A method and system for automatic dispatch and receipt of fire equipment maintenance tasks

Abstract

This invention relates to the field of maintenance task assignment technology, specifically to an automated assignment and receipt method and system for fire equipment maintenance tasks. The method includes the following steps: automatically generating maintenance work orders based on fire equipment ledgers and maintenance plan databases; automatically pushing the work orders to the corresponding responsible party's terminal; automatically activating and continuously collecting data to form structured execution data packets; calling a preset multi-factor automatic verification rule set to perform multi-source verification; if any verification fails, assigning the data packets to an audit terminal for manual review. This invention automates preventative maintenance and ensures the authenticity of maintenance operations from the technical source, forming a tightly linked and logically consistent electronic evidence chain, providing irrefutable evidence for quality traceability and responsibility determination. It also enables complementary human and machine auditing, with machines handling massive amounts of routine data while humans focus on complex anomalies, allowing for rapid and accurate decision-making and improved processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of maintenance task assignment technology, and more specifically, to an automated assignment and receipt method and system for fire equipment maintenance tasks. Background Technology

[0002] With the increasing demands of urban construction and public safety, the number and types of fire-fighting equipment are growing daily, leading to a significant increase in maintenance tasks. Regular maintenance and upkeep of fire-fighting equipment are crucial for ensuring its wartime availability and preventing fire risks. Currently, the mainstream fire-fighting maintenance management model is still in a semi-informatized or rudimentary informatized stage, typically relying on manual task planning, manual work order assignment, and manual submission of maintenance results, resulting in the following problems: Low efficiency in work order processing: Task generation and assignment mainly rely on manual operation, which is prone to delays or omissions. Untraceable execution process: Maintenance execution lacks real-time monitoring, making it difficult for management to grasp the progress and personnel status; Non-standard receipts: The information uploaded by maintenance personnel is not in a consistent format and has missing content, making it difficult to review; The review mechanism is outdated: abnormal tasks need to be manually screened and processed one by one, which is inefficient and prone to misjudgment; Data traceability is difficult: historical maintenance records are stored in a scattered manner, making it difficult to query and statistically analyze them in a unified manner; Therefore, existing fire equipment maintenance methods are insufficient to meet the current needs of integrated smart fire protection and supervision. There is an urgent need for an automated method and system based on an information platform to realize the automatic generation, execution management, receipt verification and archiving of work orders, thereby improving the efficiency and quality of maintenance operations. Summary of the Invention

[0003] The purpose of this invention is to provide an automated method and system for assigning and receiving feedback on fire equipment maintenance tasks, so as to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, one objective of this invention is to provide an automated method for assigning and receiving feedback on fire equipment maintenance tasks, comprising the following steps: S1. Based on the fire equipment ledger and maintenance plan database, maintenance work orders are automatically generated, and a responsibility mapping relationship is preset. The maintenance work orders are then automatically pushed to the corresponding responsible party's terminal. S2. Receive and display the maintenance work order through the mobile terminal. When the maintenance personnel confirm the start of execution, automatically activate and continuously collect spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence, and forcibly collect on-site media data and detection parameter data associated with the target device in the work order to form a structured execution data packet. S3. Receive the execution data packet and call the preset multi-factor automatic verification rule set to perform multi-source verification; If all verifications pass, a standard electronic receipt will be automatically generated, and the work order status will be updated to "pending review". If any verification fails, the work order status is marked as "pending review" and the specific verification exception is recorded; S4. Assign "pending review" or disputed work orders to the review terminal for manual review of the executed data packets; If the review is approved, the work order status will be updated to "completed", and the data will be stored in a traceable digital maintenance file throughout the entire lifecycle. If the review fails, it will be returned to the responsible party's terminal for re-execution.

[0005] Preferably, step S1, which automatically generates maintenance work orders based on the fire equipment ledger and maintenance plan database, includes the following steps: Construct a fire equipment ledger database to create digital archives for fire equipment, and a maintenance plan rules database that defines the maintenance standards and cycles for various types of equipment; It has a built-in high-precision time scheduler that runs at the smallest time granularity. In each scheduling cycle, it associates and matches the fire equipment ledger database with the maintenance plan rule database, so that the maintenance work order responds to the preset periodic maintenance plan trigger conditions or the real-time abnormal alarm signal of the fire equipment. The preset standard maintenance work order data template is used to fill in maintenance data to generate a maintenance work order, which is then entered into the waiting queue. The work order status is marked as "planned".

