Operation management method, device, medium and system

Abstract

The invention provides a job management method, device and system, and a medium. According to the method, an image recognition technology is adopted to perform recognition processing on a field image set of each operation stage to obtain a recognition result so as to recognize whether the operation stage corresponding to the field image set does not conform to the specification or not, and then the number and the positions of the operation stages which do not conform to the specification are determined according to the recognition result. Different prompt information is generated according to the number and the positions of the operation stages which do not accord with the specifications, an operator is reminded to know which operation stage has problems in time so as to solve the problems in time, management is easier compared with an existing scheme, the operation efficiency and the completion degree are improved, and the working efficiency is improved. Therefore, the problem that traditional operation management which is not easy to manage seriously affects the operation efficiency and the completion degree is solved.

Description

Technical Field

[0001] The present application relates to the field of operation monitoring technology, and specifically, to an operation management method, device, medium and system. Background Art

[0002] The number and complexity of operations are increasing, such as high-altitude operations, confined space operations, and high-temperature operations. These operations often have high safety risks and management challenges. Traditional operation management often uses paper or electronic spreadsheets for reporting and recording, which has problems such as untimely information, difficult management, and high safety risks.

[0003] In particular, traditional job management that is difficult to manage seriously affects job efficiency and completion. Summary of the invention

[0004] The main purpose of this application is to provide a job management method, device, medium and system to at least solve the problem that traditional job management that is difficult to manage seriously affects job efficiency and completion.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a job management method is provided, which includes: obtaining a set of on-site images of multiple job stages, the set of on-site images including multiple on-site images; using image recognition technology to perform recognition processing on the set of on-site images of each job stage to obtain a recognition result to identify whether there are any non-compliant issues in the job stage corresponding to the set of on-site images; determining the number and position of the job stages that do not meet the standards based on the recognition results, and generating different prompt information based on the number and position of the job stages that do not meet the standards.

[0006] Optionally, determining the number and positions of the operation stages that do not meet the specifications based on the recognition results includes: storing all the on-site image sets of the first target stages into a first sequence, the first target stage being the operation stage corresponding to the on-site images that do not meet the specifications in the recognition results; storing all the on-site image sets of the second target stages into a second sequence, the second target stage being the operation stage corresponding to the on-site images that meet the specifications in the recognition results; and determining the positions of the operation stages that do not meet the specifications based on all the first target stages in the first sequence.

[0007] Optionally, after storing all the on-site image sets of the first target stage into the first sequence, the method further includes: generating an alarm message when the number of the first target stages in the first sequence is greater than a preset number, wherein the alarm message is used to prompt that the current operation as a whole does not meet the specifications.

[0008] Optionally, the position of the operation stage that does not meet the specifications is determined based on all the first target stages in the first sequence, including: if there are two first target stages in the first sequence whose sequential representations are adjacent to each other, the corresponding two first target stages are planned as a first cluster; if there are three first target stages in the first sequence whose sequential representations are adjacent to each other, the corresponding three first target stages are planned as a second cluster; if there are more than three first target stages in the first sequence whose sequential representations are adjacent to each other, the corresponding three or more first target stages are planned as a third cluster.

[0009] Optionally, different prompt information is generated according to the number and location of the non-compliant operation stages, including: when the number of the first target stages in the first cluster is greater than 1, determining that the current abnormality level is a first-level abnormality; when the number of the first target stages in the second cluster is greater than 2, determining that the current abnormality level is a second-level abnormality; when the number of the first target stages in the third cluster is greater than 3, determining that the current abnormality level is a third-level abnormality; different prompt information is generated according to the current abnormality level.

[0010] Optionally, image recognition technology is used to identify and process the on-site image set of each operation stage to obtain a recognition result, including: using a convolutional neural network to extract image features of the on-site image set through multi-layer convolution and pooling operations; using a model trained by a machine learning or deep learning algorithm to identify and process the image features to obtain the recognition result.

[0011] Optionally, the method also includes: acquiring the temperature, humidity and air pressure of the working environment, as well as the status indicators and operating status indicators of the working equipment in real time to obtain monitoring data; generating early warning instructions of different levels according to the size of the monitoring data, and the early warning instructions are used to monitor risks in the working process.

