Smart factory data monitoring and prediction analysis system and method based on cloud platform

Abstract

The invention provides a smart factory data monitoring and prediction analysis system and method based on a cloud platform. The system comprises the following steps: assisting a user to decide a target management task online; wherein the target management task is a task for performing data monitoring and prediction analysis on a factory; issuing the target management task to a cloud platform for execution; wherein the cloud platform performs data monitoring butt joint with a factory in advance; and obtaining an execution result of the cloud platform and returning the execution result to the user. According to the smart factory data monitoring and prediction analysis system and method based on the cloud platform, when a user decides a target management task, the system assists the target management task, the task decision efficiency of the user is greatly improved, secondly, after the decision of the target management task is completed, the target management task is issued to the cloud platform to be executed, and the user experience is improved. The cloud platform can be used by the user anytime and anywhere, the convenience is high, the cloud platform is a service provider, operation and maintenance are not needed, and the operation and maintenance cost of a factory is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud platforms, and particularly to a smart factory data monitoring and predictive analysis system and method based on a cloud platform. Background Art

[0002] Currently, in order to ensure the stable operation of a factory, it is necessary to conduct data monitoring and predictive analysis on it, monitor various operating data of the factory, and predict and analyze possible abnormal situations in the future based on this data. However, most of the current data monitoring and predictive analysis tasks in factories are decided by on-site management personnel according to the actual situation.

[0003] However, the factory area is large, the on-site situation is complex, the number of tasks that management personnel need to handle is huge, and there is a lack of effective task decision-making assistance tools, resulting in low decision-making efficiency. In addition, existing local monitoring and predictive analysis software is usually only used in a local environment or a specific location, lacking convenience, and requires continuous operation and maintenance, resulting in relatively high operation and maintenance costs.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] One of the objectives of the present invention is to provide a smart factory data monitoring and predictive analysis system based on a cloud platform. When a user manages a task with a decision-making goal, the system assists the user, greatly improving the user's task decision-making efficiency. Secondly, after the decision-making of the goal management task is completed, it is sent to the cloud platform for execution. The cloud platform can be used by the user anytime and anywhere, with high convenience, and the cloud platform is the service provider and does not require operation and maintenance, reducing the operation and maintenance costs of the factory.

[0006] The smart factory data monitoring and predictive analysis system based on a cloud platform provided by an embodiment of the present invention includes:

[0007] A decision-making assistance module, which is used to assist the user in making decisions on goal management tasks online; wherein, the goal management task is a task of monitoring and predicting and analyzing data of the factory;

[0008] A task distribution module, which is used to send the goal management task to the cloud platform for execution; wherein, the cloud platform is pre-connected to the factory for data monitoring;

[0009] A result return module, which is used to obtain the execution result of the cloud platform and return it to the user.

[0010] Optionally, the decision-making assistance module assisting the user in making decisions on goal management tasks online includes:

[0011] When the user enters the factory and has made a decision on the historical management task, and the historical management task that the user has decided meets the first auxiliary trigger condition, obtain the movement trajectory generated by the user's movement in the factory from the moment when the user first started to make a decision on the historical management task to the current moment;

[0012] Determine the operating equipment that meets the second auxiliary trigger condition with the movement trajectory and meets the third auxiliary trigger condition with the historical management task that the user has decided from the indoor map of the factory;

[0013] Display the management task list of the operating equipment to the user;

[0014] Receive the target management task selected by the user from the management task list;

[0015] Among them, the first auxiliary trigger condition includes:

[0016] The historical management task that the user has decided is unique;

[0017] Or, there is a task association set in the historical management task that the user has decided; at least two historical management tasks are included in the same task association set, and there is the same standard intention association relationship between the management intentions represented by the historical management tasks included in the same task association set;

[0018] Among them, the second auxiliary trigger condition includes:

[0019] The site location of the operating equipment falls within the intended management area represented by the movement trajectory in the indoor map;

[0020] The third auxiliary trigger condition:

[0021] If the historical management task that the user has decided is unique, the operating equipment needs to belong to the management object indicated by the management intention represented by the historical management task that the user has decided; otherwise, the operating equipment needs to belong to the management object indicated by the management intention represented by any historical management task in the task association set.

