Method for monitoring and managing working state of oil pressure press

Abstract

The invention relates to a method for monitoring and managing the working state of an oil pressure press, and belongs to the technical field of oil pressure press management.The method comprises the steps that a knowledge graph is constructed according to pressure performance characteristic data under all working parameters, and the real-time working parameters of each oil pressure press are obtained; and finally, according to the knowledge graph and the real-time working parameters of each oil pressure press, the real-time pressure performance of the oil pressure press is obtained, and the current pressure minimum demand data information is obtained. And intelligent management is conducted on the oil pressure presses according to the real-time pressure performance of the oil pressure presses, the current pressure minimum demand data information and the maximum pressure performance characteristic data of each oil pressure press within the preset time. According to the method, the pressure performance of the oil pressure press is evaluated, so that the working parameters of the oil pressure press are adjusted according to the evaluation result, the working requirements are met, and normal operation of the oil pressure press is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic press management, and in particular to a method for monitoring and managing the working state of a hydraulic press. Background Art

[0002] A hydraulic press (a type of hydraulic press) is a machine that uses special hydraulic oil as the working medium and a hydraulic pump as the power source. The hydraulic oil enters the cylinder / piston through the hydraulic pipeline by the force of the pump. Then there are several sets of seals that cooperate with each other in the cylinder / piston. The seals in different positions are different, but they all play a sealing role to prevent the hydraulic oil from leaking. Finally, the hydraulic oil circulates in the oil tank through a one-way valve to make the cylinder / piston circulate and do work, thereby completing a certain mechanical action as a kind of productivity. However, during the service process of the hydraulic press, the pressure performance will degrade, which will cause the hydraulic press to work abnormally and be insufficient to meet the work requirements. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method for monitoring and managing the working state of an oil hydraulic press.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides a method for monitoring and managing the working state of a hydraulic press, comprising: Acquire maximum pressure performance change characteristic data of the oil hydraulic press through big data, and construct a maximum pressure performance characteristic prediction model according to the maximum pressure performance change characteristic data of the oil hydraulic press; Predicting the maximum pressure performance characteristic data of each hydraulic press within a preset time by using the maximum pressure performance characteristic prediction model; By testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed according to the pressure performance characteristic data under each working parameter to obtain the real-time working parameters of each hydraulic press; The real-time pressure performance of the hydraulic press is obtained according to the knowledge graph and the real-time working parameters of each hydraulic press, and the current minimum pressure requirement data information is obtained. The hydraulic press is intelligently managed according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information and the maximum pressure performance characteristic data of each hydraulic press within a preset time.

[0005] Furthermore, in this method, the maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed according to the maximum pressure performance change characteristic data of the hydraulic press, specifically: The maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed based on a deep neural network, and a Markov random field model is constructed; Inputting the maximum pressure performance change characteristic data of the oil hydraulic press into the Markov random field model, taking the maximum pressure performance characteristic data of each timestamp as a state vector, and calculating the probability value of each state vector transferring to another state vector; When the probability value is greater than a preset probability threshold, the corresponding state vector is updated to another state vector; when the probability value is not greater than the preset probability threshold, the current state vector is maintained unchanged, and a state vector matrix is ​​constructed according to the state vector; The state vector matrix is ​​input into the maximum pressure performance characteristic prediction model for training. When the prediction accuracy of the maximum pressure performance characteristic prediction model is within a preset prediction accuracy range, the model parameters of the maximum pressure performance characteristic prediction model are saved and the training ends.

[0006] Furthermore, in the present method, the maximum pressure performance characteristic data of each hydraulic press within a preset time is predicted by the maximum pressure performance characteristic prediction model, specifically including: Acquire the maximum pressure performance change characteristic data of each hydraulic press within a preset time, and input the maximum pressure performance change characteristic data of each hydraulic press within the preset time into the maximum pressure performance characteristic prediction model for prediction; By prediction, the maximum pressure performance characteristic data of each hydraulic press within a preset time is obtained, and the maximum pressure performance characteristic data of each hydraulic press within the preset time is output.