[0006] Preferably, the preset responsibility mapping relationship in S1 includes the following steps: The area is divided into multiple responsibility zones, and each zone is assigned a maintenance team. Mark maintenance personnel's skill tags and qualification certificates, and map them to all equipment series they are responsible for, forming professional groups; Work order allocation: Read the maintenance data of the maintenance work order, determine whether the professional team needs to intervene based on the equipment type. If so, prioritize finding the maintenance worker with the lowest current load in the area from the professional team. If not, prioritize finding the maintenance worker with the lowest load in the maintenance team bound to the area. The work order is pushed to the corresponding maintenance worker's App, and the work order status changes from "planned" to "issued". The maintenance worker clicks "Accept" or "Reject" on the mobile app. If "Accept" is clicked, the work order status changes to "Pending Execution," the work order is officially locked to this person, and the planned execution timeline begins to be recorded. If you click "Reject" or there is no response after a timeout, the work order will be reassigned and re-distributed until the work order is accepted.

[0007] Preferably, the automatic activation and continuous collection of spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence in step S2 includes the following steps: Once the maintenance technician arrives on site, he opens the mobile app, clicks "Start Task," and the status changes to "Executing." The mobile app automatically activates background location services to record spatiotemporal trajectories, forming a sequence of geographical locations and a continuous sequence of timestamps; these are then encrypted and appended to a temporary log file on the mobile device that is uniquely bound to the current work order ID.

[0008] Preferably, the forced acquisition of on-site media data and detection parameter data associated with the target device in the work order in S2 includes the following steps: Synchronously load the digital acquisition template bound to the maintenance work order type. The digital acquisition template is used to record the media data list and the test parameter list. A linear guided workflow is used to fill in the digital data acquisition template.

[0009] Preferably, the linear guided workflow includes the following steps: During media capture, the system automatically redirects to the camera interface and overlays an AR guide box to guide alignment. Capture a photo, save the image file, and automatically write the current precise GPS coordinates, timestamp, and device ID as metadata into the photo file; In the parameter entry stage, the input fields are mandatory, and the associated device ID and entry time are recorded in the data structure.

[0010] Preferably, the multi-factor automatic verification rule set performs multi-source verification, including: The first stage is the execution time and space compliance verification: it is determined whether the start and end times of the execution are within the tolerance range of the work order's planned time, and whether the distance between the collected execution geographical location and the geographical location registered in the standard maintenance work order data is less than a preset threshold. If it passes, it proceeds to the next stage; if it fails, it is terminated immediately, the status is marked as "pending review", and the anomaly is recorded. The second stage is to perform logical verification of the execution data: check whether the device identification information in the execution data packet is consistent with the target device identification in the work order. If it passes, proceed to the third stage; if it fails, mark it as "pending review" and record the exception. The third stage, auxiliary verification of the authenticity of the operation: analyze the on-site media data, identify the equipment nameplates and appearance features contained therein, and compare them with the standard information in the equipment ledger.

[0011] Preferably, the auxiliary verification of the operation authenticity also includes: Based on the continuous timestamp sequence and the corresponding geographic location sequence, a maintenance execution trajectory is generated; Verify whether the shooting timestamp and geographical location information of at least one photo in the on-site media data are included in the spatiotemporal point set of the maintenance execution trajectory. Only when both spatial dimension matching and temporal dimension matching are satisfied will the verification pass the output.

[0012] Preferably, the manual review in S4 includes the following steps: The verification anomalies of "Pending Review" work orders are added to the review pool as structured tags; Work orders are grouped in the review pool, including groups for spatiotemporal anomalies, data logic anomalies, and questionable authenticity, to facilitate subsequent allocation by specialty and to calculate and sort them by comprehensive priority score. The execution data packets of the work orders are retrieved according to the sorting order, matched with the reviewers, and a review workbench is formed for manual review.