[0022] Optionally, the steps for determining the intended management area represented by the movement trajectory are as follows:

[0023] Based on the trajectory prediction model, according to the movement trajectory, predict the user's predicted movement trajectory within a preset future time in the indoor map; among them, the trajectory prediction model is obtained by training a neural network with a large number of historical movement trajectories generated by managers' movement during equipment management decision-making in the factory until convergence;

[0024] If the predicted movement trajectory and the movement trajectory form a return trajectory, use the equipment area passed by the return trajectory in the indoor map as the intended management area represented by the movement trajectory; otherwise, use the equipment area passed by the predicted movement trajectory as the intended management area represented by the movement trajectory.

[0025] Optionally, the intelligent factory data monitoring and predictive analysis system based on the cloud platform further includes:

[0026] An MR interaction module, configured to:

[0027] Analyze the execution result to determine multiple result items;

[0028] When the running devices related to at least one result item in the factory remain within the user's real-time MR field of view for a duration exceeding the duration threshold, divide each result item into multiple result item sets based on the result item division constraint;

[0029] Based on the recommended MR field of view planning constraint, plan the recommended MR field of view for each result item set;

[0030] Simultaneously guide the user to maintain the recommended MR field of view for each result item set;

[0031] When the user maintains any recommended MR field of view, based on the associated display constraint, all result items in the result item set of the recommended MR field of view maintained by the user are associated and displayed within the recommended MR field of view maintained by the user;

[0032] Wherein, the result item division constraint includes:

[0033] The result type set of all result items in the same result item set matches the standard result type set;

[0034] Wherein, the recommended MR field of view planning constraint includes:

[0035] Each recommended MR field of view completely includes the running devices related to all result items in the same result item set and the associated display space corresponding to the completely included running devices;

[0036] Wherein, the associated display constraint includes:

[0037] All result items in the result item set of the recommended MR field of view maintained by the user are respectively set in the associated display space corresponding to the relevant running devices in the recommended MR field of view maintained by the user.

[0038] Optionally, the intelligent factory data monitoring and predictive analysis system based on the cloud platform further includes

[0039] An access support module, configured to support the user to access the cloud platform.

[0040] A method for intelligent factory data monitoring and predictive analysis based on the cloud platform provided by an embodiment of the present invention includes:

[0041] Online assist the user in the decision-making target management task; wherein, the target management task is the task of data monitoring and predictive analysis of the factory.

[0042] Send the target management task to the cloud platform for execution; among them, the cloud platform is pre-connected to the factory for data monitoring.

[0043] Obtain the execution result of the cloud platform and return it to the user.

[0044] Optionally, the online assisted user decision-making target management task includes:

[0045] When the user has made a decision on the historical management task after entering the factory and the historical management tasks decided by the user meet the first auxiliary trigger condition, obtain the movement trajectory generated by the user's movement in the factory from the moment when the user first started to make a decision on the historical management task to the current moment;

[0046] Determine the operating equipment that meets the second auxiliary trigger condition with the movement trajectory and meets the third auxiliary trigger condition with the historical management tasks decided by the user from the indoor map of the factory;

[0047] Display the management task list of the operating equipment to the user;

[0048] Receive the target management task selected by the user from the management task list;

[0049] Among them, the first auxiliary trigger condition includes:

[0050] The historical management tasks decided by the user are unique;

[0051] Or, there is a task association set in the historical management tasks decided by the user; at least two historical management tasks are included in the same task association set, and there is the same standard intention association relationship between the management intentions represented by the historical management tasks included in the same task association set;

[0052] Among them, the second auxiliary trigger condition includes:

[0053] The site location of the operating equipment falls within the intended management area represented by the movement trajectory in the indoor map;

[0054] The third auxiliary trigger condition:

[0055] If the historical management tasks decided by the user are unique, the operating equipment needs to belong to the management object indicated by the management intention represented by the historical management tasks decided by the user; otherwise, the operating equipment needs to belong to the management object indicated by the management intention represented by any historical management task in the task association set.

[0056] Optionally, the steps for determining the intended management area represented by the movement trajectory are as follows:

[0057] Based on the trajectory prediction model, the user's predicted movement trajectory within a preset time in the future is predicted in the indoor map according to the movement trajectory; wherein the trajectory prediction model is obtained by training the neural network until convergence using a large number of historical movement trajectories generated by managers' movement when making equipment management decisions in the factory in the past;

[0058] If the predicted moving trajectory and the moving trajectory form a return trajectory, the device area passed by the return trajectory in the indoor map is used as the intended management area represented by the moving trajectory; otherwise, the device area passed by the predicted moving trajectory is used as the intended management area represented by the moving trajectory.

