Engineering anomaly monitoring method, device and equipment based on electronic job ticket and medium

Abstract

The invention relates to an engineering abnormity monitoring method, device and equipment based on an electronic work ticket and a medium, and belongs to the technical field of infrastructure engineering, and the method comprises the steps: obtaining the engineering plan information of the infrastructure engineering and a to-be-processed electronic work ticket, dividing the electronic work ticket according to the process type, the work ticket type and the work difficulty, and obtaining the work plan information of the infrastructure engineering and the to-be-processed electronic work ticket; the method comprises the following steps: determining a plurality of job ticket data sets, adopting corresponding anomaly detection modes for different types of job ticket data sets, identifying an abnormal job ticket data set and an abnormal electronic job ticket, and finally generating alarm information according to abnormal information. The technical effects of accurately monitoring the abnormal condition in the infrastructure project, improving the project management efficiency and reducing the safety risk are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of infrastructure projects, and in particular to a method, device, equipment and medium for monitoring engineering anomalies based on electronic work tickets. Background Art

[0002] With the rapid development of information technology, the application of electronic work tickets in infrastructure project management is becoming increasingly widespread. In the infrastructure field, work tickets are important documents that record construction activities and ensure construction safety. Their management and monitoring are crucial to project quality and safety.

[0003] Currently, there's no way to monitor anomalies like missed or under-invoiced work tickets during construction. Furthermore, monitoring for abnormal work tickets relies primarily on manual review and simple rule-based matching, which is inefficient and prone to omissions. Furthermore, due to the complexity and diversity of work ticket data, it's difficult to accurately identify all anomalies through simple statistical analysis or rule-based engines. Consequently, some potential problems during construction can't be discovered and addressed promptly, increasing construction risks and costs. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an engineering anomaly monitoring method, device, equipment and medium based on electronic work tickets, aiming to solve at least one of the above technical problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present application provides a method for monitoring engineering anomalies based on electronic work tickets, which adopts the following technical solutions:

[0007] A method for monitoring engineering anomalies based on electronic work tickets, comprising:

[0008] Obtaining construction project plan information and all pending electronic work tickets for the infrastructure project, wherein the construction plan information includes each process and the difficulty of each process, and the pending electronic work tickets include time information, personnel information, and implementation information;

[0009] Dividing all pending electronic work tickets based on the engineering plan information to obtain multiple work ticket data sets for the infrastructure project, and determining a corresponding anomaly detection method according to the type of each work ticket data set;

[0010] For each of the job ticket data sets, performing anomaly detection on the job ticket data set based on an anomaly detection method to determine an abnormal job ticket data set and an abnormal electronic job ticket;

[0011] Based on all the abnormal work ticket data sets and all the abnormal electronic work tickets, abnormal information of the infrastructure project is determined, and alarm information of the infrastructure project is generated based on the abnormal information.

[0012] The beneficial effects of the present invention are: first, by obtaining engineering plan information and pending electronic work tickets, the integrity and accuracy of the monitoring data are ensured; second, electronic work tickets are classified and divided based on engineering plan information, which improves the pertinence and efficiency of anomaly detection; third, according to different types of work ticket data sets, corresponding anomaly detection methods are adopted to accurately locate abnormal work ticket data sets and abnormal electronic work tickets, thereby reducing the false alarm rate and missed alarm rate; finally, alarm information is generated based on all abnormal information, providing timely and effective decision-making support for engineering management, and improving the overall safety and reliability of the project.

[0013] On the basis of the above technical solution, the present invention can also be improved as follows.

[0014] Furthermore, all the electronic work tickets to be processed are divided based on the engineering plan information to obtain multiple work ticket data sets, including:

[0015] Dividing all the electronic work tickets to be processed according to the categories of each process to obtain multiple first work ticket data sets;

[0016] Dividing all the electronic job tickets to be processed according to the types of the job tickets to obtain multiple second job ticket data sets;

[0017] Dividing all the electronic work tickets to be processed according to the difficulty of each process to obtain a plurality of third work ticket data sets;

[0018] Based on the plurality of first job ticket data sets, the plurality of second job ticket data sets, and the plurality of third job ticket data sets, a plurality of job ticket data sets of the infrastructure project are determined.

[0019] The beneficial effect of adopting the above further scheme is: dividing the job ticket data set through the three dimensions of process category, job ticket type, and job difficulty, covering the key elements of engineering management (such as high-risk processes and special job types), and avoiding the limitations of single-dimensional detection. By dividing all pending electronic job tickets according to the category of each process, the job ticket set corresponding to each process can be clearly defined, which is convenient for management based on the characteristics of different processes. Dividing by the type of job ticket helps to identify the characteristics of different types of job tickets and further refine the management granularity. Dividing according to the difficulty of each process, differentiated management strategies can be implemented for job tickets of different difficulty levels.

