A task scheduling method and system based on a gig system

Abstract

The application relates to a task scheduling method and system based on a zero-work system, and relates to the technical field of data processing. The method comprises the following steps: receiving a task publishing instruction, calling a task level and a task type based on the task publishing instruction; determining a capability level based on the task type, and inquiring whether the capability level is less than preset capability information of an order-receiving user; if the capability level is less than the preset capability information of the order-receiving user, filtering the task for the order-receiving user; if the capability level is not less than the preset capability information of the order-receiving user, opening a task receiving permission for the order-receiving user; receiving a receiving instruction of the order-receiving user based on the opened task receiving permission, generating a task package based on the receiving instruction, and taking down the task; collecting identity information based on the task package, and after the identity information is consistent with preset order-receiving user information, a supervision method is matched and supervision is performed. The application has the effect of improving the task completion rate.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a task scheduling method and system based on a gig economy system. Background Technology

[0002] The gig economy marketplace is an intelligent platform that provides a wide range of services to employers, including task outsourcing, recruitment, and commission settlement.

[0003] Currently, when companies need to outsource, they mainly rely on posting the required tasks, allowing others to freely accept the orders and complete them according to the company's requirements within the specified time.

[0004] Regarding the aforementioned technologies, this operational model allows anyone, including those without prior experience, to accept tasks when the company assigns them. This task assignment method, where anyone can freely accept tasks, results in a low task completion rate and has room for improvement. Summary of the Invention

[0005] To improve task completion rates, this invention provides a task scheduling method and system based on a gig economy system, which improves task completion rates by matching users of the same level as the task type.

[0006] In a first aspect, the present invention provides a task scheduling method and system based on a gig economy system, employing the following technical solution:

[0007] A task scheduling method based on a gig economy system includes:

[0008] S1: Receive a task release instruction, and retrieve the task level and task type based on the task release instruction;

[0009] S2: Determine the ability level based on the task type and the task level, and query whether the preset ability information of the order-receiving user is lower than the ability level; if it is lower, filter the task for the order-receiving user; if it is not lower, grant the order-receiving user the permission to accept tasks.

[0010] S3: Based on the open task acceptance permission, receive the task acceptance instruction from the user accepting the order, generate a task package based on the task acceptance instruction, and remove the task from the platform.

[0011] S4: Collect identity information based on the task package. After the identity information matches the preset order-receiving user information, match the supervision method and carry out supervision.

[0012] By adopting the above technical solution, users of the same level as the task type are matched, and these users are given the right to accept orders. When the task starts, the identity of the user accepting the order is verified to confirm that the user is the one performing the task. This matching of people of the same level as the task improves the completion rate of the task.

[0013] Optional, also includes:

[0014] S40: Match the receiving instruction with the supervision instruction, and acquire the working image based on the supervision instruction;

[0015] S41: Select a processing image from the working image based on preset finished product features, match the corresponding finished product image based on the processing image, and match the processing features based on the finished product image;

[0016] S42: Determine the detection area in the processed image based on the comparison relationship between the processing features and the finished product image;

[0017] S43: Collect the features to be processed in the detection area;

[0018] S44: A production score determined based on a comparison between the feature to be processed and the processed feature;

[0019] S45: If the production score is lower than the preset requirement score, a modification reminder is issued, and the modification method for the processing feature is determined based on the processing feature; if the production score is not lower than the preset requirement score, the detection continues, and the processing time is recorded.

[0020] By adopting the above technical solution, regulatory instructions are matched to monitor the processing characteristics of users when processing products, thereby determining whether the processed products meet the requirements. If they do not meet the requirements, modifications are prompted, thereby improving the accuracy of production.

[0021] Optional, also includes:

[0022] S410: Identify and mark the arm features from the working image based on preset features, and calculate the intersection points on the extension lines of adjacent positions;

[0023] S411: Within a preset unit of time, connect the intersections of the same location sequentially to obtain the motion trend; determine the direction of movement based on the motion trend;

[0024] S412: When the direction of movement is consistent with the preset approach direction, collect the warning distance between the intersection point and the preset warning line;

[0025] S413: Issue a warning when the warning distance is less than the preset reference distance; accumulate the disappearance time when the finished product feature is not included in the working image;

[0026] S4140: If the disappearance time is not greater than the preset production time, then continue the detection;

[0027] S4141: If the disappearance time is greater than the preset production time, an alarm will be issued to prompt the order-receiving user to display the product and collect the display image, and record the detection time;

[0028] S415: Determine the image change area based on the displayed image and the working image before the cumulative disappearance time;

[0029] S416: Collect the number of cost features in the image change area and define it as the new addition number. Calculate the addition efficiency based on the new addition number and the detection time.

[0030] S4170: If the newly added efficiency is greater than the preset production efficiency, an alarm will be triggered and recorded;

[0031] S4171: If the new efficiency is not greater than the preset production efficiency, a prompt will be given and the number of detections will be recorded. If the number of detections is greater than the preset maximum number of detections or if cheating is detected, the task will be stopped and the user will be prompted to stop the task.

[0032] By adopting the above technical solution, the movement trend of the intersection of arm features in the working image is detected to determine whether cheating will occur. When the finished product features cannot be detected, the disappearance time is recorded. When the disappearance time is greater than the production time, the new efficiency is determined. When the new efficiency is greater than the preset production efficiency, an alarm is issued. This is used to monitor whether cheating occurs during user production.

[0033] Optional, also includes:

[0034] S4100: Based on the working image, select the arm features in the image and define them as the detection image;

[0035] S4101: Determine the area of ​​the selected box based on the detected image;

[0036] S4102: When the selected area reaches a preset alert area, determine the occlusion position of the detection image in the working image; determine the gap image in the working image based on the occlusion position; determine the avoidance vector based on the gap image; determine the preset rotation angle of the camera device based on the avoidance vector.

[0037] S4103: Perform evasion movement according to the evasion vector, rotate according to the rotation angle, and update the working image;

[0038] S4104: If the selected area of ​​the updated working image is the same as the selected area of ​​the original image, then the preset auxiliary monitoring method shall be used for monitoring.

[0039] By adopting the above technical solution, when the area of ​​the arm feature detected in the working image is larger than the preset area, the required avoidance vector and rotation angle are determined and rotated to make the working image free of obstructions and prevent occlusion.

