Integrated building operation and maintenance platform system and method

Abstract

The present application relates to the technical field of maintenance management, in particular to an integrated building operation and maintenance platform system and method, the system comprising a path identification module, a trajectory acquisition module, a node response module, a capability evaluation module and a result induction module. The present application can complete the transition from result non-traceable to process verifiable and quantifiable in the operation and maintenance process by constructing the correlation between the device path node, personnel trajectory and operation behavior, and can identify the jump, omission and reverse sequence violation behavior based on the collaborative evaluation of the task execution quality based on spatial coverage integrity and time sequence continuity. The data correlation between the physical space, personnel behavior and management tasks is broken through, forming a unified and calculable operation and maintenance process archive, which provides a reliable basis for detailed management, performance evaluation and strategy optimization. The process and standardized execution framework improves the forward-looking nature of management, supports abnormal task positioning, and provides intuitive support for job specification optimization and personnel training.

Description

Technical Field

[0001] This invention relates to the field of maintenance and management technology, and in particular to an integrated building operation and maintenance platform system and method. Background Technology

[0002] The field of maintenance management technology involves organizing and controlling the operational status, maintenance needs, and maintenance processes of various equipment in building facilities. Its core aspects include equipment inspection, fault diagnosis, maintenance execution, maintenance plan formulation, and task scheduling. The overall approach involves acquiring equipment operation data through status information collection methods, formulating operation and maintenance plans by combining preset maintenance strategies or historical fault data, and then completing maintenance operations through personnel assignment and on-site execution. The execution process and results are recorded and archived to support subsequent management and optimization. Among them, the traditional integrated building operation and maintenance platform system refers to an information system for unified organization of equipment operation and maintenance tasks in building scenarios. The technical aspects it addresses are the centralized management and scheduling of the operational information of different equipment in the building to support daily maintenance and emergency repairs. Traditional methods usually rely on manually entering equipment information and fault records based on structured data. Operation and maintenance personnel formulate inspection plans according to the maintenance cycle and provide feedback on maintenance status through on-site paper records. In some scenarios, environmental sensors are introduced to collect basic parameters such as equipment temperature, humidity, and voltage in real time. However, the data sources are scattered, and the feedback path depends on manual process processing.

[0003] In existing technologies, equipment path information is not linked to maintenance tasks in a recognizable manner, and trajectory data lacks the support of sequential structure between nodes. In complex scenarios or areas with dense equipment, task execution behavior is difficult to accurately associate with specific path nodes. The form of operation and maintenance process records tends to be uniform, node annotations lack behavioral attribute differentiation, and a clear mapping relationship cannot be formed between path execution integrity and task completion status. This results in a lack of process support for task behavior backtracking, no way to establish path deviation judgment, difficulty in clearly representing task node coverage, and structured status output limited by isolated basic information, which cannot support a systematic consistency evaluation based on path sequence and operation behavior. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide an integrated building operation and maintenance platform system and method.

[0005] On the one hand, an integrated building operation and maintenance platform system is provided, which includes:

[0006] The path recognition module is used to obtain the equipment number, floor number, elevator and staircase interface number and passage number of the floor distribution box and supply and exhaust fan unit, extract the access node number sequence from the entrance to the target location of the equipment, map the equipment number to the node number sequence, and obtain the equipment path structure data set.

[0007] The trajectory acquisition module is used to filter the trajectory node numbers and timestamps collected by maintenance personnel based on the device path structure data set, extract the continuous trajectory segments associated with the device, and obtain the path matching structure data set.

[0008] The node response module is used to extract the sequence of operation node numbers based on the path matching structure data group, identify the node numbers with operation behavior in the trajectory, and obtain a list of task response nodes according to the task numbers corresponding to the node order.

[0009] The capability assessment module is used to determine the coverage and order relationship between operation nodes and path nodes based on the operation node numbers in the task response node list, and obtain a task structure consistency status mapping table.

[0010] The result summarization module is used to obtain an integrated building operation and maintenance configuration status set by calling the task number, device number, floor information and node status fields, corresponding to the task structure information, based on the task structure consistency status mapping table.

[0011] As a further embodiment of the present invention, the device path structure data set includes device number, access node number sequence, floor number, elevator interface number, stair interface number, and passage number; the path matching structure data set includes task number, trajectory segment number, trajectory node number sequence, and path matching status; the task response node list includes task number, operation node number, node sequence identifier, and node behavior label; the task structure consistency status mapping table includes task number, path coverage status, node sequence continuity, and structure consistency flag; the integrated building operation and maintenance configuration status set includes device number, task number, floor information, path structure information, and node status fields.

[0012] As a further aspect of the present invention, the access node number sequence refers to the numbered sequence list of the passageways, stairs, and elevator interface nodes that the device passes through along the way from the entrance to the target location;

[0013] The continuous trajectory segment associated with the device refers to a segment of continuous moving nodes in the trajectory data of maintenance personnel that matches the device and task and is in continuous time sequence.

[0014] The node number with operational behavior refers to the node number in the trajectory that not only has been reached but also recorded as having performed an operation or behavior;

[0015] The task structure information refers to a structured task execution framework that organizes task number, equipment number, floor and path, and node status fields, which can be used for verification and analysis.

[0016] As a further aspect of the present invention, the path recognition module includes:

[0017] The equipment information extraction submodule is used to obtain the equipment number, floor number, elevator interface number, stair interface number and passage number of the floor distribution box and supply and exhaust fan unit in the building structure drawings, extract the equipment number and floor number, and obtain the equipment basic information dataset according to the position relationship of the interface number and passage number.

[0018] The access node parsing submodule is used to extract the channel number sequence associated between the entrance and the target location based on the equipment number and floor number in the equipment basic information dataset, and to extract the channel number, elevator interface number and stair interface number nodes to obtain the access node sequence dataset;

[0019] The path correspondence processing submodule is used to obtain a set of device path structure data based on the device number in the access node sequence dataset and the device basic information dataset, corresponding to the device number and the access node number.

[0020] As a further aspect of the present invention, the trajectory acquisition module includes:

[0021] The trajectory node filtering submodule is used to collect the trajectory node number and timestamp sequence from the operation and maintenance personnel terminal based on the device number and access node number sequence in the device path structure data set, compare the consistency between the trajectory node number and the device number, extract the node number associated with the device number and the corresponding time information, and obtain the trajectory node associated dataset.

