Equipment information determination method, computer equipment and storage medium

Abstract

The invention relates to an equipment information determination method and device, computer equipment, a storage medium and a computer program product, and can be applied to the technical field of computers. The method comprises the following steps: acquiring operation information of operation on an operation object by operation equipment; according to the operation information, determining candidate equipment collaborative operation information; candidate device cooperative work information indicating a candidate cooperative work mode in which the work device performs cooperative work on the work object; predicting a predicted collaborative operation result corresponding to the candidate equipment collaborative operation information according to the simulated collaborative operation corresponding to the candidate equipment collaborative operation information; and screening out the collaborative operation information of the target equipment from the collaborative operation information of the candidate equipment according to the predicted collaborative operation result. By adopting the method, the efficiency of determining the equipment collaborative operation information can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer device, storage medium, and computer program product for determining device information. Background Technology

[0002] With the development of intelligent manufacturing and automation technologies, collaborative operations between equipment are playing an increasingly important role in industrial production. Therefore, how to efficiently determine information on collaborative operations between equipment has become an important research direction.

[0003] Traditional techniques typically involve manually analyzing equipment information to determine collaborative operation information. However, this method requires a significant amount of manual processing time, resulting in low efficiency in determining collaborative operation information. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer device, computer-readable storage medium, and computer program product for determining equipment information that can improve the efficiency of determining equipment collaborative operation information in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for determining device information. The method includes:

[0006] Obtain operation information on the work equipment performing operations on the work object;

[0007] Based on the job information, candidate equipment collaborative job information is determined; the candidate equipment collaborative job information represents the candidate collaborative job mode in which the job equipment performs collaborative work on the job object;

[0008] Based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information;

[0009] Based on the predicted collaborative operation results, the collaborative operation information of the target equipment is selected from the collaborative operation information of the candidate equipment.

[0010] In one embodiment, predicting the predicted collaborative operation result corresponding to the candidate device collaborative operation information based on the simulated collaborative operation corresponding to the candidate device collaborative operation information includes:

[0011] Using a digital twin model, simulated collaborative operations are performed corresponding to the collaborative operation information of the candidate devices to obtain the simulated collaborative operation results corresponding to the collaborative operation information of the candidate devices.

[0012] Based on the simulated collaborative operation results, the predicted collaborative operation results corresponding to the candidate device collaborative operation information are predicted.

[0013] In one embodiment, the operating equipment belongs to logistics sorting equipment; the logistics sorting equipment includes a circular sorting machine, a linear sorting machine, and / or an intelligent sorting cabinet;

[0014] The method further includes:

[0015] Obtain the logistics sorting equipment information of the operating equipment;

[0016] Based on the logistics sorting equipment information, a virtual logistics sorting equipment model of the operating equipment is generated;

[0017] The virtual logistics sorting equipment model is fused to obtain the digital twin model.

[0018] In one embodiment, determining the candidate device collaborative operation information based on the operation information includes:

[0019] Based on the job information, the job equipment and the job object are combined to obtain the combination method between the job equipment and the job object;

[0020] Based on the combination method, candidate collaborative operation methods for the working equipment to perform collaborative operations on the working object are determined, and these are used as candidate equipment collaborative operation information.

[0021] In one embodiment, determining the candidate collaborative operation mode for the working equipment to perform collaborative operation on the working object according to the combination mode includes:

[0022] Based on the job information, the combination method is subjected to genetic iteration processing to obtain alternative combination methods;

[0023] Based on the predicted collaborative operation time corresponding to the alternative combination methods, candidate combination methods are selected from the alternative combination methods;

[0024] Based on the candidate combination method, the candidate collaborative operation method is determined.

[0025] In one embodiment, the operating equipment is a logistics sorting equipment; the operating object is an object to be sorted.

[0026] The step of performing genetic iteration processing on the combination methods based on the job information to obtain alternative combination methods includes:

[0027] The constraints for the collaborative sorting of the objects to be sorted by the logistics sorting equipment are determined; the constraints include a first constraint, a second constraint, a third constraint, and / or a fourth constraint; the first constraint is a sorting constraint between the type of the operating equipment and the type of the operating object; the second constraint is a sorting constraint between different types of operating objects and the operating equipment; the third constraint is a timeliness constraint between operating objects of the same type; and the fourth constraint is a priority constraint between different types of operating objects.

[0028] Based on the job information, the constraints, and the type of the job object, the combination method is subjected to genetic iteration to obtain the alternative combination method.

[0029] In one embodiment, the step of filtering out target device collaborative operation information from the candidate device collaborative operation information based on the predicted collaborative operation result includes:

[0030] Based on the predicted collaborative operation results, the predicted collaborative operation time corresponding to the candidate device collaborative operation information is determined;

[0031] Based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, the candidate device collaborative operation information with the smallest predicted collaborative operation time is selected from the candidate device collaborative operation information and used as the target device collaborative operation information.

[0032] In one embodiment, after selecting the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, and using it as the target device collaborative operation information, the method further includes:

[0033] Obtain the current collaborative operation time corresponding to the current collaborative operation information of the operating equipment;

[0034] If the comparison value between the predicted collaborative operation time corresponding to the collaborative operation information of the target equipment and the current collaborative operation time is greater than a preset threshold, the collaborative operation information of the target equipment is applied to the collaborative operation of the operation equipment.

