Work order assessment time limit determination method and device, electronic equipment and work order assessment system

Abstract

The invention discloses a work order assessment time limit determination method and device, electronic equipment and a work order assessment system. The method comprises the following steps: acquiring a plurality of operation site images collected according to a time sequence for an operation site, the plurality of operation site images being operation site images associated with a target work order; the information gain of a target operation site image in the multiple operation site images relative to a previous operation site image is determined, and the target operation site image is any operation site image except the first operation site image in the multiple operation site images; determining the operation progress of the operation site according to the information gain; and determining the predicted operation duration of the operation site according to the operation progress, and determining the work order assessment time limit according to the predicted operation duration. The technical problem that the work order cannot be accurately assessed due to the fact that the work order assessment time limit is set to be a fixed value in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing, and more specifically, to a method, apparatus, electronic device, and work order assessment system for determining work order assessment time limits. Background Technology

[0002] In related technologies, the evaluation time limit for work orders is usually a fixed preset value. The problem with this approach is that the actual work duration is a dynamically changing value depending on the progress of the work on-site. Therefore, setting a fixed evaluation time limit for work orders makes it impossible to accurately evaluate them.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and work order assessment system for determining work order assessment time limits, in order to at least solve the technical problem that work orders cannot be accurately assessed due to setting the work order assessment time limit to a fixed value in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for determining the assessment time limit of a work order is provided, comprising: acquiring multiple work site images collected in chronological order, wherein the multiple work site images are work site images associated with a target work order; determining the information gain of a target work site image relative to a previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; determining the work progress of the work site based on the information gain; determining the predicted work duration of the work site based on the work progress; and determining the work order assessment time limit based on the predicted work duration.

[0006] Optionally, determining the work progress at the work site based on information gain information includes: determining the differential grid cluster quotient matrix corresponding to the target work site image based on information gain, wherein the elements in the differential grid cluster quotient matrix represent the changes of each image square in the target work site image relative to the previous work site image; determining the time matrix, wherein the elements in the time matrix represent the change duration corresponding to each image square; and determining the work progress at the work site based on the differential grid cluster quotient matrix and the time matrix.

[0007] Optionally, determining the information gain of the target work site image relative to the previous work site image among multiple work site images includes: dividing each work site image in the multiple work site images into multiple image squares; determining multiple image square sets, wherein the image squares in the same image square set have the same relative position in the corresponding work site image; in each image square set, sorting them in order from front to back according to the shooting time of the work site image in which the image square is located; and after sorting, starting from the second image square in the sequence, determining the image square information gain of each image square relative to the previous image square.

[0008] Optionally, after determining the information gain of each image square relative to the previous image square, the method further includes: in the image square set, determining the information gain difference between adjacent image squares according to the sequence, wherein the information gain difference is the difference obtained by subtracting the information gain of the image square from the information gain of the image square that is ranked later; determining the secondary gain difference between adjacent information gain differences, and grouping the secondary gain differences according to a preset threshold, and determining the unchanged area, the work area, and the deviation area of ​​the work site based on the grouping results, wherein the deviation area is the area that does not match the work site, and the work area is the area where the work progress matches the predicted work progress.

[0009] Optionally, the method further includes: dividing each work site image in the multiple work images into blocks, dividing each work site image into multiple image squares; determining the entropy of each image square, and determining the image square with the smallest entropy as the target image square, wherein the entropy includes the conditional entropy relative to the work state; and determining the predicted work result of the work site based on the target image square.

[0010] Optionally, after determining the predicted work results at the work site based on the target image grid, the method further includes: determining the work result description information of the work order to be assessed; if the work result description information and the predicted work results are consistent, determining that the work order to be assessed has passed the assessment; if the work result description information and the predicted work results are inconsistent, determining that the work order to be assessed needs to be manually reviewed.

[0011] Optionally, multiple images of the work site can be taken from the same location and angle.

[0012] According to another aspect of the embodiments of this application, a work order assessment system is also provided, including: a data acquisition module, a mirroring module, and an assessment duration adjustment module. The data acquisition module is used to acquire multiple work site images; the assessment duration adjustment module is used to determine the information gain of a target work site image relative to a previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; determine the work progress of the work site based on the information gain; determine the predicted work duration based on the work progress; and determine the work order assessment time limit based on the predicted work duration; the mirroring module is used to generate a work order mirror image of the work order to be assessed.

