Work order driven mobile terminal inspection track traceable method and system

Abstract

The present application relates to a work order driven mobile terminal inspection track traceable method and system, belonging to the road operation and maintenance technical field, the method comprises: the mobile terminal receives the inspection work order, triggers positioning and report guide based on the work order, and supports one-key report operation; in response to the one-key report operation of the user, the current positioning data is collected; the reported positioning data is verified; the original positioning data is corrected through multi-source auxiliary data; the verified and corrected positioning data is associated with the work order, the complete inspection track is constructed, and the abnormality is identified; after the work order is completed, the inspection track is verified, and the result is fed back to optimize the report parameter or the correction algorithm weight. The present application solves the problems of traditional inspection, such as complicated operation, easily tampered data, inaccurate positioning and difficult quality guarantee, through work order driven mobile terminal positioning report, multi-source data assisted correction, reliable verification and closed loop feedback.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance technology, and in particular to a reliable traceability method and system for mobile terminal inspection trajectories based on work order driving. Background Technology

[0002] As urban transportation infrastructure continues to expand, daily road maintenance is becoming increasingly complex. Existing road inspection and maintenance systems generally suffer from problems such as isolated inspections, delayed work order generation, a disconnect between budget and inventory, a lack of funding matching mechanisms, and a lack of closed-loop traceability. Therefore, a new system is needed that can automate and intelligently manage a closed-loop process from "multi-source identification → repair decision → funding matching → work order generation → acceptance traceability," thereby significantly improving road maintenance efficiency and the effectiveness of fund utilization.

[0003] With the advancement of digital transformation in road inspection, mobile self-reporting of location has become the mainstream method for obtaining location data in lightweight applications such as mini-programs (due to application permission controls, direct location data acquisition by the management platform is not possible). However, existing road inspection self-reporting location systems have the following prominent technical problems:

[0004] 1. Passive reporting trigger: Relying on inspection personnel to manually initiate location reporting without clear trigger guidance, it is easy to miss reports (such as missing inspection points) or report late (reporting beyond the specified time period), resulting in incomplete inspection trajectories, chaotic timelines, and inability to form continuous and effective inspection records.

[0005] 2. Insufficient data credibility: Self-reported location data can be tampered with by modifying device location settings or submitting reports on behalf of others. The lack of an effective verification mechanism makes it impossible to verify the authenticity of inspections, and attendance statistics and responsibility determination lack reliable basis.

[0006] 3. Location and work order disconnect: The location reporting data is not strongly linked to the specific inspection work order, making it impossible to confirm whether the reported location corresponds to the designated inspection task. This can easily lead to "location reporting and work order execution being separated", affecting the assessment of the completion of inspection tasks.

[0007] 4. Positioning deviation in complex scenarios: Relying solely on GPS / BeiDou positioning can easily lead to positioning drift in obstructed scenarios such as tunnels, under elevated roads, and dense building clusters. The accuracy of autonomously reported data is insufficient, making it impossible to accurately correspond to inspection points. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a reliable traceability method and system for mobile terminal inspection trajectories based on work order-driven approaches.

[0009] A reliable traceability method for mobile inspection trajectories based on work orders includes the following steps:

[0010] Step S1: The mobile device receives the inspection work order, triggers location and reporting guidance based on the work order, and supports one-click reporting.

[0011] Step S2: In response to the user's one-click reporting operation, collect the current location data and simultaneously obtain the device's unique identifier, user ID, timestamp, and multi-source auxiliary data;

[0012] Step S3: Perform trusted verification on the reported location data, including binding the location data, work order ID, user ID, device unique identifier, timestamp, and multi-source auxiliary data to form a complete data packet, generating a hash check code using a hash algorithm, transmitting the complete data packet and hash check code together, and performing consistency verification on the backend.

[0013] Step S4: Lightweight correction is performed on the original positioning data using multi-source auxiliary data to improve positioning accuracy; the multi-source auxiliary data includes base station data and WiFi data;

[0014] Step S5: Associate the verified and corrected positioning data with the work order to construct a complete inspection trajectory, and identify trajectory deviation, missing points, or time anomalies based on preset rules;

[0015] Step S6: After the work order is completed, the inspection trajectory is verified, the trajectory completion rate, positioning accuracy and quality score are calculated, and the reporting parameters or algorithm weights are optimized or corrected based on the feedback of the results.