[0035] Secondly, this application also provides a device for determining device information. The device includes:

[0036] The information acquisition module is used to acquire operation information of the working equipment performing operations on the work object;

[0037] The information determination module is used to determine candidate equipment collaborative operation information based on the operation information; the candidate equipment collaborative operation information represents the candidate collaborative operation mode in which the operation equipment performs collaborative operation on the operation object;

[0038] The result prediction module is used to predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information based on the simulated collaborative operation corresponding to the candidate device collaborative operation information.

[0039] The information filtering module is used to filter out the target device collaborative operation information from the candidate device collaborative operation information based on the predicted collaborative operation results.

[0040] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0041] Obtain operation information on the work equipment performing operations on the work object;

[0042] Based on the job information, candidate equipment collaborative job information is determined; the candidate equipment collaborative job information represents the candidate collaborative job mode in which the job equipment performs collaborative work on the job object;

[0043] Based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information;

[0044] Based on the predicted collaborative operation results, the collaborative operation information of the target equipment is selected from the collaborative operation information of the candidate equipment.

[0045] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0046] Obtain operation information on the work equipment performing operations on the work object;

[0047] Based on the job information, candidate equipment collaborative job information is determined; the candidate equipment collaborative job information represents the candidate collaborative job mode in which the job equipment performs collaborative work on the job object;

[0048] Based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information;

[0049] Based on the predicted collaborative operation results, the collaborative operation information of the target equipment is selected from the collaborative operation information of the candidate equipment.

[0050] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0051] Obtain operation information on the work equipment performing operations on the work object;

[0052] Based on the job information, candidate equipment collaborative job information is determined; the candidate equipment collaborative job information represents the candidate collaborative job mode in which the job equipment performs collaborative work on the job object;

[0053] Based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information;

[0054] Based on the predicted collaborative operation results, the collaborative operation information of the target equipment is selected from the collaborative operation information of the candidate equipment.

[0055] The aforementioned equipment information determination method, apparatus, computer equipment, storage medium, and computer program product acquire operational information of a working device performing operations on a work object; determine candidate equipment collaborative operation information based on the operational information; the candidate equipment collaborative operation information represents candidate collaborative operation methods in which the working device performs collaborative operations on the work object; predict the predicted collaborative operation result corresponding to the candidate equipment collaborative operation information based on the simulated collaborative operation corresponding to the candidate equipment collaborative operation information; and filter out target equipment collaborative operation information from the candidate equipment collaborative operation information based on the predicted collaborative operation result. This scheme determines candidate equipment collaborative operation information based on the operational information of the working device performing operations on the work object, predicts the corresponding predicted collaborative operation result based on the simulated collaborative operation corresponding to the candidate equipment collaborative operation information, and filters out target equipment collaborative operation information based on the predicted collaborative operation result. Thus, when determining equipment information, by simulating different candidate collaborative operation methods and predicting the corresponding collaborative operation results, target equipment collaborative operation information is filtered out, automatically determining the most effective equipment collaborative operation information, thereby improving the efficiency and accuracy of determining equipment collaborative operation information. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating a method for determining device information in one embodiment;

[0058] Figure 2 This is a flowchart illustrating the steps for determining the predicted collaborative work results in one embodiment;

[0059] Figure 3 This is a flowchart illustrating the steps for determining a digital twin model in one embodiment;

[0060] Figure 4 This is a flowchart illustrating the steps for determining candidate device collaborative operation information in one embodiment;

[0061] Figure 5 This is a flowchart illustrating the steps for determining candidate collaborative operation methods in one embodiment;

[0062] Figure 6 This is a structural block diagram of a device information determination apparatus in one embodiment;

[0063] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0066] In logistics sorting scenarios, transit yards contain numerous small-item sorting devices, such as circular sorters, linear sorters, and intelligent sorting cabinets. During shift sorting, site personnel flexibly arrange different sorting devices based on the volume of incoming items. This strategy is ad-hoc and random, involving a high degree of human intervention. Because the volume of incoming items is not predicted in advance, and equipment usage strategies are manually formulated without big data support or a verification environment, improper equipment use and waste of resources such as equipment, personnel, and electricity may occur. This application makes the following improvements: optimizing the collaborative operation strategy between sorting devices within logistics transit yards, utilizing digital twin and operations research optimization technologies to optimize equipment use, maximizing equipment utilization and improving regional productivity; it can be applied not only to sorting equipment in logistics transit yards but also to other fields related to equipment scheduling, such as airports, warehouses, and industrial parks.

[0067] Based on this, this application provides a method, apparatus, computer device, storage medium, and computer program product for determining device information. First, the method for determining device information provided in this application will be described.

[0068] In one exemplary embodiment, such as Figure 1 As shown, a method for determining device information is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc.; the server can be a standalone server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0069] Step S101: Obtain the operation information of the working equipment performing operations on the work object.

[0070] Among them, the operating equipment can be various operating equipment that participate in collaborative operations. For example, in a logistics scenario, the operating equipment can be different sorting equipment, such as a circular sorting machine, a linear sorting machine, and an intelligent sorting cabinet.

[0071] The work object can be an object that requires work equipment to perform operations. For example, in a logistics scenario, the work object can be a package that needs to be sorted (or a workpiece and / or a courier).

[0072] The operational information can include technical parameters of each operational device (such as sorting equipment) in terms of type, speed, and capacity.

[0073] Optionally, the terminal obtains operation information of different operating equipment performing operations on different operating objects. For example, it obtains technical parameter information such as speed and capacity of various types of operating equipment when performing operations on different types of operating objects, as operation information.