[0020] Furthermore, for each of the job ticket data sets, performing anomaly detection on the job ticket data set based on an anomaly detection method, and determining an abnormal job ticket data set and an abnormal electronic job ticket, including:

[0021] For each of the first job ticket data sets, determining whether the number of to-be-processed electronic job tickets in the first job ticket data set reaches a set number threshold for the corresponding process;

[0022] For each of the first job ticket data sets, if the number of electronic job tickets to be processed in the first job ticket data set does not reach a set number threshold of the corresponding process, determining the first job ticket data set corresponding to the process as an abnormal job ticket data set;

[0023] For each of the first job ticket data sets, if the number of unprocessed electronic job tickets in the first job ticket data set reaches a set number threshold, then, based on the planned time information and planned personnel information corresponding to the process, perform an anomaly detection on each unprocessed electronic job ticket in the first job ticket data set, and determine whether there is an abnormal first abnormal electronic job ticket in the first job ticket data set;

[0024] For each second job ticket data set, obtaining historical abnormal job ticket information based on the job ticket type corresponding to the second job ticket data set;

[0025] For each second job ticket data set, performing anomaly detection on each to-be-processed electronic job ticket in the second job ticket data set based on historical abnormal job ticket information, and determining whether there is an abnormal second abnormal electronic job ticket in the second job ticket data set;

[0026] For each of the third job ticket data sets, based on a preset job difficulty anomaly detection rule, anomaly detection is performed on each to-be-processed electronic job ticket in the third job ticket data set, and a third abnormal electronic job ticket in the third job ticket data set is determined;

[0027] An abnormal electronic job ticket is determined based on each of the first abnormal electronic job tickets, each of the second abnormal electronic job tickets, and each of the third abnormal electronic job tickets.

[0028] The beneficial effect of adopting the above further solution is that by comparing the number of electronic work tickets to be processed in the first work ticket data set with the set number threshold, it is possible to quickly identify abnormal work ticket data sets with insufficient number, thereby avoiding engineering risks caused by insufficient work ticket number;

[0029] For the first job ticket data set whose number reaches the threshold, anomaly detection is performed in combination with the planned time information and planned personnel information corresponding to the process. This can accurately locate electronic job tickets with time anomalies or personnel anomalies, further improving the accuracy and efficiency of project management.

[0030] Using the job ticket type corresponding to the second job ticket dataset to obtain historical abnormal job ticket information, and based on this, perform anomaly detection to help discover potential repetitive problems;

[0031] By detecting the electronic work tickets in the third work ticket data set based on the preset work difficulty anomaly detection rules, anomalies related to work difficulty can be effectively identified, ensuring the work quality of high-difficulty processes.

[0032] Furthermore, for each of the first job ticket data sets, based on the planned time information and planned personnel information corresponding to the process, anomaly detection is performed on each to-be-processed electronic job ticket in the first job ticket data set, and determining a first abnormal electronic job ticket that is abnormal in the first job ticket data set includes:

[0033] For each of the first job ticket data sets, determining whether the time information in each of the to-be-processed electronic job tickets has a time anomaly based on the planned time information corresponding to the process, and determining whether the personnel information in each of the to-be-processed electronic job tickets has a personnel anomaly based on the planned personnel information of the corresponding process;

[0034] For each of the first job ticket data sets, if there is a time anomaly or a personnel anomaly in the electronic job ticket to be processed in the first job ticket data set, the electronic job ticket to be processed is determined to be a first abnormal electronic job ticket.

[0035] The beneficial effect of adopting this further solution is that, for each pending electronic work ticket in the first work ticket dataset, the system can determine whether the time information is abnormal based on the planned time information of the process, and whether the personnel information is abnormal based on the planned personnel information of the process. If a time anomaly or a personnel anomaly is detected, the first abnormal electronic work ticket can be accurately identified. This solution improves the accuracy of electronic work ticket anomaly detection, helping to promptly identify potential scheduling and staffing issues in infrastructure projects, thereby effectively reducing project risks.

[0036] Further, the performing of anomaly detection on each to-be-processed electronic job ticket in the second job ticket data set based on the historical abnormal job ticket information, and determining that an abnormal second abnormal electronic job ticket exists in the second job ticket data set, includes:

[0037] Based on the historical abnormal operation ticket information, determining a plurality of abnormal keywords;

[0038] Based on the word vector model, a semantic space is constructed and each abnormal keyword is vectorized to obtain a vector of each abnormal keyword in the semantic space, where the semantic space represents the semantic relationship between words;

[0039] Based on the vector corresponding to each abnormal keyword, calculating the similarity between each abnormal keyword and other words in the semantic space;

[0040] Determining similar keywords corresponding to each abnormal keyword based on the similarity between each abnormal keyword and other words in the semantic space;

[0041] Based on each abnormal keyword, similar keywords corresponding to the abnormal keyword and implementation information of each electronic job ticket to be processed, an abnormality detection is performed on each electronic job ticket to be processed to determine a second abnormal electronic job ticket with an abnormality in the second job ticket data set.

[0042] The beneficial effect of adopting the above further scheme is: it can extract abnormal keywords based on historical abnormal job ticket information, and use the word vector model to build a semantic space to achieve semantic expansion of abnormal keywords, thereby determining similar keywords. Based on the comparison of abnormal keywords and their similar keywords with the implementation information of the electronic job ticket to be processed, the second abnormal electronic job ticket with an abnormality in the second job ticket data set can be accurately located, which not only improves the accuracy of anomaly detection, but also can cover potential abnormal situations more comprehensively.

[0043] Furthermore, the method of performing anomaly detection on each to-be-processed electronic work ticket in the third work ticket data set based on a preset work difficulty anomaly detection rule, and determining a third abnormal electronic work ticket in the third work ticket data set, includes:

[0044] For each of the pending electronic job tickets in any of the third job ticket data sets, determining the operation efficiency of the pending electronic job ticket based on the operation difficulty, time information, and operation content of the pending electronic job ticket;

[0045] For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, determining whether the operation efficiency of the to-be-processed job ticket meets a set operation efficiency requirement;

[0046] For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, if the operation efficiency of the to-be-processed electronic job ticket does not meet the set operation efficiency requirement, the to-be-processed electronic job ticket is determined to be a third abnormal electronic job ticket.