[0040] Optional, also includes:

[0041] S41040: Based on the gap image, segment it with a preset dividing line to obtain segmented regions; select segmented regions with large areas from the segmented regions and define them as rotation regions; determine the rotation angle according to the rotation regions;

[0042] S41041: Determine the preset movable reflector based on the rotation angle, and determine the reflection angle of the movable reflector based on the preset reflection position and the processing image;

[0043] S41042: Control the rotation of the movable reflector based on the reflection angle, and update the working image according to the rotation angle.

[0044] By adopting the above technical solution, the gap area is divided into segmented areas, a large rotating area is selected from the segmented areas, the rotation angle is determined according to the rotating area, and the working image is updated by rotating the movable reflector. In this way, when there is occlusion and the image cannot be moved, the image is detected by mirror reflection, thus preventing the user from cheating.

[0045] Optional, also includes:

[0046] S400: Acquire a comparison image on a preset comparison area in the working image;

[0047] S401: Determine the contrast color difference based on the contrast image and the preset benchmark contrast features;

[0048] S403: When the contrast color difference is less than the preset maximum contrast color difference value, a replacement command is issued;

[0049] S404: Based on the replacement instruction, the preset protective film is replaced and the working image is updated.

[0050] By adopting the above technical solution, the color difference is determined by comparing the image of the preset comparison area with the reference features. If the color difference is less than the maximum color difference value, the protective film is replaced, thereby reducing the situation where the protective film accumulates dust due to long-term disuse, resulting in unclear working images.

[0051] Optional, also includes:

[0052] S405: When replacing, output a cleaning command, and in response to the cleaning command, control a preset light-emitting device to irradiate the protective film and collect the light intensity transmitted through the protective film;

[0053] S406: Select the cleaning area based on the light intensity and the preset light transmittance, and determine the cleaning intensity according to the light intensity;

[0054] S407: Based on the cleaning area, control the preset cleaning device to align with the cleaning area, clean the protective film according to the cleaning intensity, and control the light-emitting device to emit light;

[0055] S408: Stop cleaning when the light intensity is not less than the preset light transmittance.

[0056] By adopting the above technical solution, after the protective film is replaced, light is transmitted through the protective film to determine the light intensity, so as to select the cleaning area of ​​the protective film and clean the cleaning area. After cleaning, light is transmitted again. When the light intensity is not less than the preset light transmission intensity, the light transmission is stopped, so that the protective film full of dust can be cleaned after replacement and can be reused.

[0057] Optional, also includes:

[0058] S50: Based on preset coverage features, the coverage area selected from the working image is defined as the coverage image, and a removal instruction is output;

[0059] S51: Based on the removal instruction, retrieve historical working images within a preset unit time period, select the magnified images based on the historical working images, and match the covering material according to the magnified images;

[0060] S52: Match the blowing intensity based on the covering material;

[0061] S53: Control the preset blowing device to blow up the covering material according to the blowing intensity and update the working image;

[0062] S54: Issue an alarm when the working image remains unchanged.

[0063] By adopting the above technical solution, when a covering feature is detected, the covering material is determined by retrieving historical working images through a removal instruction, and the blowing intensity is matched to blow away the covering material. If the covering material cannot be blown away, an alarm is issued. Thus, when the working image is covered due to environmental reasons, the covering material can be blown away by blowing to ensure the supervision of the production process.

[0064] Optional, also includes:

[0065] S440: Based on the processing time, determine the completion time of the finished product feature in the working image and the appearance time of the preset blank feature in the working image.

[0066] Calculate the user's stage efficiency based on the completion time and the occurrence time;

[0067] S441: When a processing feature is detected, record the number of detections. When the number of detections reaches a preset baseline number,

[0068] To determine the initial and final time points of each processing stage, and to calculate the stage efficiency of each processing stage when processing different processing features, so as to determine the stage with the highest efficiency;

[0069] S4420: If the highest efficiency stages are different, extract the working images of each highest efficiency stage and record the common features in the working images;

[0070] S44200: If common features appear in the remaining working images, check their sorting order. If the sorting order is higher than that of the working images that do not have common features, record it as a useful feature.

[0071] S44201: Determine a simulation method based on the aforementioned beneficial features and output simulation instructions;

[0072] S44202: Based on the simulation instruction, prompt the customer to simulate the production environment using the simulation method;

[0073] S4421: If the stages with the highest production efficiency are the same, then the task station is determined based on the highest production efficiency;

[0074] S44210: Based on the task station notification, the order-receiving user arrives at the task station; monitoring is performed after the order-receiving user arrives at the task station.

[0075] By adopting the above technical solution, the user's most efficient stage is determined. If the stages are different, the user is prompted to simulate the beneficial features. If the stages are the same, the user is directed to the matched workstation for production. This improves the user's production efficiency through reasonable allocation or simulation, thereby increasing the task completion rate.

[0076] Secondly, this application provides a task scheduling system based on a gig economy system, which adopts the following technical solution:

[0077] A task scheduling system based on a gig economy system includes:

[0078] The acquisition module is used to acquire task release instructions, acceptance instructions, identity information, features to be processed, warning distance, comparison images, light intensity, removal instructions, and simulation instructions;

[0079] A memory, used to store the production program for any of the above-mentioned new energy motor housings;

[0080] A processor is used to load, execute, and implement programs stored in memory.

[0081] In summary, this application includes at least one of the following beneficial technical effects:

[0082] 1. By matching task level and type when a task is posted, and matching people with the same task level, the completion rate of the task can be improved;

[0083] 2. The rotation angle is determined by defining the rotation area, and the movable reflector is rotated to update the working image. When there is occlusion and the image cannot be moved, the processing image is detected by mirror reflection to prevent the user from cheating.

[0084] 3. By rationally allocating or simulating the environment, user productivity can be improved, thereby increasing task completion rates. Attached Figure Description

[0085] Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention;

[0086] Figure 2 This is a schematic diagram of the camera structure of the present invention;

[0087] Figure 3 This is a flowchart of a task scheduling method based on a gig economy system.