[0007] Furthermore, in this method, by testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed according to the pressure performance characteristic data under each working parameter, specifically including: By testing the pressure performance of each hydraulic press, we obtain the pressure performance characteristic data under each working parameter, build a knowledge graph, and introduce a graph neural network. Inputting the pressure performance characteristic data obtained under each working parameter into the graph neural network, using the working parameter as the first node and the pressure performance characteristic data as the second node; Construct a directed description relationship, connect the first node with the second node based on the directed description relationship to form a topological structure graph, obtain a related adjacency matrix based on the topological structure graph, and input the related adjacency matrix into the knowledge graph for storage.

[0008] Furthermore, in this method, the real-time pressure performance of the hydraulic press is obtained according to the knowledge graph and the real-time working parameters of each hydraulic press, specifically: Input the real-time working parameters of each hydraulic press into the knowledge graph for data matching, and obtain sample data corresponding to a Euclidean distance less than a preset Euclidean distance; The real-time pressure performance of the hydraulic press under the real-time working parameters of the hydraulic press is obtained according to the sample data corresponding to the Euclidean distance being less than the preset Euclidean distance, and the real-time pressure performance of the hydraulic press is output.

[0009] Furthermore, in this method, the current minimum pressure requirement data information is obtained, and the hydraulic press is intelligently managed according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information, and the maximum pressure performance characteristic data of each hydraulic press within a preset time, specifically: Acquire current minimum pressure requirement data information, introduce a genetic algorithm, set a genetic algebra according to the genetic algorithm, use the current minimum pressure requirement data information as a first constraint condition, and use the maximum pressure performance characteristic data as a second constraint condition; Setting an adjustment threshold range according to the first constraint condition and the second constraint condition, and determining whether the real-time pressure performance of the oil hydraulic press is within the adjustment threshold range; When the real-time pressure performance of the hydraulic press is within the adjustment threshold range, the current working parameters of the hydraulic press are maintained and the working state of the hydraulic press is continuously monitored; When the real-time pressure performance of the hydraulic press is not within the adjustment threshold range, genetic iteration is performed based on the genetic algebra to readjust the working parameters of the current hydraulic press, and obtain the real-time pressure performance corresponding to the working parameters of the current hydraulic press until the corresponding real-time pressure performance is within the adjustment threshold range.

[0010] A second aspect of the present invention provides a working status monitoring and management system for a hydraulic press, comprising a memory and a processor, wherein the memory comprises a working status monitoring and management method program for the hydraulic press, and when the working status monitoring and management method program for the hydraulic press is executed by the processor, the steps of any one of the working status monitoring and management methods for the hydraulic press are implemented.

[0011] The third aspect of the present invention provides a computer-readable storage medium, including a working status monitoring and management method program for a hydraulic press. When the working status monitoring and management method program for a hydraulic press is executed by a processor, the steps of any one of the working status monitoring and management methods for a hydraulic press are implemented.

[0012] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention obtains the maximum pressure performance change characteristic data of the oil hydraulic press through big data, and constructs a maximum pressure performance characteristic prediction model based on the maximum pressure performance change characteristic data of the oil hydraulic press, and then predicts the maximum pressure performance characteristic data of each oil hydraulic press within a preset time through the maximum pressure performance characteristic prediction model, thereby obtaining the pressure performance characteristic data under each working parameter by testing the pressure performance of each oil hydraulic press, and constructing a knowledge graph based on the pressure performance characteristic data under each working parameter, obtaining the real-time working parameters of each oil hydraulic press, and finally obtaining the real-time pressure performance of the oil hydraulic press based on the knowledge graph and the real-time working parameters of each oil hydraulic press, obtaining the current minimum pressure demand data information, and intelligently managing the oil hydraulic press based on the real-time pressure performance of the oil hydraulic press, the current minimum pressure demand data information, and the maximum pressure performance characteristic data of each oil hydraulic press within a preset time. The present invention evaluates the pressure performance of the oil hydraulic press, and adjusts the working parameters of the oil hydraulic press according to the evaluation results, so as to adapt to the work requirements and ensure the normal operation of the oil hydraulic press. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The overall flow chart of the working status monitoring and management method of the hydraulic press is shown; Figure 2 A partial flow chart showing a method for monitoring and managing the working status of an oil hydraulic press; Figure 3 The system block diagram of the working status monitoring and management system of the hydraulic press is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] like Figure 1 As shown, the first aspect of the present invention provides a method for monitoring and managing the working state of a hydraulic press, comprising: S102: obtaining maximum pressure performance change characteristic data of the hydraulic press through big data, and constructing a maximum pressure performance characteristic prediction model according to the maximum pressure performance change characteristic data of the hydraulic press; S104: predicting the maximum pressure performance characteristic data of each hydraulic press within a preset time by using a maximum pressure performance characteristic prediction model; S106: By testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed according to the pressure performance characteristic data under each working parameter to obtain the real-time working parameters of each hydraulic press; S108: Obtain the real-time pressure performance of the hydraulic press according to the knowledge graph and the real-time working parameters of each hydraulic press, obtain the current minimum pressure requirement data information, and intelligently manage the hydraulic press according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information, and the maximum pressure performance characteristic data of each hydraulic press within a preset time.