[0059] Optional cloud-based smart factory data monitoring and predictive analysis methods also include:

[0060] Analyze the execution results and determine multiple result items;

[0061] When the time duration for which the relevant operating equipment of at least one result item in the factory remains in the real-time MR field of view of the user exceeds a time threshold, each result item is divided into a plurality of result item sets based on a result item division constraint;

[0062] Based on the recommended MR field of view planning constraints, plan the recommended MR field of view for each result item set;

[0063] At the same time, the user is guided to maintain the recommended MR field of view for each result item set;

[0064] When the user retains any recommended MR field of view, all result items in the result item set of the recommended MR field of view retained by the user are associated and displayed within the recommended MR field of view retained by the user based on the associated display constraint;

[0065] The result item partitioning constraints include:

[0066] The result type set of all result items in the same result item set matches the standard result type set;

[0067] Among them, the recommended MR field of view planning constraints include:

[0068] Each recommended MR field of view completely includes the relevant operating devices of all result items in the same result item set and the associated display space corresponding to the completely included relevant operating devices;

[0069] The associated display constraints include:

[0070] All result items in the result item set of the recommended MR field of view maintained by the user are respectively set in the associated display space corresponding to the corresponding related operating device in the recommended MR field of view maintained by the user.

[0071] Optional, cloud-based smart factory data monitoring and predictive analysis methods also include

[0072] Supports users to access the cloud platform.

[0073] Other features and advantages of the present invention will be described in the subsequent specification, and in part will become apparent from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structure specifically pointed out in the written specification and the accompanying drawings.

[0074] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0075] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0076] Figure 1 is a schematic diagram of a smart factory data monitoring and predictive analysis system based on a cloud platform in an embodiment of the present invention;

[0077] Figure 2 is a schematic diagram of a smart factory data monitoring and predictive analysis method based on a cloud platform in an embodiment of the present invention. Detailed Embodiments

[0078] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0079] The embodiment of the present invention provides a smart factory data monitoring and predictive analysis system based on a cloud platform, as Figure 1 shown, including:

[0080] A decision-making assistance module 1, which is used to assist users in making decisions on target management tasks online; wherein, the target management task is a task of data monitoring and predictive analysis for a factory;

[0081] A task distribution module 2, which is used to distribute the target management task to the cloud platform for execution; wherein, the cloud platform is pre-connected to the factory for data monitoring docking;

[0082] A result return module 3, which is used to obtain the execution result of the cloud platform and return it to the user.

[0083] In the above technical solution, the user is a management staff of the factory; the cloud platform is a cloud platform that provides data monitoring and predictive analysis services for the factory, and it has been pre-connected with the project for data monitoring; when the user makes a decision on the target management task, the system assists in it. After the decision-making of the target management task is completed, it is sent to the cloud platform for execution. The cloud platform can obtain the relevant operation data of the factory, realize monitoring, and perform predictive analysis on the obtained data to determine the possible abnormal situations that may occur in the factory in the future and return them to the user as the execution result; the cloud platform can use artificial intelligence technology, big data analysis technology, etc. to provide data monitoring and predictive analysis services for the factory.

[0084] When the user of this application makes a decision on the target management task, the system assists in it, which greatly improves the task decision-making efficiency of the user. Secondly, after the decision-making of the target management task is completed, it is sent to the cloud platform for execution. The cloud platform can be used by the user anytime and anywhere, with high convenience. Moreover, the cloud platform is the service provider and does not require operation and maintenance, reducing the operation and maintenance costs of the factory.

[0085] In one embodiment, the decision-making assistance module online assists the user in making a decision on the target management task, including:

[0086] When the user has made a decision on the historical management task after entering the factory and the historical management task decided by the user meets the first auxiliary trigger condition, obtain the movement trajectory generated by the user's movement in the factory from the moment when the user first starts to make a decision on the historical management task to the current moment;

[0087] Determine the operating equipment that meets the second auxiliary trigger condition with the movement trajectory and meets the third auxiliary trigger condition with the historical management task decided by the user from the indoor map of the factory;

[0088] Display the management task list of the operating equipment to the user;

[0089] Receive the target management task selected by the user from the management task list;

[0090] Among them, the first auxiliary trigger condition includes:

[0091] The historical management task decided by the user is unique;

[0092] Or, there is a task association set in the historical management tasks decided by the user; at least two historical management tasks are included in the same task association set, and the management intentions represented by the historical management tasks included in the same task association set have the same standard intention association relationship pairwise;

[0093] Among them, the second auxiliary trigger condition includes:

[0094] The site location of the operating equipment falls within the intended management area represented by the movement trajectory in the indoor map;

[0095] Third auxiliary trigger condition:

[0096] If the historical management tasks that the user has decided on are unique, the operating device needs to belong to the management object indicated by the management intention represented by the historical management tasks that the user has decided on; otherwise, the operating device needs to belong to the management object indicated by the management intention represented by any historical management task in the task association set.