[0088] The parts referred to by the numbers in the above attached diagrams are as follows: 1. Movable reflector; 2. Camera device; 3. Bracket; 4. Protective film; 5. Replacement area. Detailed Implementation

[0089] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0090] This application discloses a task scheduling method and system based on a gig economy system, referring to... Figure 1 The present invention controls the camera device 2 on the bracket 3 to rotate when the image does not change, and drives the movable reflector 1 installed on the bracket 3 to rotate so that it can rotate when it cannot move to avoid the cover, and detects and processes the image by reflecting the image in the reflector.

[0091] Reference Figure 1 and Figure 2When the protective film 4 in front of the camera is detected to be covered by dust, the motor in the replacement area 5 is controlled to rotate to move the protective film 4, and the protective film 4 is moved to the back of the camera, and the replacement protective film 4 in the back of the camera is moved to the front of the camera lens.

[0092] Reference Figure 3 A task scheduling method based on a gig economy system includes the following steps:

[0093] Step 1: Receive the task release instruction and retrieve the task level and task type based on the task release instruction.

[0094] Task level refers to the lowest level at which the task can be completed, and task type refers to the classification of the job. Task level and task type can be manually entered by the employer, which will not be elaborated here.

[0095] A task posting instruction is an instruction from an employer to post a task. The task posting instruction can be sent by the employer and received by the server. After receiving the task posting instruction, the server will retrieve the corresponding task level and task type.

[0096] Step 2: Determine the ability level based on the task type and task level, and check if the user's preset ability information is lower than the ability level. If it is lower, filter the task for the user. If it is not lower, grant the user the right to accept tasks.

[0097] "Order-receiving user" refers to a user who accepts orders. All users on this platform who need to accept orders are order-receiving users.

[0098] The ability level refers to the level of a user's ability. The ability level can be determined by querying the user's ability information to see if their ability level for this type of task meets the task level. If it does, the user is granted permission to accept tasks; otherwise, the task is filtered out for the user.

[0099] Open task acceptance permission refers to granting task acceptance permission to qualified users. When a user's ability level for the corresponding task type reaches the task level, task acceptance permission is granted.

[0100] Capability information refers to data that records the capabilities of users in various task categories. Capability information can be determined by querying the user's historical orders, job content, and job skills. Capability information can be used to match capability levels.

[0101] The ability level is determined based on the task type and task level. This means that the corresponding ability level needs to be matched according to the type of task being posted. For example, if the task is manual production, then the ability level of the user who accepted the order will be matched to the manual production task level.

[0102] Step 3: Based on the open task acceptance permission, receive the task acceptance instruction from the user who accepts the order, generate a task package based on the acceptance instruction, and then remove the task from the platform.

[0103] A task acceptance instruction is an instruction issued by a user after accepting a task. The instruction process involves identifying whether the user has accepted the task, issuing the instruction upon acceptance, removing the corresponding task from the platform, and sending a task package to the user for task completion. The task package contains the necessary content for the task and is activated when the task begins.

[0104] Step 4: Collect identity information based on the task package. After the identity information matches the preset order-receiving user information, match the supervision method and carry out supervision.

[0105] Identity information refers to the user's identity information. This information can be collected by the camera controlled by the task package after the task package is activated. The camera model is selected by the staff based on the actual situation and will not be elaborated here.

[0106] Order-receiving user information refers to the information of the user who receives the order. This information can be determined in advance by the user who receives the order, and will not be elaborated on here.

[0107] Once the task package is activated, the identity information of the user who activated it is collected. If the collected identity information does not match the information of the user who accepted the order, the user will be prevented from working. If they match, a supervision method will be used to supervise the user.

[0108] The supervision method refers to the method used to supervise the production of users who accept orders. The supervision method is determined according to the type of task. In this embodiment, the supervision method is the supervision method for manual production.

[0109] The regulatory approach includes the following steps:

[0110] Step 40: Match the receiving instruction with the supervisory instruction, and collect working images based on the supervisory instruction.

[0111] Regulatory instructions refer to instructions used for supervision. Regulatory instructions can be matched after receiving the receiving instruction. Work images refer to images of the user working on the order. Work images can be captured using a camera. The camera model is selected by the staff according to the actual situation, and will not be elaborated here.

[0112] Step 41: Select the processing image from the working image based on the preset finished product features, match the corresponding finished product image based on the processing image, and match the processing features based on the finished product image.

[0113] Finished product characteristics refer to the characteristics of the completed product. Finished product characteristics can be manually entered by staff, which will not be elaborated here.

[0114] By annotating a large number of images with finished product features, and then inputting these annotated images into the YOLO large model, the finished product features are extracted by repeatedly stacking the images using a PyTorch network architecture. The error between the results and the data is calculated, and the model is put into use when the error is less than 1%.

[0115] The working image is input into the large model. When the finished product features are identified, the finished product features are marked and selected from the working image. The selected image is the processing image.

[0116] The finished product image refers to the image after processing. The finished product image can be obtained by querying the finished product record table, which records the finished product features corresponding to the task. The finished product image is determined based on the initial features that appear during processing.

[0117] Processing features refer to the characteristics of a product when it is processed by the user. These features can be determined by querying the feature data table, which refers to the processing features corresponding to different finished product images that have been determined in advance through experiments.

[0118] Step 42: Determine the detection area in the processed image based on the comparison relationship between the processing features and the finished product image.

[0119] The detection area refers to the area used to detect processing features. By importing the finished product image into the processing image and selecting the different areas in the finished product image and the processing image, this area is the required detection area.

[0120] Step 43: Collect the features to be processed in the detection area.

[0121] Features to be processed refer to the features that the order-receiving user needs to process when processing the product. Features to be processed can be identified from the detection area by image recognition technology, which identifies the features processed by the order-receiving user in the detection area.

[0122] Step 44: The production score is determined based on the comparison between the feature to be processed and the processed feature.

[0123] Production score refers to the score generated after producing the processing feature. It can be determined by comparing the processed feature with the feature to be processed. By comparing the completed feature to be processed with the processed feature, areas with incorrect position or shape are selected to lower the production score; otherwise, the score is not lowered.

[0124] Step 45: If the production score is lower than the preset requirement score, issue a modification reminder and determine the modification method for the processing feature based on the processing feature; if the production score is not lower than the preset requirement score, continue the inspection and record the processing time.

[0125] The requirement score refers to the score of the minimum production requirements for this production. The requirement score is manually entered by the staff, which will not be elaborated here.