[0018] It should be noted that the present invention evaluates the pressure performance of the hydraulic press and adjusts the working parameters of the hydraulic press according to the evaluation results to meet the working requirements and ensure the normal operation of the hydraulic press.

[0019] like Figure 2 As shown, further, in this method, the maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed according to the maximum pressure performance change characteristic data of the hydraulic press, specifically: S202: Obtain maximum pressure performance change characteristic data of the hydraulic press through big data, and construct a maximum pressure performance characteristic prediction model based on a deep neural network, and construct a Markov random field model; S204: inputting the maximum pressure performance change characteristic data of the oil hydraulic press into the Markov random field model, taking the maximum pressure performance characteristic data of each timestamp as a state vector, and calculating the probability value of each state vector transferring to another state vector; S206: When the probability value is greater than a preset probability threshold, the corresponding state vector is updated to another state vector; when the probability value is not greater than the preset probability threshold, the current state vector is maintained unchanged, and a state vector matrix is ​​constructed according to the state vector; S208: Input the state vector matrix into the maximum pressure performance characteristic prediction model for training. When the prediction accuracy of the maximum pressure performance characteristic prediction model is within a preset prediction accuracy range, save the model parameters of the maximum pressure performance characteristic prediction model and the training ends.

[0020] It should be noted that, since the maximum pressure performance of the hydraulic press will degrade to a certain extent during use, the Markov random field model can better describe the uncertain performance change process. When the probability value is greater than the preset probability threshold, the corresponding state vector is updated to another state vector. When the probability value is not greater than the preset probability threshold, the current state vector is maintained unchanged. The performance status can be updated in time, thereby improving the prediction accuracy of the maximum pressure performance characteristics of the hydraulic press.

[0021] Furthermore, in the present method, the maximum pressure performance characteristic data of each hydraulic press within a preset time is predicted by the maximum pressure performance characteristic prediction model, specifically including: Acquire the maximum pressure performance change characteristic data of each hydraulic press within a preset time, and input the maximum pressure performance change characteristic data of each hydraulic press within the preset time into the maximum pressure performance characteristic prediction model for prediction; By prediction, the maximum pressure performance characteristic data of each hydraulic press within a preset time is obtained, and the maximum pressure performance characteristic data of each hydraulic press within the preset time is output.

[0022] Furthermore, in this method, by testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed according to the pressure performance characteristic data under each working parameter, specifically including: By testing the pressure performance of each hydraulic press (setting the maximum pressure performance characteristic data as the upper threshold value to test the pressure performance of the hydraulic press), the pressure performance characteristic data under each working parameter is obtained, a knowledge graph is constructed, and a graph neural network is introduced; The pressure performance characteristic data obtained under each working parameter is input into the graph neural network, with the working parameter as the first node and the pressure performance characteristic data as the second node; Construct a directed description relationship, connect the first node with the second node based on the directed description relationship to form a topological structure graph, obtain a related adjacency matrix based on the topological structure graph, and input the related adjacency matrix into the knowledge graph for storage.