[0074] Step S102: Determine candidate equipment collaborative operation information based on the operation information; candidate equipment collaborative operation information indicates the candidate collaborative operation mode in which the operating equipment performs collaborative operation on the operation object.

[0075] Among them, the candidate equipment collaborative operation information can be a collaborative operation combination scheme of different operating equipment set according to the operation information, and these collaborative operation combination schemes can be candidate schemes.

[0076] Optionally, the terminal determines possible collaborative operation methods for the working equipment to perform collaborative operations on the working object based on the acquired work information, and uses these as candidate collaborative operation methods. These candidate collaborative operation methods represent different combinations of working equipment to perform collaborative operations on the working object; and the candidate collaborative operation methods are used as candidate equipment collaborative operation information.

[0077] Step S103: Based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information.

[0078] Among them, simulated collaborative operation can be a process of simulating the collaborative operation information of each candidate device to realize virtual collaborative operation. For example, simulated collaborative operation specifically uses a digital twin model to simulate various candidate device collaboration schemes (candidate collaborative operation methods) to simulate their sorting process.

[0079] Among them, the predicted collaborative operation results can be the collaborative operation results obtained by simulating collaborative operations. For example, the predicted collaborative operation results can be the virtual sorting results of each candidate solution obtained by simulation through a digital twin model, such as efficiency, cost and other indicator information.

[0080] Optionally, the terminal simulates collaborative operations based on the determined candidate collaborative operation methods, such as simulating the sorting process of each candidate scheme; based on the simulated collaborative operation, it predicts the collaborative operation results corresponding to each candidate collaborative operation method to obtain the predicted collaborative operation results corresponding to the candidate device collaborative operation information.

[0081] Step S104: Based on the predicted collaborative operation results, filter out the collaborative operation information of the target equipment from the collaborative operation information of the candidate equipment.

[0082] Among them, the target equipment collaborative operation information can be the candidate equipment collaborative operation information with the best predicted collaborative operation result. For example, the target equipment collaborative operation information can be the candidate equipment collaborative operation information with the highest efficiency, such as the equipment collaboration scheme with the highest efficiency in the corresponding simulated collaborative operation.

[0083] Optionally, based on the predicted collaborative operation results, the terminal selects the candidate device collaborative operation information with the best effect from all candidate device collaborative operation information (candidate collaborative operation methods) and uses it as the target device collaborative operation information.

[0084] The aforementioned method for determining equipment information involves: acquiring operational information of the working equipment performing operations on the work object; determining candidate equipment collaborative operation information based on the operational information; the candidate equipment collaborative operation information representing candidate collaborative operation methods of the working equipment performing collaborative operations on the work object; predicting the predicted collaborative operation result corresponding to the candidate equipment collaborative operation information based on the simulated collaborative operation corresponding to the candidate equipment collaborative operation information; and selecting the target equipment collaborative operation information from the candidate equipment collaborative operation information based on the predicted collaborative operation result. This scheme determines candidate equipment collaborative operation information based on the operational information of the working equipment performing operations on the work object, predicts the corresponding predicted collaborative operation result based on the simulated collaborative operation corresponding to the candidate equipment collaborative operation information, and selects the target equipment collaborative operation information based on the predicted collaborative operation result. Thus, when determining equipment information, by simulating different candidate collaborative operation methods and predicting the corresponding collaborative operation results, the target equipment collaborative operation information is selected, automatically determining the most effective equipment collaborative operation information, thereby improving the efficiency and accuracy of determining equipment collaborative operation information.

[0085] In one exemplary embodiment, such as Figure 2 As shown, in step S103, based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, the predicted collaborative operation result corresponding to the candidate device collaborative operation information is predicted, specifically including the following:

[0086] Step S201: Using a digital twin model, simulate collaborative operation corresponding to the candidate equipment collaborative operation information is performed to obtain the simulated collaborative operation result corresponding to the candidate equipment collaborative operation information.

[0087] Step S202: Based on the simulated collaborative operation results, predict the predicted collaborative operation results corresponding to the candidate equipment collaborative operation information.

[0088] Among them, a digital twin model can be a virtual model of various operating equipment built using digital technology, which can simulate the working process of physical equipment.

[0089] Among them, simulated collaborative operation can be a sorting simulation process carried out in a virtual environment using a digital twin model to simulate the collaborative schemes (candidate collaborative operation methods) of various candidate equipment.

[0090] Among them, the simulated collaborative operation results can be the output of the simulation process, such as specific data such as the operating status and sorting efficiency of the operating equipment under each candidate collaborative operation mode.

[0091] Optionally, the terminal imports information such as equipment collaboration relationships from the collaborative operation information of each candidate device into the corresponding digital twin model; through the digital twin model, it simulates the operation of the candidate collaborative operation mode in a virtual environment based on information such as equipment technical parameters; it records and obtains the simulation results (predicted collaborative operation results) such as equipment status changes and sorting efficiency of each candidate collaborative operation mode during the simulation operation; based on the simulation results of each candidate collaborative operation mode, it performs predictive analysis on its predictive effect in the real environment, for example, predicting the values ​​of each candidate collaborative operation mode in terms of sorting throughput, equipment utilization rate and other indicators, thereby obtaining the predicted collaborative operation results corresponding to the candidate device collaborative operation information.

[0092] The technical solution provided in this embodiment establishes a virtual model of the equipment using digital twin technology, performs virtual simulation of collaborative operation information of different candidate equipment, obtains simulation result data, and then predicts and evaluates the performance level of collaborative operation information of each candidate equipment in reality based on these simulation data. This helps to predict more accurate collaborative operation results corresponding to the collaborative operation information of candidate equipment, thereby improving the accuracy of determining collaborative operation information of equipment.