[0047] The beneficial effect of adopting this further solution is that it can accurately calculate the work efficiency of each pending electronic work ticket in a dataset of work tickets with varying degrees of difficulty, taking into account the work difficulty, time information, and work content. Furthermore, by determining whether the work efficiency meets preset requirements, it effectively identifies electronic work tickets that fail to meet the work efficiency standards, thereby accurately identifying the third abnormal electronic work ticket. This helps to promptly identify potential risk points in infrastructure projects, providing important support for project safety management and progress control.

[0048] Furthermore, determining abnormal information of the infrastructure project based on all the abnormal work ticket data sets and all the abnormal electronic work tickets includes:

[0049] According to each process of the infrastructure project, count the abnormal type and number of abnormal electronic work tickets of each process;

[0050] Determining a first risk level for each of the processes based on the abnormal type and quantity of the abnormal electronic work ticket for each of the processes;

[0051] Determining a second risk level corresponding to each of the processes based on all of the abnormal work ticket data sets;

[0052] Determining the safety risk of each of the processes based on the first risk level and the second risk level corresponding to each of the processes;

[0053] Based on the safety risk of each process, the abnormal type of the abnormal electronic ticket and the number of abnormal electronic tickets, the abnormal information of the infrastructure project is determined.

[0054] The beneficial effect of adopting this further solution is that it can comprehensively count the types and number of abnormal electronic work tickets for each process in the infrastructure project, thereby accurately assessing the primary risk level of each process. Simultaneously, based on the dataset of all abnormal work tickets, the secondary risk level of each process is further determined, and the safety risk of each process is derived by combining the two. Ultimately, by combining the safety risk of each process with the types and number of abnormal electronic tickets, the overall abnormality information of the infrastructure project can be accurately determined, providing a scientific basis for project management and effectively improving the accuracy and efficiency of project abnormality monitoring.

[0055] In a second aspect, the present application provides an engineering anomaly monitoring device based on an electronic work ticket, which adopts the following technical solutions:

[0056] An engineering abnormality monitoring device based on an electronic work ticket, comprising:

[0057] An acquisition module is used to acquire the engineering plan information of the infrastructure project and all pending electronic work tickets, wherein the engineering plan information includes each process and the difficulty of each process, and the pending electronic work tickets include status information, time information, personnel information, and implementation information;

[0058] a division module, which divides all pending electronic work tickets based on the project plan information to obtain multiple work ticket data sets of the infrastructure project, and determines a corresponding anomaly detection method according to the type of each work ticket data set;

[0059] An anomaly detection module, configured to perform an anomaly detection on each of the job ticket data sets based on an anomaly detection method, and determine an abnormal job ticket data set and an abnormal electronic job ticket;

[0060] An alarm module is used to determine abnormal information of the infrastructure project based on the abnormal work ticket data set and the abnormal electronic work ticket, and generate alarm information of the infrastructure project based on the abnormal information.

[0061] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0062] An electronic device comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the engineering anomaly monitoring method based on an electronic work ticket as described in any one of the first aspects.

[0063] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0064] A computer-readable storage medium stores a computer program that can be loaded by a processor and executes the engineering anomaly monitoring method based on electronic work tickets described in any one of the first aspects.

[0065] Additional aspects and advantages of the present application will be given in part in the following description, which will become apparent from the following description, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flowchart of a method for monitoring engineering anomalies based on electronic work tickets is provided as an embodiment of the present invention;

[0067] Figure 2 A schematic structural diagram of an engineering anomaly monitoring device based on an electronic work ticket provided in one embodiment of the present invention;

[0068] Figure 3 The present invention provides a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0070] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0071] An embodiment of the present application provides an engineering anomaly monitoring method based on an electronic work ticket, which can be executed by an electronic device, which can be a server or a mobile terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services; the mobile terminal device can be a laptop computer, a desktop computer, etc., but is not limited to this.

[0072] like Figure 1 As shown, a method for monitoring engineering anomalies based on electronic work tickets includes:

[0073] Step S101: Acquire engineering plan information and all pending electronic work tickets of the infrastructure project, wherein the engineering plan information includes each process and the difficulty of each process, and the pending electronic work tickets include time information, personnel information, and implementation information;

[0074] In an embodiment of the present application, all pending electronic work tickets related to the infrastructure project are searched in the work ticket database, and SQL or other database query languages ​​are used to query based on the unique identifier of the infrastructure project.

[0075] Step S102: dividing all pending electronic work tickets based on the project plan information to obtain multiple work ticket data sets of the infrastructure project, and determining a corresponding anomaly detection method according to the type of each work ticket data set;

[0076] In an embodiment of the present application, the anomaly detection method is pre-set according to different types of job ticket data sets, and all electronic job tickets to be processed are divided according to multiple dimensions to obtain multiple job ticket data sets, each dimension being a process dimension, a job difficulty dimension, and a job ticket type dimension. It can also be divided according to the job ticket priority dimension and the time dimension. This embodiment is not limited to this.