[0022] The task trajectory recognition submodule is used to filter continuous node segments with the same task number, trajectory segment number and device number based on the node number and time order in the trajectory node association dataset, determine the time order continuity between nodes, extract continuous trajectory segment information, and obtain the task trajectory segment dataset.

[0023] The path matching processing submodule is used to call the access node number sequence in the device path structure data set based on the node number sequence in the task trajectory segment dataset, corresponding to the trajectory node and the access node, to obtain the path matching structure data group.

[0024] As a further aspect of the present invention, the node response module includes:

[0025] The task node extraction submodule is used to read the operation node number sequence in the task configuration file based on the task number and trajectory segment number in the path matching structure data group, extract the operation node number associated with the device according to the task number, verify the consistency of the number format with the task field, and obtain a set of operation node number sequences.

[0026] The operation node identification submodule is used to obtain the trajectory node number and timestamp data in the corresponding trajectory segment based on the device number and node number in the operation node number sequence set, match the trajectory node number, identify the node number including operation behavior information, extract the node number and organize it according to the task number to obtain the operation behavior node number list.

[0027] The node sequence association submodule is used to compare the node number and task number in the operation behavior node number list with the operation node number sequence according to the sequence result, associate the operation node number under each task number, and obtain the task response node list.

[0028] As a further aspect of the present invention, the capability assessment module includes:

[0029] The node coverage detection submodule is used to extract the corresponding device number according to the task number and operation node number in the task response node list, call the access path node number sequence in the device path structure data set, and analyze whether the operation node exists in the path according to the corresponding operation node number and access path node number to obtain the path node corresponding data set.

[0030] The sequential continuity judgment submodule is used to detect the numbering relationship between adjacent operation nodes based on the operation node number sequence under the task number in the corresponding path node dataset, and to analyze the sequential connection status to obtain the sequential continuity of path nodes dataset.

[0031] The state structure extraction submodule is used to extract the path response status under the task number based on the task number, path existence status and sequence status content in the path node corresponding status dataset and the path node sequential continuity status dataset, merge the path coverage status and sequence connection status, and obtain the task structure consistency status data table.

[0032] As a further aspect of the present invention, in the process of extracting the device number in the node coverage detection: according to the correspondence between the task number and the operation node number in the task response node list, the device number is extracted by the task number; through the device number in the device path structure data set, the sequence of access path node numbers is retrieved; and the operation node number is compared with the access path node number to analyze whether the operation node is in the path, thereby obtaining a dataset of path node correspondence.

[0033] In the process of analyzing the sequence of operation node numbers in the sequential continuity judgment: based on the sequence of operation node numbers under the task number in the path node corresponding situation dataset, the relationship between adjacent operation node numbers is checked according to the node number order information, the sequential connection state is analyzed, and a dataset of sequential continuity of path nodes is obtained.

[0034] In the process of generating the task structure consistency state data table during the state structure extraction: by combining the task number, path existence status and sequence status information in the path node corresponding situation dataset and the path node sequential continuity situation dataset, the path response status under the task number is extracted to obtain the task structure consistency state data table.

[0035] As a further aspect of the present invention, the result summarization module includes:

[0036] The status reading submodule is used to locate the corresponding status content by task number based on the task number and structure status field in the task structure consistency status mapping table, extract the device number, node number and node status information, and obtain the task status data field set.

[0037] The field extraction submodule is used to call the task number to match the corresponding device number and floor information based on the task status data field set, parse the corresponding content of device number and floor information under task number, and obtain task device floor field lookup table.

[0038] The structure comparison submodule is used to match the field values ​​of the functional structure, path structure and response structure under the task number based on the task equipment floor field comparison table, and perform parallel comparison according to the task number to obtain an integrated building operation and maintenance configuration status set.

[0039] On the other hand, a verification method for building operation and maintenance processes is provided, based on the system described above, the method comprising the following steps:

[0040] Path identification steps: Obtain the equipment number, floor number, elevator and staircase interface number and passage number of the floor distribution box and supply and exhaust fan unit; extract the access node number sequence from the entrance to the target location of the equipment; map the equipment number to the node number sequence to obtain the equipment path structure data set.

[0041] Trajectory acquisition steps: Based on the device path structure data set, filter the trajectory node numbers and timestamps collected by maintenance personnel, extract the continuous trajectory segments associated with the device, and obtain the path matching structure data set; Node response steps: Based on the path matching structure data set, extract the operation node number sequence, identify the node numbers with operation behaviors in the trajectory, and obtain the task response node list according to the node order corresponding to the task number.

[0042] Capability assessment steps: Based on the operation node numbers in the task response node list, determine the coverage and sequence relationships between operation nodes and path nodes to obtain a task structure consistency status mapping table;

[0043] Results summary steps: Based on the task structure consistency status mapping table, call the task number, device number, floor information and node status fields, corresponding to the task structure information, to obtain the integrated building operation and maintenance configuration status set.

[0044] Compared with existing technologies, the integrated building operation and maintenance platform system and method provided by this invention have the following significant advantages:

[0045] 1. It has achieved a fundamental transformation in the operation and maintenance process from a "black box of results" to a "transparent and quantifiable process." By constructing a structured mapping relationship between equipment path node sequences and personnel trajectories and operational behaviors, the system can automatically and accurately trace back whether operation and maintenance personnel have reached the designated location, performed the designated operations, and whether the execution order is correct. This completely solves the problems of difficult traceability of the operation and maintenance process and reliance on subjective human judgment in the traditional approach.

[0046] 2. A novel method for quantifying operation and maintenance quality based on the collaborative evaluation of "spatial coverage integrity" and "temporal sequence continuity" is proposed. The capability assessment module does not rely on a single indicator but innovatively combines the coverage check of operational nodes to the theoretical path with the analysis of the sequential continuity between nodes for collaborative evaluation. This method can effectively identify complex violations such as "shortcuts," "skipping steps," "missed checks," and "reverse operations," enabling the execution quality of operation and maintenance tasks to be accurately and objectively quantified and scored (e.g., consistency scores and grades), providing a reliable basis for refined management.