[0013] The second objective of this invention is to provide an automated assignment and receipt system for fire equipment maintenance tasks, including any of the above-mentioned automated assignment and receipt methods for fire equipment maintenance tasks, comprising a maintenance work order generation unit, an execution data packet acquisition unit, a multi-source verification unit, and a manual review unit. The maintenance work order generation unit is used to automatically generate maintenance work orders based on the fire equipment ledger and maintenance plan database, and to preset the responsibility mapping relationship, and automatically push the maintenance work order to the corresponding responsible party terminal. The execution data packet acquisition unit is used to receive and display the maintenance work order through a mobile terminal. When the maintenance personnel confirm the start of execution, it automatically activates and continuously collects spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence, and forcibly collects on-site media data and detection parameter data associated with the target equipment in the work order to form a structured execution data packet. The multi-source verification unit is used to receive the execution data packet and call the preset multi-factor automatic verification rule set to perform multi-source verification. If any verification fails, the work order status is marked as "pending review" and the specific verification exception is recorded. The manual review unit is used to allocate "pending review" or disputed work orders to the review terminal for manual review execution data packets.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention automates preventative maintenance through a proactive and automatic mode driven by a dual engine of periodic planning and real-time anomaly alarms. It eliminates manual delays, omissions, and misassignments in task generation and assignment, allowing maintenance tasks to flow automatically and precisely like an industrial assembly line. This frees management personnel from heavy scheduling and coordination work, improving management efficiency by hundreds of times, and especially saving valuable time for emergency rescue. Secondly, by forcibly guiding data collection through process locks and automatically injecting immutable time and space stamps and equipment IDs into each piece of field data, the backend service continuously and automatically records the continuous time and space trajectory of the entire operation process. This ensures the authenticity of maintenance operations from the technical source and forms an interlocking and logically consistent electronic evidence chain, providing ironclad evidence for quality traceability and responsibility determination. Furthermore, preliminary verification is conducted through a set of intelligent verification rules that cross-validate multi-level and multi-source data. This not only far surpasses the efficiency of manual verification but also uncovers deep-seated contradictions that are difficult for the human eye to detect. At the same time, the selected abnormal work orders are pushed to the reviewers, and the discrete work order information, abnormal reports, map trajectories, media evidence, historical data, etc. are visualized and integrated, enabling penetrating data review. This allows for human-machine complementarity, with machines handling massive amounts of routine data and humans focusing on complex anomalies, enabling rapid and accurate decision-making and improving processing efficiency. Attached Figure Description

[0015] Figure 1 This is the overall flowchart of Example 1; Figure 2 The flowchart for executing multi-source verification of the multi-factor automatic verification rule set in Example 1 is shown. Detailed Implementation

[0016] 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.

[0017] Example 1 like Figures 1-2 As shown, one of the objectives of this invention is to provide an automated method for assigning and receiving feedback on fire equipment maintenance tasks, comprising the following steps: S1. Maintenance work orders are automatically generated based on the fire equipment ledger and maintenance plan database, and a responsibility mapping relationship is preset. The maintenance work orders are automatically pushed to the corresponding responsible party's terminal, which is conducive to the automatic progress of tasks like on an assembly line. This greatly reduces the management costs of manual coordination, tracking, and expediting, and significantly improves efficiency.

[0018] Specifically, the automatic generation of maintenance work orders in S1 based on the fire equipment ledger and maintenance plan database includes the following steps: A fire equipment ledger database is constructed to create digital archives for fire equipment (such as fire extinguishers, fire hydrants, alarms, and water pumps). Each record includes static and dynamic attributes such as equipment code, name, model, installation location (accurate to GPS coordinates or building code), region, responsible person, production date, and last maintenance date. A maintenance plan rule database defines the maintenance standards and cycles for various types of equipment. For example: "Dry powder fire extinguishers: monthly appearance and pressure inspection, annual comprehensive inspection and refilling"; "Fire alarm controllers: daily functional self-check, quarterly linkage test." Each rule is associated with the equipment type and model. It incorporates a high-precision time scheduler (such as a task scheduling framework like Quartz), operating at the smallest time granularity (e.g., "every morning"). In each scheduling cycle, it correlates and matches the fire equipment ledger database with the maintenance plan rule database, ensuring that maintenance work orders respond to preset periodic maintenance plan trigger conditions or real-time abnormal alarm signals from fire equipment. Specifically: First, for all equipment whose planned dates fall within this time window, a periodic maintenance plan is automatically created for it, and the periodic maintenance plan is set as the planned maintenance date for that equipment. A response is issued when the maintenance date is reached. Second, through the device IoT data access gateway, it monitors abnormal signals from the automatic fire alarm system, equipment sensors, and IoT modules in real time. The signals follow standard protocols (such as Modbus, BACnet, GB / T). (e.g., 26875) When the gateway receives a signal predefined as "abnormal" or "fault" (such as "smoke detector malfunction", "water pump pressure below threshold", "fire door normally open alarm"), it parses the device identification code or physical address in the alarm signal, performs a quick search in the fire equipment ledger database, accurately locates the specific device that is malfunctioning, and generates an emergency maintenance work order. The priority of such work orders is usually marked as "high" or "urgent", the planned execution time is immediately or the current time, and a specific alarm description is attached (e.g., "fault code: F001"). Regardless of the triggering mode, the final step in generating a work order is data population based on a template. A pre-defined standard maintenance work order data template is used to populate maintenance data, generating a work order that enters the assignment queue. The work order status is marked as "planned." Specifically, this involves automatically filling the corresponding fields in the template with "Basic Equipment Information" (code, location, model) from the equipment ledger, "Task Information" (planned time or alarm time, task type) from the trigger source, and "Execution Standards and Requirements" (items to be checked, standard parameter values, required photo angles) extracted from the maintenance plan rules. Simultaneously, the system generates a globally unique work order serial number and records the work order's... The generation time and trigger source (plan / alarm and specific reason) are then complete, and a standard work order containing all necessary information is generated and entered into the assignment queue. This frees managers from tedious calendar scheduling and Excel spreadsheet creation. Monthly / quarterly plan preparation and breakdown, which used to take several days, can now be completed automatically in milliseconds within minutes, improving efficiency by hundreds of times. It achieves a second-level response from "equipment malfunction" to "maintenance task assignment," automatically identifying, locating, and creating the work order. This completely eliminates the intermediate steps and time delays of manual alarm receiving, recording, searching equipment information, and manually placing orders, saving valuable time for rapid response.