[0012] According to another aspect of the present application, a job management device is provided, which includes: an acquisition unit, used to acquire a set of on-site images of multiple job stages, the set of on-site images including multiple on-site images; a first processing unit, used to use image recognition technology to perform recognition processing on the set of on-site images of each job stage to obtain a recognition result, so as to identify whether the job stage corresponding to the set of on-site images has any problem of non-compliance with the specifications; a second processing unit, used to determine the number and position of the job stages that do not comply with the specifications based on the recognition result, and generate different prompt information based on the number and position of the job stages that do not comply with the specifications.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] According to another aspect of the present application, a job management system is provided, which includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0015] By applying the technical solution of the present application, the on-site image sets of each operation stage are identified and processed by using image recognition technology to obtain recognition results, so as to identify whether there are any problems that do not meet the specifications in the operation stage corresponding to the on-site image set, and then determine the number and location of the operation stages that do not meet the specifications based on the recognition results, and generate different prompt information based on the number and location of the operation stages that do not meet the specifications, so as to promptly remind the operating personnel to know which operation stage has problems so that they can be solved in time. Compared with the existing solutions, it is easier to manage, improves the operation efficiency and completion rate, and thus solves the problem that the traditional operation management that is difficult to manage seriously affects the operation efficiency and completion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A schematic diagram of a process flow of a job management method provided according to an embodiment of the present application is shown;

[0018] Figure 2 A structural block diagram of a job management device provided according to an embodiment of the present application is shown;

[0019] Figure 3 A schematic diagram of a job management system provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] As introduced in the background technology, the number and complexity of operations are increasing, such as high-altitude operations, confined space operations, high-temperature operations, etc. These operations often have high safety risks and management challenges. Traditional operation management often uses paper or electronic spreadsheets for reporting and recording, which has problems such as untimely information, difficult management, and high safety risks. In order to solve the problem that traditional operation management that is difficult to manage seriously affects operation efficiency and completion, the embodiments of the present application provide a method, device, medium and system for operation management.

[0024] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0025] In this embodiment, a job management method running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 FIG. 1 is a flow chart of a method for job management provided according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0027] Step S101, obtaining a set of on-site images of multiple operation stages, wherein the set of on-site images includes multiple on-site images;

[0028] Step S102, using image recognition technology to perform recognition processing on the above-mentioned on-site image set of each of the above-mentioned operation stages to obtain a recognition result, so as to identify whether the above-mentioned operation stage corresponding to the above-mentioned on-site image set has a problem of not meeting the specification;

[0029] Image recognition technology is a technology that uses computer vision technology to analyze and process images to identify target objects, scenes or features in images. Image recognition technology includes image classification, target detection, face recognition, text recognition and other aspects. It can be applied to various fields, such as intelligent security, medical image analysis, and unmanned driving. The development of image recognition technology has made great progress. The development of deep learning technology, especially convolutional neural network (CNN), has significantly improved the accuracy and speed of image recognition technology.

[0030] The image recognition technology is used to perform recognition processing on the above-mentioned on-site image set of each of the above-mentioned operation stages to obtain recognition results, including:

[0031] A convolutional neural network is used to extract image features of the above-mentioned on-site image set through multi-layer convolution and pooling operations;

[0032] Specifically, the Convolutional Neural Network (CNN) is used as the core algorithm for image feature extraction. Through multiple layers of convolution and pooling operations, CNN can automatically learn features at all levels in the image, from edges and textures to more complex patterns and objects. Convolution operation: Slide the filter (or convolution kernel) on the image and perform weighted summation on the local area to detect specific features in the image, such as edges, lines, or local features of specific objects. Pooling operation: Usually follows the convolution layer, it is used to reduce the dimension of the feature map, improve the model's invariance and robustness to the input, and reduce the amount of calculation.

[0033] The above-mentioned image features are recognized and processed by a model trained with a machine learning or deep learning algorithm to obtain the above-mentioned recognition results.