[0047] 3. The system breaks down data barriers between "physical space, personnel behavior, and management tasks," generating machine-readable, structured operation and maintenance process archives. The "integrated building operation and maintenance configuration status set" output by the system is no longer a discrete log or report, but a structured data object that deeply integrates equipment, floor, task, path node status, operational behavior, and evaluation results. This provides a solid and unified data foundation for subsequent big data analysis, task pattern mining, automated performance evaluation, and operation and maintenance strategy optimization.

[0048] 4. Enhanced the initiative and accuracy of operation and maintenance management. Managers can quickly locate abnormal tasks (such as low-scoring tasks or tasks with excessive path deviations) based on the configuration status set generated by the system, achieving an upgrade in management mode from "post-event remediation" to "in-process intervention" and "pre-event prevention." Simultaneously, the structured task execution framework provides an intuitive digital template for new employee training and standardized operating procedures (SOP) optimization. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a system flowchart of the present invention;

[0051] Figure 2 This is a flowchart of the path recognition module in this invention;

[0052] Figure 3 This is a flowchart of the trajectory acquisition module in this invention;

[0053] Figure 4 This is a flowchart of the node response module in this invention;

[0054] Figure 5 This is a flowchart of the capability assessment module in this invention;

[0055] Figure 6 This is a flowchart of the result summarization module in this invention. Detailed Implementation

[0056] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0057] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0058] This invention provides an integrated building operation and maintenance platform system, such as... Figure 1 The diagram shown illustrates an integrated building operation and maintenance platform system, which includes:

[0059] The path recognition module obtains the equipment number, floor number, elevator interface number, stair interface number and passage number of the floor distribution box and supply and exhaust fan unit in the building structure drawings, extracts the access node number sequence that the equipment passes through from the entrance to the target location, maps the equipment number to the access node number sequence, and obtains the equipment path structure data set.

[0060] The trajectory acquisition module filters the trajectory node number and timestamp sequence collected by the maintenance personnel terminal based on the node sequence and device number in the device path structure data set, extracts continuous data segments with the same task number, trajectory segment number and device number, matches the trajectory path and device path structure, and obtains the path matching structure data group.

[0061] The node response module extracts the sequence of device operation node numbers from the task configuration file based on the task number and trajectory segment number in the path matching structure data group. It identifies the node numbers with operation behavior records from the trajectory data, sorts, identifies and encapsulates them according to the node order, and associates the task number and operation node number according to the node sequence to obtain the task response node list.

[0062] The capability assessment module, based on the operation node number under the task number in the task response node list, calls the sequence of accessible path nodes in the device path structure data set according to the device number, determines whether the operation node covers the path node, determines the continuity of the node order, and obtains the task structure consistency status mapping table.

[0063] The results summarization module reads the structural status information corresponding to the task number in the task structure consistency status mapping table, calls the equipment number, task number, floor information and node status fields, and obtains the integrated building operation and maintenance configuration status set by comparing the task number with the information of task function structure, path structure and response structure.

[0064] The equipment path structure data set includes equipment number, access node number sequence, floor number, elevator interface number, stair interface number, and passage number. The path matching structure data set includes task number, trajectory segment number, trajectory node number sequence, and path matching status. The task response node list includes task number, operation node number, node sequence identifier, and node behavior label. The task structure consistency status mapping table includes task number, path coverage status, node sequence continuity, and structure consistency flag. The integrated building operation and maintenance configuration status set includes equipment number, task number, floor information, path structure information, and node status fields.

[0065] Specifically, such as Figure 2 As shown, the path recognition module includes:

[0066] The equipment information extraction submodule obtains the equipment number, floor number, elevator interface number, stair interface number and passage number of the floor distribution box and supply and exhaust fan unit in the building structure drawings, extracts the equipment number and floor number, and obtains the equipment basic information dataset according to the positional relationship of the interface number and passage number.

[0067] First, the equipment information extraction submodule establishes a data reading channel with the Building Information Modeling (BIM) database, retrieving the digitized building structure drawings stored therein. These drawings are stored in either an industrial basic format or a general computer-aided design vector format. The module's internal primitive analysis unit performs layered scanning of the drawings, using feature filtering algorithms to identify target layers containing electrical equipment, HVAC components, and the building's basic structure. For extracting equipment and floor numbers, the module calls a pre-trained deep optical character recognition (DCR) network. This network model includes an input layer that receives tile data normalized to 64x64 pixels, followed by a 7-layer convolutional neural network structure, with the first three layers using 3x3 convolutional kernels. This module extracts local stroke features, employs 5x5 convolutional kernels in the middle two layers to capture character structure features, and integrates semantic information in the last two layers. Batch normalization is used in inter-layer connections to accelerate convergence, and a linear rectified function is used as the activation function to enhance non-linear expression. The output layer is mapped to the character dictionary space through a fully connected structure. This module matches the recognized text string, such as DB-L1-01, with the coordinates of the geometric centers of nearby primitives. It determines the attribution relationship based on the overlap rate between the text box and the primitive bounding box, thereby resolving the device number. For floor numbers, it scans the attribute fields of the bounding box area to extract specific metadata identifying floor information. When extracting interface numbers and channel numbers and establishing associations, this module performs computational geometry-based proximity analysis. The module identifies specific symbolic representations of elevator shafts and stairwells by using the closed spatial contours in the historical drawings, extracts their geometric center point coordinates, and assigns unique interface numbers. Simultaneously, it extracts skeleton lines from corridors and passageways to generate centerline vectors representing travel paths and assigns passageway numbers. This module sets a distance threshold of 3000 mm for position association determination. This threshold is based on a statistical analysis of 100 standard building floor samples, calculating the average distance from the equipment operating surface to the nearest passageway centerline as 1800 mm, and using twice its standard deviation as the redundancy limit. The module performs distance calculations to obtain the x-coordinate X1 and y-coordinate Y1 of the equipment center point, and the x-coordinate X2 and y-coordinate Y2 of the projection point on the passageway skeleton line, calculating the x-coordinate difference. The module calculates the difference between the device's coordinates and its ordinate, squares these differences, sums them, and then takes the square root of the sum to obtain the Euclidean distance. For example, when the device's coordinates are x-coordinate 10500 and ordinate 20000, and the channel's projection point coordinates are x-coordinate 10500 and ordinate 22000, the module calculates a ordinate difference of 2000 and an x-coordinate difference of 0. Taking the square root of the squared ordinate difference (4000000), the module calculates a physical distance of 2000 mm between the two points. Since this distance is less than the set threshold of 3000 mm, the module determines that the device and the channel are associated. The module repeats this process to calculate the distance between the device and surrounding elevator and stairwell interfaces, selecting the interface with the smallest distance as the associated object.Finally, the extracted device ID and floor ID are used as primary keys, and the associated passage ID, elevator interface ID, and stair interface ID are used as attribute values ​​to construct a key-value pair structure, thus obtaining the device basic information dataset.