[0019] Furthermore, the preset responsibility mapping relationship in S1 includes the following steps: Divide the area into multiple responsibility zones, and assign a maintenance team to each zone. Note that each maintenance team should be equipped with personnel with the corresponding qualifications for that zone as much as possible. Mark maintenance personnel's skill tags and qualification certificates, and map them to all equipment series they are responsible for, forming professional groups; Work order allocation: Read the maintenance data of the maintenance work order, determine whether the professional team needs to intervene based on the equipment type. If so, prioritize finding the maintenance worker with the lowest current load in the area from the professional team. If not, prioritize finding the maintenance worker with the lowest load in the bound maintenance team in the area. Through professional mapping, ensure that complex equipment is handled by the most knowledgeable person, guarantee the professionalism and quality of maintenance work from the source, assign responsibility to individuals, and eliminate mutual shirking of responsibility. The work order can be pushed to the corresponding maintenance personnel's App (the push method includes lock screen reminder and ringing). You can also choose to send an SMS as a backup. The push content includes a work order summary, urgency level and direct link. The work order status changes from "planned" to "issued". The maintenance worker clicks "Accept" or "Reject" on the mobile app. If "Accept" is clicked, the work order status changes to "Pending Execution," the work order is officially locked to this person, and the planned execution timeline begins to be recorded. If "Reject" is clicked or there is no response after a timeout, the work order will be reassigned and re-distributed until the work order is accepted, ensuring the continuity and robustness of the operational process.

[0020] S2. The maintenance work order is received and displayed via a mobile terminal. When the maintenance personnel confirm the start of execution, the system automatically activates and continuously collects spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence. It also forcibly collects on-site media data and detection parameter data associated with the target equipment in the work order, forming a structured execution data package. This facilitates a unified work order data template and execution process, standardizes the on-site operation and feedback format of maintenance personnel, improves the usability and standardization of data, facilitates subsequent verification, effectively eliminates fake maintenance and perfunctory work, and ensures the authenticity and quality of maintenance operations.

[0021] Specifically, S2 involves automatically activating and continuously collecting spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequences, including the following steps: When the maintenance worker arrives at the site, he opens the mobile app and clicks "Start Task." The status changes to "Executing." This click is not a simple interface jump, but a strong status change command sent to the system backend. The app immediately changes the work order status to "Executing" and disables the "Start Execution" button to prevent duplicate operations. After receiving the command, the server accurately records the "actual start execution timestamp (t0)" of the work order in the database and synchronizes the status. At the same time, the backend sends an authorization command to the app to "activate" the trajectory collection task. The mobile app automatically activates background location services to record spatiotemporal trajectories, forming a sequence of geographical locations and continuous timestamps. Even if the user switches the app to the background or briefly locks the screen, the service continues to work. Upon initial startup or when necessary, it dynamically requests and ensures that it obtains continuous location permissions from the user. At the same time, a continuous notification (such as "Recording maintenance trajectory - Work order number: XXXX") is displayed in the phone's notification bar, clearly informing the user that data collection is in progress, enhancing compliance and preventing accidental closure. The data is also encrypted and appended to a temporary log file uniquely bound to the current work order ID on the mobile device.