[0126] The modification reminder is a prompt used to remind the order taker to modify the product. When the production score is lower than the required score, the system will control the sound to remind the order taker to modify the product. The model of the sound system is selected by the staff according to the actual situation, and will not be elaborated here.

[0127] The modification method refers to the method used to modify product features. The modification method can identify the processing methods of the order-receiving user through working images, determine the means by which the order-receiving user has lowered the score, and match the correct processing methods to enable the order-receiving user to carry out production.

[0128] Production time refers to the time it takes for a user to complete the production of a product after receiving an order. Production time can be determined by the time recorded by a camera, which will not be elaborated on here.

[0129] Cheating detection methods include the following steps:

[0130] Step 410: Identify and mark the arm from the working image based on the preset arm features, and calculate the intersection points on the extension lines of adjacent positions.

[0131] Arm features refer to the characteristics of the arms, which are pre-entered by staff and will not be elaborated upon here. Intersection points refer to the points where arms intersect, which can be created by extending lines from the arms; the points where these extended lines intersect are the intersection points.

[0132] Step 411: Within a preset unit of time, connect the intersections of the same location sequentially to obtain the movement trend; determine the direction of movement based on the movement trend.

[0133] Unit time refers to a pre-set time, which is set by the staff according to the actual situation and will not be elaborated here. Movement trend refers to the trend of arm movement. It can be determined by connecting the intersections in the work image within the unit time and determining the next movement direction based on the connection sequence; this movement direction is the movement trend. Movement direction refers to the direction of movement within the movement trend, which can be determined by extending the connected line segments.

[0134] Step 412: When the moving direction is consistent with the preset approach direction, collect the warning distance between the intersection point and the preset warning line.

[0135] The approach direction refers to the direction in which the intersection point approaches the warning line. The approach direction is manually entered by the staff and will not be elaborated here. The warning line is a boundary line used to alert users to whether they are cheating. The warning line is the line segment at the boundary of the production table as detected by the camera during production.

[0136] When the direction of movement is consistent with the preset approach direction, it indicates that the user's movement in that direction may indicate a cheating tendency. At this time, the distance between the intersection point and the warning line is collected.

[0137] Step 413: Issue a warning when the warning distance is less than the preset baseline distance; accumulate the disappearance time when the working image does not contain finished product features.

[0138] When the warning distance is less than the preset baseline distance, it indicates that the order-taking user may be engaging in fraudulent production. At this time, a warning will be issued to remind the order-taking user to increase the distance between their arm and the warning line.

[0139] If the finished product features are not present in the working image, it indicates that the user is not heeding the warning. In this case, the disappearance time of the finished product features should be recorded.

[0140] The disappearance time refers to the time when the finished product feature disappears. The disappearance time is determined by recording the time point when the finished product feature disappears and continuously recording the time. For example, if the finished product feature disappears at 10:00, the entire time after the finished product feature disappears is the disappearance time.

[0141] Step 4140: If the disappearance time is not greater than the preset production time, continue the detection.

[0142] Production time refers to the time required to complete the cheat production. The production time is entered by the staff based on the actual situation, and will not be elaborated here.

[0143] If the disappearance time is not greater than the preset production time, it means that the disappearance time is too short to complete the cheating production. In this case, the production process of the order-receiving user will continue to be detected.

[0144] Step 4141: If the disappearance time is longer than the preset production time, an alarm will be issued to prompt the user who received the order to display the product and collect the display image, and record the detection time.

[0145] If the disappearance time is longer than the preset production time, it means that the user who accepted the order may engage in fraudulent production during this period. At this time, an alarm will be issued to prompt the user to display the product.

[0146] The displayed image refers to the image shown by the user. The displayed image can be obtained through a camera, which will not be elaborated on here.

[0147] The detection time refers to the total time of the disappearance period, that is, the time point when the production feature appears within the disappearance period, and the entire disappearance period is recorded as the detection time. For example, if the start time of the disappearance period is 10:00, the recording time begins, and when the time reaches 10:20, the user displays the product, at which point the detection time is 20 minutes.

[0148] Step 415: Determine the image change area based on the displayed image and the working image before the cumulative disappearance time.

[0149] The image change region refers to the image region that changes during the detection time. The image change region can be extracted by comparing the working image and the display image before the disappearance time. The finished product features that are not in the working image are these finished product features.

[0150] Step 416: Collect the number of cost features in the image change area and define it as the new addition number. Calculate the addition efficiency based on the new addition number and the detection time.

[0151] The number of cost features collected in the image change area represents the number of newly added finished product features detected within the detection time. The number of newly added features refers to the number of newly added finished product features. For example, if a user needs to produce a flower, the flower has 10 petals when the finished product features have not disappeared, and 20 petals when they reappear, then the number of newly added petals is 20-10=10.

[0152] New production efficiency refers to the production efficiency of the user receiving the order within the testing time. New production efficiency can be determined by calculating the quotient of the new production quantity and the testing time. For example, if 10 petals are completed in 20 minutes of testing time, then the production efficiency is 10 / 20 = 0.5 petals / minute.

[0153] Step 4170: If the newly added efficiency is greater than the preset production efficiency, an alarm will be issued and recorded.

[0154] Production efficiency refers to the production efficiency of the order-receiving user. Production efficiency can be determined by querying the efficiency record table of the order-receiving user. The efficiency record table is a table that records the production efficiency range of the order-receiving user in the past.

[0155] Step 4171: If the new efficiency is not greater than the preset production efficiency, a prompt will be given and the number of detections will be recorded. If the number of detections exceeds the preset maximum number of detections or if cheating is detected, the task will be stopped and the user will be prompted to stop the task.

[0156] If the new production efficiency is not greater than the preset production efficiency, it means that the user has not engaged in cheating production. At this time, the number of times the user accepts the order is recorded. When the number exceeds the maximum number of detections, the user is prompted to stop the task.

[0157] The number of detections refers to the number of times a finished product feature cannot be detected in the working image. The number of detections can be determined by the number of times the detection time is acquired; each detection time is recorded as one detection count. The maximum number of detections refers to the number of detections that are allowed. The maximum number of detections is manually entered by the staff and will not be elaborated here.

[0158] The occlusion detection method includes the following steps:

[0159] Step 4100: Based on the working image, select the arm features in the image and define them as the detection image.