[0013] According to another aspect of the embodiments of this application, a work order assessment time limit determination device is also provided, comprising: a first processing module, configured to acquire multiple work site images collected in chronological order for a work site, wherein the multiple work site images are work site images associated with a target work order; a second processing module, configured to determine the information gain of a target work site image relative to a previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; a third processing module, configured to determine the work progress of the work site based on the information gain; and a fourth processing module, configured to determine the predicted work duration of the work site based on the work progress, and determine the work order assessment time limit based on the predicted work duration.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, and the program controls the device where the non-volatile storage medium is located to execute a work order assessment time limit determination method when it runs.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the program executes a work order assessment time limit determination method during runtime.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements a method for determining work order assessment time limits.

[0017] In this embodiment, multiple work site images are acquired sequentially over time, each image being associated with a target work order. The information gain of the target work site image relative to the previous image is determined, where the target work site image is any image other than the first one. The work progress is determined based on the information gain. The predicted work duration is then determined based on the work progress, and the work order assessment time limit is determined based on the predicted work duration. By determining the predicted work duration and then using it to determine the work order assessment time limit, the goal of adjusting the work order assessment time limit based on the actual work progress is achieved. This results in setting a more reasonable work order assessment time limit, thus solving the technical problem of inaccurate work order assessment caused by setting a fixed assessment time limit in related technologies. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a schematic diagram of the structure of a computer terminal (mobile device) according to an embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating a method for determining the assessment time limit for work orders according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the structure of a work order assessment system according to an embodiment of this application;

[0022] Figure 4 This is a flowchart illustrating a work order assessment process according to an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of a work order assessment time limit determination device provided according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0027] Emergency repair: Emergency repair generally refers to physically connecting optical cables, electrical cables, etc., when a line or base station fails, and completing the data configuration to enable the equipment to operate normally. This type of work order can often be assessed within the predetermined time.

[0028] Repair: Repair refers to the laying and fixing of optical cables and electrical cables in accordance with construction specifications. This process is often affected by factors such as road restoration, factory restoration, and weather. This type of work order cannot be assessed and completed according to a fixed time.

[0029] Currently, in the work order management process, due to the significant differences in the objective environment on site, some faults are only temporarily repaired and then a work order is issued, requiring further work such as line and equipment repair and rectification. The current work order management system cannot provide full-process management and multi-environment recognition for the subsequent work.

[0030] While related technologies have proposed methods for modularizing and differentiating work orders, in the monitoring of work orders for comprehensive on-site maintenance of lines and other infrastructure, it is necessary to monitor the entire process of work orders for emergency repairs and restorations. Furthermore, some work orders cannot be scheduled for return due to factors such as road construction parties, and a single return time limit cannot meet the needs of the work orders.

[0031] To address this issue and improve the efficiency and accuracy of work order processing, this application provides relevant solutions, which are detailed below.

[0032] According to an embodiment of this application, a method embodiment for determining the assessment time limit of a work order is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The method embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a method to determine work order assessment time limits is shown. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the work order assessment time limit determination method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned work order assessment time limit determination method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0037] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the above operating environment, this application provides a method for determining the work order assessment time limit, such as... Figure 2 As shown, the method includes the following steps:

[0039] Step S202: Obtain multiple work site images collected in chronological order, where the multiple work site images are work site images associated with the target work order;

[0040] In the technical solution provided in step S202, multiple images of the work site are taken from the same location and angle. The images also carry information such as the time of capture and latitude / longitude. Besides images taken by cameras or video cameras, the images can also be satellite images.

[0041] In some embodiments of this application, after obtaining the images arranged chronologically as described above, a decision tree algorithm from machine learning can be used to improve the accuracy of the photo comparison analysis results. The accuracy of the comparison analysis results can be calculated using the following information gain formula:

[0042]

[0043] In the above formula, IG(X;Y) is the information gain, H(Y) is the entropy of Y, and X... i H(Y|X) is the i-th value of X (i.e., the i-th photo taken in the same environment). i ) is a given X i The conditional entropy of Y. Y represents preset conditions, such as repair progress.