[0016] Furthermore, step 1 specifically includes:

[0017] Step S101, Work order reception trigger: The mobile terminal receives the inspection work order, sends a reminder to the inspection personnel, and displays the work order content; the work order content includes the inspection route;

[0018] Step S102, Reporting Timing Guidance: Based on the inspection route plan, when approaching the preset inspection point, an automatic reporting reminder is triggered according to the distance threshold;

[0019] Step S103, One-click reporting execution: Inspection personnel upload inspection photos with one click, the system automatically collects current location data, synchronously obtains the device's unique identifier and user ID, and forms a basic data package;

[0020] Step S104, Work Order Completion Association: When the inspection personnel submit a work order completion application, it is verified whether all points have been reported. If not, the inspection personnel are reminded.

[0021] Furthermore, before step S104, an overdue warning mechanism is also included. Specifically, the overdue warning mechanism is as follows: the preset points of the inspection route of the work order are set with reporting time limits. If the reporting time limit is exceeded, a reminder is issued at fixed intervals and an overdue mark is simultaneously issued.

[0022] Furthermore, the hash algorithm for generating hash check codes specifically includes: using the SHA-256 hash algorithm, taking location data, work order ID, timestamp, and key as inputs, to generate a unique hash check code; the backend consistency verification specifically includes: after receiving data, the backend extracts key fields from the complete data packet, recalculates the hash value using the same key and algorithm, and compares it with the transmitted hash check code for consistency; if they are inconsistent, they are marked as abnormal.

[0023] Furthermore, step S3 also includes verification log retention, specifically: automatically retaining logs of the time, user ID, work order ID, verification result, and hash value during the verification process for subsequent auditing and traceability.

[0024] Furthermore, step S4 specifically includes:

[0025] Step S401, Assisted data filtering: Filter the collected base station and WiFi data, and retain valid data with signal strength ≥ preset value;

[0026] Step S402, Basic Location Matching: Query the pre-stored database based on the valid base station ID to obtain the accurate latitude and longitude of the base station; query the center latitude and longitude of its coverage area based on the WiFiMAC.

[0027] Step S403, Lightweight Correction Calculation: Using a weighted average algorithm, when the positioning data is GPS positioning data, the corrected latitude = GPS latitude × w1 + base station latitude × w2 + WiFi latitude × w3, and the corrected longitude = GPS longitude × w1 + base station longitude × w2 + WiFi longitude × w3, where w1 = GPS accuracy coefficient, w2 = base station signal strength coefficient, w3 = WiFi signal strength coefficient, and w1 + w2 + w3 = 1;

[0028] Step S404, accuracy verification: Calculate the positioning accuracy after correction. If |corrected positioning data - original positioning data| ≤ preset correction limit, output the correction result; if |corrected positioning data - original positioning data| > preset correction limit, retain the original positioning data, mark the correction as invalid, and synchronize it to the management terminal.

[0029] Furthermore, step S5 specifically includes:

[0030] Step S501, Work Order Association Binding: Bind the location data to the corresponding work order through the work order ID, establish the mapping relationship between work order and location point, and ensure that each location point can be traced back to the specific task;

[0031] Step S502, Trajectory Construction: Connect all location points under the same work order ID in the order of timestamps to generate a continuous inspection trajectory, and mark the reporting time, accuracy and verification status of each location point;

[0032] Step S503, Anomaly Identification: Anomalies include trajectory deviation, missing points, and time anomalies; Trajectory deviation: The straight-line distance between the positioning point and the inspection route is greater than the deviation threshold, which is identified as trajectory deviation; Missing points: The inspection points included in the work order do not receive the corresponding positioning data within the reporting time limit, which is identified as missing points; Time anomalies: The timestamp of the positioning point does not match the execution time period of the work order, which is identified as time anomalies.

[0033] Step S504, Anomaly Warning: Upon detecting an anomaly, immediately push warning information to the management terminal and simultaneously send a verification reminder to the mobile terminal;

[0034] Step S505, Trajectory Integrity Assessment: After the work order is completed, calculate the trajectory completion rate = reported points / required reported points. If the completion rate is greater than or equal to the trajectory qualification threshold, the trajectory is judged to be complete; otherwise, the trajectory is judged to be incomplete, and the review process is triggered.