[0034] Specifically, regarding the training model, machine learning or deep learning algorithms, such as support vector machines (SVM), random forests (RF) or deep neural networks (DNN), are used to train on a large number of annotated image data sets to identify and classify objects in images or judge the compliance of images. Recognition processing: The extracted image features are input into the trained model, and the model processes the features according to the learned patterns and rules, and finally outputs the recognition results. For example, it determines whether special operators are wearing safety equipment that meets the standards, or identifies whether the construction site meets the conditions for safe operation.

[0035] Step S103, determining the number and position of the operation stages that do not meet the specifications according to the recognition result, and generating different prompt information according to the number and position of the operation stages that do not meet the specifications.

[0036] In the above steps, the on-site image sets of each operation stage are identified and processed by using image recognition technology to obtain recognition results, so as to identify whether there are any problems that do not meet the specifications in the operation stage corresponding to the on-site image set, and then determine the number and location of the operation stages that do not meet the specifications based on the recognition results, and generate different prompt information based on the number and location of the operation stages that do not meet the specifications, so as to promptly remind the operators to know which operation stage has problems so that they can be solved in time. Compared with the existing solutions, it is easier to manage, improves the operation efficiency and completion rate, and thus solves the problem that the traditional operation management that is difficult to manage seriously affects the operation efficiency and completion rate.

[0037] Compared with traditional desktop or manual filling methods, this application significantly improves the convenience and real-time performance of data entry and access. Workers can report operations at any location, update data in real time, and improve work efficiency. This application can identify potential risks earlier and take preventive measures, which helps prevent accidents and enhances the initiative and foresight of operational safety management. This application adopts a strict management system for the application, approval, supervision, and acceptance of special operations, which will further improve safety while speeding up the efficiency of special operations.

[0038] The number and location of the above-mentioned operation stages that do not meet the specifications are determined according to the above-mentioned identification results, including:

[0039] storing all the on-site image sets of the first target stage into a first sequence, wherein the first target stage is the operation stage corresponding to the on-site images that do not meet the specification in the recognition results;

[0040] Among them, after all the above-mentioned on-site image sets of the first target stage are stored in the first sequence, when the number of the above-mentioned first target stages in the above-mentioned first sequence is greater than the preset number, an alarm message is generated, and the above-mentioned alarm message is used to prompt that the current operation as a whole does not meet the specifications.

[0041] Specifically, the number of stages in the first sequence is counted and marked as B. If B is greater than a preset value b, it means that the special operation as a whole does not meet the standard.

[0042] storing all the above-mentioned on-site image sets of the second target stage into a second sequence, wherein the above-mentioned second target stage is the above-mentioned operation stage corresponding to the above-mentioned on-site images that meet the specifications in the above-mentioned recognition results;

[0043] The locations of the operation stages that do not meet the specifications are determined based on all the first target stages in the first sequence.

[0044] Among them, if in the above-mentioned first sequence, there are two of the above-mentioned first target stages whose sequential representations are adjacent to each other, the corresponding two of the above-mentioned first target stages are planned as the first cluster; if in the above-mentioned first sequence, there are three of the above-mentioned first target stages whose sequential representations are adjacent to each other in sequence, the corresponding three of the above-mentioned first target stages are planned as the second cluster; if in the above-mentioned first sequence, there are more than three of the above-mentioned first target stages whose sequential representations are adjacent to each other in sequence, the corresponding three or more of the above-mentioned first target stages are planned as the third cluster.

[0045] Specifically, the on-site images when each stage of the special operation is completed are marked as Ai, i=1…n, where n is a positive integer; image recognition is performed on Ai, and if an unqualified point is identified, the stage corresponding to Ai is placed in the first sequence; if no unqualified point is identified, the stage corresponding to Ai is placed in the second sequence; the number of stages in the first sequence is counted and marked as B. If B is greater than a preset value b, it means that the special operation as a whole does not meet the standard; if B is less than or equal to the preset value b, the next step is entered, that is, the position of the stage in the first sequence is identified. If two stages are adjacent to each other, the corresponding two stages are planned to the first cluster; if three stages are adjacent to each other, the corresponding three stages are planned to the second cluster; if more than three stages are adjacent to each other, the corresponding more than three stages are planned to the third cluster; the number of all stages in the first cluster, the second cluster, and the third cluster are counted and marked as X, Y, and Z respectively.