[0068] The access node parsing submodule extracts the channel number sequence associated between the entrance and the target location based on the device number and floor number in the device basic information dataset. It then extracts the channel number, elevator interface number and stair interface number nodes to obtain the access node sequence dataset.

[0069] First, the passage node parsing submodule reads the target device number and its floor number from the device basic information dataset. This module loads the vectorized road network topology model for that floor, which abstracts the building space as a graph structure composed of nodes and edges. The module defines the starting point for path planning as the main entrance of the building or the core traffic entrance of the current floor, and the ending point as the coordinates of the passage associated with the target device. To generate the optimal passage sequence, this module executes an improved A* search algorithm, setting a heuristic evaluation function based on the actual movement cost from the starting point to the current node and the estimated cost from the current node to the destination. Composed of two parts, the actual movement cost is calculated based on the physical channel length, while the estimated cost is calculated using Manhattan distance. During the search process, this module traverses adjacent nodes in the graph structure and records the channel entity ID traversed for each hop. When path planning involves cross-level movement, this module identifies vertical traffic nodes connecting different elevation planes in the path, checks the path node attributes, and extracts the corresponding elevator or stairwell interface number if the node is marked as a vertical transition point. This module then classifies and sorts all extracted nodes by type. For example, if the path starts from the lobby on the 1st floor, passes through the elevator hall, takes the elevator to the 3rd floor, and then passes through a corridor... Upon reaching the equipment room, this module sequentially extracts the 1st floor lobby passage number, the 1st floor elevator interface number, the 3rd floor elevator interface number, and the 3rd floor corridor passage number. To ensure the physical connectivity of the sequence, this module executes connectivity verification logic, checking whether the spatial entities represented by two adjacent numbers in the sequence share a boundary or connection point in the road network database. If the adjacency weight of adjacent nodes in the database is infinite, a path replanning mechanism is triggered. This module also calculates the total path weight to verify optimality, setting the corridor weight to 1 and the elevator equivalent weight to 5. For example, the path includes a 50-meter corridor, one vertical elevator crossing, and 30... For the target floor corridor, this module sums the weight values ​​of the 50-meter corridor, the weighted equivalent value of the vertical elevator (100, i.e., 20 meters multiplied by 5), and the weight value of the 30-meter target floor corridor to obtain the total cost value of the path, which is 180. In addition, this module adds directional labels to the extracted node sequence to clarify the order of entering and leaving nodes. For channel nodes, it records the coordinates of their entrance and exit endpoints. This module organizes all the verified and labeled channel numbers, elevator interface numbers, and stair interface numbers into a linear linked list structure according to the spatiotemporal order from the start point to the end point to obtain the passable node sequence dataset.

[0070] The path mapping processing submodule obtains a set of device path structure data based on the device number in the access node sequence dataset and the device basic information dataset, and matches the device number with the access node number.

[0071] First, the path mapping submodule performs the core data mapping and structured assembly tasks. It initializes an empty hash table in memory to store the mapping between devices and paths. This module iterates through the device basic information dataset and inserts each unique device number as a key into the hash table. Then, it reads the pass-through node sequence dataset and extracts the corresponding node sequence as the value based on the device association identifier carried in the dataset. During the mapping process, this module performs data enrichment operations. For each node number in the sequence, it calls the building space attribute library to obtain additional information about the node, including the fire compartment code to which the node belongs, the space type of the node, and the geometric dimensions of the node, and embeds this attribute information into the node object. Next, this module calculates the topological depth index of the path, sets the index value of the starting node to 0, and increments it by 1 for each subsequent node along the path direction. Each node is assigned a unique sequence index value. This module also needs to calculate and record the overall attributes of the path, including the total path length and the total number of nodes. The total path length is calculated by accumulating the length values ​​of all channel segments in the sequence and the equivalent length values ​​of vertical traffic nodes. For example, this module sums the 10-meter length of channel A, the 15-meter length of channel B, and the 20-meter equivalent length of the elevator's vertical displacement to obtain a total path length of 45 meters. This module writes the calculation results into the header metadata of the device path structure. To optimize data retrieval efficiency, this module serializes the data structure into a binary stream or JavaScript object markup string. The final output set establishes a one-to-one strong association, ensuring that each device number is precisely bound to a chain of access nodes containing complete spatial attributes and sequence information, thus obtaining the device path structure data set.

[0072] Specifically, such as Figure 3 As shown, the trajectory acquisition module includes:

[0073] The trajectory node filtering submodule is based on the sequence of device number and access node number in the device path structure data set. It collects the trajectory node number and timestamp sequence from the terminal of the operation and maintenance personnel, compares the consistency between the trajectory node number and the device number, extracts the node number associated with the device number and the corresponding time information, and obtains the trajectory node associated dataset.

[0074] First, the trajectory node filtering submodule collects location data packets sent by the smart terminals carried by maintenance personnel at high frequency through the IoT gateway interface. The data packets contain the terminal's unique Media Access Control address, the captured location tag number (such as an RFID tag or Bluetooth beacon ID), and a millisecond-level timestamp. This module first segments the raw data stream into time windows to extract data segments that overlap with the current maintenance task's time period. Then, it loads the device path structure data set to extract the standard access node number sequence associated with the target device and defines it as a valid node whitelist. This module executes filtering and comparison logic to traverse each collected trajectory node number and check if it exists in the whitelist. If the trajectory node number is in the whitelist, the data point is determined to be valid and retained. If it is not in the whitelist, for example, if a person enters a non-task area, the data point is determined to be noise and removed. While processing the nodes, this module also cleans the timestamp information. Since signal fluctuations may cause multiple duplicate records to be generated at the same location in a short period of time, this module performs deduplication and merging operations and sets the time jitter threshold to 5 seconds. If the same node number appears consecutively within 5 seconds, this module retains the timestamp of the first record as the entry time and the timestamp of the last record as the exit time, and calculates the difference between the two as the dwell time. For example, in the original data, node A is recorded at 10:00:01, 10:00:03, and 10:00:05. This module merges them into one record and calculates its time span, that is, from 10:00:01 to 10:00:05, and finds that the dwell time of this node is 4 seconds. This module reassembles the selected matching node numbers, the processed time information, and the associated device numbers to obtain the trajectory node association dataset.