[0077] Specifically, all electronic work tickets to be processed are divided based on the engineering plan information to obtain multiple work ticket data sets, including:

[0078] Dividing all the electronic work tickets to be processed according to the categories of each process to obtain multiple first work ticket data sets;

[0079] Dividing all the electronic job tickets to be processed according to the types of the job tickets to obtain multiple second job ticket data sets;

[0080] Dividing all the electronic work tickets to be processed according to the difficulty of each process to obtain a plurality of third work ticket data sets;

[0081] Based on the plurality of first job ticket data sets, the plurality of second job ticket data sets, and the plurality of third job ticket data sets, a plurality of job ticket data sets of the infrastructure project are determined.

[0082] In this embodiment, work tickets are categorized by process in the project plan to facilitate process-specific management. For example, if a capital construction project plan includes three processes: "pile foundation construction," "concrete pouring," and "rebar tying," then the first work ticket dataset 1 includes all work tickets related to "pile foundation construction," the first work ticket dataset 2 includes all work tickets related to "concrete pouring," and the first work ticket dataset 3 includes all work tickets related to "rebar tying."

[0083] Categorize job tickets by type to facilitate unified processing of similar job tickets. For example, if the job ticket types include "Safety Job Ticket," "Quality Inspection Ticket," and "Equipment Commissioning Ticket," then the second job ticket dataset 1 includes all safety job tickets, the second job ticket dataset 2 includes all quality inspection tickets, and the third job ticket dataset 3 includes all equipment commissioning tickets.

[0084] Categorizing processes by difficulty level facilitates focused monitoring of high-difficulty processes. For example, the difficulty level is categorized into high, medium, and low. The third job ticket dataset 1 includes all job tickets for high-difficulty processes, the third job ticket dataset 2 includes all job tickets for medium-difficulty processes, and the third job ticket dataset 3 includes all job tickets for low-difficulty processes.

[0085] The job ticket data set is divided into three dimensions: process category, job ticket type, and job difficulty, covering the key elements of engineering management and avoiding the limitations of single-dimensional detection.

[0086] Step S103, for each of the job ticket data sets, performing anomaly detection on the job ticket data set based on an anomaly detection method, and determining an abnormal job ticket data set and an abnormal electronic job ticket;

[0087] In the embodiment of the present application, specifically, step S103 mainly includes the following sub-steps:

[0088] Step S1031: for each of the first job ticket data sets, determining whether the number of electronic job tickets to be processed in the first job ticket data set reaches a set number threshold of the corresponding process;

[0089] Step S1032: for each first job ticket data set, if the number of electronic job tickets to be processed in the first job ticket data set does not reach a set number threshold of the corresponding process, then determining the first job ticket data set corresponding to the process as an abnormal job ticket data set;

[0090] Step S1033: For each of the first job ticket data sets, if the number of pending electronic job tickets in the first job ticket data set reaches a set number threshold, then, based on the planned time information and planned personnel information corresponding to the process, perform an anomaly detection on each pending electronic job ticket in the first job ticket data set to determine if there is an abnormal first abnormal electronic job ticket in the first job ticket data set;

[0091] Step S1034: for each second job ticket data set, obtaining historical abnormal job ticket information based on the job ticket type corresponding to the second job ticket data set;

[0092] Step S1035: for each second job ticket data set, performing anomaly detection on each to-be-processed electronic job ticket in the second job ticket data set based on historical abnormal job ticket information, and determining whether there is an abnormal second abnormal electronic job ticket in the second job ticket data set;

[0093] Step S1036: For each of the third job ticket data sets, based on a preset job difficulty anomaly detection rule, perform anomaly detection on each to-be-processed electronic job ticket in the third job ticket data set, and determine a third abnormal electronic job ticket in the third job ticket data set;

[0094] Step S1037 : determining an abnormal electronic job ticket based on each of the first abnormal electronic job tickets, each of the second abnormal electronic job tickets, and each of the third abnormal electronic job tickets.

[0095] In an embodiment of the present application, if the set quantity threshold of a certain process is 50 work tickets, but the actual number is only 40, then the data set is determined to be abnormal.

[0096] By comparing the number of electronic work tickets to be processed in the first work ticket data set with the set quantity threshold, abnormal work ticket data sets with insufficient quantity can be quickly identified to avoid engineering risks caused by insufficient work ticket quantity;

[0097] For each unprocessed electronic job ticket in the first job ticket data set, anomalies can be determined by comparing the deviation between the actual operation time and the planned time, or verifying whether the personnel qualifications meet the requirements.

[0098] Optionally, for each first job ticket data set, based on the planned time information and planned personnel information corresponding to the process, performing anomaly detection on each to-be-processed electronic job ticket in the first job ticket data set, and determining a first abnormal electronic job ticket that is abnormal in the first job ticket data set includes:

[0099] For each of the first job ticket data sets, determining whether the time information in each of the to-be-processed electronic job tickets has a time anomaly based on the planned time information corresponding to the process, and determining whether the personnel information in each of the to-be-processed electronic job tickets has a personnel anomaly based on the planned personnel information of the corresponding process;

[0100] For each of the first job ticket data sets, if there is a time anomaly or a personnel anomaly in the electronic job ticket to be processed in the first job ticket data set, the electronic job ticket to be processed is determined to be a first abnormal electronic job ticket.

[0101] For each pending electronic work ticket in the first work ticket dataset, the system can determine whether there are any anomalies in the time information based on the planned time information of the process, and whether there are any anomalies in the personnel information based on the planned personnel information of the process. If an anomaly in time or personnel is detected, the first abnormal electronic work ticket can be accurately identified. This improves the accuracy of electronic work ticket anomaly detection, helping to promptly identify potential scheduling and staffing issues in infrastructure projects, thereby effectively reducing project risks.