[0097] In the above technical solution, if we want to assist the user in making decisions on target management tasks, we need to accurately determine the user's management intention; when the user enters the factory, there are two types of information reflecting their management intention. The first is the work content they have completed, and the second is their movement trajectory within the factory; and this first type of information can also directly reflect whether the user is in the factory for the purpose of making decisions on target management tasks. Therefore, a first auxiliary trigger condition is set to determine whether the historical management tasks that the user has decided on (which can be completed independently or with the assistance of the system) can reflect the user's management intention; when the user has decided on historical management tasks after entering the factory and the historical management tasks that the user has decided on meet the first auxiliary trigger condition, it means that assistance to the user can begin and subsequent operations are triggered. Specifically, in the first auxiliary trigger condition, when the historical management tasks that the user has decided on are unique, it means that the user has only decided on and completed one historical management task, which can directly reflect the user's management intention alone; or, when the historical management tasks that the user has decided on are multiple, if there is a task association set among them that can directly reflect the user's management intention, it also means that the historical management tasks that the user has decided on can reflect the user's management intention; in order for the task association set to achieve this reflection function, for historical management tasks that can represent management intentions (for example: if the historical management task is to perform data monitoring and predictive analysis on a certain device of type A, the represented management intention is to manage other devices of type A), if there is the same standard intention association relationship (the standard intention association relationship can be that the management objects are the same or overlapping, the absolute value of the management time difference does not exceed 10 minutes, the management levels are the same, etc.) between the management intentions represented by different historical management tasks, it means that at least these two historical management tasks can be used to reflect the same management intention of the user. Therefore, it is restricted that at least two historical management tasks are included in the same task association set, and the management intentions represented by the historical management tasks included in the same task association set have the same standard intention association relationship pairwise.

[0098] When performing subsequent operations, it is necessary to comprehensively determine the user's management intention by combining the above first type of information and the second type of information. First, obtain the movement trajectory generated by the user's movement in the factory from the moment when the user first starts to make decisions on historical management tasks (the first start of decision-making is their independent decision-making) to the current moment. The acquisition of the movement trajectory can be realized based on the positioning function on the intelligent terminal carried by the user; set the second auxiliary trigger condition and the third auxiliary trigger condition. When the operating device meets the second auxiliary trigger condition with the movement trajectory in the indoor map of the factory (the indoor map can be obtained by pre-measuring the map) and meets the third auxiliary trigger condition with the historical management tasks that the user has already decided, it indicates that it is very likely the object that the user wants to make management decisions on. Display the management task list of the operating device (there are pre-set tasks in the management task list that can perform different types of data monitoring and predictive analysis on the operating device) to the user for selection, that is, the assistance is completed. Specifically, in the second auxiliary trigger condition, the movement trajectory will characterize the intended management area, and the intended management area is the equipment area that the user wants to manage. If the operating device is within this area, it means that the operating device is the object that the user wants to make management decisions on. Secondly, in the third auxiliary trigger condition, if the historical management tasks that the user has already decided are unique, the operating device needs to belong to the management object indicated by the management intention characterized by it (the management intention includes managing the device and indicates the management object). Otherwise, the operating device needs to belong to the management object indicated by the management intention characterized by any historical management task in the task association set, which further indicates that the operating device is the object that the user wants to make management decisions on.

[0099] In the embodiment of the present invention, after the user enters the factory, there is no need to input an auxiliary request, let alone input relevant auxiliary requirements. The system can automatically determine the auxiliary timing and implement assistance to the user, realizing non-intrusive assistance. The user only needs to perform their own management work, which improves the user experience. Secondly, the first auxiliary trigger condition is introduced to trigger subsequent operations, reducing the auxiliary resources of the system and improving the auxiliary efficiency of the system. The second auxiliary trigger condition and the third auxiliary trigger condition are introduced to determine the operating device that the user wants to manage, display the management task list of the operating device to the user, and receive the target management task selected by the user from the management task list, greatly improving the accuracy and comprehensiveness of determining the device that the user wants to manage, and further improving the decision-making efficiency of the user.