[0075] The task trajectory recognition submodule filters continuous node segments with the same task number, trajectory segment number and device number based on the node number and time order in the trajectory node association dataset, judges the time order continuity between nodes, extracts continuous trajectory segment information, and obtains the task trajectory segment dataset.

[0076] First, the task trajectory recognition submodule performs time-series clustering analysis on the discrete associated node data. Based on the task number and device number in the task allocation table, it groups the records in the trajectory node associated dataset. Within each group, nodes are sorted in ascending order according to their entry timestamp. The core task of this module is to identify continuous movement trajectories, eliminating breaks caused by prolonged interruptions or task switching. This module sets the maximum allowable node gap time threshold to 180 seconds. This threshold is determined with reference to the average walking speed of maintenance personnel moving between adjacent nodes (1.2 meters per second) and the maximum physical distance between nodes (150 meters). The module calculates the theoretical maximum time to be 125 seconds and adds approximately 44% redundancy (55 seconds) to this, thus determining the total threshold to be 180 seconds. This module then iterates through the sorted data... The node sequence calculates the time difference between two adjacent nodes, which is the entry time stamp of the later node minus the departure time stamp of the earlier node. If the calculated time difference is less than or equal to 180 seconds, the two nodes are considered to be continuous in time and belong to the same trajectory segment. If the time difference is greater than 180 seconds, the trajectory is considered to be broken at this point, that is, the previous trajectory segment ends and the later node starts a new trajectory segment. This module assigns a unique trajectory segment number to each identified continuous segment and counts the number of nodes contained in each segment. If the number of nodes in a segment is less than 3, it is considered an invalid fragment and is discarded. This module extracts the start time and end time of the valid segments and packages the contained node number sequence, task number, device number and trajectory segment number to obtain the task trajectory segment dataset.

[0077] The path matching processing submodule, based on the node number sequence in the task trajectory segment dataset, calls the access node number sequence in the device path structure data set to match the trajectory node with the access node, and obtains the path matching structure data set.

[0078] First, the path matching submodule performs structured alignment between the actual trajectory and the theoretical path. It reads the actual walking node sequence from the task trajectory segment dataset and retrieves the standard passable node sequence from the device path structure data set. This module uses a variant of the longest common subsequence algorithm for sequence matching, constructing a two-dimensional matching matrix. The rows of the matrix represent standard path nodes, and the columns represent actual trajectory nodes. This module scans the matrix to find matching node numbers. When a match is found, it records the index position of the node in both sequences. This module identifies three states: matching, missing, and redundant. If a node in the standard sequence has a corresponding node in the actual sequence, it is marked as a match; otherwise, it is marked as a redundancy. If a node is found outside the standard sequence in the actual sequence, it is marked as missing. If a node outside the standard sequence appears in the actual sequence, it is marked as deviated. This module calculates the index jump value to quantify the continuity of the match. For two adjacent matching nodes in the actual sequence, the index values ​​of the nodes in the standard sequence are obtained and the difference is calculated. If the difference is equal to 1, it means that the sequence is perfectly continuous. If the difference is greater than 1, it means that there is a jump in the middle. For example, if the actual nodes are A and C and the standard sequence is A to B to C, where A corresponds to index 1 and C corresponds to index 3, this module calculates that the index difference between the two is 2, thus determining that the maintenance personnel skipped the intermediate node B with index 2. This module records the matching status, index mapping relationship and jump situation of all nodes in detail to obtain the path matching structure data group.

[0079] Specifically, such as Figure 4 As shown, the node response module includes:

[0080] The task node extraction submodule reads the operation node number sequence from the task configuration file based on the task number and trajectory segment number in the path matching structure data group, extracts the operation node number associated with the device according to the task number, verifies the consistency of the number format with the task field, and obtains a set of operation node number sequences.

[0081] First, the task node extraction submodule is responsible for parsing the business logic constraints of the maintenance task. Based on the task number in the path matching structure data group, it indexes the corresponding standard operating procedure file from the task configuration database. This module parses the file content and extracts the list of nodes that the task must perform, i.e., the operation nodes. These nodes are usually in front of the distribution box, the fan maintenance port, or the instrument reading position. This module performs strict validation of the number format. According to the preset regular expression conforming to the enterprise coding standard, such as a 3-letter prefix plus a 3-digit middle section plus a 2-letter suffix, this module matches each extracted operation node number with the regular expression. For example, for the number FAN-101-CK, the module validates... If the format meets the requirements, and a match fails, the number is marked as having an abnormal format and is removed during sequence generation to ensure data purity. At the same time, this module verifies the consistency between the operation node and the task field, and checks whether the extracted operation node belongs to the device or area targeted by the current task. This is achieved by comparing the location segment code in the node number with the floor area code in the task field. For example, if the task specifies the area as 3F-ZoneA, and the extracted node number contains 2F, it is determined to be inconsistent and removed. This module arranges the verified operation node numbers into an ordered sequence according to the execution order specified in the standard operating procedure, and attaches the task number and trajectory segment number to obtain the operation node number sequence set.

[0082] The operation node identification submodule obtains the trajectory node number and timestamp data in the corresponding trajectory segment based on the device number and node number in the operation node number sequence set, matches the trajectory node number, identifies the node number including operation behavior information, extracts the node number and organizes it according to the task number to obtain a list of operation behavior node numbers.