[0093] In one exemplary embodiment, such as Figure 3 As shown, it also includes the steps of determining the digital twin model, specifically including the following:

[0094] Step S301: Obtain logistics sorting equipment information for the operating equipment;

[0095] Step S302: Generate a virtual logistics sorting equipment model based on the logistics sorting equipment information.

[0096] Step S303: The virtual logistics sorting equipment model is fused to obtain a digital twin model.

[0097] Among these, the operating equipment falls under the category of logistics sorting equipment; logistics sorting equipment includes circular sorting machines, linear sorting machines, and / or intelligent sorting cabinets. Circular sorting machines, linear sorting machines, and intelligent sorting cabinets are different types of logistics sorting equipment.

[0098] The equipment information can include the technical parameters of each piece of equipment, such as specific technical data like equipment size, speed, and functions.

[0099] Among them, logistics sorting equipment information can be equipment information of logistics sorting equipment, such as equipment information of circular sorting machines, equipment information of linear sorting machines and / or equipment information of intelligent sorting cabinets.

[0100] Among them, the virtual device model can be a digital model of each physical device that is simulated using digital technology.

[0101] The virtual logistics sorting equipment model can be a virtual equipment model of logistics sorting equipment, such as a virtual equipment model of a circular sorting machine, a virtual equipment model of a linear sorting machine, and / or a virtual equipment model of an intelligent sorting cabinet.

[0102] Among them, fusion processing can be the process of integrating the established individual virtual logistics sorting equipment models, connecting their input and output relationships, and thus forming a complete equipment system model.

[0103] Optionally, the terminal acquires logistics sorting equipment information of the operating equipment, such as acquiring the technical parameters of each sorting equipment in the logistics transfer station; based on the logistics sorting equipment information of each operating equipment, it establishes models of each in the digital space to generate individual virtual logistics sorting equipment models of each operating equipment; it integrates the generated individual virtual logistics sorting equipment models, such as connecting their input and output relationships, and uses the fully integrated virtual logistics sorting equipment model as a digital twin model to simulate the entire sorting operation process.

[0104] The technical solution provided in this embodiment establishes virtual equipment models for each operating device based on the device information. These individual virtual equipment models are then integrated and connected to form a digital twin model that can simulate the entire operation process. This approach helps to obtain a more accurate digital twin model, thereby improving the accuracy of determining equipment collaborative operation information.

[0105] In one exemplary embodiment, such as Figure 4 As shown, in step S102, the candidate equipment collaborative operation information is determined based on the operation information, specifically including the following:

[0106] Step S401: Based on the work information, perform combination processing on the work equipment and the work object to obtain the combination method between the work equipment and the work object;

[0107] Step S402: Based on the combination method, determine the candidate collaborative operation mode for the working equipment to perform collaborative operation on the working object, and use it as candidate equipment collaborative operation information.

[0108] The job information may also include equipment information of the job equipment and object information of the job objects. For example, the object information of the job objects may include the quantity and type of the job objects.

[0109] Among them, combination processing can be a process of matching and combining operating equipment and operating objects. For example, combination processing can be used to match which types of operating equipment perform operations on which types of operating objects, such as matching which sorting equipment is used to sort which type of express delivery.

[0110] The combination method can be a matching relationship between the working equipment and the working object, such as which types of working equipment perform the work on which types of working objects.

[0111] Among them, the candidate equipment collaborative operation information can be the optional scheme of how the operating equipment should cooperate in a certain operation, such as which equipment should sort different types of express mail in what order.

[0112] Optionally, the terminal obtains equipment information of the operating equipment and object information of the operating object from the operation information; matches the correspondence between the operating equipment (such as different types of sorting equipment) and the operating object (such as different types of express mail) according to the equipment information and object information to obtain the combination method between the operating equipment and the operating object; determines the feasible plan of how each operating equipment can cooperate to complete the operation task in this operation according to the combination method, and obtains the candidate collaborative operation method of the operating equipment to the operating object; and identifies each candidate collaborative operation method as candidate equipment collaborative operation information.

[0113] The technical solution provided in this embodiment combines the working equipment and the working object, and determines the candidate equipment collaborative operation information according to the obtained combination method. This is beneficial to efficiently and accurately determine the candidate equipment collaborative operation information, thereby improving the efficiency and accuracy of determining the equipment collaborative operation information.

[0114] In one exemplary embodiment, such as Figure 5 As shown, based on the combination method, candidate collaborative operation methods for the working equipment to perform collaborative operations on the working object are determined, specifically including the following:

[0115] Step S501: Based on the job information, perform genetic iteration processing on the combination methods to obtain alternative combination methods;

[0116] Step S502: Based on the predicted collaborative work time corresponding to the alternative combination methods, select candidate combination methods from the alternative combination methods;

[0117] Step S503: Determine the candidate collaborative operation mode based on the candidate combination mode.

[0118] Among them, genetic iteration processing can be a process of iterative optimization through genetic algorithms.

[0119] Among them, the alternative combination method can be a matching relationship scheme between the working equipment and the working object obtained by genetic algorithm optimization.

[0120] The predicted collaborative operation time corresponding to the alternative combination method can be the predicted operation time required for the corresponding alternative combination method.

[0121] Among them, the candidate combination can be one or more alternative combinations that have the smallest predicted collaborative operation time.