[0100] In one embodiment, the steps for determining the intended management area characterized by the movement trajectory are as follows:

[0101] Based on the trajectory prediction model, according to the movement trajectory, predict the predicted movement trajectory of the user within a preset future time in the indoor map; among them, the trajectory prediction model is obtained by training the neural network with a large number of historical movement trajectories generated by managers' historical equipment management decisions and movements in the factory until convergence;

[0102] If the predicted movement trajectory and the movement trajectory form a return trajectory, the equipment areas passed by the return trajectory in the indoor map are used as the intention management areas represented by the movement trajectory; otherwise, the equipment areas passed by the predicted movement trajectory are used as the intention management areas represented by the movement trajectory.

[0103] In the above technical solution, by using the historical movement trajectories generated by a large number of managers moving during equipment management decisions in the factory in the past to train the neural network, the trajectory prediction model obtained after training convergence can predict the future movement trajectory of the user based on the movement trajectory. The preset time can be 5 minutes. The user is about to make a decision on a new target management task. Therefore, predicting only the predicted movement trajectory in the short term in the future is sufficient for determining the management intention. The predicted movement trajectory and the movement trajectory forming a return trajectory means that there is a route indicating the user's return movement after the predicted movement trajectory and the movement trajectory are connected; and the user's return movement indicates that the user will go to a certain area in the future and will also return from that area along the original route, indicating that this area must be the user's intention management area. Therefore, the equipment areas passed by the return trajectory in the indoor map are used as the intention management areas represented by the movement trajectory; after determining the intention management area in this case and determining the operating equipment, the user only needs to select the target management task from the management task list of the operating equipment, without having to go to or only having to go to a part of the area that still needs to be returned from in the future, improving the user experience and its management efficiency. Otherwise, it means that no return trajectory is formed, indicating that all the areas the user will go to in the future may be intention management areas, so the equipment areas passed by the predicted movement trajectory are used as the intention management areas represented by the movement trajectory.

[0104] The embodiment of the present invention makes a short-term prediction of the user's future movement trajectory, which not only meets the technical requirement that the user is about to make a decision on a new target management task, but also enables the short-term predicted movement trajectory with higher prediction accuracy to be used to more accurately determine the user's intention management area. Secondly, for the special case where the predicted movement trajectory and the movement trajectory form a return trajectory, a corresponding intention management area determination scheme is set, which greatly improves the working efficiency of the system and the applicability of the system.

[0105] In one embodiment, the intelligent factory data monitoring and prediction analysis system based on the cloud platform further includes:

[0106] The MR interaction module is used for:

[0107] Analyze the execution result and determine multiple result items;

[0108] When the running devices associated with at least one result item in the factory remain within the user's real-time MR field of view for a duration exceeding the duration threshold, divide each result item into multiple result item sets based on the result item division constraint;

[0109] Based on the recommended MR field of view planning constraint, plan the recommended MR field of view for each result item set;

[0110] At the same time, guide the user to maintain the recommended MR field of view for each result item set;

[0111] When the user maintains any recommended MR field of view, based on the associated display constraint, all result items in the result item set of the recommended MR field of view maintained by the user are associated and displayed within the recommended MR field of view maintained by the user;

[0112] Among them, the result item division constraint includes:

[0113] The result type set of all result items in the same result item set matches the standard result type set;

[0114] Among them, the recommended MR field of view planning constraint includes:

[0115] Each recommended MR field of view completely includes the running devices associated with all result items in the same result item set and the associated display space corresponding to the completely included running devices;

[0116] Among them, the associated display constraint includes:

[0117] All result items in the result item set of the recommended MR field of view maintained by the user are respectively set within the associated display space corresponding to the running devices associated with the recommended MR field of view maintained by the user.

[0118] In the above technical solution, the execution result includes multiple result items, and each result item has a related operating device. For example, if the result item is that device B will have a fault in the future, the related operating device is device B. The user wears MR (Mixed Reality) glasses in the factory, and the real-time MR vision is the vision of the glasses when using the MR glasses to view the factory, which can be obtained by docking with the MR glasses. The duration threshold can be 10 seconds. When the duration that the related operating device of at least one result item in the factory remains within the user's real-time MR vision exceeds the duration threshold, it indicates that the user wants to view the historical monitoring prediction analysis situation during the process of continuing decision-making management. The user only needs to stand still and can view it in the MR glasses without having to return to the vicinity of the related operating device. However, since the execution result contains multiple result items, if all of them are output in the MR glasses worn by the user, it may cause the user to feel confused and not know where to start. Therefore, based on the result item division constraint, each result item is divided into multiple result item sets, and based on the recommended MR vision planning constraint, the recommended MR vision of each result item set is planned. At the same time, the user is guided to maintain the recommended MR vision of each result item set (when guiding, the corresponding guiding animation can be output in the MR glasses). The user can choose to enter any recommended MR vision. When the user maintains any recommended MR vision, based on the associated display constraint, all the result items in the result item set of the recommended MR vision maintained by the user are associated and displayed within the recommended MR vision maintained by the user; realizing the recommendation of the MR vision to the user and displaying the result items batch by batch and specifically. Secondly, the current operating information of the related operating device and the like can also be displayed in the associated display space.