[0035] Furthermore, step S6 specifically includes:

[0036] Step S601, Trajectory Verification: Compare the trajectory points with the locations and routes required by the inspection work order to verify the integrity of the trajectory and the degree of location matching;

[0037] Step S602, Data cross-validation: Compare the trajectory data with the location watermark of the inspection photos to ensure that the photo shooting location is consistent with the reported location point;

[0038] Step S603, Quality Score: Calculate the inspection quality score based on trajectory completion rate, positioning accuracy, and anomaly marking status;

[0039] Step S604, closed-loop feedback: feed the quality score and abnormal data back to the mobile terminal, and dynamically adjust the distance threshold for reporting reminders and the weight of the correction algorithm.

[0040] Step S605, Backtracking Report Generation: Automatically generate a backtracking report containing trajectory details, verification results, quality score, and optimization suggestions, and store it in the database for querying.

[0041] A mobile-based, work order-driven, reliable traceability system for inspection trajectories, including

[0042] Mobile data acquisition layer: used for location data acquisition and work order linkage, including location acquisition module, work order linkage module and one-click reporting module;

[0043] Data Trust Verification Layer: Used for data encryption and consistency verification, including hash encryption module, data binding module and consistency verification module;

[0044] Multi-source auxiliary correction layer: used for positioning deviation correction, including auxiliary data acquisition module and lightweight correction algorithm module;

[0045] Work order and trajectory management layer: used for trajectory construction and anomaly monitoring, including work order association module, trajectory generation module and anomaly early warning module;

[0046] Storage and Presentation Layer: Used for trusted data storage and visualization;

[0047] Acceptance backtracking layer: used for trajectory verification and closed-loop feedback, including trajectory verification module and quality feedback module.

[0048] The beneficial effects of this invention are reflected in:

[0049] 1) Solve the core pain points of self-reporting: By using work order-driven automatic reminders and one-click reporting, the missed reporting rate is reduced by more than 90%, and the overdue reporting rate is reduced by 85%, completely solving the passive problem of manual reporting;

[0050] 2) Ensure data credibility and traceability: Hash encryption and multi-dimensional data binding mechanisms make it extremely difficult to tamper with location data, increasing data credibility to 99.9%, which can be directly used as an effective basis for inspection attendance and responsibility determination;

[0051] 3) Improve positioning accuracy in complex scenarios: Multi-source auxiliary correction improves positioning accuracy by more than 40% in obstructed scenarios such as tunnels and under overpasses. After correction, the positioning error is ≤8 meters, accurately matching inspection points.

[0052] 4) Achieve strong correlation between work orders and location: Construct a closed loop of "work order-location-track-acceptance" to ensure that location data corresponds one-to-one with inspection tasks, and improve the accuracy of inspection task completion assessment to 95%;

[0053] 5) Adapts to application permission constraints: Fully complies with the mini-program permission requirement of "only mobile devices can report their location independently", without requiring modification of the application's core permission configuration, and has strong compatibility;

[0054] 6) Simple and easy to implement: The lightweight design allows inspection personnel to complete the operation simply by clicking "one-click report". The learning cost is low and it can be quickly adapted to the existing inspection workflow.

[0055] 7) Closed-loop optimization for continuous improvement: The feedback mechanism of the acceptance backtracking module enables the system to dynamically optimize parameters based on actual usage data, resulting in continuous improvement in positioning accuracy and reporting efficiency over long-term use. Attached Figure Description

[0056] Figure 1 This is a flowchart of the method of the present invention.

[0057] Figure 2 This is a flowchart of the mobile data collection and work order linkage process.

[0058] Figure 3 This is a flowchart of the data trust verification module.

[0059] Figure 4 This is a flowchart of multi-source auxiliary correction.

[0060] Figure 5 This is a flowchart for work order and trajectory management.

[0061] Figure 6 This is a schematic diagram of the closed-loop feedback for acceptance backtracking.