[0102] In this embodiment, anomalies of the same type of work tickets are detected based on historical anomaly information. For example, if the second work ticket dataset is a safety work ticket dataset of the same type, historical anomaly information of the safety work ticket dataset is obtained, and the anomaly causes include: failure to wear a safety helmet, failure to set a warning sign, etc. Based on the historical anomaly information, anomaly detection is performed on each pending electronic work ticket in the second work ticket dataset to determine if there is a second abnormal electronic work ticket in the second work ticket dataset.

[0103] Optionally, performing anomaly detection on each to-be-processed electronic job ticket in the second job ticket data set based on the historical abnormal job ticket information to determine that an abnormal second abnormal electronic job ticket exists in the second job ticket data set includes:

[0104] Based on the historical abnormal operation ticket information, determining a plurality of abnormal keywords;

[0105] Based on the word vector model, a semantic space is constructed and each abnormal keyword is vectorized to obtain a vector of each abnormal keyword in the semantic space, where the semantic space represents the semantic relationship between words;

[0106] Based on the vector corresponding to each abnormal keyword, calculating the similarity between each abnormal keyword and other words in the semantic space;

[0107] Determining similar keywords corresponding to each abnormal keyword based on the similarity between each abnormal keyword and other words in the semantic space;

[0108] Based on each abnormal keyword, similar keywords corresponding to the abnormal keyword and implementation information of each electronic job ticket to be processed, an abnormality detection is performed on each electronic job ticket to be processed to determine a second abnormal electronic job ticket with an abnormality in the second job ticket data set.

[0109] In an embodiment of the present application, a word vector model (such as Word2Vec, BERT) is used to convert abnormal keywords into vectors to construct a semantic space. In the semantic space, the vector distance of words with similar semantics is closer. Afterwards, the similarity between each abnormal keyword and other words in the semantic space is calculated, and words with similarity higher than the threshold are screened as similar keywords. The abnormal keywords and similar keywords are matched with the implementation information of the electronic operation ticket to be processed. If the same keyword is matched, the electronic operation ticket to be processed is determined to be the second abnormal electronic operation ticket.

[0110] In the embodiment of the present application, based on the operation difficulty rule, the abnormality of the operation ticket of the high-difficulty process is detected. The rule of high-difficulty process (such as "height operation") is that the use record of the safety rope must be provided and the safety briefing must be conducted.

[0111] Optionally, performing anomaly detection on each to-be-processed electronic job ticket in the third job ticket data set based on a preset job difficulty anomaly detection rule to determine a third abnormal electronic job ticket in the third job ticket data set includes:

[0112] For each of the pending electronic job tickets in any of the third job ticket data sets, determining the operation efficiency of the pending electronic job ticket based on the operation difficulty, time information, and operation content of the pending electronic job ticket;

[0113] For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, determining whether the operation efficiency of the to-be-processed job ticket meets a set operation efficiency requirement;

[0114] For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, if the operation efficiency of the to-be-processed electronic job ticket does not meet the set operation efficiency requirement, the to-be-processed electronic job ticket is determined to be a third abnormal electronic job ticket.

[0115] In an embodiment of the present application, efficiency thresholds for different job difficulties are defined based on historical data or industry standards, and the job type, start time, end time, total duration, and workload are extracted from the job ticket. Afterwards, the corresponding efficiency calculation formula is selected based on the job difficulty and the job efficiency of the electronic job ticket to be processed is calculated. The calculated job efficiency is compared with the set efficiency threshold. Job tickets that do not meet the efficiency requirements are marked as third abnormal electronic job tickets.

[0116] By determining whether work efficiency meets preset requirements, we can effectively identify electronic work tickets that do not meet the work efficiency standards, thereby accurately determining the third abnormal electronic work ticket. This helps to timely identify potential risk points in infrastructure projects and provides important support for project safety management and progress control.

[0117] Step S104 : determining abnormal information of the infrastructure project based on all the abnormal work ticket data sets and all the abnormal electronic work tickets, and generating alarm information of the infrastructure project based on the abnormal information.

[0118] Determining abnormal information of the infrastructure project based on all the abnormal work ticket data sets and all the abnormal electronic work tickets includes:

[0119] According to each process of the infrastructure project, count the abnormal type and number of abnormal electronic work tickets of each process;

[0120] Determining a first risk level for each of the processes based on the abnormal type and quantity of the abnormal electronic work ticket for each of the processes;

[0121] Determining a second risk level corresponding to each of the processes based on all of the abnormal work ticket data sets;

[0122] Determining the safety risk of each of the processes based on the first risk level and the second risk level corresponding to each of the processes;

[0123] Based on the safety risk of each process, the abnormal type of the abnormal electronic ticket and the number of abnormal electronic tickets, the abnormal information of the infrastructure project is determined.

[0124] In an embodiment of the present application, the following further features are included: determining the flow level of abnormal information based on the risk level of each process, and issuing an alarm based on the flow level. Based on the risk level of the process, the flow level of abnormal information is dynamically adjusted to ensure that abnormal information from high-risk processes is processed first, thereby reducing safety risks.