[0044] Step S204: Determine the information gain of the target work site image relative to the previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images;

[0045] In the technical solution provided in step S204, the step of determining the information gain of the target work site image relative to the previous work site image in multiple work site images includes: determining multiple image grid sets, wherein the image grids in the same grid image set have the same relative position in the corresponding site image; in each image grid set, sorting them in order from front to back according to the shooting time of the site image where the image grid is located; and after sorting, starting from the second image grid in the sequence, determining the image grid information gain of each image grid relative to the previous image grid.

[0046] In some embodiments of this application, after determining the information gain of each image square relative to a reference image square, the method further includes: determining the information gain difference between adjacent image squares in the image square set according to their sequence, wherein the information gain difference is the difference obtained by subtracting the information gain of the preceding image square from the information gain of the later-ranked image square; determining the secondary gain difference between adjacent information gain differences, and grouping the secondary gain differences according to a preset threshold, and determining the unchanged area, the work area, and the deviation area of ​​the work site based on the grouping results, wherein the deviation area is the area that does not match the work site, and the work area is the area where the work progress matches the predicted work progress. It should be noted that, in the embodiments of this application, "matching the predicted work progress" means that the work progress is not lower than the preset work progress. The deviation area may be the area captured due to a deviation in position or angle when the image is captured, and this area is not in the work site.

[0047] In some embodiments of this application, the aforementioned unchanged area refers to an area that has not changed relative to the previous image or whose change is within a preset range. The predicted work progress is determined based on multiple captured images and the work plan.

[0048] In some embodiments of this application, different image squares in an image square set can be represented by A, B, C, etc., and the information gain difference between adjacent image squares can be represented as AIG. AB =IG B (X;Y)-IG A (X;Y), AIG BC =IG C (X;Y)-IG B (X; Y). The difference in second-order gain can be represented as BIG. ABC =AIG BC -AIG AB .

[0049] In some embodiments of this application, the secondary gain difference can be classified using preset thresholds B0 and B1. Image squares corresponding to secondary gain differences less than the preset threshold B0 are considered indifferent image squares, i.e., image squares corresponding to unchanged regions. Image squares with values ​​not less than the preset threshold B0 and less than the preset threshold B1 are considered image squares with minimal difference changes, i.e., image squares corresponding to clearly repaired regions.

[0050] It should be noted that as the number of images collected increases, the regions corresponding to the aforementioned image squares may change. As an optional implementation, the region type corresponding to each image square in the most recently collected image can be used as the current region type.

[0051] In some embodiments of this application, the differences in quadratic gain within the same group after grouping can be compared using a Poisson distribution, as shown in the following formula:

[0052]

[0053] In the above formula, X represents the difference in second-order gain. Then, by referring to typical examples, the K value at which P is maximized can be determined using the above formula, along with the corresponding grid information, thus allowing for a preliminary assessment of areas (such as road surfaces) that are being repaired according to the same location and time schedule. Taking repair work as an example, typical examples can include before-and-after photos of the repair process. For instance, when the road surface was excavated, the perimeter was cement-like, the center was yellow mud, and after repair, both the front and back of the road surface were cement-like.

[0054] In some embodiments of this application, the process of sorting image squares according to information gain values ​​reveals which regions in the image show the most significant changes and which regions remain relatively stable. Squares with low information gain tend to show little change and may represent areas that have been repaired or do not require repair; while squares with high information gain undergo significant changes and may be key areas that are being actively repaired.

[0055] Optionally, calculating the information gain difference between adjacent squares can identify local fluctuations in repair activity. If the information gain of a square suddenly increases significantly, it means that the repair work may be accelerating in that area, while the opposite may indicate stagnation or slowdown.

[0056] The quadratic interpolation calculation further enhances the system's understanding of repair patterns and its ability to detect anomalies. If the quadratic interpolation shows a stable gradient within a certain region, it indicates that the repair work follows a certain sequence or pattern; if an abnormal peak in the quadratic interpolation occurs, there may be a sudden or abnormal repair activity.

