is passed, an in-home safety inspection will be conducted, and on-site photos containing the specified gas facilities will be taken and uploaded. S105. Analyze the photograph for any abnormalities such as reuse, insufficient clarity, or missing specified objects through image recognition; S106. Based on the comprehensive verification and identification results, quantitatively evaluate and judge the security inspection operation, complete the recording, and implement the corresponding status management.

2. The method for preventing cheating in gas pipeline safety inspections according to claim 1, characterized in that, The dynamic encryption factor is obtained based on the user's gas consumption fluctuation characteristics, historical security inspection compliance, and abnormal user operation data within a preset period.

3. The method for preventing cheating in gas pipeline safety inspections according to claim 1, characterized in that, The specific content of the synchronization verification includes: Verify whether the electronic dynamic QR code is within its validity period, whether the distance between the current location of the terminal and the geographical location information in the QR code is less than or equal to 100 meters, and whether the deviation between the dynamic encryption factor value in the QR code and the current value calculated by the system in real time is less than or equal to 0.

05.

4. The method for preventing cheating in gas pipeline safety inspections according to claim 1, characterized in that, The analysis of the on-site photos using image recognition technology includes: The system calculates the similarity between on-site photos and historical photos. If the similarity is greater than or equal to 90%, it is considered a reuse. The system also calculates the clarity score of on-site photos. If the score is lower than a preset threshold, it is considered insufficient clarity. Finally, the system identifies whether all the required key gas facilities are included in the on-site photos. If any are missing, it is considered non-compliant.

5. A method for preventing cheating in gas pipeline safety inspections according to claim 1, characterized in that, The quantitative assessment and judgment include: quantitatively scoring the operation of security personnel based on at least one of the following: abnormal photo conditions, compliance of security inspection procedures, completeness of reported potential hazards, and user complaint information, and determining the level of violation based on the scoring results.

6. A method for preventing cheating in gas pipeline safety inspections according to claim 5, characterized in that, The method also includes setting a corresponding status recovery time and recovery conditions based on the level of violation. The recovery conditions include the completion of rectification of related hidden dangers, no new violations during the recovery period, and no valid user complaints.

7. A system for using a gas inlet safety inspection anti-cheating method as described in any one of claims 1-6, characterized in that, It includes a user-side module, a staff-side module, and a back-end management module; The user-end module is configured on the user's mobile terminal and is used to enable users to complete real-name authentication and generate and display electronic dynamic QR codes containing dynamic encryption factors. The staff terminal module, configured on the mobile terminal of security personnel, has a positioning function and is used to scan the electronic dynamic QR code, obtain and upload the real-time location, and perform on-site security inspection operations and data collection. The backend management module communicates with the user terminal module and the staff terminal module. It is used to store user data and security inspection records, generate the dynamic encryption factor based on the user's gas usage history, security inspection history and behavior data, and generate an electronic dynamic QR code accordingly. It performs real-time multi-verification of the scanning information and location information uploaded by the staff terminal APP module, and performs anomaly analysis based on image recognition on the uploaded on-site photos.

8. The system according to claim 7, characterized in that, When generating the electronic dynamic QR code, the backend management module encodes the dynamic encryption factor, validity period information, and user geographical location information into the QR code.

9. The system according to claim 7, characterized in that, The real-time verification of QR code information and location information by the backend management module includes: QR code validity period verification, geographical proximity judgment, and dynamic encryption factor consistency verification.

10. The system according to claim 7, characterized in that, The background management module performs anomaly analysis on on-site photos, including: duplicate similarity detection, sharpness scoring, and key object identification.

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