[0125] In the embodiment of the present application, the abnormal type and number of abnormal electronic work tickets are counted by process. All abnormal electronic work tickets are traversed and grouped by process name. For each process, the number of different abnormal types is counted. Then, based on the abnormal type and number, the first risk level of each process is calculated. The first risk level represents the degree of abnormality of each process. Based on all the abnormal work ticket data sets, the second risk level corresponding to each process is determined. The second risk level represents the degree of missed tickets for each process. Finally, based on the first risk level and the second risk level corresponding to each process, the safety risk of each process is determined. Combining the safety risk of each process with the abnormal type and number of abnormal electronic tickets, the overall abnormal information of the infrastructure project is accurately determined, providing a scientific basis for project management and effectively improving the accuracy and efficiency of project abnormality monitoring.

[0126] For example, if a process's anomalies are concentrated in delays and occur frequently, the risk is considered high. The alarm generation unit generates alarm information based on statistical results, supporting multiple output formats. For example, if a major anomaly is detected, an alarm can be immediately sent to the relevant person in charge.

[0127] This method improves the pertinence and efficiency of anomaly detection by classifying electronic work tickets based on project plan information. Subsequently, it adopts corresponding anomaly detection methods according to different types of work ticket datasets to accurately locate abnormal work ticket datasets and abnormal electronic work tickets, reducing false positive and false negative rates. Finally, it generates alarm information based on all abnormal information, providing timely and effective decision support for project management and improving the overall safety and reliability of the project.

[0128] Figure 2 A structural diagram of an engineering abnormality monitoring device 200 based on an electronic work ticket is shown.

[0129] like Figure 2 As shown, an engineering abnormality monitoring device 200 based on an electronic work ticket mainly includes:

[0130] Acquisition module 201 is used to acquire the construction project plan information and all pending electronic work tickets, wherein the construction plan information includes each process and the difficulty of each process, and the pending electronic work tickets include status information, time information, personnel information, and implementation information;

[0131] A division module 202 divides all pending electronic work tickets based on the project plan information to obtain multiple work ticket data sets for the infrastructure project, and determines a corresponding anomaly detection method based on the type of each work ticket data set;

[0132] An anomaly detection module 203 is configured to perform an anomaly detection on each of the job ticket data sets based on an anomaly detection method, and determine an abnormal job ticket data set and an abnormal electronic job ticket;

[0133] The display module 204 is configured to determine abnormal information of the infrastructure project based on the abnormal operation ticket data set and the abnormal electronic operation ticket, and generate alarm information of the infrastructure project based on the abnormal information.

[0134] Optionally, the division module 202 is specifically configured to:

[0135] Dividing all the electronic work tickets to be processed according to the categories of each process to obtain multiple first work ticket data sets;

[0136] Dividing all the electronic job tickets to be processed according to the types of the job tickets to obtain multiple second job ticket data sets;

[0137] Dividing all the electronic work tickets to be processed according to the difficulty of each process to obtain a plurality of third work ticket data sets;

[0138] Based on the plurality of first job ticket data sets, the plurality of second job ticket data sets, and the plurality of third job ticket data sets, a plurality of job ticket data sets of the infrastructure project are determined.

[0139] Optionally, the anomaly detection module 203 includes:

[0140] A first anomaly detection submodule is configured to determine, for each of the first job ticket data sets, whether the number of to-be-processed electronic job tickets in the first job ticket data set reaches a set number threshold for the corresponding process;

[0141] For each of the first job ticket data sets, if the number of electronic job tickets to be processed in the first job ticket data set does not reach a set number threshold of the corresponding process, determining the first job ticket data set corresponding to the process as an abnormal job ticket data set;

[0142] For each of the first job ticket data sets, if the number of unprocessed electronic job tickets in the first job ticket data set reaches a set number threshold, then, based on the planned time information and planned personnel information corresponding to the process, perform an anomaly detection on each unprocessed electronic job ticket in the first job ticket data set, and determine whether there is an abnormal first abnormal electronic job ticket in the first job ticket data set;

[0143] A second anomaly detection submodule is configured to obtain, for each second job ticket data set, historical abnormal job ticket information based on the job ticket type corresponding to the second job ticket data set;

[0144] For each second job ticket data set, performing anomaly detection on each to-be-processed electronic job ticket in the second job ticket data set based on historical abnormal job ticket information, and determining whether there is an abnormal second abnormal electronic job ticket in the second job ticket data set;

[0145] A third anomaly detection submodule is configured to perform an anomaly detection on each of the electronic work tickets to be processed in the third work ticket data set based on a preset work difficulty anomaly detection rule for each of the third work ticket data sets, and determine a third abnormal electronic work ticket in the third work ticket data set;

[0146] An abnormal electronic job ticket is determined based on each of the first abnormal electronic job tickets, each of the second abnormal electronic job tickets, and each of the third abnormal electronic job tickets.

[0147] Optionally, the first anomaly detection submodule is specifically configured to:

[0148] For each of the first job ticket data sets, determining whether the time information in each of the to-be-processed electronic job tickets has a time anomaly based on the planned time information corresponding to the process, and determining whether the personnel information in each of the to-be-processed electronic job tickets has a personnel anomaly based on the planned personnel information of the corresponding process;

[0149] For each of the first job ticket data sets, if there is a time anomaly or a personnel anomaly in the electronic job ticket to be processed in the first job ticket data set, the electronic job ticket to be processed is determined to be a first abnormal electronic job ticket.