[0083] First, the operation node identification submodule aims to infer actual operation behavior through spatiotemporal data features. It iterates through each theoretical operation node in the set of operation node number sequences, searching for the corresponding trajectory record in the task trajectory segment dataset for each node. When a matching trajectory node is found, the module initiates the behavior determination logic. This logic is primarily based on dwell time analysis. The module calculates the effective dwell time of the maintenance personnel at the node, which is the difference between the last signal capture time and the first signal capture time. The module then retrieves a preset minimum operation time threshold, for example, 15 seconds for meter reading tasks and 30 seconds for inspection tasks. The value is derived by subtracting one standard deviation from the average time taken from the video analysis data of 500 standard operations. This module compares the actual dwell time with a threshold. If the actual dwell time is greater than or equal to the threshold, the node is determined to have performed a substantive operation. If it is less than the threshold, it is determined to have only passed by without operation. For example, if the threshold is set to 30 seconds, and the actual dwell time is 45 seconds, it is determined to be valid because it exceeds the threshold. If the actual dwell time is only 10 seconds, it is determined to be invalid. This module extracts the node numbers that are determined to be valid and attaches the start and end times of their actual operations. Finally, these nodes are summarized by task number to obtain a list of operation behavior node numbers.

[0084] The node sequence association submodule compares the node number and task number in the operation behavior node number list with the operation node number sequence according to the sequence result, and associates the operation node number under each task number to obtain the task response node list.

[0085] First, the node sequence association submodule performs compliance verification of the operation sequence. It simultaneously reads the list of operation behavior node numbers and the set of operation node number sequences, mapping and comparing the two sets of sequences by task number. This module first sorts the operation behavior nodes by time based on the start timestamp of the actual operation to generate the actual execution sequence. Then, it compares the actual execution sequence with the theoretically required sequence, checking whether the relative order of the actual nodes in the theoretical sequence remains monotonically increasing. For example, if the theoretical sequence is N1 to N2 to N3, and the actual execution time is N1 at 9:00 and N2 at 9:05... If the index order is 1 and 2 and remains increasing, it is determined to be forward. If the actual execution time N2 is 9:00 and N1 is 9:05, the index order becomes 2 and 1, which is reversed, so it is determined to be reverse. This module generates an associated record for each operation node, including the theoretical index value, the actual execution index value, and the order status label. This module also handles the skipped order case. That is, if N1 and N3 are actually executed but N2 is skipped, this module marks N2 as unresponsive and N3 as skipped response. Finally, the comparison results and status labels of all nodes are integrated to obtain the task response node list.

[0086] Specifically, such as Figure 5As shown, the competency assessment module includes:

[0087] The node coverage detection submodule extracts the corresponding device number based on the task number and operation node number in the task response node list, calls the access path node number sequence in the device path structure data set, and analyzes whether the operation node exists in the path according to the corresponding operation node number and access path node number to obtain the path node corresponding data set.

[0088] First, the node coverage detection submodule analyzes the distribution of operation nodes in the overall travel path. Based on the task number, it extracts all executed operation node numbers from the task response node list. Simultaneously, using the device number associated with the task, it retrieves the complete standard travel path node sequence for that device from the device path structure mapping set. This module executes set membership detection logic, traversing each operation node to check if it is included in the standard path sequence. This step aims to verify whether the operation node is located on the prescribed travel route to prevent maintenance personnel from reaching the operation point via illegal paths. The module calculates the path matching degree; if operation node N1 exists... In the standard path set, the path attribute is marked as "within the path"; if it does not exist, it is marked as "outside the path". In addition, this module calculates the coverage rate of critical path nodes, identifies key necessary nodes in the standard path such as security gates or specific corridors, and checks whether these nodes appear in the operation behavior or trajectory record. This module determines the coverage rate by calculating the ratio of the number of key nodes actually passed to the total number of key nodes in the standard path. For example, if 4 out of a total of 5 key nodes are matched, the coverage rate is calculated to be 0.8. This module writes the path attribute mark of each operation node and the overall coverage data into the dataset to obtain the path node corresponding data set.

[0089] The sequential continuity judgment submodule is based on the sequence of operation node numbers under the task number in the path node corresponding data set. It refers to the node number order information to detect the numbering relationship between adjacent operation nodes, analyze the sequential connection status, and obtain the path node sequential continuity data set.

[0090] First, the sequential continuity judgment submodule deeply analyzes the topological connection continuity between nodes, reads the dataset of path node correspondences, and arranges the nodes under the same task in chronological order. This module calculates the index span between two adjacent actual nodes by referring to the node index values ​​in the standard path sequence. The calculation logic is as follows: let the current node be Nc and the next node be Nn, obtain their index values ​​in the standard path, and calculate the span difference, which is equal to the index of Nn minus the index of Nc. In the ideal point-to-point traversal scenario, the difference should be equal to 1, indicating that the nodes are closely adjacent. If the difference is greater than 1, it means that several nodes have been skipped. This module further analyzes the nature of the skipped nodes. If the skipped node includes a physical barrier or checkpoint that must be traversed, the connection status is determined to be discontinuous. If the skipped node is only a logical intermediate point, it is determined to be logically continuous. This module also needs to detect backtracking behavior, i.e., cases where the difference is less than 0. If a negative difference is found, the connection status is determined to be reversed. This module calculates the sequential continuity coefficient by averaging the ratio of the number of consecutive connections to the total number of connections throughout the entire task. For example, if there are a total of 10 node jumps, with 8 being consecutive and 2 being discontinuous, the coefficient calculated by this module is 0.8. This module encapsulates the connection status determination results of each pair of nodes and the overall coefficient to obtain a dataset of sequential continuity of path nodes.

[0091] The state structure extraction submodule extracts the task number, path existence status and sequence status content from the path node corresponding status dataset and the path node sequential continuity status dataset, merges the path coverage status and sequence connection status, extracts the path response status under the task number, and obtains the task structure consistency status data table.

[0092] First, the state structure extraction submodule performs logical merging of multi-source states. Using the task number as an index, it retrieves coverage states from the dataset corresponding to the path nodes and connection states from the dataset of consecutive path node sequences. This module applies a weighted scoring model to calculate the task's structural consistency score. The model sets the coverage weight Wc to 0.5, the continuity weight Ws to 0.3, and the sequence correctness weight Wo to 0.2. The module performs a weighted calculation: the actual coverage value is multiplied by 100 and then weighted by Wc; the continuity coefficient is multiplied by 100 and then weighted by Ws; and the sequence correctness is multiplied by 100 and then weighted by Wo. The module sums these three scores. For example, if a task has 100% coverage (i.e., 1.0), a continuity coefficient of 0.8, and a sequence correctness of 0.9, the module calculates a contribution of 50 points for coverage, 24 points for continuity, and 18 points for sequence correctness, resulting in a total score of 92. The module then determines the consistency level based on the score: 90 points or above is excellent, 80 to 89 points is good, 60 to 79 points is acceptable, and below 60 points is poor. Simultaneously, the module generates a comprehensive status description string. If the coverage is complete but there are minor skips in the order, the module integrates the task number, consistency score, level assessment, and status description to obtain a task structure consistency status data table.