[0023] It should be noted that the pressure performance of the hydraulic press will change due to different working parameters, such as different hydraulic oil flow, pressure, flow direction and other factors. This method can form a knowledge graph to analyze the pressure performance under various working parameters, thereby forming a knowledge graph. Since the maximum pressure performance characteristic data will degenerate, the test results within different pressure performance characteristic threshold ranges stored in the knowledge graph, that is, different pressure performance characteristic threshold ranges will have different working parameters to achieve the same pressure requirement.

[0024] Furthermore, in this method, the real-time pressure performance of the hydraulic press is obtained according to the knowledge graph and the real-time working parameters of each hydraulic press, specifically: Input the real-time working parameters of each hydraulic press into the knowledge graph for data matching, and obtain sample data corresponding to a Euclidean distance less than a preset Euclidean distance; The real-time pressure performance of the hydraulic press under the real-time working parameters of the hydraulic press is obtained according to the sample data corresponding to the Euclidean distance being less than the preset Euclidean distance, and the real-time pressure performance of the hydraulic press is output.

[0025] Furthermore, in this method, the current minimum pressure requirement data information is obtained, and the hydraulic press is intelligently managed according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information, and the maximum pressure performance characteristic data of each hydraulic press within a preset time, specifically: Obtaining current pressure minimum demand data information, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, taking the current pressure minimum demand data information as a first constraint condition, and taking the maximum pressure performance characteristic data as a second constraint condition; Setting an adjustment threshold range according to the first constraint condition and the second constraint condition, and determining whether the real-time pressure performance of the oil hydraulic press is within the adjustment threshold range; When the real-time pressure performance of the hydraulic press is within the adjustment threshold range, the current working parameters of the hydraulic press are maintained and the working status of the hydraulic press is continuously monitored; When the real-time pressure performance of the hydraulic press is not within the adjustment threshold range, genetic iteration is performed based on genetic algebra to readjust the working parameters of the current hydraulic press, and obtain the real-time pressure performance corresponding to the working parameters of the current hydraulic press until the corresponding real-time pressure performance is within the adjustment threshold range.

[0026] It should be noted that this method can improve the rationality of control over the hydraulic press, so that in some pressure demand scenarios, the real-time pressure performance is within the adjustment threshold range, thereby achieving intelligent management.

[0027] In addition, the method further comprises: When the inheritance reaches the maximum inheritance generation, if the real-time pressure performance of the hydraulic press is not within the adjustment threshold range, a non-working hydraulic press in the target area is obtained, and the maximum pressure performance characteristic data of the non-working hydraulic press is obtained; When the maximum pressure performance characteristic data of the non-working hydraulic press is greater than the current minimum pressure requirement data information, the non-working hydraulic press is used as a candidate for recommendation; Acquire historical service data information of the hydraulic press, and input the historical service data information of the hydraulic press into a Bayesian network for training, to obtain a trained Bayesian network; Obtaining service data information of the candidate recommended hydraulic press within a preset time, inputting the service data information of the candidate recommended hydraulic press within the preset time into the trained Bayesian network, and predicting the failure time of the candidate recommended hydraulic press; A candidate recommended hydraulic press whose failure time is not within the work demand time is selected as a recommended hydraulic press, and the recommended hydraulic press is displayed in a preset manner.

[0028] It should be noted that this method can further evaluate and analyze the hydraulic presses in the target area, thereby improving the rationality of the management of the hydraulic presses.

[0029] like Figure 3 As shown, the second aspect of the present invention provides a working status monitoring and management system 4 for a hydraulic press, including a memory 41 and a processor 42. The memory 41 includes a working status monitoring and management method program for the hydraulic press. When the working status monitoring and management method program for the hydraulic press is executed by the processor 42, any technical solution of the working status monitoring and management method for the hydraulic press is implemented.

[0030] Among them, include: The maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed based on the maximum pressure performance change characteristic data of the hydraulic press; Predicting the maximum pressure performance characteristic data of each hydraulic press within a preset time by using a maximum pressure performance characteristic prediction model; By testing the pressure performance of each hydraulic press, the pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed based on the pressure performance characteristic data under each working parameter to obtain the real-time working parameters of each hydraulic press; The real-time pressure performance of the hydraulic press is obtained based on the knowledge graph and the real-time working parameters of each hydraulic press, and the current minimum pressure requirement data information is obtained. The hydraulic press is intelligently managed based on the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information and the maximum pressure performance characteristic data of each hydraulic press within a preset time.