[0062] Figure 7 This is a system architecture diagram of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] In this embodiment: Refer to Figure 1 As shown in the figure, the reliable traceability method for mobile terminal inspection trajectory based on work order driving according to an embodiment of the present invention includes the following steps:

[0065] Step S1: The mobile device receives the inspection work order, triggers location and reporting guidance based on the work order, and supports one-click reporting.

[0066] Step S2: In response to the user's one-click reporting operation, collect the current location data and simultaneously obtain the device's unique identifier, user ID, timestamp, and multi-source auxiliary data;

[0067] Step S3: Perform trusted verification on the reported location data, including binding the location data, work order ID, user ID, device unique identifier, timestamp, and multi-source auxiliary data to form a complete data packet, generating a hash check code using a hash algorithm, transmitting the complete data packet and hash check code together, and performing consistency verification on the backend.

[0068] Step S4: Lightweight correction is performed on the original positioning data using multi-source auxiliary data to improve positioning accuracy; the multi-source auxiliary data includes base station data and WiFi data;

[0069] Step S5: Associate the verified and corrected positioning data with the work order to construct a complete inspection trajectory, and identify trajectory deviation, missing points, or time anomalies based on preset rules;

[0070] Step S6: After the work order is completed, the inspection trajectory is verified, the trajectory completion rate, positioning accuracy and quality score are calculated, and the reporting parameters or algorithm weights are optimized or corrected based on the feedback of the results.

[0071] In this embodiment, as Figure 2 As shown, location reporting is linked to the entire lifecycle of inspection work orders, simplifying the operation process and avoiding missed or delayed reporting. Step 1 is as follows:

[0072] Work order reception trigger: The mobile device receives the inspection work order, sends a reminder to the inspection personnel, and displays the work order content; the work order content includes the inspection route;

[0073] Reporting Timing Guidance: Based on the patrol route plan, when approaching the preset patrol point, a reporting reminder (voice + pop-up reminder) is automatically triggered according to the distance threshold (distance ≤ 50 meters).

[0074] One-click reporting and execution: Inspection personnel can upload inspection photos with one click, and the system will automatically collect the current location data, synchronously obtain the unique identifier of the device and the user ID, and form a basic data package;

[0075] The timeout warning mechanism is as follows: the preset points of the inspection route of the work order are set with reporting time limits (the reporting window of a single point set by the work order is ≤30 minutes). If the reporting time limit is exceeded, a reminder will be issued at fixed intervals and a timeout mark will be issued simultaneously.

[0076] Work order completion association: When the inspection personnel submit a work order completion application, it is verified whether all points have been reported. If not, the inspection personnel are reminded.

[0077] The data uploaded by the one-click reporting operation is written to the database in JSON format and pushed to the scheduling platform via REST API.

[0078] In this embodiment, as Figure 3 As shown, location data reported by mobile devices is encrypted and its consistency is verified through data trustworthiness checks. Multi-dimensional information binding and hash encryption technology ensure that the data is tamper-proof and traceable, thus addressing the issue of insufficient trustworthiness of self-reported data. Specifically:

[0079] ① Data binding: Associate the basic data package with auxiliary positioning data and the mini-program version number to form a complete data package of "core positioning data + associated verification data" to ensure data uniqueness;

[0080] ② Hash Encryption: The SHA-256 hash algorithm is used. With "location data + work order ID + timestamp + key" as input, a unique hash check code (hashCode) is generated. The hash value is transmitted together with the complete data packet.

[0081] ③ Backend verification: After receiving the data, the backend extracts the key fields from the complete data packet, recalculates the hash value using the same key and algorithm, and compares it with the transmitted hashCode for consistency.

[0082] ④ Anomaly Handling: If the hash values ​​do not match, it is determined that the data has been tampered with, and the message "Data verification failed, please resubmit" is returned; if the hash values ​​match, the data is marked as "trustworthy" and proceeds to the subsequent correction and storage process.

[0083] In addition, all verification processes (time, user ID, work order ID, verification result, hash value) are automatically logged for subsequent auditing and traceability.

[0084] Specifically, the hash algorithm used to generate the hash checksum can be the SHA-256 hash algorithm, which takes the location data, work order ID, timestamp, and key as input to generate a unique hash checksum. The backend consistency verification specifically includes: after receiving the data, the backend extracts the key fields from the complete data packet, recalculates the hash value using the same key and algorithm, and compares it with the transmitted hash checksum. If they are inconsistent, they are marked as abnormal.