[0046] In step S103, different prompt information is generated according to the number and position of the above-mentioned operation stages that do not meet the specifications, including: when the number of the above-mentioned first target stages in the above-mentioned first cluster is greater than 1, determining that the current abnormality level is a first-level abnormality; when the number of the above-mentioned first target stages in the above-mentioned second cluster is greater than 2, determining that the current abnormality level is a second-level abnormality; when the number of the above-mentioned first target stages in the above-mentioned third cluster is greater than 3, determining that the current abnormality level is a third-level abnormality; generating different prompt information according to the above-mentioned current abnormality level.

[0047] Specifically, if X>1, it means that the special operation has a first-level abnormality that is continuously not up to standard; if Y>2, it means that the special operation has a second-level abnormality that is continuously not up to standard; if Z>3, it means that the special operation has a third-level abnormality that is continuously not up to standard. Different prompt information is then generated according to the above current abnormality level, so that the operator can promptly know the current abnormality level.

[0048] In one embodiment of the present application, the above method also includes: acquiring the temperature, humidity and air pressure of the working environment, as well as the status indicators and operating status indicators of the working equipment in real time to obtain monitoring data; generating different levels of early warning instructions according to the size of the above monitoring data, and the above early warning instructions are used to monitor risks in the operation process.

[0049] Specifically, for example, if the temperature of the working environment is 40°C and the temperature threshold is 36°C, an early warning instruction for excessive temperature is generated to achieve the purpose of detecting risks in the working process.

[0050] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the job management method of the present application will be described in detail below in conjunction with specific embodiments.

[0051] This embodiment relates to a specific job management method, including:

[0052] Acquire a set of on-site images at multiple operation stages, wherein the set of on-site images includes multiple on-site images;

[0053] A convolutional neural network is used to extract image features of the above-mentioned on-site image set through multi-layer convolution and pooling operations;

[0054] Using a model trained by a machine learning or deep learning algorithm to identify the above image features to obtain the above recognition results;

[0055] storing all the on-site image sets of the first target stage into a first sequence, wherein the first target stage is the operation stage corresponding to the on-site images that do not meet the specification in the recognition results;

[0056] storing all the above-mentioned on-site image sets of the second target stage into a second sequence, wherein the above-mentioned second target stage is the above-mentioned operation stage corresponding to the above-mentioned on-site images that meet the specifications in the above-mentioned recognition results;

[0057] Determine the location of the operation phase that does not meet the specification based on all the first target phases in the first sequence;

[0058] When the number of the first target stages in the first sequence is greater than a preset number, an alarm message is generated, wherein the alarm message is used to indicate that the current operation as a whole does not meet the specification;

[0059] When the number of the first target stages in the first sequence is less than or equal to a preset number, generating different prompt information according to the number and position of the non-compliant operation stages;

[0060] Specifically, when the number of the above-mentioned first target stages in the above-mentioned first cluster is greater than 1, the current abnormality level is determined to be a level one abnormality; when the number of the above-mentioned first target stages in the above-mentioned second cluster is greater than 2, the current abnormality level is determined to be a level two abnormality; when the number of the above-mentioned first target stages in the above-mentioned third cluster is greater than 3, the current abnormality level is determined to be a level three abnormality; different prompt information is generated according to the above-mentioned current abnormality level.

[0061] Obtain the temperature, humidity and air pressure of the working environment, as well as the status indicators and operating indicators of the working equipment in real time to obtain monitoring data;

[0062] Different levels of early warning instructions are generated according to the size of the above monitoring data, and the above early warning instructions are used to monitor the risks in the operation process.

[0063] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0064] The embodiment of the present application also provides a kind of operation management device, it should be noted that the operation management device of the embodiment of the present application can be used to execute the operation management method provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiment and preferred implementation mode, and the description has been made no further. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0065] The following is an introduction to the job management device provided in an embodiment of the present application.