[0031] It should be noted that the present invention evaluates the pressure performance of the hydraulic press and adjusts the working parameters of the hydraulic press according to the evaluation results to meet the working requirements and ensure the normal operation of the hydraulic press.

[0032] Furthermore, in this method, the maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed according to the maximum pressure performance change characteristic data of the hydraulic press, specifically: The maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed based on a deep neural network, and a Markov random field model is constructed; Input the maximum pressure performance change characteristic data of the hydraulic press into the Markov random field model, take the maximum pressure performance characteristic data of each time stamp as a state vector, and calculate the probability value of each state vector transferring to another state vector; When the probability value is greater than the preset probability threshold, the corresponding state vector is updated to another state vector. When the probability value is not greater than the preset probability threshold, the current state vector is maintained unchanged, and a state vector matrix is ​​constructed according to the state vector. The state vector matrix is ​​input into the maximum pressure performance characteristic prediction model for training. When the prediction accuracy of the maximum pressure performance characteristic prediction model is within a preset prediction accuracy range, the model parameters of the maximum pressure performance characteristic prediction model are saved and the training ends.

[0033] It should be noted that, since the maximum pressure performance of the hydraulic press will degrade to a certain extent during use, the Markov random field model can better describe the uncertain performance change process. When the probability value is greater than the preset probability threshold, the corresponding state vector is updated to another state vector. When the probability value is not greater than the preset probability threshold, the current state vector is maintained unchanged. The performance status can be updated in time, thereby improving the prediction accuracy of the maximum pressure performance characteristics of the hydraulic press.

[0034] Furthermore, in the present method, the maximum pressure performance characteristic data of each hydraulic press within a preset time is predicted by the maximum pressure performance characteristic prediction model, specifically including: Acquire the maximum pressure performance change characteristic data of each hydraulic press within a preset time, and input the maximum pressure performance change characteristic data of each hydraulic press within the preset time into the maximum pressure performance characteristic prediction model for prediction; By prediction, the maximum pressure performance characteristic data of each hydraulic press within a preset time is obtained, and the maximum pressure performance characteristic data of each hydraulic press within the preset time is output.

[0035] Furthermore, in this method, by testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed according to the pressure performance characteristic data under each working parameter, specifically including: By testing the pressure performance of each hydraulic press (setting the maximum pressure performance characteristic data as the upper threshold value to test the pressure performance of the hydraulic press), the pressure performance characteristic data under each working parameter is obtained, a knowledge graph is constructed, and a graph neural network is introduced; The pressure performance characteristic data obtained under each working parameter is input into the graph neural network, with the working parameter as the first node and the pressure performance characteristic data as the second node; Construct a directed description relationship, connect the first node with the second node based on the directed description relationship to form a topological structure graph, obtain a related adjacency matrix based on the topological structure graph, and input the related adjacency matrix into the knowledge graph for storage.

[0036] It should be noted that the pressure performance of the hydraulic press will change due to different working parameters, such as different hydraulic oil flow, pressure, flow direction and other factors. This method can form a knowledge graph to analyze the pressure performance under various working parameters, thereby forming a knowledge graph. Since the maximum pressure performance characteristic data will degenerate, the test results within different pressure performance characteristic threshold ranges stored in the knowledge graph, that is, different pressure performance characteristic threshold ranges will have different working parameters to achieve the same pressure requirement.

[0037] Furthermore, in this method, the real-time pressure performance of the hydraulic press is obtained according to the knowledge graph and the real-time working parameters of each hydraulic press, specifically: Input the real-time working parameters of each hydraulic press into the knowledge graph for data matching, and obtain sample data corresponding to a Euclidean distance less than a preset Euclidean distance; The real-time pressure performance of the hydraulic press under the real-time working parameters of the hydraulic press is obtained according to the sample data corresponding to the Euclidean distance being less than the preset Euclidean distance, and the real-time pressure performance of the hydraulic press is output.