[0160] The detection image refers to the image used to detect arm features. The detection image is determined by identifying arm features in the working image and then selecting the arm features by using the largest circumscribed quadrilateral.

[0161] Step 4101: Determine the bounding box area based on the detected image.

[0162] The bounding box area refers to the area of ​​the detected image. It can be determined by calculating the length and width of the largest circumscribed quadrilateral. If the length of the largest circumscribed quadrilateral in the detected image is 5cm and the width is 7cm, then the bounding box area is 5 x 7 = 35cm. 2 .

[0163] Step 4102: When the selected area reaches the preset alert area, determine the occlusion position of the detection image in the working image; determine the gap image in the working image based on the occlusion position, determine the avoidance vector based on the gap image; determine the preset rotation angle of the camera device 2 based on the avoidance vector.

[0164] The alert area refers to the maximum area used for the alert selection box. This area is manually entered by the staff and will not be elaborated upon here. When the selected area reaches the alert area, it indicates that the user may intend to cover the camera with their arm. The occlusion position refers to the location of the detected image. This position is defined as the location of the entire detected image within the working image by detecting the position of the selected area. The gap image refers to the remaining image in the working image excluding the detected image. By identifying the occlusion image at the occlusion position in the working image, removing the occlusion image from the working image, the remaining image is the gap image.

[0165] The avoidance vector refers to the direction and distance the camera moves when avoiding detection images. It is determined by examining the removed detection image within the gap image and comparing the distance the detection image reaches the edge of the gap image. The side with the greater distance is defined as the avoidance direction. Image recognition technology is used to measure the maximum distance the detection image reaches the edge of the working image. This maximum distance is then used to query a movement distance data table, which represents the movement distance corresponding to different maximum distances determined experimentally. A smaller maximum distance corresponds to a longer movement distance.

[0166] The rotation angle refers to the angle at which the camera device 2 rotates. The rotation angle can be determined by consulting a rotation data table, which shows the rotation angles corresponding to different moving distances determined in advance through experiments. The longer the moving distance, the larger the rotation angle.

[0167] Camera device 2 refers to the camera used to capture working images. The model of the camera is selected by the staff according to the actual situation, and will not be elaborated here.

[0168] Step 4103: Perform evasion movement according to the evasion vector, rotate according to the rotation angle, and update the working image.

[0169] The camera device 2 is controlled to move according to the avoidance vector, and rotates according to the rotation angle while moving. After the rotation is completed, the working image is updated.

[0170] Step 4104: If the selected area of ​​the updated working image is the same as the selected area of ​​the original image, then the preset auxiliary monitoring method is used for monitoring.

[0171] If the selected area of ​​the updated working image is the same as the selected area of ​​the original image, it indicates that the camera device 2 cannot be moved. In this case, a preset auxiliary monitoring method is used for monitoring. The auxiliary monitoring method can be viewed in steps 41040 to 41042.

[0172] The auxiliary regulatory methods include the following steps:

[0173] Step 41040: Segment the image based on the gaps using preset dividing lines to obtain segmented regions; select the segmented regions with larger areas from the segmented regions and define them as rotating regions; determine the rotation angle based on the rotating regions.

[0174] A dividing line is a line segment used to divide a gap area. A divided area is the region formed after the gap area is divided by the dividing line. The dividing area is formed by identifying the shortest vertical distance that can be divided in the gap area and connecting them.

[0175] If the detection image is located in the center and the dividing line cannot be determined, the gap area is two unconnected areas on the left and right. The rotation area is the gap area with the larger area. If the areas are the same, the left and right can be rotated arbitrarily.

[0176] A rotation region is a region used to determine the direction of rotation. By comparing the areas of the segmented regions, the segmented region with the larger area is defined as the rotation region.

[0177] The rotation angle refers to the angle used to rotate the camera device 2. There are only two rotation angles: positive 90 degrees and negative 90 degrees. The rotation angle is determined by the position of the rotation area. If the rotation area is on the left, the rotation angle is negative 90 degrees; if the rotation area is on the right, the rotation angle is positive 90 degrees.

[0178] Step 41041: Determine the preset movable reflector 1 based on the rotation angle, and determine the reflection angle of the movable reflector 1 based on the preset reflection position and the processed image.

[0179] The movable reflector 1 refers to a rotatable mirror used to reflect the working environment. The movable reflector 1 can be selected by the staff according to the actual situation, which will not be described in detail here. There are two movable reflectors 1, one on the left and one on the right. The movable reflectors 1 are mounted on the bracket 3. There is a motor for rotating the reflector 1 below it. The motor and the bracket are selected by the staff according to the actual situation, which will not be described in detail here.

[0180] The reflective position refers to the location of the reflector. The reflective position is manually entered and determined by the staff, which will not be elaborated here.

[0181] The processing image position refers to the location of the processing image within the working image. The processing image can be identified and selected using image recognition technology, and the selected position within the working image is the processing image position.

[0182] The reflection angle refers to the angle used to move the reflector. It can be found in a reflection angle data table by inputting the reflector's position and the processed image into the table. This data table represents the reflection angles corresponding to different reflector positions and processed images, determined beforehand through experiments. With the processed image position constant, the longer the reflection distance, the smaller the reflection angle. Conversely, with the reflection distance and position constant, the farther the processed image is from the reflector, the smaller the reflection angle.

[0183] Step 41042: Control the rotation of the active reflector 1 based on the reflection angle, and update the working image according to the rotation angle.

[0184] The rotating motor under the movable reflector 1 is rotated by controlling the reflection angle, thereby driving the movable reflector 1 to rotate. The model of the rotating motor is selected by the staff according to the actual situation, and will not be described in detail here.

[0185] After the rotation is complete, the camera device 2 captures the image in the movable reflector 1 to update the working image.

[0186] The verification method includes the following steps:

[0187] Step 400: Acquire a comparison image on a preset comparison area in the working image.

[0188] The contrast area refers to the area used to detect dust accumulation. This area is pre-set by the staff and will not be elaborated upon here. The contrast image is the image of the contrast area. Image recognition technology can be used to identify the contrast area within the working image; this image is the contrast image.

[0189] Step 401: Determine the contrast color difference based on the contrast image and the preset baseline contrast features.

[0190] Protective film 4 refers to a tough, soft film used to protect the camera lens. Protective film 4 is selected by the staff according to the actual situation, and will not be elaborated here.