[0057] By comparing the squares sorted by information gain, the actual progress of the repair project can be identified more accurately, and areas with different repair speeds can be distinguished. This is crucial for the rational allocation of resources and the improvement of work order processing efficiency.

[0058] In addition, the assessment period for work orders can be intelligently adjusted based on the information gain change trend of the repair area. For areas where repair progress is smooth, the assessment period can be shortened; while for areas where progress is slow or stalled, the assessment time can be appropriately extended to more fairly evaluate the work of maintenance personnel.

[0059] In some embodiments of this application, the determination of work progress at the work site based on images can be achieved in the following ways:

[0060] First, the system implementing the work order assessment duration determination method provided in this application acquires the latest work site image and subdivides it into multiple uniformly sized image squares as the basic unit of analysis. Next, using the previously described information gain algorithm, the information gain value of each image square relative to a reference image square is calculated. The reference image is typically the first site image taken at the start of the work process, and it is considered the reference point for the initial state.

[0061] After calculating the information gain value, the system sorts all image squares according to their information gain value. This sorting is done in ascending order from the minimum information gain value to the maximum information gain value. This step aims to distinguish between areas with weak changes and areas with significant changes, so as to facilitate subsequent analysis and classification.

[0062] Next, the system focuses on the sorted image squares and calculates the information gain difference between adjacent image squares. This operation involves subtracting the information gain values ​​of two consecutive image squares. The sign of the difference reflects the changing trend of the operation process; a positive value indicates that the operation is progressing, while a negative value may indicate that a problem or accident has occurred.

[0063] Based on this, the system further determines the secondary gain difference between adjacent information gain differences. This calculation is equivalent to analyzing the rate of change during the operation, i.e., whether the operation is progressing smoothly, accelerating, or decelerating. The system sets a series of predefined thresholds to classify these secondary gain differences into different groups, each group representing different state characteristics of the operation site.

[0064] Unchanged areas: Image grids with an absolute value of the second gain difference less than the first threshold B0 are considered unchanged areas. This means that even during the operation, the appearance or state of these areas does not change significantly, possibly because they did not require the operation in the first place, or because the operation has not yet affected them.

[0065] Work area: Image squares whose absolute value of the second-order gain difference is between B0 and a higher threshold B1 are classified as work areas. Work progress in these areas is stable, meeting or exceeding the preset work schedule, demonstrating that the work process is proceeding in an orderly manner according to plan.

[0066] Deviation Region: The system identifies regions where the absolute value of the secondary gain difference exceeds the preset threshold B1 as deviation regions. Changes in these regions may not be directly related to the work process, but may be caused by shooting angle, lighting conditions or other external factors, or may reflect the existence of unexpected situations or abnormal work activities.

[0067] Step S206: Determine the work progress at the work site based on information gain;

[0068] In the technical solution provided in step S206, determining the work progress at the work site based on information gain information includes: determining the differential grid cluster quotient matrix corresponding to the target work site image based on information gain, wherein the elements in the differential grid cluster quotient matrix represent the changes of each image square in the target work site image relative to the previous work site image; determining the time matrix, wherein the elements in the time matrix represent the change duration corresponding to each image square; and determining the work progress at the work site based on the differential grid cluster quotient matrix and the time matrix. The aforementioned change duration can be the elapsed time when the area corresponding to the image square changes. The elements in the differential grid cluster quotient matrix can be the information gain of each image square, the information gain difference, or the second difference of information gain.

[0069] Optionally, the aforementioned change duration can be the ratio of the amount of change to the rate of change of the area corresponding to the image grid. When a large number of images are collected, this ratio can be determined based on earlier collected images. For example, when more than three images are collected, the ratio can be determined based on the first three images, or a preset number of images. Alternatively, the ratio can be calculated based on all currently collected images. For areas that have not changed, this ratio can be set to a preset fixed value.

[0070] If the number of images is too small to calculate the above ratio, historical work records that are similar to the current work situation can be identified based on historical construction data, and the above ratio can be calculated based on the historical work records.