[0038] Furthermore, in this method, the current minimum pressure requirement data information is obtained, and the hydraulic press is intelligently managed according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information, and the maximum pressure performance characteristic data of each hydraulic press within a preset time, specifically: Obtaining current pressure minimum demand data information, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, taking the current pressure minimum demand data information as a first constraint condition, and taking the maximum pressure performance characteristic data as a second constraint condition; Setting an adjustment threshold range according to the first constraint condition and the second constraint condition, and determining whether the real-time pressure performance of the oil hydraulic press is within the adjustment threshold range; When the real-time pressure performance of the hydraulic press is within the adjustment threshold range, the current working parameters of the hydraulic press are maintained and the working status of the hydraulic press is continuously monitored; When the real-time pressure performance of the hydraulic press is not within the adjustment threshold range, genetic iteration is performed based on genetic algebra to readjust the working parameters of the current hydraulic press, and obtain the real-time pressure performance corresponding to the working parameters of the current hydraulic press until the corresponding real-time pressure performance is within the adjustment threshold range.

[0039] It should be noted that this method can improve the rationality of control over the hydraulic press, so that in some pressure demand scenarios, the real-time pressure performance is within the adjustment threshold range, thereby achieving intelligent management.

[0040] The third aspect of the present invention provides a computer-readable storage medium, including a working status monitoring and management method program for a hydraulic press. When the working status monitoring and management method program for a hydraulic press is executed by a processor, any technical solution of the working status monitoring and management method for a hydraulic press is implemented.

[0041] In summary, the present invention obtains the maximum pressure performance change characteristic data of the hydraulic press through big data, and constructs a maximum pressure performance characteristic prediction model based on the maximum pressure performance change characteristic data of the hydraulic press, and then predicts the maximum pressure performance characteristic data of each hydraulic press within the preset time through the maximum pressure performance characteristic prediction model, thereby testing the pressure performance of each hydraulic press, obtaining the pressure performance characteristic data under each working parameter, and constructing a knowledge graph based on the pressure performance characteristic data under each working parameter, obtaining the real-time working parameters of each hydraulic press, and finally obtaining the real-time pressure performance of the hydraulic press based on the knowledge graph and the real-time working parameters of each hydraulic press, obtaining the current minimum pressure demand data information, and intelligently managing the hydraulic press based on the real-time pressure performance of the hydraulic press, the current minimum pressure demand data information, and the maximum pressure performance characteristic data of each hydraulic press within the preset time. The present invention evaluates the pressure performance of the hydraulic press, and adjusts the working parameters of the hydraulic press according to the evaluation results, so as to adapt to the work requirements and ensure the normal operation of the hydraulic press.

[0042] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0043] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0044] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0045] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0046] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0047] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring and managing the working status of a hydraulic press, characterized in that: include: Acquire maximum pressure performance change characteristic data of the hydraulic press through big data, and construct a maximum pressure performance characteristic prediction model based on the maximum pressure performance change characteristic data of the hydraulic press; Predicting the maximum pressure performance characteristic data of each hydraulic press within a preset time by using the maximum pressure performance characteristic prediction model; By testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed according to the pressure performance characteristic data under each working parameter to obtain the real-time working parameters of each hydraulic press; The real-time pressure performance of the hydraulic press is obtained according to the knowledge graph and the real-time working parameters of each hydraulic press, and the current minimum pressure requirement data information is obtained. The hydraulic press is intelligently managed according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information and the maximum pressure performance characteristic data of each hydraulic press within a preset time.

2. A method for monitoring and managing the working status of a hydraulic press according to claim 1, characterized in that: The maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed according to the maximum pressure performance change characteristic data of the hydraulic press, specifically: The maximum pressure performance change characteristic data of the hydraulic press is obtained through big data, and a maximum pressure performance characteristic prediction model is constructed based on a deep neural network, and a Markov random field model is constructed; Inputting the maximum pressure performance change characteristic data of the oil hydraulic press into the Markov random field model, taking the maximum pressure performance characteristic data of each timestamp as a state vector, and calculating the probability value of each state vector transferring to another state vector; When the probability value is greater than a preset probability threshold, the corresponding state vector is updated to another state vector; when the probability value is not greater than the preset probability threshold, the current state vector is maintained unchanged, and a state vector matrix is ​​constructed according to the state vector; The state vector matrix is ​​input into the maximum pressure performance characteristic prediction model for training. When the prediction accuracy of the maximum pressure performance characteristic prediction model is within a preset prediction accuracy range, the model parameters of the maximum pressure performance characteristic prediction model are saved and the training ends.