[0191] The baseline contrast feature refers to the feature used to detect whether there is dust accumulation. The baseline contrast feature is a small square pattern that is pre-set on the protective film 4. After the protective film 4 is photographed by the camera, a small square for comparison will appear in the lower right corner of the working image. The small square is covered by dust. The state of the small square shown in the photograph is the baseline contrast feature.

[0192] RGB values ​​refer to the numerical values ​​used for red, green, and blue colors. RGB values ​​can be determined through image recognition technology, which will not be elaborated on here.

[0193] Contrast color difference refers to the difference between the color difference in a contrasting image and the color difference of a reference contrast feature. Contrast color difference can be calculated by identifying the RGB values ​​of the contrasting image and the RGB values ​​of the reference contrast feature using image recognition technology, and then calculating the color difference between the two. This color difference is the contrast color difference.

[0194] Substituting into the formula: E = √((R1 - R2)) 2 + (G1-G2) 2 +(B1-B2) 2 ).

[0195] For example, image color is represented by three color components: red (R), blue (G), and green (B). The baseline contrast feature simulates the RGB values ​​of a dusty photo. For example, if the RGB values ​​of the baseline contrast feature are (174.238.238) and the contrast image has RGB values ​​of (187.255.255), then the color difference is E, and E = √(187-174). 2 +(255-238) 2 +(255-238) 2 ) =√(132+172+172), and further calculations give E =√(169+289+289) =√747≈27.

[0196] Step 403: When the contrast color difference is less than the preset maximum contrast color difference value, issue a replacement command.

[0197] The maximum contrast color difference value refers to the maximum value of the contrast color difference value. The maximum contrast color difference value is manually entered by the staff and will not be described in detail here.

[0198] When the calculated contrast color difference value is less than the preset maximum contrast color difference value, it means that the protective film 4 needs to be replaced, and the server issues a replacement command.

[0199] Step 404: Replace the preset protective film 4 based on the replacement instruction and update the working image.

[0200] The replacement device refers to the device used to replace the protective film 4. The replacement device is selected by the staff according to the actual situation, and will not be described in detail here.

[0201] Upon receiving a replacement instruction, the control device is activated. By rotating the replacement device, the protective film 4 is moved, thereby replacing the protective film 4.

[0202] The cleaning method includes the following steps:

[0203] Step 405: When replacing, output a cleaning command, and in response to the cleaning command, control the preset light-emitting device to irradiate the protective film 4 and collect the light intensity transmitted through the protective film 4.

[0204] The cleaning instruction refers to the instruction for cleaning the protective film 4, and is issued after the replacement instruction is completed. The light-emitting device refers to the light bulb used for emitting light; the bulb model is selected by the staff based on the actual situation and will not be elaborated here. The light intensity refers to the intensity of light passing through the protective film 4; the light intensity can be determined by a luminance meter, the model of which is selected by the staff based on the actual situation and will not be elaborated here.

[0205] Step 406: Select the cleaning area based on the light intensity and the preset light transmittance, and determine the cleaning intensity according to the light intensity.

[0206] Light transmittance refers to the intensity of light emitted by the light-emitting device when it passes through the protective film 4. The light transmittance is selected by the staff according to the actual situation, and will not be elaborated here. The cleaning area is the area that needs to be cleaned. The cleaning area can be selected by outlining the area where the light intensity is lower than the light transmittance; this area is the light transmittance area.

[0207] Cleaning intensity refers to the water pressure used to clean the protective film 4. The cleaning intensity can be determined by consulting a cleaning data table, which records the cleaning intensity corresponding to different light intensities, determined beforehand through experiments. The lower the light intensity, the greater the cleaning intensity.

[0208] Step 407: Based on the cleaning area control, the preset cleaning device is aligned with the cleaning area, and the protective film 4 is cleaned according to the cleaning intensity, and the light-emitting device is controlled to emit light.

[0209] The cleaning device refers to the water gun and water pump used to clean the protective membrane 4. The model of the water gun and water pump is selected by the staff according to the actual situation, and will not be described in detail here. Control the water gun to aim at the cleaning area, control the water pump to set the cleaning intensity to spray water to rinse the protective membrane 4, and control the light-emitting device to emit light.

[0210] Step 408: Stop cleaning when the light intensity is not less than the preset light transmission intensity.

[0211] When the detected light intensity is not lower than the light transmittance, control the water gun to stop cleaning.

[0212] The air blowing removal method includes the following steps:

[0213] Step 50: Based on the preset coverage features, the coverage area selected from the working image is defined as the coverage image, and a removal instruction is output.

[0214] Coverage features refer to the features that are covered in the working image. These features are preset and input by the staff, and will not be elaborated upon here. Coverage image refers to the area image covered by the coverage feature. The coverage image can be used to select the coverage feature from the working image using image recognition technology. Removal command refers to the command used to remove the coverage feature. The removal command is issued after the coverage image is recognized.

[0215] Step 51: Based on the removal command, retrieve historical working images within a preset unit time period, and select nearby images that show a magnifying trend based on the historical working images. Then, match the covering material according to the nearby images. The unit time refers to the preset time period to be retrieved, which is set by the staff according to the actual situation and will not be elaborated here. Historical working images refer to the working images within the preset unit time period, which can be determined by retrieving the working images within that unit time period.

[0216] A closer image refers to an image that gradually enlarges from a smaller size. A closer image can be obtained by retrieving historical working images, playing them according to time, and selecting the gradually enlarging image. This gradually enlarging image is the closer image.

[0217] Covering material refers to the material covering the camera. Covering material can be matched by bringing the nearby image into the coverage record table.

[0218] Step 52: Match the blowing intensity based on the covering material.

[0219] The blowing intensity refers to the intensity used to blow away the covering material. The blowing intensity can be obtained by looking up the blowing data table, which refers to the blowing intensity corresponding to different covering materials determined in advance through experiments.

[0220] Step 53: Control the preset air blowing device to blow the covering material according to the blowing intensity and update the working image.

[0221] The air blowing device refers to the air pump and air inlet used to blow away the covering material. The model of the air pump and air inlet is selected by the operator according to the actual situation, and will not be elaborated here. After matching the blowing intensity, the air blowing device is controlled to blow away the covering material according to the blowing intensity, and the working image is updated.