[0022] Furthermore, the forced collection of on-site media data and detection parameter data associated with the target device in the work order in S2 includes the following steps: Synchronously load the digital data acquisition template bound to the maintenance work order type. The digital data acquisition template is used to record the media data list: the content, quantity, and key elements of the photos / videos that must be taken. For example, for fire extinguisher inspection, the template requires taking: one "panoramic photo of the fire extinguisher and its surrounding environment", one "close-up photo of the pressure gauge pointer", and one "photo of the safety pin and latch". It also includes a list of test parameters: the parameter items that must be filled in or read, their format, and the legal value range. For example, it requires entering: "Pressure value (MPa)", which must be entered numerically, with a normal range of 1.2-1.4; "Visual inspection", which requires selecting "intact, rusted, or damaged" from the drop-down menu. A linear guided workflow is used to fill in the digital data acquisition template. Maintenance personnel must complete each step according to the preset steps. If the previous step is not passed, the next step cannot be performed.

[0023] Specifically, the linear guided workflow includes the following steps: During media capture, the system automatically redirects to the camera interface and overlays an AR guidance frame for alignment. Users cannot skip this step to the next. It can integrate lightweight real-time analysis, such as detecting the presence of a circular dial outline in the image; if it doesn't match, it prompts for a retake, technically ensuring the basic validity of the captured photos. Capture a photo, save the image file, and automatically write the current precise GPS coordinates, timestamp, and device ID as metadata into the photo file (such as EXIF ​​information), so that the photo itself carries immutable information about "when, where, and which device it belongs to". In the parameter filling process, the input boxes are mandatory, and the associated device ID and entry time are recorded in its data structure. This firmly locks the data with the specific physical device, location, and time, rendering the forgery of a single photo meaningless and greatly increasing the cost and technical threshold of forgery.

[0024] S3. Receive the execution data packet and call the preset multi-factor automatic verification rule set to perform multi-source verification, freeing managers from heavy and inefficient manual review, automatically completing most compliance checks, and requiring only manual handling of a few abnormal cases, thus improving both review quality and efficiency. If all verifications pass, a standard electronic receipt will be automatically generated, and the work order status will be updated to "pending review". If any verification fails, the work order status is marked as "pending review" and the specific verification exception is recorded.

[0025] It is worth explaining, specifically as follows: Figure 2 The multi-factor automatic verification rule set performs multi-source verification, including: Phase 1: Execution Time and Space Compliance Verification. This phase determines whether the start and end times of execution are within the tolerance range of the work order's planned time and whether the distance between the collected execution geographic location and the geographic location registered in the standard maintenance work order data is less than a preset threshold. The verification engine parses key timestamps and core geographic locations in the work trajectory from the data packet (usually the centroid of the trajectory point set or the coordinates of the equipment scanning point). It calculates the relationship between the planned execution time and the actual execution time window. For emergency work orders, it verifies whether the response time is within the specified time limit after the alarm (e.g., 30 minutes). Simultaneously, it calculates the spherical distance between the geographic location registered in the standard maintenance work order data and the equipment registration location obtained from the equipment ledger, and determines whether this distance is less than a preset geofence threshold (e.g., 50 meters). This verification is automatically completed using a geographic calculation formula (e.g., the Haversine formula). If it passes, it proceeds to the next phase; if it fails, it terminates immediately, marks the status as "pending review," and records anomalies such as "work location deviates from equipment by more than 50 meters" or "execution time exceeds the planned window." The second stage involves data logic verification: Checking if the device identification information in the execution data package matches the target device identification in the work order. If it passes, proceed to the third stage; otherwise, mark it as "pending review" and record any anomalies such as "device identification mismatch" or "missing evidence for critical work steps," including: Identifier consistency verification: Compare whether the device identifier carried in the data packet (from the scanning result) is completely consistent with the target device identifier specified in the original work order. This is the most basic error prevention check to prevent the work order and the device from being mismatched. Business logic conflict check: Based on the preset business rule library, the judgment is made. For example, if the work order type is "replace fire extinguisher", but the data packet does not contain the required photos of "old fire extinguisher recycling" and "new fire extinguisher installation", and the parameter "pressure value" is entered as 0 MPa, but "pressure status" is selected as "normal". The third stage, auxiliary verification of the authenticity of the operation: Analyze the on-site media data, identify the equipment nameplates and appearance features (including instrument readings), and compare them with the standard information in the equipment ledger. Specifically: OCR-based nameplate recognition: The engine automatically selects photos marked "equipment panorama" or "nameplate close-up" from the data package, calls the optical character recognition service to perform text recognition on the equipment nameplate area in the photo, and automatically compares the recognized equipment number, model and other information with the standard records in the equipment ledger. If the matching degree is higher than the set threshold (such as 95%), it is judged as passing; otherwise, an exception is triggered. Image feature comparison based on computer vision (CV): For specific types of equipment, analyze their appearance feature photos, use computer vision (CV) models (such as trained lightweight convolutional neural networks or feature matching algorithms) to calculate feature similarity between the uploaded equipment appearance photos and the standard template images or historical photos of the equipment stored in the ledger, to help confirm whether they are the same equipment, especially suitable for equipment without clear nameplates; Image recognition based on instrument readings: This method analyzes close-up photos of instruments such as pressure gauges and level gauges, employing pointer / digital instrument recognition algorithms. For pointer gauges, the pointer angle is identified and converted into an actual value; for digital gauges, the digital content is directly identified. The algorithm-recognized readings are cross-validated with the test parameters manually entered by maintenance personnel. If the difference is within the allowable error range, the reading is considered valid, effectively identifying false entries or misreading issues.