[0150] Optionally, the second anomaly detection submodule is specifically configured to:

[0151] Based on the historical abnormal operation ticket information, determining a plurality of abnormal keywords;

[0152] Based on the word vector model, a semantic space is constructed and each abnormal keyword is vectorized to obtain a vector of each abnormal keyword in the semantic space, where the semantic space represents the semantic relationship between words;

[0153] Based on the vector corresponding to each abnormal keyword, calculating the similarity between each abnormal keyword and other words in the semantic space;

[0154] Determining similar keywords corresponding to each abnormal keyword based on the similarity between each abnormal keyword and other words in the semantic space;

[0155] Based on each abnormal keyword, similar keywords corresponding to the abnormal keyword and implementation information of each electronic job ticket to be processed, an abnormality detection is performed on each electronic job ticket to be processed to determine a second abnormal electronic job ticket with an abnormality in the second job ticket data set.

[0156] Optionally, the third anomaly detection submodule is specifically configured to:

[0157] For each of the pending electronic job tickets in any of the third job ticket data sets, determining the operation efficiency of the pending electronic job ticket based on the operation difficulty, time information, and operation content of the pending electronic job ticket;

[0158] For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, determining whether the operation efficiency of the to-be-processed job ticket meets a set operation efficiency requirement;

[0159] For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, if the operation efficiency of the to-be-processed electronic job ticket does not meet the set operation efficiency requirement, the to-be-processed electronic job ticket is determined to be a third abnormal electronic job ticket.

[0160] Optional alarm module, specifically used for:

[0161] According to each process of the infrastructure project, count the abnormal type and number of abnormal electronic work tickets of each process;

[0162] Determining a first risk level for each of the processes based on the abnormal type and quantity of the abnormal electronic work ticket for each of the processes;

[0163] Determining a second risk level corresponding to each of the processes based on all of the abnormal work ticket data sets;

[0164] Determining the safety risk of each of the processes based on the first risk level and the second risk level corresponding to each of the processes;

[0165] Based on the safety risk of each process, the abnormal type of the abnormal electronic ticket and the number of abnormal electronic tickets, the abnormal information of the infrastructure project is determined.

[0166] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0167] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call programs. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0168] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical solutions.

[0169] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0170] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] Figure 3 This is a structural block diagram of an electronic device 300 according to an embodiment of the present application.

[0172] like Figure 3As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include one or more of an information input / information output (I / O) interface 303 , a communication component 304 , and a communication bus 305 .

[0173] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps in the above-mentioned engineering anomaly monitoring method based on electronic work tickets. The memory 302 is used to store various types of data to support the operation of the electronic device 300. For example, these data may include instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0174] The I / O interface 303 provides an interface between the processor 301 and other interface modules, which may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used to test wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more thereof, may include: a Wi-Fi component, a Bluetooth component, and an NFC component.

[0175] Communication bus 305 may include a path for transmitting information between the aforementioned components. Communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, for example. Communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.

[0176] The electronic device 300 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the engineering anomaly monitoring method based on electronic work tickets given in the above embodiment.

[0177] The computer-readable storage medium provided in the embodiment of the present application is introduced below. The computer-readable storage medium described below and the engineering anomaly monitoring method based on electronic work tickets described above can be referenced to each other.

[0178] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned engineering anomaly monitoring method based on electronic work tickets are implemented.

[0179] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0180] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0181] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A method for monitoring engineering anomalies based on electronic work tickets, characterized in that: include: Obtaining engineering plan information and all pending electronic work tickets for the infrastructure project, wherein the engineering plan information includes each process and the difficulty of each process; Dividing all pending electronic work tickets based on the engineering plan information to obtain multiple work ticket data sets for the infrastructure project, and determining a corresponding anomaly detection method according to the type of each work ticket data set; For each of the job ticket data sets, performing anomaly detection on the job ticket data set based on an anomaly detection method to determine an abnormal job ticket data set and an abnormal electronic job ticket; Based on all the abnormal work ticket data sets and all the abnormal electronic work tickets, abnormal information of the infrastructure project is determined, and alarm information of the infrastructure project is generated based on the abnormal information.

2. The method for monitoring engineering anomalies based on electronic work tickets according to claim 1, characterized in that: The method divides all pending electronic work tickets based on the engineering plan information to obtain multiple work ticket data sets, including: Dividing all the electronic work tickets to be processed according to the categories of each process to obtain multiple first work ticket data sets; Dividing all the electronic job tickets to be processed according to the types of the job tickets to obtain multiple second job ticket data sets; Dividing all the electronic work tickets to be processed according to the difficulty of each process to obtain a plurality of third work ticket data sets; Based on the plurality of first job ticket data sets, the plurality of second job ticket data sets, and the plurality of third job ticket data sets, a plurality of job ticket data sets of the infrastructure project are determined.