[0122] Optionally, the terminal encodes the combination method (initial combination method), sets genetic operators (such as crossover, mutation, etc.), and generates new combination methods as candidate combination methods through repeated selection, crossover, and mutation. It predicts the merits of the candidate combination methods (for example, the smaller the predicted collaborative work time, the better), selects and retains the optimal candidate combination method, and obtains the candidate combination method. Based on each candidate combination method, it determines the specific plan of how the working equipment should cooperate to complete the work task, and obtains the candidate collaborative work method corresponding to each candidate combination method.

[0123] The technical solution provided in this embodiment optimizes the original combination method through a genetic algorithm to obtain a new alternative combination method. Based on the predicted operation time of the alternative combination method, a candidate collaborative operation method is obtained. This is beneficial to obtaining a better candidate collaborative operation method, thereby improving the accuracy of determining equipment collaborative operation information.

[0124] In an exemplary embodiment, based on the job information, a genetic iteration process is performed on the combination methods to obtain alternative combination methods, specifically including the following:

[0125] Define the constraints for collaborative sorting of objects by logistics sorting equipment; the constraints include a first constraint, a second constraint, a third constraint, and / or a fourth constraint; the first constraint is the sorting constraint between the type of operating equipment and the type of operating object; the second constraint is the sorting constraint between different types of operating objects and operating equipment; the third constraint is the time constraint between operating objects of the same type; the fourth constraint is the priority constraint between different types of operating objects.

[0126] Based on the task information, constraints, and type of task object, the combination methods are subjected to genetic iteration to obtain alternative combination methods.

[0127] Among them, the operating equipment belongs to logistics sorting equipment; the operating object belongs to the object to be sorted.

[0128] The items to be sorted can be workpieces, parcels, or express packages.

[0129] The first constraint can be that the same work equipment sorts only one type of work object at the same time.

[0130] The second constraint can be that each type of work object is sorted on only one work machine at any given time and that the machine cannot be stopped midway.

[0131] The third constraint can be a sequential constraint for sorting the same type of work objects, a constraint that sorts the more time-sensitive objects first and the less time-sensitive objects later, or a constraint that sorts different types of work objects without a sequential order.

[0132] The fourth constraint can be that different types of work objects have the same priority and the same departure time.

[0133] Optionally, the terminal determines the constraints for collaborative sorting of objects by logistics sorting equipment as a first constraint, a second constraint, a third constraint, and a fourth constraint. The first constraint is that the same equipment sorts only one type of object at any given time. The second constraint is that each type of object is sorted on only one equipment at any given time and cannot be interrupted. The third constraint is that sorting of the same type of object has a priority order: higher-efficiency objects are sorted first, lower-efficiency objects are sorted later, and sorting of different types of objects has no priority order. The fourth constraint is that different types of objects have the same priority and the same departure time. Based on the operation information, constraints, and object types, a genetic iteration process is performed on the combination methods to obtain alternative combination methods.

[0134] The technical solution provided in this embodiment determines the constraints of the collaborative sorting of objects by logistics sorting equipment, and performs genetic iteration processing on the combination methods in combination with the constraints, which helps to obtain more accurate alternative combination methods, thereby improving the accuracy of determining the collaborative operation information of the equipment in the future.

[0135] In an exemplary embodiment, in step S104, target device collaborative operation information is selected from candidate device collaborative operation information based on the predicted collaborative operation results. Specifically, this includes: determining the predicted collaborative operation time corresponding to the candidate device collaborative operation information based on the predicted collaborative operation results; and selecting the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, and using it as the target device collaborative operation information.

[0136] Among them, the predicted collaborative operation time corresponding to the candidate equipment collaborative operation information can be the operation time required for the predicted corresponding candidate equipment collaborative operation information.

[0137] Optionally, the terminal obtains the predicted collaborative operation time corresponding to each candidate device collaborative operation information from the predicted collaborative operation results; based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, it filters out the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information, and uses the candidate device collaborative operation information with the smallest predicted collaborative operation time as the target device collaborative operation information.

[0138] The technical solution provided in this embodiment selects the candidate equipment collaborative operation information with the best effect from multiple generated candidate solutions as the final target equipment collaborative operation information, thereby improving the efficiency and accuracy of determining equipment collaborative operation information.

[0139] In an exemplary embodiment, after selecting the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, and using it as the target device collaborative operation information, the following steps are also included: obtaining the current collaborative operation time corresponding to the current device collaborative operation information of the operating device; and applying the target device collaborative operation information to the collaborative operation of the operating device if the comparison value between the predicted collaborative operation time corresponding to the target device collaborative operation information and the current collaborative operation time is greater than a preset threshold.

[0140] Among them, the current equipment collaborative operation information can be the equipment collaborative operation scheme currently being used by the operating equipment.

[0141] The current collaborative operation time can be the actual time required for the working equipment to perform collaborative operations based on the current equipment collaborative operation information.

[0142] The comparison value can be the difference between the predicted collaborative operation time and the current collaborative operation time, which reflects the relative advantages and disadvantages of the two in terms of time.

[0143] The preset threshold can be a set time difference value used to determine the degree of difference between the predicted collaborative operation time and the current collaborative operation time.