[0071] In some embodiments of this application, the step of determining the differential grid clustering quotient matrix provides basic data for intelligent analysis of work progress by accurately quantifying the changes in each image grid in the target work site image. The specific implementation of this process is as follows:

[0072] The system receives a set of images of the work site, arranged chronologically. The first image serves as a baseline, recording the state at the very beginning of the repair work. Each subsequent image captures a different moment in the repair process and is included in the analysis as a target work site image. The system first compares the first and second images using an information gain algorithm. This algorithm was chosen because it can effectively measure how much additional information the new image provides compared to the old image.

[0073] Image grid division: The target work site image is divided into multiple small squares. For example, an image may be divided into 10x10 squares, forming a two-dimensional array of 100 image squares.

[0074] Information gain calculation: For each image square, the system calculates its information gain value. This value reflects the information difference between the square in the target work site image and the corresponding square in the previous image. By calculating the information gain value, the magnitude of the change in state of the target square compared to the previous square can be determined.

[0075] Matrix element assignment: The calculated information gain values ​​are assigned as elements in the differential grid quotient matrix. This matrix is ​​the same size as the grid division of the image. Each element represents the information gain of the corresponding image square. For example, the element at position (1,1) of the matrix might record the information gain value of the top-left square of the image, reflecting the degree of variation in that region.

[0076] Through the above implementation steps, the intelligent monitoring system for the entire work order process can generate a detailed, differentiated grid clustering matrix. This matrix not only reveals the repair progress at the work site but also provides solid data support for subsequent work duration prediction and work order assessment duration adjustment. This quantitative analysis based on information gain enables the system to monitor every subtle change in the work order execution process more intelligently and accurately, thereby improving the overall efficiency and level of work order management.

[0077] Step S208: Determine the predicted operation time at the work site based on the work progress, and determine the work order assessment time limit based on the predicted operation time.

[0078] In the technical solution provided in step S208, the method further includes: performing block processing on each work site image in the multiple work images, dividing each work site image into multiple image squares; determining the entropy of each image square, and determining the image square with the smallest entropy as the target image square, wherein the entropy includes the conditional entropy relative to the work state; and determining the predicted work result of the work site based on the target image square.

[0079] In some embodiments of this application, after determining the predicted work result of the work site based on the target image grid, the method further includes: determining the work result description information of the work order to be assessed; if the work result description information and the predicted work result are consistent, determining that the work order to be assessed has passed the assessment; if the work result description information and the predicted work result are inconsistent, determining that the work order to be assessed needs to be manually reviewed.

[0080] By acquiring multiple work site images sequentially over time, each image being associated with a target work order; determining the information gain of the target work site image relative to the previous image (where the target image is any image except the first one); determining the work progress based on the information gain; determining the predicted work duration based on the work progress; and determining the work order assessment time limit based on the predicted work duration, this method achieves the goal of adjusting the work order assessment time limit according to the actual work progress. This results in setting a more reasonable work order assessment time limit and solves the technical problem of inaccurate work order assessment caused by setting a fixed assessment time limit in related technologies.

[0081] This application provides a work order assessment system. For example... Figure 3As shown, the system includes: a data acquisition module 30, a mirroring module 32, and an assessment duration adjustment module 34. The data acquisition module 30 acquires multiple images of the work site. The assessment duration adjustment module 34 determines the information gain of a target work site image relative to the previous work site image among the multiple work site images, where the target work site image is any work site image other than the first one. The system determines the work progress based on the information gain, determines the predicted work duration based on the work progress, and determines the work order assessment time limit based on the predicted work duration. The mirroring module 32 generates a work order mirror of the work order to be assessed.

[0082] In some embodiments of this application, the system reliability of the work order assessment system can be calculated using the following formula:

[0083] A = 1 - (1 - R) N

[0084] In the above formula, R refers to the mean or median reliability of a single component or work order module in the system, and N is the number of redundant components or redundant work order modules.

[0085] In some embodiments of this application, the work order evaluation system performs the following evaluation process for work orders: Figure 4 As shown, the process includes steps such as receiving network management work orders, scene filtering, work order assessment duration marking, work order transfer, process feedback, return receipt, and work order inspection. The work order assessment duration refers to the time limit for work order assessment.