[0119] In the result item division constraint, the standard result type set contains multiple result item types that have joint work assistance value for the user. For example, the types of multiple result items with fault causality, the types of result items of devices within the user's maintenance ability range, etc. In the recommended MR vision planning constraint, it is ensured that each recommended MR vision completely contains the related operating devices of all the result items in the same result item set, and also completely contains the associated display space corresponding to the related operating devices it contains (the associated display space is a display space for displaying result items, and its size can be preset by technicians). In the associated display constraint, all the result items in the result item set of the recommended MR vision maintained by the user are respectively set in the associated display space corresponding to the related operating devices in the recommended MR vision maintained by the user. Thus, every time the user enters a recommended MR vision, not only can the user view the content items that have joint work assistance value for him / her, but also can view its related devices, so as to carry out targeted management work.

[0120] In the embodiments of the present invention, by dividing the result items into multiple result item sets and planning the recommended MR fields of view for each result item set, information overload can be avoided, helping users manage and process different types of prediction results more efficiently. Without having to shuttle around the factory or return to the equipment, users only need to stand in place and use the MR glasses to view relevant information in the recommended MR fields of view, enabling users to focus on the information being viewed currently, saving users' time and energy, improving users' decision-making efficiency, and allowing users to view information such as future fault predictions of equipment in real time, so that preventive measures can be taken faster and the production process of the factory can be optimized.

[0121] In one embodiment, the intelligent factory data monitoring and predictive analysis system based on the cloud platform further includes

[0122] an access support module for supporting users to access the cloud platform.

[0123] In the above technical solution, users can also access the cloud platform to perform historical target management tasks and retrieve historical execution results, etc.

[0124] The embodiments of the present invention provide an intelligent factory data monitoring and predictive analysis method based on the cloud platform, as Figure 2 shown, including:

[0125] S1. Online assist users in decision-making target management tasks; wherein, the target management task is a task of performing data monitoring and predictive analysis on the factory;

[0126] S2. Send the target management task to the cloud platform for execution; wherein, the cloud platform is pre-connected to the factory for data monitoring;

[0127] S3. Obtain the execution result of the cloud platform and return it to the user.

[0128] The online assisting users in decision-making target management tasks includes:

[0129] When the user has made a decision on historical management tasks after entering the factory and the historical management tasks decided by the user meet the first auxiliary trigger condition, obtain the movement trajectory generated by the user's movement in the factory between the moment when the user first started making decisions on historical management tasks and the current moment;

[0130] Determine the operating equipment that meets the second auxiliary trigger condition with the movement trajectory and meets the third auxiliary trigger condition with the historical management tasks decided by the user from the indoor map of the factory;

[0131] Display the management task list of the operating equipment to the user;

[0132] Receive the target management task selected by the user from the management task list;

[0133] Among them, the first auxiliary trigger condition includes:

[0134] The historical management tasks decided by the user are unique;

[0135] Or, there is a task association set among the historical management tasks decided by the user; at least two historical management tasks are included in the same task association set, and there is the same standard intention association relationship between the management intentions represented by the historical management tasks included in the same task association set;

[0136] Among them, the second auxiliary trigger condition includes:

[0137] The site location of the operating device falls within the intention management area represented by the movement trajectory in the indoor map;

[0138] The third auxiliary trigger condition:

[0139] If the historical management tasks decided by the user are unique, the operating device needs to belong to the management object indicated by the management intention represented by the historical management tasks decided by the user; otherwise, the operating device needs to belong to the management object indicated by the management intention of any historical management task in the task association set.

[0140] The steps for determining the intention management area represented by the movement trajectory are as follows:

[0141] Based on the trajectory prediction model, according to the movement trajectory, predict the predicted movement trajectory of the user within a preset future time in the indoor map; among them, the trajectory prediction model is obtained by training a neural network with a large number of historical movement trajectories generated by managers during equipment management decision-making in the factory until convergence;

[0142] If the predicted movement trajectory and the movement trajectory form a return trajectory, the equipment area passed by the return trajectory in the indoor map is used as the intention management area represented by the movement trajectory; otherwise, the equipment area passed by the predicted movement trajectory is used as the intention management area represented by the movement trajectory.