[0066] Figure 2 is a structural block diagram of a job management device provided according to an embodiment of the present application. Figure 2 As shown, the device comprises:

[0067] An acquisition unit 21 is used to acquire a set of on-site images of multiple operation stages, wherein the set of on-site images includes multiple on-site images;

[0068] The first processing unit 22 is used to use image recognition technology to perform recognition processing on the above-mentioned on-site image set of each of the above-mentioned operation stages to obtain a recognition result, so as to identify whether the above-mentioned operation stage corresponding to the above-mentioned on-site image set has a problem of not meeting the specification;

[0069] The second processing unit 23 is used to determine the number and position of the operation stages that do not meet the specifications according to the recognition result, and generate different prompt information according to the number and position of the operation stages that do not meet the specifications.

[0070] In the above-mentioned device, the on-site image set of each operation stage is identified and processed by using image recognition technology to obtain an identification result, so as to identify whether there are any problems that do not meet the specifications in the operation stage corresponding to the on-site image set, and then determine the number and position of the operation stages that do not meet the specifications based on the identification result, and generate different prompt information based on the number and position of the operation stages that do not meet the specifications, so as to promptly remind the operating personnel to know which operation stage has problems so that they can be solved in time. Compared with the existing solutions, it is easier to manage, improves the operation efficiency and completion rate, and thus solves the problem that the traditional operation management that is difficult to manage seriously affects the operation efficiency and completion rate.

[0071] In one embodiment of the present application, the second processing unit includes a first processing module, a second processing module and a third processing module, the first processing module is used to store the above-mentioned on-site image sets of all first target stages into a first sequence, the above-mentioned first target stage is the above-mentioned operation stage corresponding to the above-mentioned on-site images that do not meet the specifications in the above-mentioned recognition results; the second processing module is used to store the above-mentioned on-site image sets of all second target stages into a second sequence, the above-mentioned second target stage is the above-mentioned operation stage corresponding to the above-mentioned on-site images that meet the specifications in the above-mentioned recognition results; the third processing module is used to determine the position of the above-mentioned operation stage that does not meet the specifications based on all the first target stages in the above-mentioned first sequence.

[0072] In one embodiment of the present application, the second processing unit includes a fourth processing module, which is used to generate an alarm message after storing all the above-mentioned on-site image sets of the first target stage into the first sequence, when the number of the above-mentioned first target stages in the above-mentioned first sequence is greater than a preset number, and the above-mentioned alarm message is used to prompt that the current operation as a whole does not meet the specifications.

[0073] In one embodiment of the present application, the third processing module includes a first processing sub-module, a second processing sub-module and a third processing sub-module. The first processing sub-module is used for planning the corresponding two first target stages as a first cluster if there are two sequential representations of the first target stages in the first sequence and they are adjacent to each other, and the second processing sub-module is used for planning the corresponding three first target stages as a second cluster if there are three sequential representations of the first target stages in the first sequence and they are adjacent to each other, and the third processing sub-module is used for planning the corresponding three or more first target stages as a third cluster if there are more than three sequential representations of the first target stages in the first sequence and they are adjacent to each other, and the third processing sub-module is used for planning the corresponding three or more first target stages as a third cluster if there are more than three sequential representations of the first target stages in the first sequence and they are adjacent to each other, and the third processing sub-module is used for planning the corresponding three or more first target stages as a third cluster.

[0074] In one embodiment of the present application, the second processing unit includes a first determination module, a second determination module, a third determination module and a fifth processing module; the first determination module is used to determine that the current abnormality level is a first-level abnormality when the number of the above-mentioned first target stages in the above-mentioned first cluster is greater than 1; the second determination module is used to determine that the current abnormality level is a second-level abnormality when the number of the above-mentioned first target stages in the above-mentioned second cluster is greater than 2; the third determination module is used to determine that the current abnormality level is a third-level abnormality when the number of the above-mentioned first target stages in the above-mentioned third cluster is greater than 3; the fifth processing module is used to generate different prompt information according to the above-mentioned current abnormality level.