[0144] Optionally, the terminal obtains the current collaborative operation time corresponding to the current collaborative operation information of the working equipment; determines whether the comparison value between the predicted collaborative operation time corresponding to the target equipment collaborative operation information and the current collaborative operation time is greater than a preset threshold; if the comparison value between the predicted collaborative operation time corresponding to the target equipment collaborative operation information and the current collaborative operation time is greater than the preset threshold, it indicates that the target equipment collaborative operation information (target scheme) has a significant advantage over the current equipment collaborative operation information (current scheme), and applies the target equipment collaborative operation information to the actual collaborative operation performed by the working equipment.

[0145] The technical solution provided in this embodiment selects the target equipment collaborative operation information with the best effect and compares it with the current equipment collaborative operation information. If the target equipment collaborative operation information is significantly better than the current equipment collaborative operation information, the target equipment collaborative operation information is applied to the actual collaborative operation, thereby improving the efficiency of collaborative operation.

[0146] The following example illustrates the device information determination method provided in this application. This example demonstrates the application of this method to a terminal, and the main steps include:

[0147] The first step is for the terminal to obtain the operation information of the working equipment on the work object.

[0148] The second step is for the terminal to combine the work equipment and the work object based on the work information to obtain the combination method between the work equipment and the work object.

[0149] The third step is for the terminal to perform genetic iteration processing on the combination methods based on the job information to obtain candidate combination methods; based on the predicted collaborative job time corresponding to the candidate combination methods, candidate combination methods are selected from the candidate combination methods; based on the candidate combination methods, candidate collaborative job methods are determined; candidate equipment collaborative job information indicates the candidate collaborative job methods for the working equipment to perform collaborative jobs on the work object.

[0150] The fourth step is for the terminal to acquire the equipment information of the operating equipment; based on the equipment information, to generate a virtual equipment model of the operating equipment; and to perform fusion processing on the virtual equipment model to obtain a digital twin model.

[0151] Fifth, the terminal uses a digital twin model to simulate collaborative operations corresponding to the candidate device collaborative operation information, and obtains the simulated collaborative operation results corresponding to the candidate device collaborative operation information; based on the simulated collaborative operation results, it predicts the predicted collaborative operation results corresponding to the candidate device collaborative operation information.

[0152] The sixth step is for the terminal to determine the predicted collaborative operation time corresponding to the candidate device collaborative operation information based on the predicted collaborative operation results; and to select the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information as the target device collaborative operation information.

[0153] Step 7: The terminal obtains the current collaborative operation time corresponding to the current collaborative operation information of the working equipment; if the comparison value between the predicted collaborative operation time corresponding to the collaborative operation information of the target equipment and the current collaborative operation time is greater than a preset threshold, the collaborative operation information of the target equipment is applied to the collaborative operation of the working equipment.

[0154] The technical solution provided in this embodiment determines candidate equipment collaborative operation information based on the operation information of the working equipment performing operations on the work object, predicts the corresponding predicted collaborative operation results based on the simulated collaborative operation corresponding to the candidate equipment collaborative operation information, and filters out the target equipment collaborative operation information based on the predicted collaborative operation results. In this way, when determining equipment information, by simulating different candidate collaborative operation methods and predicting the corresponding collaborative operation results, the target equipment collaborative operation information is filtered out, so as to automatically determine the equipment collaborative operation information with the best effect, thereby improving the efficiency and accuracy of determining equipment collaborative operation information.

[0155] The following application example illustrates the device information determination method provided in this application. This application example demonstrates the application of this method to a terminal, and the main steps include:

[0156] The first step is for the terminal to collect data.

[0157] For example, taking the small parcel area of ​​a logistics transit center as an example, optimize the collaborative strategy of all sorting equipment in the small parcel area. The transit center is generally divided into four areas: unloading area, matrix area, small parcel area, and loading area. In the data collection phase, it is first necessary to establish system data pathways to automatically acquire parcel data and sorting process data. Secondly, it is necessary to acquire equipment data through methods such as reading CAD (Computer-Aided Design), design drawings, product manuals, and manual measurements. For example: 1. Collect equipment mechanism data in the small parcel area, such as the inbound packaging line, unloading port, feeding table, sorting machine, sorting cabinet, outbound packaging line, etc., including equipment dimensions, speed, texture, friction, and upstream and downstream relationships; 2. Train the equipment data model in the small parcel area, such as the distribution of scanning rate, personnel efficiency, unloading speed, bag placement success rate, drop rate, and return rate, and learn the entire sorting process and logic.

[0158] The second step is to establish a digital twin system for the terminal.

[0159] Specifically, 1. For devices with input and output data, a realistic single-device model is created; 2. For devices that cannot collect input and output data, they are treated as black boxes and merged with other single devices to optimize realism as a whole; 3. All digital twin models are established, with each device containing a corresponding 3D model, mechanism model, and data model. The overall realism is optimized end-to-end. When the realism reaches 99%, the digital twin model is considered to reflect the real physical world.

[0160] The third step involves the terminal solving the problem using a genetic algorithm.

[0161] For example, suppose a shift includes consolidated goods, loose goods, and special items (such as five-easy-handle and two-cold-handle items). Small item sorting equipment includes circular sorters, linear sorters, and intelligent sorting cabinets. The following constraints apply to collaborative sorting: 1. At any given time, the same machine can only sort one type of parcel; 2. Each type of parcel can only be sorted on one machine at a given time, without interruption; 3. Sorting of the same type of parcel has a priority constraint: higher-time-demand parcels are sorted first, lower-time-demand parcels are sorted later; sorting of different types of parcels has no priority order; 4. Different types of parcels have the same priority and the same departure time. The equipment collaboration problem satisfying these constraints can be transformed into a flexible job shop scheduling problem, which can be solved using a process-based coding method. Sorting parcels according to their timeliness represents a process, parcel type represents a workpiece, and sorting time represents processing time.