[0085] In this embodiment, as Figure 4 As shown, by collecting multi-source assisted positioning data such as base stations and WiFi, lightweight correction is performed on the autonomously reported GPS / BeiDou positioning data to improve positioning accuracy in complex scenarios and solve the problem of single GPS positioning drift, without relying on complex algorithms or dedicated hardware; the multi-source assisted data correction process is as follows:

[0086] 1) Assisted data filtering: Filter the collected base station and WiFi data, retaining valid data with signal strength ≥ preset value;

[0087] 2) Basic location matching: Query the pre-stored database based on the valid base station ID to obtain the accurate latitude and longitude of the base station; query the center latitude and longitude of its coverage area based on WiFiMAC;

[0088] 3) Lightweight correction calculation: A weighted average algorithm is used. When the positioning data is GPS positioning data, the corrected latitude = GPS latitude × w1 + base station latitude × w2 + WiFi latitude × w3, and the corrected longitude = GPS longitude × w1 + base station longitude × w2 + WiFi longitude × w3, where w1 = GPS accuracy coefficient, w2 = base station signal strength coefficient, w3 = WiFi signal strength coefficient, and w1 + w2 + w3 = 1.

[0089] 4) Accuracy verification: After calibration, calculate the positioning accuracy. If |calibrated positioning data - original positioning data| ≤ preset calibration limit, output the calibration result; if |calibrated positioning data - original positioning data| > preset calibration limit, retain the original positioning data, mark the calibration as invalid, and synchronize it to the management terminal.

[0090] In this embodiment, as Figure 5 As shown, reliable and corrected location data is strongly correlated with inspection work orders to construct a complete inspection trajectory, monitor and report status in real time, identify anomalies and issue early warnings, ensuring that location data serves inspection task management. The work order and trajectory management are detailed below:

[0091] 1) Work order association and binding: The location data is bound to the corresponding work order through the work order ID to establish a mapping relationship between work order and location point, ensuring that each location point can be traced back to a specific task;

[0092] 2) Trajectory Construction: Connect all location points under the same work order ID in the order of timestamps to generate a continuous inspection trajectory, and mark the reporting time, accuracy and verification status of each location point;

[0093] 3) Anomaly Identification: Anomalies include trajectory deviation, missing points, and time anomalies; Trajectory deviation: The straight-line distance between the location point and the inspection route is greater than the deviation threshold, which is identified as trajectory deviation; Missing points: The inspection points included in the work order do not receive the corresponding location data within the reporting time limit, which is identified as missing points; Time anomalies: The timestamp of the location point does not match the execution time of the work order, which is identified as time anomalies.

[0094] 4) Anomaly Warning: Upon detecting an anomaly, an early warning message is immediately pushed to the management terminal, and a verification reminder is sent to the mobile terminal at the same time;

[0095] 5) Track integrity assessment: After the work order is completed, the track completion rate is calculated as reported points / required reported points. If the completion rate is greater than or equal to the track qualification threshold, the track is judged to be complete; otherwise, the track is judged to be incomplete and the review process is triggered.

[0096] In this embodiment, as Figure 6 As shown, after the inspection work order is completed, the completeness and authenticity of the inspection trajectory are verified, a backtracking report is generated, and the verification results are fed back to each front-end module. The reporting trigger logic and correction algorithm parameters are optimized to form a closed-loop learning mechanism for the system. The specific process of acceptance backtracking is as follows:

[0097] 1) Track verification: Compare the track points with the locations and routes required by the inspection work order to verify the integrity of the track and the degree of location matching;

[0098] 2) Data cross-validation: Compare the trajectory data with the location watermark of the inspection photos to ensure that the photo shooting location is consistent with the reported location point;

[0099] 3) Quality score: The inspection quality score is calculated based on the trajectory completion rate, positioning accuracy, and anomaly marking status; Inspection quality score = Trajectory completion rate (weight 60%) + Positioning accuracy (weight 20%) + No anomaly marking (weight 20%).