[0086] from Figure 4 As can be seen from the embodiments of this application, the work order assessment system will adjust the initially set work order assessment time limit in the two steps of process feedback and return, thereby avoiding the problem of mismatch between assessment time limit and actual progress caused by fixed preset work order assessment time limit.

[0087] In some embodiments of this application, Figure 3 The work order evaluation system shown below includes the following steps in its evaluation process:

[0088] The first step involves system startup, where the acquisition module 30 acquires a set of images of the work site. These images are arranged chronologically, with the first image recording the initial state of the work. As time progresses, each subsequent image captures a different stage in the work process. These images will be used to analyze and evaluate the progress of the work at the site.

[0089] The second step involves setting the first image of the work site as the baseline, with each subsequent image becoming a target image for comparison. The assessment duration determination module first processes the second target image, using an information gain algorithm to calculate the information difference between each image square and the baseline square. This algorithm quantifies the amount of new information provided by each square in the target image, reflecting the progress of the work activity. During the calculation, the assessment duration determination module focuses on images other than the first one, as they contain crucial information about the work process.

[0090] The third step involves determining the information gain of the target image, after which the system begins analyzing the work progress at the work site. Work progress is assessed based on the information gain value; a higher information gain value indicates more significant work activity in that image square, thus advancing the overall work progress.

[0091] The fourth step involves the system determining the predicted task duration based on the assessment results of the task progress. This prediction is not fixed but dynamically adjusted according to the current task progress, reflecting the actual progress of the task and its possible future completion time. The determination of the predicted task duration provides a key parameter for the operation of the assessment duration adjustment module 34, which will use this prediction to determine the assessment time limit for the work order.

[0092] The fifth step, in the stage of determining the assessment time limit, involves the system intelligently adjusting the assessment criteria by comparing the predicted job duration with the current processing time of the work order. For work orders where the job progress is faster than expected, the assessment time limit may be shortened to incentivize maintenance personnel to work efficiently; while for work orders where the job progress is slower than expected, the system will appropriately extend the assessment time limit to provide sufficient processing time and ensure job quality.

[0093] The sixth step involves the mirror module 32 in the system generating a work order mirror for each work order to be assessed. The work order mirror not only replicates the content of the original work order but also integrates the prediction results from the assessment duration adjustment module 34, providing maintenance personnel and managers with complete information including work progress, predicted work duration, and adjusted assessment time limit.

[0094] In the seventh step, as the work progresses, the acquisition module 30 continues to periodically acquire the latest images of the work site. The system then repeats the processes of information gain calculation, work progress assessment, predicted work duration updates, and assessment time limit adjustments, forming a closed loop of continuous monitoring and intelligent feedback. When processing work orders, maintenance personnel can receive the latest work instructions and assessment standards provided by the system in a timely manner, promoting the efficient and high-quality completion of work orders.

[0095] Throughout the implementation process, the system achieves intelligent monitoring and management of the work order processing through image acquisition by the acquisition module 30, dynamic analysis by the assessment duration adjustment module 34, and work order copying and information integration by the mirror module 32. This process ensures the fairness and rationality of work order assessment, while also providing maintenance personnel with effective on-site operation guidance and optimizing the maintenance efficiency of the communication network.

[0096] This application provides a device for determining the time limit for work order assessment. Figure 5 This is a schematic diagram of the device. From... Figure 5 As can be seen from the diagram, the device includes: a first processing module 50, used to acquire multiple work site images collected in chronological order, wherein the multiple work site images are work site images associated with a target work order; a second processing module 52, used to determine the information gain of a target work site image relative to the previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; a third processing module 54, used to determine the work progress of the work site based on the information gain; and a fourth processing module 56, used to determine the predicted work duration of the work site based on the work progress, and to determine the work order assessment time limit based on the predicted work duration.

[0097] In some embodiments of this application, multiple images of the work site are taken from the same location and at the same angle.

[0098] In some embodiments of this application, the step of the second processing module 52 in determining the information gain of a target work site image relative to the previous work site image in multiple work site images includes: performing block processing on each work site image in the multiple work site images, dividing each work site image into multiple image squares; determining multiple image square sets, wherein the image squares in the same image square set have the same relative position in the corresponding work site image; in each image square set, sorting them in order from front to back according to the shooting time of the work site image where the image square is located; and after sorting, starting from the second image square in the sequence, determining the image square information gain of each image square relative to the previous image square.