[0162] (1) Encoding and decoding:

[0163] Each chromosome consists of a sequence of n×m genes (where n can represent the number of jobs / work objects and m can represent the number of machines / work equipment) representing processes, and is a permutation of all processes. Its characteristic is that any permutation of gene strings can represent a feasible schedule. For a scheduling problem involving n jobs processed by m machines, its chromosome consists of n×m genes, with each job number appearing m times in the chromosome. Scanning the chromosome from left to right, the job number appearing k times (where k represents a number) represents the k-th process for that job.

[0164]

[0165] J1, J2, J3, and J4 can represent different types of workpieces. This problem can be encoded and decoded.

[0166] (2) Selection operation:

[0167] The optimal individual preservation method is adopted, which directly copies the best individual from the parent group into the next generation.

[0168] (3) Cross operations:

[0169] The IPOX (Process Interval Division Crossover Operator) is based on process coding. The specific operation process of IPOX is as follows, where P1 and P2 are two parent chromosomes, and crossover produces offspring C1 and C2. The IPOX crossover operation process is as follows: 1) Randomly divide all workpieces into two sets G1 and G2. G1 = {1, 3}, G2 = {2, 4}; 2) Copy the workpieces contained in G1 from P1 to C1, and copy the workpieces contained in G2 from P2 to C2, keeping their positions unchanged; 3) Copy the workpieces contained in G1 from P1 to C2, and copy the workpieces contained in G2 from P2 to C1, keeping their order unchanged.

[0170] (4) Mutation operation:

[0171] Mutation based on machine-assigned coding. Since each process can be completed by multiple machines, two processes are randomly selected, and then one machine is selected from the set of machines that execute these two processes (the one with the shorter processing time is preferred), and the selected machine number is placed into the corresponding gene string based on machine-assigned coding.

[0172] The fourth step involves the terminal undergoing verification and iteration within the digital twin system:

[0173] For example, the terminal inputs the device collaboration strategy obtained in the above manner into a highly realistic digital twin system to simulate the on-site operation process and output virtual results. If there is a significant improvement compared to the original results, the new strategy is applied to the transfer site. If there is no improvement or the improvement is not significant, the crossover or mutation operation is changed, and the process is iterated to find the optimal solution.

[0174] The technical solution provided in this application example uses digital twin technology and operations research optimization algorithms to verify the output equipment collaboration strategy within the digital twin system. Based on the feedback, the optimization algorithm is adjusted, and after multiple iterations, the optimal result is output. Here, digital twin technology can improve the upper limit of algorithm optimization, finding the optimal equipment collaboration strategy quickly and efficiently at low cost, saving human and material resources, thereby reducing operating costs. Through collaborative optimization, each sorting device can better complete sorting tasks based on mutual cooperation, avoiding waste or dropped goods caused by independent operation of a certain link; and improving the efficiency and accuracy of determining equipment collaborative operation information.

[0175] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0176] Based on the same inventive concept, this application also provides a device for determining device information to implement the device information determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more device information determination device embodiments provided below can be found in the limitations of the device information determination method described above, and will not be repeated here.

[0177] In one exemplary embodiment, such as Figure 6 As shown, a device information determining apparatus is provided, the apparatus 600 may include:

[0178] The information acquisition module 601 is used to acquire the operation information of the working equipment performing operations on the working object;

[0179] The information determination module 602 is used to determine candidate equipment collaborative operation information based on the operation information; the candidate equipment collaborative operation information represents the candidate collaborative operation methods in which the operating equipment performs collaborative operations on the operation object.

[0180] The result prediction module 603 is used to predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information based on the simulated collaborative operation corresponding to the candidate device collaborative operation information.

[0181] The information filtering module 604 is used to filter out the target equipment collaborative operation information from the candidate equipment collaborative operation information based on the predicted collaborative operation results.

[0182] In an exemplary embodiment, the result prediction module 603 is further configured to perform simulated collaborative operation corresponding to the candidate device collaborative operation information through a digital twin model, and obtain the simulated collaborative operation result corresponding to the candidate device collaborative operation information; and predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information based on the simulated collaborative operation result.

[0183] In an exemplary embodiment, the device 600 further includes: the operating equipment belongs to logistics sorting equipment; the logistics sorting equipment includes a circular sorting machine, a linear sorting machine, and / or an intelligent sorting cabinet; a model generation module is used to obtain logistics sorting equipment information of the operating equipment; generate a virtual logistics sorting equipment model of the operating equipment based on the logistics sorting equipment information; and perform fusion processing on the virtual logistics sorting equipment model to obtain a digital twin model.

[0184] In an exemplary embodiment, the information determination module 602 is further configured to perform combination processing on the work equipment and the work object according to the work information to obtain the combination method between the work equipment and the work object; and determine the candidate collaborative work method for the work equipment to perform collaborative work on the work object according to the combination method, as candidate equipment collaborative work information.

[0185] In an exemplary embodiment, the information determination module 602 is further configured to perform genetic iteration processing on the combination methods according to the job information to obtain candidate combination methods; select candidate combination methods from the candidate combination methods according to the predicted collaborative job time corresponding to the candidate combination methods; and determine candidate collaborative job methods according to the candidate combination methods.