3. The method for monitoring and managing the working status of a hydraulic press according to claim 1, characterized in that: Predicting the maximum pressure performance characteristic data of each hydraulic press within a preset time by the maximum pressure performance characteristic prediction model specifically includes: Acquire the maximum pressure performance change characteristic data of each hydraulic press within a preset time, and input the maximum pressure performance change characteristic data of each hydraulic press within the preset time into the maximum pressure performance characteristic prediction model for prediction; By prediction, the maximum pressure performance characteristic data of each hydraulic press within a preset time is obtained, and the maximum pressure performance characteristic data of each hydraulic press within the preset time is output.

4. A method for monitoring and managing the working status of a hydraulic press according to claim 1, characterized in that: By testing the pressure performance of each hydraulic press, pressure performance characteristic data under each working parameter is obtained, and a knowledge graph is constructed based on the pressure performance characteristic data under each working parameter, specifically including: By testing the pressure performance of each hydraulic press, we obtain the pressure performance characteristic data under each working parameter, build a knowledge graph, and introduce a graph neural network. Inputting the pressure performance characteristic data obtained under each working parameter into the graph neural network, using the working parameter as the first node and the pressure performance characteristic data as the second node; Construct a directed description relationship, connect the first node with the second node based on the directed description relationship to form a topological structure graph, obtain a related adjacency matrix based on the topological structure graph, and input the related adjacency matrix into the knowledge graph for storage.

5. The method for monitoring and managing the working status of a hydraulic press according to claim 1, characterized in that: The real-time pressure performance of the hydraulic press is obtained according to the knowledge graph and the real-time working parameters of each hydraulic press, specifically: Input the real-time working parameters of each hydraulic press into the knowledge graph for data matching, and obtain sample data corresponding to a Euclidean distance less than a preset Euclidean distance; The real-time pressure performance of the hydraulic press under the real-time working parameters of the hydraulic press is obtained according to the sample data corresponding to the Euclidean distance being less than the preset Euclidean distance, and the real-time pressure performance of the hydraulic press is output.

6. The method for monitoring and managing the working status of a hydraulic press according to claim 1, characterized in that: Obtain the current minimum pressure requirement data information, and intelligently manage the hydraulic press according to the real-time pressure performance of the hydraulic press, the current minimum pressure requirement data information, and the maximum pressure performance characteristic data of each hydraulic press within a preset time, specifically: Acquire current minimum pressure requirement data information, introduce a genetic algorithm, set a genetic algebra according to the genetic algorithm, use the current minimum pressure requirement data information as a first constraint condition, and use the maximum pressure performance characteristic data as a second constraint condition; Setting an adjustment threshold range according to the first constraint condition and the second constraint condition, and determining whether the real-time pressure performance of the oil hydraulic press is within the adjustment threshold range; When the real-time pressure performance of the hydraulic press is within the adjustment threshold range, the current working parameters of the hydraulic press are maintained and the working state of the hydraulic press is continuously monitored; When the real-time pressure performance of the hydraulic press is not within the adjustment threshold range, genetic iteration is performed based on the genetic algebra to readjust the working parameters of the current hydraulic press, and obtain the real-time pressure performance corresponding to the working parameters of the current hydraulic press until the corresponding real-time pressure performance is within the adjustment threshold range.

7. A system for monitoring and managing the working state of a hydraulic press, characterized in that: It comprises a memory and a processor, wherein the memory comprises a program for monitoring and managing the working status of a hydraulic press, and when the program for monitoring and managing the working status of a hydraulic press is executed by the processor, the steps of the method for monitoring and managing the working status of a hydraulic press as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that: It includes a working status monitoring and management method program for a hydraulic press, which, when executed by a processor, implements the steps of the working status monitoring and management method for a hydraulic press as described in any one of claims 1 to 6.

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