[0222] Step 54: Issue an alarm when there is no change in the working image.

[0223] If the updated working image does not change after blowing air, an alarm will be issued.

[0224] The air blowing removal method includes the following steps:

[0225] Step 440: Determine the completion time of the finished product features in the working image and the appearance time of the preset blank features in the working image based on the processing time, and calculate the user's stage efficiency based on the completion time and appearance time.

[0226] The completion time point refers to the time when the finished product features are completed. The completion time point can be determined by querying the working images within the recorded processing time to find the time when the finished product features appear.

[0227] The blank features refer to the characteristics of the workpiece at the very beginning. The blank features are manually entered by the staff and will not be described in detail here.

[0228] The occurrence time point refers to the time point when the blank appears. The occurrence time point can be determined by querying the recorded processing time and finding the time point when the blank characteristics appear within that processing time.

[0229] The production time is calculated based on the completion time and the occurrence time, and the production time is the stage efficiency. For example, if the start time is 12:00 and the occurrence time is 12:10, the stage efficiency is 10 minutes.

[0230] Step 441: When a processing feature is detected, record the number of detections. When the number of detections reaches the preset baseline number, determine the initial time point and completion time point of each processing time, and calculate the stage efficiency of each processing time when processing different processing features, so as to determine the stage with the highest efficiency.

[0231] The number of inspections refers to the number of times a processing feature is detected. The number of inspections increments by 1 each time a processing feature is detected. The baseline number of inspections refers to the number of inspections used to check processing efficiency. The baseline number is manually entered by the staff and will not be elaborated upon here.

[0232] Once the number of tests reaches a preset baseline, the initial and completion times for each processing time will be extracted, and the production efficiency for each time period will be determined based on these times. The production efficiencies will then be sorted, and the highest-efficiency group will be selected.

[0233] Step 4420: If the highest efficiency stages are different, extract the working images of each highest efficiency stage and record the common features in the working images.

[0234] The highest efficiency stage is different because the production efficiency is the same when producing different processing features. When determining the recording processing time, the processing time is assigned a number according to the processing feature to calculate the processing efficiency of the corresponding feature. Common features refer to the features shared by these working images. Common features can be determined by extracting features that have appeared in the working images of each highest efficiency stage. In this embodiment, the common feature is sound feature.

[0235] When different peak efficiency stages are obtained, the working images of each peak efficiency stage are extracted, and the common features appearing in the working images are recorded.

[0236] For example, a finished product has five different processing characteristics: A, B, C, D, and E. These five characteristics have the same production time, but some steps differ. After completing the five finished products, there are 25 recorded times: A1 to A5, B1 to B5, C1 to C5, D1 to D5, and E1 to E5. Calculating the processing efficiency of these 25 recorded times reveals that the highest production efficiency group is A1, B2, and C4. This indicates that the production efficiency is highest when producing A1, B2, and C4. Since the production stages differ, the common characteristics of this group's production efficiency are recorded.

[0237] Step 44200: If common features appear in the remaining working images, check their sorting order. If the sorting order is higher than that of the working images that do not have common features, then record it as a useful feature.

[0238] Beneficial features are those that help users who receive orders to carry out production. Beneficial features can be identified by substituting them into other work images to find other work images with common features. The work time of these work images will be sorted, and it will be seen which common features appear in the high ranking and which features appear in the low ranking. The common features with high ranking are the beneficial features.

[0239] For example, common features include music, rain sounds, footsteps, and car noises. Extract all working images with music features and rank them by processing efficiency. If the ranking of processed images with music features is greater than 50%, they are recorded as beneficial features; otherwise, they are not.

[0240] Step 44201: Determine the simulation method based on the beneficial features and output the simulation instructions.

[0241] The simulation method refers to the method used to simulate beneficial features. The simulation method is determined by extracting beneficial features and substituting them into a simulation data table, which refers to the simulation methods corresponding to different beneficial features determined in advance through sound. After matching the simulation method, a simulation command is output.

[0242] Step 44202: Based on the simulation instructions, prompt the customer to simulate the production environment using simulation methods.

[0243] After issuing the simulation command, the system will prompt the customer to perform the simulation according to the instructions via audio.

[0244] Step 4421: If the stages with the highest production efficiency are the same, then determine the task station based on the highest production efficiency.

[0245] The same stage of highest production efficiency indicates that when users process products, they are only good at processing a single processing feature. The task station refers to the work position that processes a single processing feature, which will not be elaborated here.

[0246] At this point, the processing characteristics it excels at are determined based on its highest production efficiency. The task station is then determined based on these processing characteristics.

[0247] Step 44210: Based on the task station prompt, notify the order-receiving user to arrive at the task station; monitor the user after they arrive at the task station.

[0248] After matching the required workstation, the system will prompt the order taker to arrive at the corresponding workstation via audio. Once the order taker arrives at the workstation, production will continue.

[0249] Based on the same inventive concept, embodiments of the present invention provide a task scheduling system based on a gig economy system, comprising:

[0250] The acquisition module is used to acquire task release instructions, acceptance instructions, identity information, features to be processed, warning distance, comparison images, light intensity, removal instructions, and simulation instructions;

[0251] A memory is used to store any of the above-mentioned task scheduling methods based on a gig system;

[0252] A processor is used to load, execute, and implement programs stored in memory.