[0075] In one embodiment of the present application, the first processing unit includes a sixth processing module and a seventh processing module. The sixth processing module is used to extract image features of the above-mentioned on-site image set through multi-layer convolution and pooling operations using a convolutional neural network; the seventh processing module is used to use a model trained by a machine learning or deep learning algorithm to identify and process the above-mentioned image features to obtain the above-mentioned recognition results.

[0076] In one embodiment of the present application, the above-mentioned device also includes a third processing unit and a fourth processing unit. The third processing unit is used to obtain the temperature, humidity and air pressure of the working environment, as well as the status indicators and operating status indicators of the working equipment in real time to obtain monitoring data; the fourth processing unit is used to generate different levels of early warning instructions according to the size of the above-mentioned monitoring data, and the above-mentioned early warning instructions are used to monitor risks in the operation process.

[0077] The above-mentioned operation management device includes a processor and a memory, and the above-mentioned acquisition unit, the first processing unit, the second processing unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0078] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the problem of difficult-to-manage traditional job management seriously affecting job efficiency and completion can be solved by adjusting kernel parameters.

[0079] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0080] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the job management method.

[0081] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the job management method is executed when the program is running.

[0082] The embodiment of the present invention provides a device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements at least the following steps when executing the program: obtaining a set of on-site images of multiple operation stages, the set of on-site images includes multiple on-site images; using image recognition technology to perform recognition processing on the set of on-site images of each of the above operation stages to obtain a recognition result, so as to identify whether the above operation stage corresponding to the above on-site image set has a problem of not meeting the specification; determining the number and position of the above operation stages that do not meet the specification based on the above recognition result, and generating different prompt information based on the number and position of the above operation stages that do not meet the specification. The device in this article can be a server, PC, PAD, mobile phone, etc.

[0083] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes at least the following method steps: obtaining a set of on-site images of multiple operation stages, the above-mentioned on-site image set including multiple on-site images; using image recognition technology to perform recognition processing on the above-mentioned on-site image set of each of the above-mentioned operation stages to obtain a recognition result, so as to identify whether there is a problem that the above-mentioned operation stage corresponding to the above-mentioned on-site image set does not meet the specifications; determining the number and position of the above-mentioned operation stages that do not meet the specifications based on the above-mentioned recognition results, and generating different prompt information based on the number and position of the above-mentioned operation stages that do not meet the specifications.

[0084] A job management system is provided, the system comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for executing any of the above methods. By using image recognition technology to identify and process the on-site image set of each job stage, a recognition result is obtained to identify whether there is a problem that does not meet the specifications in the job stage corresponding to the on-site image set, and then the number and location of the job stages that do not meet the specifications are determined according to the recognition result, and different prompt information is generated according to the number and location of the job stages that do not meet the specifications, so as to promptly remind the operator to know which job stage has a problem, so that it can be solved in time. Compared with the existing solution, it is easier to manage, improves the job efficiency and completion, and thus solves the problem that the traditional job management that is difficult to manage seriously affects the job efficiency and completion.

[0085] A specific embodiment can be as follows Figure 3 As shown, the job management system includes a mobile terminal, a background service terminal and a job monitoring module; the mobile terminal includes a job application module and a job acceptance module, wherein the job application module is used to apply for special job reports to the background service terminal and receive the approval results of the background service terminal; the job acceptance module is used to collect on-site images when each stage of the special job is completed, and then send the collected on-site images to the background service terminal for analysis; the background service terminal includes a job approval module and a risk warning module, wherein the job approval module is used to approve the special job report applied for by the job application module upon receiving it, and send the approval result back to the mobile terminal, and at the same time, analyze the on-site images of the special job sent when each stage is completed, and output the analysis results to the mobile terminal; the job monitoring module is used to monitor the temperature, humidity, air pressure and other indicators of the working environment, as well as the status and operation of the working equipment during the special operation, and send the monitoring information to the background service terminal for analysis, and the risk warning module of the background service terminal issues a warning of the corresponding level according to the analysis result, and then feeds the warning result back to the mobile terminal.