[0143] The intelligent factory data monitoring and prediction analysis method based on the cloud platform further includes:

[0144] Analyze the execution results to determine multiple result items;

[0145] When the duration for which at least one result item's related operating device remains within the user's real-time MR field of view in the factory exceeds the duration threshold, based on the result item division constraint, divide each result item into multiple result item sets;

[0146] Based on the recommended MR field of view planning constraint, plan the recommended MR field of view for each result item set;

[0147] At the same time, guide the user to maintain the recommended MR field of view for each result item set;

[0148] When the user maintains any recommended MR view, based on the associated display constraint, all result items in the result item set of the recommended MR view maintained by the user are associated and displayed within the recommended MR view maintained by the user;

[0149] Among them, the result item division constraint includes:

[0150] The result type set of all result items in the same result item set matches the standard result type set;

[0151] Among them, the recommended MR view planning constraint includes:

[0152] Each recommended MR view completely includes the relevant operating devices of all result items in the same result item set and the associated display space corresponding to the completely included relevant operating devices;

[0153] Among them, the associated display constraint includes:

[0154] All result items in the result item set of the recommended MR view maintained by the user are respectively set in the associated display space corresponding to the relevant operating devices in the recommended MR view maintained by the user.

[0155] The intelligent factory data monitoring and predictive analysis method based on the cloud platform further includes

[0156] Supporting the user to access the cloud platform.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A cloud platform-based smart factory data monitoring and prediction analysis system, characterized in that: include: The decision-making support module is used to assist users in making online decisions on target management tasks; the target management task is to monitor and predict the data of the factory; The task delivery module is used to deliver the target management tasks to the cloud platform for execution; the cloud platform is pre-connected with the factory for data monitoring; The result return module is used to obtain the execution results of the cloud platform and return them to the user.

2. The cloud platform-based smart factory data monitoring and prediction analysis system according to claim 1, characterized in that: The decision support module assists users in making decision-making target management tasks online, including: When the user has decided on a historical management task after entering the factory and the historical management task decided by the user meets the first auxiliary trigger condition, the movement trajectory of the user in the factory between the time when the user first started to decide on the historical management task and the current time is obtained; Determine, from the indoor map of the factory, an operating device that meets the second auxiliary trigger condition with respect to the movement trajectory and meets the third auxiliary trigger condition with respect to the historical management task decided by the user; Displays a management task list of running equipment to the user; Receive the target management task selected by the user from the management task list; The first auxiliary triggering condition includes: The historical management tasks decided by the user are unique; Or, there is a task association set in the historical management tasks that the user has decided; the same task association set contains at least two historical management tasks, and the management intentions represented by the historical management tasks contained in the same task association set have the same standard intention association relationship between each other; The second auxiliary trigger condition includes: The location of the operating equipment falls within the intended management area represented by the movement trajectory in the indoor map; The third auxiliary trigger condition: If the historical management task decided by the user is unique, the running device must belong to the management object indicated by the management intent represented by the historical management task decided by the user; otherwise, the running device must belong to the management object indicated by the management intent represented by any historical management task in the task association set.

3. The cloud platform-based smart factory data monitoring and prediction analysis system according to claim 2, characterized in that: The steps for determining the intended management area represented by the movement trajectory are as follows: Based on the trajectory prediction model, the user's predicted movement trajectory within a preset time in the future is predicted in the indoor map according to the movement trajectory; wherein the trajectory prediction model is obtained by training the neural network until convergence using a large number of historical movement trajectories generated by managers' movement when making equipment management decisions in the factory in the past; If the predicted moving trajectory and the moving trajectory form a return trajectory, the device area passed by the return trajectory in the indoor map is used as the intended management area represented by the moving trajectory; otherwise, the device area passed by the predicted moving trajectory is used as the intended management area represented by the moving trajectory.