[0253] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A task scheduling method based on a gig economy system, characterized in that, include: S1: Receive a task release instruction, and retrieve the task level and task type based on the task release instruction; S2: Determine the capability level based on the task type and the task level, and query whether the preset capability information of the order-receiving user is less than the capability level; if it is less, filter the task for the order-receiving user; if it is not less, grant the order-receiving user the permission to accept tasks. S3: Based on the open task acceptance permission, receive the task acceptance instruction from the user accepting the order, generate a task package based on the task acceptance instruction, and remove the task from the platform. S4: Collect identity information based on the task package; after the identity information matches the preset order-receiving user information, match the supervision method and carry out supervision. The regulatory methods include: S40: Match the receiving instruction with the supervision instruction, and acquire the working image based on the supervision instruction; S41: Select a processing image from the working image based on preset finished product features, match the corresponding finished product image based on the processing image, and match the processing features based on the finished product image; S42: Determine the detection area in the processed image based on the comparison relationship between the processing features and the finished product image; S43: Collect the features to be processed in the detection area; S44: Determine a production score based on a comparison between the feature to be processed and the processed feature; S45: If the production score is lower than the preset requirement score, a modification reminder is issued, and a modification method for the processing feature is determined based on the processing feature; if the production score is not lower than the preset requirement score, the detection continues, and the processing time is recorded. The method for detecting cheating is further included between steps S41 and S42, and the cheating detection method includes: S410: Identify and mark the arm features from the working image based on preset features, and calculate the intersection points on the extension lines of adjacent positions; S411: Within a preset unit of time, connect the intersections of the same location sequentially to obtain the movement trend; determine the direction of movement based on the movement trend; S412: When the moving direction is consistent with the preset approach direction, collect the warning distance between the intersection point and the preset warning line; S413: Issue a warning when the warning distance is less than the preset reference distance; accumulate the disappearance time when the finished product feature is not included in the working image; S4140: If the disappearance time is not greater than the preset production time, then continue the detection; S4141: If the disappearance time is greater than the preset production time, an alarm will be issued to prompt the order-receiving user to display the product and collect the display image, and record the detection time; S415: Determine the image change area based on the displayed image and the working image before the cumulative disappearance time; S416: Collect the number of finished product features in the image change area and define it as the new addition number. Calculate the addition efficiency based on the new addition number and the detection time. S4170: If the newly added efficiency is greater than the preset production efficiency, an alarm will be issued and recorded; S4171: If the newly added efficiency is not greater than the preset production efficiency, a prompt will be given and the number of detections will be recorded. If the recorded number of detections is greater than the preset maximum number of detections or if cheating is detected, the task will be stopped and the user will be prompted to stop the task. The method further includes an occlusion detection method between steps S410 and S411, the occlusion detection method comprising: S4100: Based on the working image, select the arm features in the image and define them as the detection image; S4101: Determine the area of ​​the selected box based on the detected image; S4102: When the selected area reaches the preset alert area, determine the occlusion position of the detection image in the working image; determine the gap image in the working image based on the occlusion position; determine the avoidance vector based on the gap image; determine the preset rotation angle of the camera device (4) based on the avoidance vector. S4103: Perform evasion movement according to the evasion vector, rotate according to the rotation angle, and update the working image; S4104: If the selected area of ​​the updated working image is the same as the selected area of ​​the original image, then the preset auxiliary monitoring method shall be used for monitoring. The auxiliary supervision method includes: S41040: Based on the gap image, a segmented region is obtained by segmenting it with a preset dividing line. The segmented region is the area formed after the gap region is divided by the dividing line. By identifying the shortest vertical distance that can be segmented in the gap region and connecting them, two regions are segmented, which are the segmented regions. The segmented region with the larger area is selected from the segmented regions and defined as the rotation region. The rotation angle is determined according to the rotation region. S41041: Determine the preset movable reflector (1) based on the rotation angle, and determine the reflection angle of the movable reflector (1) based on the preset reflection position and the processing image; S41042: Control the rotation of the active reflector (1) based on the reflection angle, and update the working image according to the rotation angle.

2. The task scheduling method based on a gig economy system according to claim 1, characterized in that, Step S40 is followed by a verification method, which includes: S400: Acquire a comparison image on a preset comparison area in the working image; S401: Determine the contrast color difference based on the contrast image and the preset benchmark contrast features; S403: When the contrast color difference is less than the preset maximum contrast color difference value, a replacement command is issued; S404: Based on the replacement instruction, the preset protective film (4) is replaced and the working image is updated.

3. The task scheduling method based on a gig economy system according to claim 2, characterized in that, Step S404 is followed by a cleaning method, which includes: S405: When replacing, output a cleaning command, and in response to the cleaning command, control the preset light-emitting device to irradiate the protective film (4) and collect the light intensity transmitted through the protective film (4); S406: Select the cleaning area based on the light intensity and the preset light transmittance, and determine the cleaning intensity according to the light intensity; S407: Based on the cleaning area, the preset cleaning device is aligned with the cleaning area, and the protective film (4) is cleaned according to the cleaning intensity, and the light-emitting device is controlled to emit light; S408: Stop cleaning when the light intensity is not less than the preset light transmittance.

4. The task scheduling method based on a gig economy system according to claim 1, characterized in that, It also includes a blowing removal method, the blowing removal method comprising: S50: Based on preset coverage features, the coverage area selected from the working image is defined as the coverage image, and a removal instruction is output; S51: Based on the removal instruction, retrieve historical working images within a preset unit time period, select the magnified images based on the historical working images, and match the covering material according to the magnified images; S52: Match the blowing intensity based on the covering material; S53: Control the preset blowing device to blow up the covering material according to the blowing intensity and update the working image; S54: Issue an alarm when the working image remains unchanged.

5. A task scheduling method based on a gig economy system according to claim 1, characterized in that, If the production score in step S45 is not lower than the preset requirement score, the method further includes an efficiency improvement method, which includes: S440: Based on the processing time, determine the completion time of the finished product feature in the working image and the appearance time of the preset blank feature in the working image, and calculate the user's stage efficiency based on the completion time and the appearance time. S441: When a processing feature is detected, the number of detections is recorded. When the number of detections reaches a preset baseline number, the initial time point and the completion time point of each processing time are determined, and the stage efficiency of each processing time when processing different processing features is calculated to determine the stage with the highest efficiency. S4420: If the highest efficiency stages are different, extract the working images of each highest efficiency stage and record the common features in the working images; S44200: If common features appear in the remaining working images, check their sorting order. If the sorting order is higher than that of the working images that do not have common features, record it as a useful feature. S44201: Determine a simulation method based on the aforementioned beneficial features and output simulation instructions; S44202: Based on the simulation instruction, prompt the customer to simulate the production environment using the simulation method; S4421: If the stages with the highest production efficiency are the same, then the task station is determined according to the highest production efficiency; S44210: Based on the task station notification, the order-receiving user arrives at the task station; monitoring is performed after the order-receiving user arrives at the task station.

6. A task scheduling system based on a gig economy system, characterized in that, include: The acquisition module is used to acquire task publishing instructions, acceptance instructions, and identity information; A memory for storing a task scheduling method based on a gig economy system as described in any one of claims 1 to 5; A processor is used to load, execute, and implement programs stored in memory.

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