3. The method for monitoring engineering anomalies based on electronic work tickets according to claim 2, characterized in that: For each of the job ticket data sets, performing anomaly detection on the job ticket data set based on an anomaly detection method, and determining an abnormal job ticket data set and an abnormal electronic job ticket, including: For each of the first job ticket data sets, determining whether the number of electronic job tickets to be processed in the first job ticket data set reaches a set number threshold of the corresponding process; For each of the first job ticket data sets, if the number of electronic job tickets to be processed in the first job ticket data set does not reach a set number threshold of the corresponding process, determining the first job ticket data set corresponding to the process as an abnormal job ticket data set; For each of the first job ticket data sets, if the number of unprocessed electronic job tickets in the first job ticket data set reaches a set number threshold, then, based on the planned time information and planned personnel information corresponding to the process, perform an anomaly detection on each unprocessed electronic job ticket in the first job ticket data set, and determine whether there is an abnormal first abnormal electronic job ticket in the first job ticket data set; For each of the second job ticket data sets, obtaining historical abnormal job ticket information based on the job ticket type corresponding to the second job ticket data set; For each second job ticket data set, performing anomaly detection on each to-be-processed electronic job ticket in the second job ticket data set based on historical abnormal job ticket information, and determining whether there is an abnormal second abnormal electronic job ticket in the second job ticket data set; For each of the third job ticket data sets, based on a preset job difficulty anomaly detection rule, anomaly detection is performed on each to-be-processed electronic job ticket in the third job ticket data set, and a third abnormal electronic job ticket in the third job ticket data set is determined; An abnormal electronic job ticket is determined based on each of the first abnormal electronic job tickets, each of the second abnormal electronic job tickets, and each of the third abnormal electronic job tickets.

4. The method for monitoring engineering anomalies based on electronic work tickets according to claim 3, characterized in that: The electronic job ticket to be processed includes time information and personnel information. For each of the first job ticket data sets, based on the planned time information and planned personnel information corresponding to the process, anomaly detection is performed on each electronic job ticket to be processed in the first job ticket data set, and determining a first abnormal electronic job ticket that is abnormal in the first job ticket data set includes: For each of the first job ticket data sets, determining whether the time information in each of the to-be-processed electronic job tickets has a time anomaly based on the planned time information corresponding to the process, and determining whether the personnel information in each of the to-be-processed electronic job tickets has a personnel anomaly based on the planned personnel information of the corresponding process; For each of the first job ticket data sets, if there is a time anomaly or a personnel anomaly in the electronic job ticket to be processed in the first job ticket data set, the electronic job ticket to be processed is determined to be a first abnormal electronic job ticket.

5. The method for monitoring engineering anomalies based on electronic work tickets according to claim 3, characterized in that: The electronic job ticket to be processed further includes implementation information. The abnormality detection is performed on each electronic job ticket to be processed in the second job ticket data set based on the historical abnormal job ticket information, and determining that there is an abnormal second electronic job ticket in the second job ticket data set, including: Based on the historical abnormal operation ticket information, determining a plurality of abnormal keywords; Based on the word vector model, a semantic space is constructed and each abnormal keyword is vectorized to obtain a vector of each abnormal keyword in the semantic space, where the semantic space represents the semantic relationship between words; Based on the vector corresponding to each abnormal keyword, calculating the similarity between each abnormal keyword and other words in the semantic space; Determining similar keywords corresponding to each abnormal keyword based on the similarity between each abnormal keyword and other words in the semantic space; Based on each abnormal keyword, similar keywords corresponding to the abnormal keyword and implementation information of each electronic job ticket to be processed, an abnormality detection is performed on each electronic job ticket to be processed to determine a second abnormal electronic job ticket with an abnormality in the second job ticket data set.

6. The method for monitoring engineering anomalies based on electronic work tickets according to claim 4, characterized in that: The method of performing anomaly detection on each to-be-processed electronic job ticket in the third job ticket data set based on a preset job difficulty anomaly detection rule, and determining a third abnormal electronic job ticket in the third job ticket data set, includes: For each of the pending electronic job tickets in any of the third job ticket data sets, determining the operation efficiency of the pending electronic job ticket based on the operation difficulty, time information, and operation content of the pending electronic job ticket; For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, determining whether the operation efficiency of the to-be-processed job ticket meets a set operation efficiency requirement; For each of the to-be-processed electronic job tickets in any of the third job ticket data sets, if the operation efficiency of the to-be-processed electronic job ticket does not meet the set operation efficiency requirement, the to-be-processed electronic job ticket is determined to be a third abnormal electronic job ticket.

7. The method for monitoring engineering anomalies based on electronic work tickets according to claim 3, characterized in that: Determining abnormal information of the infrastructure project based on all the abnormal work ticket data sets and all the abnormal electronic work tickets includes: According to each process of the infrastructure project, count the abnormal type and number of abnormal electronic work tickets of each process; Determining a first risk level for each of the processes based on the abnormal type and quantity of the abnormal electronic work ticket for each of the processes; Determining a second risk level corresponding to each of the processes based on all of the abnormal work ticket data sets; Determining the safety risk of each of the processes based on the first risk level and the second risk level corresponding to each of the processes; Based on the safety risk of each process, the abnormal type of the abnormal electronic ticket and the number of abnormal electronic tickets, the abnormal information of the infrastructure project is determined.

8. An engineering abnormality monitoring device based on electronic work tickets, characterized in that: include: An acquisition module is used to acquire the engineering plan information of the infrastructure project and all pending electronic work tickets, wherein the engineering plan information includes each process and the difficulty of each process, and the pending electronic work tickets include status information, time information, personnel information, and implementation information; a division module, which divides all pending electronic work tickets based on the project plan information to obtain multiple work ticket data sets of the infrastructure project, and determines a corresponding anomaly detection method according to the type of each work ticket data set; An anomaly detection module, configured to perform an anomaly detection on each of the job ticket data sets based on an anomaly detection method, and determine an abnormal job ticket data set and an abnormal electronic job ticket; An alarm module is used to determine abnormal information of the infrastructure project based on the abnormal work ticket data set and the abnormal electronic work ticket, and generate alarm information of the infrastructure project based on the abnormal information.

9. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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