[0086] When applying for special operation reports to the backend service terminal, the operation application module needs to attach on-site construction images and full-body photos of special operators. The operation approval module analyzes the on-site construction images and full-body photos of special operators to determine whether the current construction site meets the construction standards, and whether the identity of the special operator is incorrect and whether the clothing meets the standards. This setting effectively ensures that the preparations before construction are sufficient, avoids accidents in subsequent operations due to insufficient preparation, and improves safety.

[0087] If the approval result given by the job approval module is that the job is not allowed, the rectification opinions will be attached to the approval result. The special operator can view the rectification opinions through the mobile terminal and make corresponding rectifications according to the rectification opinions. After the rectification is completed, reapply for special jobs. If the job approval module approves and rejects the job again, the special operator's credit points will be reduced. The credit points can be used to exchange gifts in the points mall, and can also be used to apply for special jobs with priority. This setting can effectively improve the user's enthusiasm for pre-job preparation, reduce the workload of the job approval module, and improve work efficiency.

[0088] The specific process of the job approval module analyzing the on-site images of the special jobs sent over when each stage is completed is as follows: the on-site images of the special jobs when each stage is completed are marked as Ai, i=1…n, where n is a positive integer; image recognition is performed on Ai, and if an unqualified point is identified, the stage corresponding to Ai is placed in the first sequence; if no unqualified point is identified, the stage corresponding to Ai is placed in the second sequence; the number of stages in the first sequence is counted and marked as B, and if B is greater than the preset value b, it means that the special job as a whole does not meet the standard; if B is less than or equal to the preset value b, proceed to the next step; the stages in the first sequence are counted and marked as B. The positions are identified. If two stages are adjacent to each other, the corresponding two stages are planned to the first cluster. If three stages are adjacent to each other, the corresponding three stages are planned to the second cluster. If more than three stages are adjacent to each other, the corresponding three or more stages are planned to the third cluster. The number of all stages in the first cluster, the second cluster, and the third cluster are counted and marked as X, Y, and Z respectively. If X>1, it means that the special operation has a first-level abnormality of continuous non-compliance; if Y>2, it means that the special operation has a second-level abnormality of continuous non-compliance; if Z>3, it means that the special operation has a third-level abnormality of continuous non-compliance.

[0089] After each stage of the special operation is completed, the operation acceptance module collects on-site images and uploads them to the background service terminal for analysis. The background service terminal analyzes in real time and feeds back the analysis results to the mobile terminal. The special operator understands the shortcomings of his current operation based on the feedback analysis results, so as to make improvements in subsequent operations. Through this round-by-round acceptance method, non-standard operations can be discovered in a timely manner, so that corrections can be made in advance to avoid greater mistakes in the future. At the same time, it can also effectively supervise the construction attitude of special operators and improve the quality of operations.

[0090] The backend service terminal builds a monitoring and analysis model based on the temperature, humidity, air pressure and other indicators of the working environment transmitted by the operation monitoring module, as well as the status indicators and operation indicators of the operating equipment. The monitoring and analysis model analyzes the abnormal indicators in the current construction environment. The risk warning module counts the number of abnormal indicators and issues warnings of different levels based on the number. The larger the number of abnormal indicators, the higher the corresponding warning level. This setting can timely discover risks in the operation process and issue warnings, thereby improving the safety of the operation.

[0091] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0092] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0098] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0100] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0101] 1) The job management method of the present application uses image recognition technology to identify and process the on-site image sets of each job stage to obtain recognition results, so as to identify whether there are any problems that do not meet the specifications in the job stage corresponding to the on-site image set, and then determine the number and location of the job stages that do not meet the specifications based on the recognition results, and generate different prompt information based on the number and location of the job stages that do not meet the specifications, so as to promptly remind the operators to know which job stage has problems so that they can be solved in time. Compared with the existing solutions, it is easier to manage, improves the job efficiency and completion, and thus solves the problem that the traditional job management that is difficult to manage seriously affects the job efficiency and completion.