[0186] In an exemplary embodiment, the operating equipment belongs to logistics sorting equipment; the operating object belongs to the object to be sorted; the information determination module 602 is further used to determine the constraints of the logistics sorting equipment in collaboratively sorting the object to be sorted; the constraints include a first constraint, a second constraint, a third constraint and / or a fourth constraint; the first constraint is the sorting constraint between the types of operating equipment and operating objects; the second constraint is the sorting constraint between different types of operating objects and operating equipment; the third constraint is the time constraint between operating objects of the same type; the fourth constraint is the priority constraint between different types of operating objects; based on the operating information, constraints and the type of operating object, the combination method is subjected to genetic iteration processing to obtain alternative combination methods.

[0187] In an exemplary embodiment, the information filtering module 604 is further configured to determine the predicted collaborative operation time corresponding to the candidate device collaborative operation information based on the predicted collaborative operation result; and to filter out the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, and use it as the target device collaborative operation information.

[0188] In an exemplary embodiment, the device 600 further includes: an information application module, configured to obtain the current collaborative operation time corresponding to the current collaborative operation information of the working equipment; and to apply the target equipment collaborative operation information to the collaborative operation of the working equipment when the comparison value between the predicted collaborative operation time corresponding to the target equipment collaborative operation information and the current collaborative operation time is greater than a preset threshold.

[0189] The modules in the aforementioned device information determination apparatus can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0190] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a device information determination method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0191] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0192] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0193] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above-described method embodiments.

[0194] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0195] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0196] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining equipment information, characterized in that, The method includes: Obtain operation information on the work equipment performing operations on the work object; Based on the job information, candidate equipment collaborative job information is determined; the candidate equipment collaborative job information represents the candidate collaborative job mode in which the job equipment performs collaborative work on the job object; Based on the simulated collaborative operation corresponding to the candidate device collaborative operation information, predict the predicted collaborative operation result corresponding to the candidate device collaborative operation information; Based on the predicted collaborative operation results, the collaborative operation information of the target equipment is selected from the collaborative operation information of the candidate equipment.

2. The method according to claim 1, characterized in that, The step of predicting the predicted collaborative operation result corresponding to the candidate device collaborative operation information based on the simulated collaborative operation information includes: Using a digital twin model, simulated collaborative operations are performed corresponding to the collaborative operation information of the candidate devices to obtain the simulated collaborative operation results corresponding to the collaborative operation information of the candidate devices. Based on the simulated collaborative operation results, the predicted collaborative operation results corresponding to the candidate device collaborative operation information are predicted.

3. The method according to claim 2, characterized in that, The operating equipment belongs to logistics sorting equipment; the logistics sorting equipment includes circular sorting machines, linear sorting machines and / or intelligent sorting cabinets; The method further includes: Obtain the logistics sorting equipment information of the operating equipment; Based on the logistics sorting equipment information, a virtual logistics sorting equipment model of the operating equipment is generated; The virtual logistics sorting equipment model is fused to obtain the digital twin model.

4. The method according to claim 1, characterized in that, The step of determining the candidate equipment collaborative operation information based on the operation information includes: Based on the job information, the job equipment and the job object are combined to obtain the combination method between the job equipment and the job object; Based on the combination method, candidate collaborative operation methods for the working equipment to perform collaborative operations on the working object are determined, and these are used as candidate equipment collaborative operation information.

5. The method according to claim 4, characterized in that, The step of determining candidate collaborative operation modes for the working equipment to perform collaborative operations on the working object based on the combination method includes: Based on the job information, the combination method is subjected to genetic iteration processing to obtain alternative combination methods; Based on the predicted collaborative operation time corresponding to the alternative combination methods, candidate combination methods are selected from the alternative combination methods; Based on the candidate combination method, the candidate collaborative operation method is determined.

6. The method according to claim 5, characterized in that, The operating equipment is a logistics sorting equipment; the object being sorted is an object to be sorted. The step of performing genetic iteration processing on the combination methods based on the job information to obtain alternative combination methods includes: The constraints for the collaborative sorting of the objects to be sorted by the logistics sorting equipment are determined; the constraints include a first constraint, a second constraint, a third constraint, and / or a fourth constraint; the first constraint is a sorting constraint between the type of the operating equipment and the type of the operating object; the second constraint is a sorting constraint between different types of operating objects and the operating equipment; the third constraint is a timeliness constraint between operating objects of the same type; and the fourth constraint is a priority constraint between different types of operating objects. Based on the job information, the constraints, and the type of the job object, the combination method is subjected to genetic iteration to obtain the alternative combination method.

7. The method according to claim 1, characterized in that, The step of filtering out target equipment collaborative operation information from the candidate equipment collaborative operation information based on the predicted collaborative operation results includes: Based on the predicted collaborative operation results, the predicted collaborative operation time corresponding to the candidate device collaborative operation information is determined; Based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, the candidate device collaborative operation information with the smallest predicted collaborative operation time is selected from the candidate device collaborative operation information and used as the target device collaborative operation information.

8. The method according to claim 7, characterized in that, After selecting the candidate device collaborative operation information with the smallest predicted collaborative operation time from the candidate device collaborative operation information based on the predicted collaborative operation time corresponding to the candidate device collaborative operation information, and using it as the target device collaborative operation information, the method further includes: Obtain the current collaborative operation time corresponding to the current collaborative operation information of the operating equipment; If the comparison value between the predicted collaborative operation time corresponding to the collaborative operation information of the target equipment and the current collaborative operation time is greater than a preset threshold, the collaborative operation information of the target equipment is applied to the collaborative operation of the operation equipment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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