[0099] In some embodiments of this application, after determining the information gain of each image square relative to the previous image square, the second processing module 52 is further configured to: determine the information gain difference between adjacent image squares in the image square set according to the sequence, wherein the information gain difference is the difference obtained by subtracting the information gain of the image square that is sorted later from the information gain of the image square that is sorted earlier; determine the secondary gain difference between adjacent information gain differences, and group the secondary gain differences according to a preset threshold, and determine the unchanged area, the work area and the deviation area of ​​the work site according to the grouping results, wherein the deviation area is the area that does not match the work site, and the work area is the area where the work progress matches the predicted work progress.

[0100] In some embodiments of this application, the step of the third processing module 54 in determining the work progress at the work site based on information gain information includes: determining the differential square cluster quotient matrix corresponding to the target work site image based on information gain, wherein the elements in the differential square cluster quotient matrix are the changes of each image square in the target work site image relative to the previous work site image; determining the time matrix, wherein the elements in the time matrix are the change duration corresponding to each image square; and determining the work progress at the work site based on the differential square cluster quotient matrix and the time matrix.

[0101] In some embodiments of this application, the work order assessment time limit determination device is further configured to: perform block processing on each work site image among multiple work images, dividing each work site image into multiple image squares; determine the entropy of each image square, and determine the image square with the smallest entropy as the target image square, wherein the entropy includes the conditional entropy relative to the work state; and determine the predicted work result of the work site based on the target image square.

[0102] In some embodiments of this application, after determining the predicted work result of the work site based on the target image grid, the work order assessment time limit determination device is further used to: determine the work result description information of the work order to be assessed; if the work result description information and the predicted work result are consistent, determine that the work order to be assessed has passed the assessment; if the work result description information and the predicted work result are inconsistent, determine that the work order to be assessed needs to be manually reviewed.

[0103] It should be noted that each module in the above-mentioned work order assessment time limit determination device can be a program module (for example, a set of program instructions to implement a certain specific function) or a hardware module. For the latter, it can be expressed in the following forms, but is not limited to them: each of the above modules is expressed as a processor, or the functions of each of the above modules are implemented by a processor.

[0104] According to an embodiment of this application, a non-volatile storage medium is provided, which stores a program. During program execution, the device containing the non-volatile storage medium executes the following work order assessment time limit determination method: acquiring multiple work site images collected sequentially over time, wherein the multiple work site images are work site images associated with a target work order; determining the information gain of a target work site image relative to the previous work site image, wherein the target work site image is any work site image other than the first work site image; determining the work progress of the work site based on the information gain; determining the predicted work duration of the work site based on the work progress; and determining the work order assessment time limit based on the predicted work duration.

[0105] According to an embodiment of this application, an electronic device is also provided, including a memory and a processor. The processor is used to run a program stored in the memory, wherein the program executes the following work order assessment time limit determination method: acquiring multiple work site images collected in chronological order for the work site, wherein the multiple work site images are work site images associated with a target work order; determining the information gain of the target work site image relative to the previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; determining the work progress of the work site based on the information gain; determining the predicted work duration of the work site based on the work progress; and determining the work order assessment time limit based on the predicted work duration.

[0106] According to an embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the following method for determining work order assessment time limits: acquiring multiple work site images collected in chronological order, wherein the multiple work site images are work site images associated with a target work order; determining the information gain of a target work site image relative to the previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; determining the work progress of the work site based on the information gain; determining the predicted work duration of the work site based on the work progress; and determining the work order assessment time limit based on the predicted work duration.

[0107] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0112] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining the assessment time limit for work orders, characterized in that, include: Acquire multiple work site images collected in chronological order, wherein the multiple work site images are work site images associated with the target work order; Determine the information gain of the target work site image relative to the previous work site image among the plurality of work site images, wherein the target work site image is any work site image among the plurality of work site images except the first work site image; The work progress at the work site is determined based on the information gain. The predicted operation time at the work site is determined based on the operation progress, and the work order assessment time limit is determined based on the predicted operation time.