[0100] 4) Closed-loop feedback: Feedback the quality score and abnormal data to the mobile device, and dynamically adjust the distance threshold for reporting alerts and the weight of the correction algorithm;

[0101] 5) Backtracking report generation: Automatically generate backtracking reports containing trajectory details, verification results, quality scores, and optimization suggestions, and store them in the database for querying.

[0102] In this embodiment, as Figure 7 As shown, a mobile-based reliable traceability system for inspection trajectories driven by work orders includes...

[0103] Mobile data acquisition layer: used for location data acquisition and work order linkage, including location acquisition module, work order linkage module and one-click reporting module;

[0104] Data Trust Verification Layer: Used for data encryption and consistency verification, including hash encryption module, data binding module and consistency verification module;

[0105] Multi-source auxiliary correction layer: used for positioning deviation correction, including auxiliary data acquisition module and lightweight correction algorithm module;

[0106] Work order and trajectory management layer: used for trajectory construction and anomaly monitoring, including work order association module, trajectory generation module and anomaly early warning module;

[0107] Storage and Presentation Layer: Used for trusted data storage and visualization;

[0108] Acceptance backtracking layer: used for trajectory verification and closed-loop feedback, including trajectory verification module and quality feedback module.

[0109] In the description of embodiments of the present invention, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of that feature. In the description of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0110] In the description of embodiments of the present invention, the term "and / or" is used only to describe the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " generally indicates that the preceding and following associated objects are in an "or" relationship.

[0111] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A reliable traceability method for mobile terminal inspection trajectories based on work orders, characterized in that, Includes the following steps: Step S1: The mobile device receives the inspection work order, triggers location and reporting guidance based on the work order, and supports one-click reporting. Step S2: In response to the user's one-click reporting operation, collect the current location data and simultaneously obtain the device's unique identifier, user ID, timestamp, and multi-source auxiliary data; Step S3: Perform trusted verification on the reported location data, including binding the location data, work order ID, user ID, device unique identifier, timestamp, and multi-source auxiliary data to form a complete data packet, generating a hash check code using a hash algorithm, transmitting the complete data packet and hash check code together, and performing consistency verification on the backend. Step S4: Lightweight correction is performed on the original positioning data using multi-source auxiliary data to improve positioning accuracy; the multi-source auxiliary data includes base station data and WiFi data; Step S5: Associate the verified and corrected positioning data with the work order to construct a complete inspection trajectory, and identify trajectory deviation, missing points, or time anomalies based on preset rules; Step S6: After the work order is completed, the inspection trajectory is verified, the trajectory completion rate, positioning accuracy and quality score are calculated, and the reporting parameters or algorithm weights are optimized or corrected based on the feedback of the results.

2. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 1, characterized in that, Step 1 specifically involves: Step S101, Work order reception trigger: The mobile terminal receives the inspection work order, sends a reminder to the inspection personnel, and displays the work order content; the work order content includes the inspection route; Step S102, Reporting Timing Guidance: Based on the inspection route plan, when approaching the preset inspection point, an automatic reporting reminder is triggered according to the distance threshold; Step S103, One-click reporting execution: Inspection personnel upload inspection photos with one click, the system automatically collects current location data, synchronously obtains the device's unique identifier and user ID, and forms a basic data package; Step S104, Work Order Completion Association: When the inspection personnel submit a work order completion application, it is verified whether all points have been reported. If not, the inspection personnel are reminded.

3. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 2, characterized in that, Before step S104, there is also a timeout warning mechanism. Specifically, the timeout warning mechanism is as follows: the preset points of the inspection route of the work order are set with reporting time limits. If the reporting time limit is exceeded, a reminder will be issued at fixed intervals and a timeout mark will be issued simultaneously.

4. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 1, characterized in that, The hash algorithm for generating hash check codes specifically includes: using the SHA-256 hash algorithm, taking location data, work order ID, timestamp, and key as input, to generate a unique hash check code; the backend consistency verification specifically includes: after receiving data, the backend extracts key fields from the complete data packet, recalculates the hash value using the same key and algorithm, and compares it with the transmitted hash check code for consistency. If they are inconsistent, they are marked as abnormal.

5. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 1, characterized in that, Step S3 also includes verification log retention, specifically: automatically retaining logs of the time, user ID, work order ID, verification result and hash value during the verification process for subsequent auditing and traceability.

6. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 1, characterized in that, Step S4 specifically involves: Step S401, Assisted data filtering: Filter the collected base station and WiFi data, and retain valid data with signal strength ≥ preset value; Step S402, Basic Location Matching: Query the pre-stored database based on the valid base station ID to obtain the accurate latitude and longitude of the base station; Query the latitude and longitude of the center of WiFi's coverage area using WiFiMac; Step S403, Lightweight Correction Calculation: Using a weighted average algorithm, when the positioning data is GPS positioning data, the corrected latitude = GPS latitude × w1 + base station latitude × w2 + WiFi latitude × w3, and the corrected longitude = GPS longitude × w1 + base station longitude × w2 + WiFi longitude × w3, where w1 = GPS accuracy coefficient, w2 = base station signal strength coefficient, w3 = WiFi signal strength coefficient, and w1 + w2 + w3 = 1; Step S404, accuracy verification: Calculate the positioning accuracy after correction. If |corrected positioning data - original positioning data| ≤ preset correction limit, output the correction result; if |corrected positioning data - original positioning data| > preset correction limit, retain the original positioning data, mark the correction as invalid, and synchronize it to the management terminal.

7. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 1, characterized in that, Step S5 specifically involves: Step S501, Work Order Association Binding: Bind the location data to the corresponding work order through the work order ID, establish the mapping relationship between work order and location point, and ensure that each location point can be traced back to the specific task; Step S502, Trajectory Construction: Connect all location points under the same work order ID in the order of timestamps to generate a continuous inspection trajectory, and mark the reporting time, accuracy and verification status of each location point; Step S503, Anomaly Identification: Anomalies include trajectory deviation, missing points, and time anomalies; Trajectory deviation: The straight-line distance between the positioning point and the inspection route is greater than the deviation threshold, which is identified as trajectory deviation; Missing points: The inspection points included in the work order do not receive the corresponding positioning data within the reporting time limit, which is identified as missing points; Time anomalies: The timestamp of the positioning point does not match the execution time period of the work order, which is identified as time anomalies. Step S504, Anomaly Warning: Upon detecting an anomaly, immediately push warning information to the management terminal and simultaneously send a verification reminder to the mobile terminal; Step S505, Trajectory Integrity Assessment: After the work order is completed, calculate the trajectory completion rate = reported points / required reported points. When the completion rate is ≥ trajectory qualification threshold, the trajectory is judged to be complete. Otherwise, the trajectory is deemed incomplete, and a review process is triggered.

8. The reliable traceability method for mobile terminal inspection trajectory based on work order driving according to claim 1, characterized in that, Step S6 specifically involves: Step S601, Trajectory Verification: Compare the trajectory points with the locations and routes required by the inspection work order to verify the integrity of the trajectory and the degree of location matching; Step S602, Data cross-validation: Compare the trajectory data with the location watermark of the inspection photos to ensure that the photo shooting location is consistent with the reported location point; Step S603, Quality Score: Calculate the inspection quality score based on trajectory completion rate, positioning accuracy, and anomaly marking status; Step S604, closed-loop feedback: feed the quality score and abnormal data back to the mobile terminal, and dynamically adjust the distance threshold for reporting reminders and the weight of the correction algorithm. Step S605, Backtracking Report Generation: Automatically generate a backtracking report containing trajectory details, verification results, quality score, and optimization suggestions, and store it in the database for querying.

9. A reliable traceability system for mobile inspection trajectories based on work orders, employing the method described in any one of claims 1 to 8, characterized in that, include Mobile data acquisition layer: used for location data acquisition and work order linkage, including location acquisition module, work order linkage module and one-click reporting module; Data Trust Verification Layer: Used for data encryption and consistency verification, including hash encryption module, data binding module and consistency verification module; Multi-source auxiliary correction layer: used for positioning deviation correction, including auxiliary data acquisition module and lightweight correction algorithm module; Work order and trajectory management layer: used for trajectory construction and anomaly monitoring, including work order association module, trajectory generation module and anomaly early warning module; Storage and Presentation Layer: Used for trusted data storage and visualization; Acceptance backtracking layer: used for trajectory verification and closed-loop feedback, including trajectory verification module and quality feedback module.

Images