[0102] 2) The job management device of the present application uses image recognition technology to identify and process the on-site image sets of each job stage to obtain a recognition result, so as to identify whether there are any problems that do not meet the specifications in the job stage corresponding to the on-site image set, and then determine the number and location of the job stages that do not meet the specifications based on the recognition result, and generate different prompt information based on the number and location of the job stages that do not meet the specifications, so as to promptly remind the operators to know which job stage has problems so that they can be solved in time. Compared with the existing solutions, it is easier to manage, improves the job efficiency and completion rate, and thus solves the problem that the traditional job management that is difficult to manage seriously affects the job efficiency and completion rate.

[0103] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for operation management, characterized in that: include: Acquire a set of on-site images at multiple operation stages, wherein the set of on-site images includes multiple on-site images; Using image recognition technology to perform recognition processing on the on-site image set of each operation stage to obtain a recognition result, so as to identify whether the operation stage corresponding to the on-site image set has a problem that does not meet the specifications; The number and position of the operation stages that do not meet the specifications are determined according to the recognition results, and different prompt information is generated according to the number and position of the operation stages that do not meet the specifications.

2. The method according to claim 1, characterized in that Determining the number and location of the operation stages that do not meet the specification according to the identification results includes: storing all the on-site image sets of the first target stage into a first sequence, the first target stage being the operation stage corresponding to the on-site images that do not meet the specification in the recognition results; storing all the on-site image sets of the second target stage into a second sequence, wherein the second target stage is the operation stage corresponding to the on-site images that meet the specification in the recognition result; The locations of the operating stages that do not meet specifications are determined based on all first target stages in the first sequence.

3. The method according to claim 2, characterized in that After storing all the live image sets of the first target stage into the first sequence, the method further includes: When the number of the first target stages in the first sequence is greater than a preset number, an alarm message is generated, where the alarm message is used to prompt that the current operation as a whole does not meet the specification.

4. The method according to claim 2, characterized in that: Determining the position of the operation phase that does not meet the specification based on all the first target phases in the first sequence includes: The order representation of the existence of two first target stages in the first sequence is a front-to-back adjacent relationship, and the corresponding two first target stages are planned as a first cluster; In the first sequence, there are three sequential representations of the first target stages that are adjacent to each other in sequence, and the corresponding three first target stages are planned as a second cluster; In the first sequence, there are three or more sequential representations of the first target stages that are adjacent to each other in sequence, and the corresponding three or more first target stages are planned as a third cluster.

5. The method according to claim 4, characterized in that Different prompt information is generated according to the number and location of the operation stages that do not meet the specifications, including: When the number of the first target stages in the first cluster is greater than 1, determining that the current abnormality level is a first-level abnormality; When the number of the first target stages in the second cluster is greater than 2, determining that the current abnormality level is a level 2 abnormality; When the number of the first target stages in the third cluster is greater than 3, determining that the current abnormality level is a level 3 abnormality; Different prompt information is generated according to the current abnormality level.

6. The method according to claim 1, characterized in that The image recognition technology is used to perform recognition processing on the on-site image set of each operation stage to obtain recognition results, including: A convolutional neural network is used to extract image features of the scene image set through multi-layer convolution and pooling operations; The image features are identified using a model trained with a machine learning or deep learning algorithm to obtain the identification result.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Obtain the temperature, humidity and air pressure of the working environment, as well as the status indicators and operating indicators of the working equipment in real time to obtain monitoring data; Different levels of early warning instructions are generated according to the size of the monitoring data, and the early warning instructions are used to monitor the risks in the operation process.

8. A work management device, characterized in that: include: An acquisition unit, used for acquiring a set of on-site images of multiple operation stages, wherein the set of on-site images includes multiple on-site images; A first processing unit is used to use image recognition technology to perform recognition processing on the on-site image set of each operation stage to obtain a recognition result so as to identify whether the operation stage corresponding to the on-site image set has a problem of not meeting the specification; The second processing unit is used to determine the number and position of the operation stages that do not meet the specifications according to the recognition result, and generate different prompt information according to the number and position of the operation stages that do not meet the specifications.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A job management system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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