2. The method for determining the work order assessment time limit according to claim 1, characterized in that, Determining the work progress at the work site based on the information gain information includes: The differential grid cluster quotient matrix corresponding to the target work site image is determined based on the information gain, wherein the elements in the differential grid cluster quotient matrix are the changes of each image square in the target work site image relative to the previous work site image. Determine a time matrix, wherein the elements in the time matrix are the change durations corresponding to each image square; The work progress at the work site is determined based on the differentiated grid clustering matrix and the time matrix.

3. The method for determining the work order assessment time limit according to claim 1, characterized in that, Determining the information gain of the target work site image relative to the previous work site image among the multiple work site images includes: Each of the multiple work site images is divided into blocks, and each work site image is divided into multiple image squares; Determine multiple sets of image grids, where the image grids in the same set of image grids have the same relative position in the corresponding field image; In each set of image squares, they are sorted in order from front to back according to the time when the scene image of the image square is captured. After sorting, starting from the second image square in the sequence, determine the image square information gain of each image square relative to the previous image square.

4. The method for determining the work order assessment time limit according to claim 3, characterized in that, After determining the image grid information gain of each image grid relative to the previous image grid, the method further includes: In the set of image squares, According to the sequence, the information gain difference between adjacent image squares is determined, wherein the information gain difference is the difference obtained by subtracting the information gain of the image square that is ranked later from the information gain of the image square that is ranked earlier. The secondary gain difference between adjacent information gain differences is determined, and the secondary gain differences are grouped according to a preset threshold. Based on the grouping results, the unchanged area, the operation area, and the deviation area of ​​the work site are determined. The deviation area is the area that does not match the work site, and the operation area is the area where the operation progress matches the predicted operation progress.

5. The method for determining the work order assessment time limit according to claim 1, characterized in that, The method further includes: Each of the multiple work site images is divided into blocks, and each work site image is divided into multiple image squares; Determine the entropy of each of the image squares, and determine the image square with the minimum entropy as the target image square, wherein the entropy includes conditional entropy relative to the job state; The predicted work results at the work site are determined based on the target image grid.

6. The method for determining the work order assessment time limit according to claim 5, characterized in that, After determining the predicted work results at the work site based on the target image grid, the method further includes: Determine the description information of the work results for the work orders to be evaluated; If the description of the work result is consistent with the predicted work result, the work order to be assessed is determined to have passed the assessment. If the job result is inconsistent with the description information and the predicted job result, it is determined that the job order to be assessed needs to be manually reviewed.

7. The method for determining the work order assessment time limit according to claim 1, characterized in that, The multiple images of the work site were taken from the same location and angle.

8. A work order assessment system, characterized in that, It includes a data collection module, a mirroring module, and an assessment duration adjustment module. The acquisition module is used to acquire multiple images of the work site; The assessment duration adjustment module is used to determine the information gain of the target work site image relative to the previous work site image among the multiple work site images, wherein the target work site image is any work site image other than the first work site image among the multiple work site images; determine the work progress of the work site based on the information gain; determine the predicted work duration based on the work progress; and determine the work order assessment time limit based on the predicted work duration. The mirror module is used to generate a mirror image of the work order to be assessed.

9. A device for determining the assessment time limit for work orders, characterized in that, include: The first processing module is used to acquire multiple work site images collected in chronological order, wherein the multiple work site images are work site images associated with the target work order; The second processing module is used to determine the information gain of the target work site image relative to the previous work site image among the multiple work site images, wherein the target work site image is any work site image among the multiple work site images except the first work site image; The third processing module is used to determine the work progress at the work site based on the information gain. The fourth processing module is used to determine the predicted operation time of the work site based on the operation progress, and to determine the work order assessment time limit based on the predicted operation time.

10. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a program, wherein when the program is executed, it controls the device where the non-volatile storage medium is located to execute the work order assessment time limit determination method according to any one of claims 1 to 7.

11. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the work order assessment time limit determination method according to any one of claims 1 to 7.

12. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the work order assessment time limit determination method according to any one of claims 1 to 7.

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