[0026] Furthermore, the auxiliary verification of the operation authenticity also includes: Based on the continuous timestamp sequence and the corresponding geographic location sequence, a maintenance execution trajectory is generated; Verify whether the shooting timestamp and geographical location information of at least one photo in the on-site media data are included in the spatiotemporal point set of the maintenance execution trajectory. Verification is only successful when both spatial and temporal matching are satisfied. Spatial matching involves finding the shooting timestamp and geographical location information of the nearest trajectory point before and after the photo (i.e., the coordinates before and after the timestamp), and calculating the spatial distance from the photo coordinates to the line segment connecting this maintenance execution trajectory point (or the estimated possible position at that time based on the movement speed). This distance must be less than a dynamic threshold, which is not a fixed 50 meters but rather considers both the positioning accuracy of the trajectory point itself and the positioning accuracy of the photo. Temporal matching involves searching for the shooting time of a key photo on the timeline of the maintenance execution trajectory. If the shooting time is on the timeline, it proves that the moment the photo was taken was within the monitored operation period, forming a logically consistent and mutually corroborating evidence loop. In auditing or liability determination, the probative value of this evidence chain is far stronger than isolated photos or sign-in records, elevating the quality management of fire protection maintenance from static review of results to a new level of dynamic verification and reliable reconstruction of the process.

[0027] S4. Assign "pending review" or disputed work orders to the review terminal for manual review of the execution data packet. On the one hand, this enables manual handling of abnormal cases and improves review efficiency. On the other hand, manual review can be performed based on the execution data packet, and work orders with disputes can be reviewed to improve accuracy. If the review is approved, the work order status will be updated to "completed" and the data will be stored in a linked manner throughout the entire lifecycle to form a traceable digital maintenance file, which will facilitate historical query and audit tracing. If the review fails, it will be returned to the responsible party's terminal for re-execution.

[0028] Specifically, the manual review in S4 includes the following steps: The verification anomalies of "Pending Review" work orders are added to the review pool as structured tags; Work orders are grouped in the review pool, including spatiotemporal anomaly group, data logic anomaly group, and authenticity doubt group, to facilitate subsequent allocation by profession. A comprehensive priority score is calculated and sorted according to the urgency of the work order (work orders triggered by emergency alarms have the highest priority), timeout risk (the longer the delay time, the higher the priority), and anomaly severity (anomalies involving safety-critical parameters have higher priority). The system retrieves work order execution data packages according to their order, matches them with reviewers, and creates a review workbench for manual verification. The mobile app interface displays basic work order information, a detailed list of anomalies from the automatic verification report, and overlays the equipment registration location (marked as A), work trajectory (continuous line segments), and the shooting location of each photo (bubble marker, clickable to view thumbnails) onto a single map, making spatiotemporal anomalies readily apparent. All on-site photos / videos are displayed in a timeline or photo gallery format, supporting zooming and comparison. Key detection parameters are displayed side-by-side with their standard values. If maintenance personnel submit written explanations, these are prominently displayed. After comprehensive evaluation, reviewers only need to perform simple operations. Click "Review Approved": The status will be automatically updated to "Completed", the final electronic receipt will be generated (with the reviewer's electronic signature / stamp), and the archiving process will be triggered; Click "Revert and Rework": A pop-up window asks you to check or add a reason for the revert (you can directly quote the exception or enter it manually), specify the specific requirements for correction. After confirmation, the work order status is reverted to "Pending Execution" in S2, and the review comments and markings are pushed back to the original maintenance personnel's mobile terminal. The original execution data package is retained as a historical version for comparison, eliminating the management costs of manual screening and allocation, and ensuring that problems can be professionally handled as quickly as possible. The review workbench breaks down data silos, visually integrating isolated photos, coordinates, and parameters in a spatiotemporal dimension, allowing reviewers to grasp the overall situation and make accurate judgments within minutes, avoiding the inefficiency of repeatedly switching between multiple pages and tables.