4. The cloud platform-based smart factory data monitoring and prediction analysis system according to claim 1, characterized in that: Also includes: MR interaction module, used for: Analyze the execution results and determine multiple result items; When the time duration for which the relevant operating equipment of at least one result item in the factory remains in the real-time MR field of view of the user exceeds a time threshold, each result item is divided into a plurality of result item sets based on a result item division constraint; Based on the recommended MR field of view planning constraints, plan the recommended MR field of view for each result item set; At the same time, the user is guided to maintain the recommended MR field of view for each result item set; When the user retains any recommended MR field of view, all result items in the result item set of the recommended MR field of view retained by the user are associated and displayed within the recommended MR field of view retained by the user based on the associated display constraint; The result item partitioning constraints include: The result type set of all result items in the same result item set matches the standard result type set; Among them, the recommended MR field of view planning constraints include: Each recommended MR field of view completely includes the relevant operating devices of all result items in the same result item set and the associated display space corresponding to the completely included relevant operating devices; The associated display constraints include: All result items in the result item set of the recommended MR field of view maintained by the user are respectively set in the associated display space corresponding to the corresponding related operating device in the recommended MR field of view maintained by the user.

5. The cloud platform-based smart factory data monitoring and prediction analysis system according to claim 1, characterized in that: Also includes The access support module is used to support users to access the cloud platform.

6. A cloud platform-based smart factory data monitoring and prediction analysis method, characterized in that: include: Online assistance for users to make decisions on target management tasks; target management tasks are tasks for data monitoring and predictive analysis of factories; Send the target management tasks to the cloud platform for execution; the cloud platform will be pre-connected with the factory for data monitoring; Get the execution results of the cloud platform and return them to the user.

7. The cloud platform-based smart factory data monitoring and prediction analysis method according to claim 6, characterized in that: The online auxiliary user decision-making target management task includes: When the user has decided on a historical management task after entering the factory and the historical management task decided by the user meets the first auxiliary trigger condition, the movement trajectory of the user in the factory between the time when the user first started to decide on the historical management task and the current time is obtained; Determine, from the indoor map of the factory, an operating device that meets the second auxiliary trigger condition with respect to the movement trajectory and meets the third auxiliary trigger condition with respect to the historical management task decided by the user; Displays a management task list of running equipment to the user; Receive the target management task selected by the user from the management task list; The first auxiliary triggering condition includes: The historical management tasks decided by the user are unique; Or, there is a task association set in the historical management tasks that the user has decided; the same task association set contains at least two historical management tasks, and the management intentions represented by the historical management tasks contained in the same task association set have the same standard intention association relationship between each other; The second auxiliary trigger condition includes: The location of the operating equipment falls within the intended management area represented by the movement trajectory in the indoor map; The third auxiliary trigger condition: If the historical management task decided by the user is unique, the running device must belong to the management object indicated by the management intent represented by the historical management task decided by the user; otherwise, the running device must belong to the management object indicated by the management intent represented by any historical management task in the task association set.

8. The cloud platform-based smart factory data monitoring and prediction analysis method according to claim 7, characterized in that: The steps for determining the intended management area represented by the movement trajectory are as follows: Based on the trajectory prediction model, the user's predicted movement trajectory within a preset time in the future is predicted in the indoor map according to the movement trajectory; wherein the trajectory prediction model is obtained by training the neural network until convergence using a large number of historical movement trajectories generated by managers' movement when making equipment management decisions in the factory in the past; If the predicted moving trajectory and the moving trajectory form a return trajectory, the device area passed by the return trajectory in the indoor map is used as the intended management area represented by the moving trajectory; otherwise, the device area passed by the predicted moving trajectory is used as the intended management area represented by the moving trajectory.

9. The cloud platform-based smart factory data monitoring and prediction analysis method according to claim 6, characterized in that: Also includes: Analyze the execution results and determine multiple result items; When the time duration for which the relevant operating equipment of at least one result item in the factory remains in the real-time MR field of view of the user exceeds a time threshold, each result item is divided into a plurality of result item sets based on a result item division constraint; Based on the recommended MR field of view planning constraints, plan the recommended MR field of view for each result item set; At the same time, the user is guided to maintain the recommended MR field of view for each result item set; When the user retains any recommended MR field of view, all result items in the result item set of the recommended MR field of view retained by the user are associated and displayed within the recommended MR field of view retained by the user based on the associated display constraint; The result item partitioning constraints include: The result type set of all result items in the same result item set matches the standard result type set; Among them, the recommended MR field of view planning constraints include: Each recommended MR field of view completely includes the relevant operating devices of all result items in the same result item set and the associated display space corresponding to the completely included relevant operating devices; The associated display constraints include: All result items in the result item set of the recommended MR field of view maintained by the user are respectively set in the associated display space corresponding to the corresponding related operating device in the recommended MR field of view maintained by the user.

10. The cloud platform-based smart factory data monitoring and prediction analysis method according to claim 6, characterized in that: Also includes Support users to access the cloud platform.

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