[0093] Specifically, such as Figure 6 As shown, the results summarization module includes:

[0094] The status reading submodule uses the task number and structure status field in the task structure consistency status mapping table to locate the corresponding status content by task number, extract the device number, node number and node status information to obtain the task status data field set;

[0095] First, the status reading submodule is responsible for transforming complex evaluation data into a standardized set of fields. It receives a list of task IDs from the query request and performs an index lookup in the task structure consistency status data table. This module extracts the core fields from the records and breaks them down into fine-grained key-value pairs. The extracted content includes the unique task identifier, associated device identifier, execution status code of each operation node, and overall consistency level. This module performs escape processing on the status codes, converting the numerical encoding in the database, such as 1, into human-readable text, such as "completed". This module performs data formatting operations, encapsulating the extracted information into standard JavaScript markup objects or XML fragments, such as status descriptions including task ID T01, device D01, level Good, and node list. This module packages all processed task data to strip away the underlying calculation logic and intermediate parameters, retaining only the final status information oriented towards business decisions, resulting in the task status data field set.

[0096] The field extraction submodule is based on the task status data field set. It calls the task number to match the corresponding device number and floor information, parses the corresponding content of the device number and floor information under the task number, and obtains the task device floor field lookup table.

[0097] First, the field extraction submodule aims to construct a full-dimensional task context view, parsing the task status data field set to obtain the task number and device number. Then, this module accesses the device basic information dataset again and uses the device number as a foreign key to perform a related query. This module extracts the physical floor number, specific room number, and functional area description of the device. For example, querying device AHU-03 reveals that it is located on the 3rd floor, in room 305, and in the North Zone. This module horizontally concatenates these physical space attribute fields with the task status field. This module also parses the floor information that may be hidden in the task number, such as F3 in task F3, and performs consistency checks with the queried physical floors to ensure data accuracy. Finally, the output lookup table contains a complete record for each row, including the task ID, the targeted device, the floor and room where the device is located, and the task execution status and score, resulting in a task device floor field lookup table.

[0098] The structure comparison submodule matches the field values ​​of the functional structure, path structure and response structure under the task number based on the task equipment floor field comparison table, and performs parallel comparison according to the task number to obtain an integrated building operation and maintenance configuration status set.

[0099] First, the structure comparison submodule performs the final data integration and visualization preparation, gathering information flows from different dimensions and aligning them with the task number as the primary key. These information flows include functional structure (equipment attributes and standard operating procedure requirements), path structure (theoretical route length and standard node count), response structure (actual trajectory length), actual operation results, and consistency scores. This module aligns these heterogeneous data into the same report using the task number as the primary key. This module calculates differential indicators such as path deviation rate. It calculates the absolute value of the difference between the actual path length and the theoretical path length, and then divides the difference by the theoretical path length to obtain the deviation ratio. If the ratio exceeds 0.2 (i.e., 20%), it is marked as a path anomaly. This module fills all the original indicators and the calculated differential indicators into the corresponding columns. The final generated set provides a panoramic view of operation and maintenance quality, enabling managers to quickly locate problematic tasks with low scores, large deviations, or insufficient coverage, thereby achieving precise operation and maintenance management and optimization, and obtaining an integrated set of building operation and maintenance configuration status.

[0100] A verification method for building operation and maintenance processes, based on the aforementioned system, the method comprising the following steps:

[0101] Path identification steps: Obtain the equipment number, floor number, elevator and staircase interface number and passage number of the floor distribution box and supply and exhaust fan unit; extract the access node number sequence from the entrance to the target location of the equipment; map the equipment number to the node number sequence to obtain the equipment path structure data set.

[0102] Trajectory acquisition steps: Based on the device path structure data set, filter the trajectory node numbers and timestamps collected by maintenance personnel, extract the continuous trajectory segments associated with the device, and obtain the path matching structure data set; Node response steps: Based on the path matching structure data set, extract the operation node number sequence, identify the node numbers with operation behaviors in the trajectory, and obtain the task response node list according to the node order corresponding to the task number.

[0103] Capability assessment steps: Based on the operation node numbers in the task response node list, determine the coverage and sequence relationships between operation nodes and path nodes to obtain a task structure consistency status mapping table;

[0104] Results summary steps: Based on the task structure consistency status mapping table, call the task number, device number, floor information and node status fields, corresponding to the task structure information, to obtain the integrated building operation and maintenance configuration status set.

[0105] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An integrated building operation and maintenance platform system, characterized in that, The system includes: The path recognition module is used to obtain the equipment number, floor number, elevator and staircase interface number and passage number of the floor distribution box and supply and exhaust fan unit, extract the access node number sequence from the entrance to the target location of the equipment, map the equipment number to the node number sequence, and obtain the equipment path structure data set. The trajectory acquisition module is used to filter the trajectory node numbers and timestamps collected by maintenance personnel based on the device path structure data set, extract the continuous trajectory segments associated with the device, and obtain the path matching structure data set. The node response module is used to extract the sequence of operation node numbers based on the path matching structure data group, identify the node numbers with operation behavior in the trajectory, and obtain a list of task response nodes according to the task numbers corresponding to the node order. The capability assessment module is used to determine the coverage and order relationship between operation nodes and path nodes based on the operation node numbers in the task response node list, and obtain a task structure consistency status mapping table. The result summarization module is used to obtain an integrated building operation and maintenance configuration status set by calling the task number, device number, floor information and node status fields, corresponding to the task structure information, based on the task structure consistency status mapping table.

2. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The device path structure data set includes device number, access node number sequence, floor number, elevator interface number, stair interface number, and passage number; The path matching structure data group includes a task number, a trajectory segment number, a sequence of trajectory node numbers, and a path matching status. The task response node list includes task number, operation node number, node sequence identifier, and node behavior label; The task structure consistency state mapping table includes task number, path coverage status, node order continuity and structure consistency flag. The integrated building operation and maintenance configuration status set includes equipment number, task number, floor information, path structure information, and node status fields.

3. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The access node number sequence refers to the sequential list of the numbered nodes of the passageways, stairs, and elevator interfaces that the equipment passes through along the way from the entrance to the target location; The continuous trajectory segment associated with the device refers to a segment of continuous moving nodes in the trajectory data of maintenance personnel that matches the device and task and is in continuous time sequence. The node number with operational behavior refers to the node number in the trajectory that not only visited but also recorded the execution of operations and behaviors; The task structure information refers to a structured task execution framework that organizes task number, equipment number, floor and path, and node status fields, which can be used for verification and analysis.

4. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The path recognition module includes: The equipment information extraction submodule is used to obtain the equipment number, floor number, elevator interface number, stair interface number and passage number of the floor distribution box and supply and exhaust fan unit in the building structure drawings, extract the equipment number and floor number, and obtain the equipment basic information dataset according to the position relationship of the interface number and passage number. The access node parsing submodule is used to extract the channel number sequence associated between the entrance and the target location based on the equipment number and floor number in the equipment basic information dataset, and to extract the channel number, elevator interface number and stair interface number nodes to obtain the access node sequence dataset; The path correspondence processing submodule is used to obtain a set of device path structure data based on the device number in the access node sequence dataset and the device basic information dataset, corresponding to the device number and the access node number.

5. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The trajectory acquisition module includes: The trajectory node filtering submodule is used to collect the trajectory node number and timestamp sequence from the operation and maintenance personnel terminal based on the device number and access node number sequence in the device path structure data set, compare the consistency between the trajectory node number and the device number, extract the node number associated with the device number and the corresponding time information, and obtain the trajectory node associated dataset. The task trajectory recognition submodule is used to filter continuous node segments with the same task number, trajectory segment number and device number based on the node number and time order in the trajectory node association dataset, determine the time order continuity between nodes, extract continuous trajectory segment information, and obtain the task trajectory segment dataset. The path matching processing submodule is used to call the access node number sequence in the device path structure data set based on the node number sequence in the task trajectory segment dataset, corresponding to the trajectory node and the access node, to obtain the path matching structure data group.

6. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The node response module includes: The task node extraction submodule is used to read the operation node number sequence in the task configuration file based on the task number and trajectory segment number in the path matching structure data group, extract the operation node number associated with the device according to the task number, verify the consistency of the number format with the task field, and obtain a set of operation node number sequences. The operation node identification submodule is used to obtain the trajectory node number and timestamp data in the corresponding trajectory segment based on the device number and node number in the operation node number sequence set, match the trajectory node number, identify the node number including operation behavior information, extract the node number and organize it according to the task number to obtain the operation behavior node number list. The node sequence association submodule is used to compare the node number and task number in the operation behavior node number list with the operation node number sequence according to the sequence result, associate the operation node number under each task number, and obtain the task response node list.

7. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The capability assessment module includes: The node coverage detection submodule is used to extract the corresponding device number according to the task number and operation node number in the task response node list, call the access path node number sequence in the device path structure data set, and analyze whether the operation node exists in the path according to the corresponding operation node number and access path node number to obtain the path node corresponding data set. The sequential continuity judgment submodule is used to detect the numbering relationship between adjacent operation nodes based on the operation node number sequence under the task number in the corresponding path node dataset, and to analyze the sequential connection status to obtain the sequential continuity of path nodes dataset. The state structure extraction submodule is used to extract the path response status under the task number based on the task number, path existence status and sequence status content in the path node corresponding status dataset and the path node sequential continuity status dataset, merge the path coverage status and sequence connection status, and obtain the task structure consistency status data table.

8. The integrated building operation and maintenance platform system according to claim 7, characterized in that, In the process of extracting device numbers in the node coverage detection: according to the correspondence between task numbers and operation node numbers in the task response node list, device numbers are extracted by task numbers. Through the device numbers in the device path structure data set, the sequence of access path node numbers is retrieved, and the operation node numbers are compared with the access path node numbers to analyze whether the operation node is in the path, thus obtaining the path node correspondence dataset. In the process of analyzing the sequence of operation node numbers in the sequential continuity judgment: based on the sequence of operation node numbers under the task number in the path node corresponding situation dataset, the relationship between adjacent operation node numbers is checked according to the node number order information, the sequential connection state is analyzed, and a dataset of sequential continuity of path nodes is obtained. In the process of generating the task structure consistency state data table during the state structure extraction: by combining the task number, path existence status and sequence status information in the path node corresponding situation dataset and the path node sequential continuity situation dataset, the path response status under the task number is extracted to obtain the task structure consistency state data table.

9. The integrated building operation and maintenance platform system according to claim 1, characterized in that, The result summarization module includes: The status reading submodule is used to locate the corresponding status content by task number based on the task number and structure status field in the task structure consistency status mapping table, extract the device number, node number and node status information, and obtain the task status data field set. The field extraction submodule is used to call the task number to match the corresponding device number and floor information based on the task status data field set, parse the corresponding content of device number and floor information under task number, and obtain task device floor field lookup table. The structure comparison submodule is used to match the field values ​​of the functional structure, path structure and response structure under the task number based on the task equipment floor field comparison table, and perform parallel comparison according to the task number to obtain an integrated building operation and maintenance configuration status set.

10. A verification method for building operation and maintenance processes, characterized in that, The method, executed according to any one of claims 1 to 9, comprises the steps of: Path identification steps: Obtain the equipment number, floor number, elevator and staircase interface number and passage number of the floor distribution box and supply and exhaust fan unit; extract the access node number sequence from the entrance to the target location of the equipment; map the equipment number to the node number sequence to obtain the equipment path structure data set. Trajectory acquisition steps: Based on the device path structure data set, filter the trajectory node numbers and timestamps collected by the maintenance personnel, extract the continuous trajectory segments associated with the device, and obtain the path matching structure data set; Node response steps: Based on the path matching structure data group, extract the operation node number sequence, identify the node numbers with operation behavior in the trajectory, and obtain the task response node list according to the node order corresponding to the task number. Capability assessment steps: Based on the operation node numbers in the task response node list, determine the coverage and sequence relationships between operation nodes and path nodes to obtain a task structure consistency status mapping table; Results summary steps: Based on the task structure consistency status mapping table, call the task number, device number, floor information and node status fields, corresponding to the task structure information, to obtain the integrated building operation and maintenance configuration status set.

Images