[0029] The second objective of this invention is to provide an automated assignment and receipt system for fire equipment maintenance tasks, including any of the above-mentioned automated assignment and receipt methods for fire equipment maintenance tasks, including a maintenance work order generation unit, an execution data packet acquisition unit, a multi-source verification unit, and a manual review unit. The maintenance work order generation unit is used to automatically generate maintenance work orders based on the fire equipment ledger and maintenance plan database, and to preset the responsibility mapping relationship, and automatically push the maintenance work order to the corresponding responsible party terminal. The execution data packet acquisition unit is used to receive and display the maintenance work order through a mobile terminal. When the maintenance personnel confirm the start of execution, it automatically activates and continuously collects spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence, and forcibly collects on-site media data and detection parameter data associated with the target equipment in the work order to form a structured execution data packet. The multi-source verification unit is used to receive the execution data packet and call the preset multi-factor automatic verification rule set to perform multi-source verification. If any verification fails, the work order status is marked as "pending review" and the specific verification exception is recorded. The manual review unit is used to allocate "pending review" or disputed work orders to the review terminal for manual review execution data packets.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated method for assigning and receiving feedback on fire equipment maintenance tasks, characterized in that, Includes the following steps: S1. Based on the fire equipment ledger and maintenance plan database, maintenance work orders are automatically generated, and a responsibility mapping relationship is preset. The maintenance work orders are then automatically pushed to the corresponding responsible party's terminal. S2. Receive and display the maintenance work order through the mobile terminal. When the maintenance personnel confirm the start of execution, automatically activate and continuously collect spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence, and forcibly collect on-site media data and detection parameter data associated with the target device in the work order to form a structured execution data packet. S3. Receive the execution data packet and call the preset multi-factor automatic verification rule set to perform multi-source verification; If all verifications pass, a standard electronic receipt will be automatically generated, and the work order status will be updated to "pending review". If any verification fails, the work order status is marked as "pending review" and the specific verification exception is recorded; S4. Assign "pending review" or disputed work orders to the review terminal for manual review of the executed data packets; If the review is approved, the work order status will be updated to "completed", and the data will be stored in a traceable digital maintenance file throughout the entire lifecycle. If the review fails, it will be returned to the responsible party's terminal for re-execution.

2. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 1, characterized in that: The S1 process automatically generates maintenance work orders based on the fire equipment ledger and maintenance plan database, including the following steps: Construct a fire equipment ledger database to create digital archives for fire equipment, and a maintenance plan rules database that defines the maintenance standards and cycles for various types of equipment; It has a built-in high-precision time scheduler that runs at the smallest time granularity. In each scheduling cycle, it associates and matches the fire equipment ledger database with the maintenance plan rule database, so that the maintenance work order responds to the preset periodic maintenance plan trigger conditions or the real-time abnormal alarm signal of the fire equipment. The preset standard maintenance work order data template is filled with maintenance data to generate a maintenance work order, which is then entered into the waiting queue. The work order status is marked as "planned".

3. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 2, characterized in that: The preset responsibility mapping relationship in S1 includes the following steps: The area is divided into multiple responsibility zones, and each zone is assigned a maintenance team. Mark maintenance personnel's skill tags and qualification certificates, and map them to all equipment series they are responsible for, forming professional groups; Work order allocation: Read the maintenance data of the maintenance work order, determine whether the professional team needs to intervene based on the equipment type. If so, prioritize finding the maintenance worker with the lowest current load in the area from the professional team. If not, prioritize finding the maintenance worker with the lowest load in the maintenance team bound to the area. The work order is pushed to the corresponding maintenance worker's App, and the work order status changes from "planned" to "issued". The maintenance worker clicks "Accept" or "Reject" on the mobile app. If "Accept" is clicked, the work order status changes to "Pending Execution," the work order is officially locked to this person, and the planned execution timeline begins to be recorded. If you click "Reject" or there is no response after a timeout, the work order will be reassigned and re-distributed until the work order is accepted.

4. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 3, characterized in that: The automatic activation and continuous collection of spatiotemporal trajectory data containing execution geographical location and continuous timestamp sequences in S2 includes the following steps: Once the maintenance technician arrives on site, he opens the mobile app, clicks "Start Task," and the status changes to "Executing." The mobile app automatically activates background location services to record spatiotemporal trajectories, forming a sequence of geographical locations and a continuous sequence of timestamps; these are then encrypted and appended to a temporary log file on the mobile device that is uniquely bound to the current work order ID.

5. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 4, characterized in that: The forced collection of on-site media data and detection parameter data associated with the target device in the work order in S2 includes the following steps: Synchronously load the digital acquisition template bound to the maintenance work order type. The digital acquisition template is used to record the media data list and the test parameter list. A linear guided workflow is used to fill in the digital data acquisition template.

6. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 5, characterized in that: The linear guided workflow includes the following steps: During media capture, the system automatically redirects to the camera interface and overlays an AR guide box to guide alignment. Capture a photo, save the image file, and automatically write the current precise GPS coordinates, timestamp, and device ID as metadata into the photo file; In the parameter entry stage, the input fields are mandatory, and the associated device ID and entry time are recorded in the data structure.

7. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 5, characterized in that: The multi-factor automatic verification rule set performs multi-source verification, including: The first stage is the execution time and space compliance verification: it is determined whether the start and end times of the execution are within the tolerance range of the work order's planned time, and whether the distance between the collected execution geographical location and the geographical location registered in the standard maintenance work order data is less than a preset threshold. If it passes, it proceeds to the next stage; if it fails, it is terminated immediately, the status is marked as "pending review", and the anomaly is recorded. The second stage is to perform logical verification of the execution data: check whether the device identification information in the execution data packet is consistent with the target device identification in the work order. If it passes, proceed to the third stage; if it fails, mark it as "pending review" and record the exception. The third stage, auxiliary verification of the authenticity of the operation: analyze the on-site media data, identify the equipment nameplates and appearance features contained therein, and compare them with the standard information in the equipment ledger.

8. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 7, characterized in that: The auxiliary verification of the authenticity of the operation also includes: Based on the continuous timestamp sequence and the corresponding geographic location sequence, a maintenance execution trajectory is generated; Verify whether the shooting timestamp and geographical location information of at least one photo in the on-site media data are included in the spatiotemporal point set of the maintenance execution trajectory. Only when both spatial dimension matching and temporal dimension matching are satisfied will the verification pass the output.

9. The automated assignment and receipt method for fire equipment maintenance tasks according to claim 5, characterized in that: The manual review in S4 includes the following steps: The verification anomalies of "Pending Review" work orders are added to the review pool as structured tags; Work orders are grouped in the review pool, including groups for spatiotemporal anomalies, data logic anomalies, and questionable authenticity, to facilitate subsequent allocation by specialty and to calculate and sort them by comprehensive priority score. The execution data packets of the work orders are retrieved according to the sorting order, matched with the reviewers, and a review workbench is formed for manual review.

10. An automated assignment and receipt system for fire equipment maintenance tasks, comprising the automated assignment and receipt method for fire equipment maintenance tasks as described in any one of claims 1-9, characterized in that: It includes a maintenance work order generation unit, an execution data packet acquisition unit, a multi-source verification unit, and a manual review unit; The maintenance work order generation unit is used to automatically generate maintenance work orders based on the fire equipment ledger and maintenance plan database, and to preset the responsibility mapping relationship, and automatically push the maintenance work order to the corresponding responsible party terminal. The execution data packet acquisition unit is used to receive and display the maintenance work order through a mobile terminal. When the maintenance personnel confirm the start of execution, it automatically activates and continuously collects spatiotemporal trajectory data containing the execution geographical location and continuous timestamp sequence, and forcibly collects on-site media data and detection parameter data associated with the target equipment in the work order to form a structured execution data packet. The multi-source verification unit is used to receive the execution data packet and call the preset multi-factor automatic verification rule set to perform multi-source verification. If any verification fails, the work order status is marked as "pending review" and the specific verification exception is recorded. The manual review unit is used to allocate "pending review" or disputed work orders to the review terminal for manual review execution data packets.

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