Carbon graphite product profiling control system and method based on multi-parameter cooperation

Abstract

The invention discloses a carbon graphite product profiling control system and method based on multi-parameter collaboration, and relates to the technical field of graphite profiling control. A technological parameter setting unit is used for setting initial technological parameters for all machining links, and a real-time monitoring unit and a feedback compensation unit are used for monitoring the initial technological parameters of all machining links; establishing a feedback mechanism for adjusting the initial process parameters by using real-time data, and realizing primary adjustment on the process parameters; then, through a technological parameter adjusting unit, the relevance among different technological parameters in a single machining link is utilized to conduct adaptive adjustment on other parameters, and secondary adjustment of the technological parameters is achieved; and finally, through a process parameter coupling unit, the process parameters of the subsequent processing link are adaptively adjusted by using the real-time process parameters of the preceding processing link, so that three-stage adjustment of the process parameters is completed. According to the invention, a three-stage adjustment mechanism for process parameters is established to optimize the profiling process of the graphite product, so that the quality of the graphite product is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of graphite compression control, and particularly relates to a carbon graphite product compression control system and method based on multi-parameter cooperation. BACKGROUND

[0002] The carbon graphite product compression process is a key link in the production process of carbon graphite, which makes raw materials into a blank body with a specific shape, size and certain density. The core is to make the plastic raw materials densified and shaped in the mold through external force, so as to provide qualified blanks for subsequent processes such as baking and graphitization. The process directly affects the key properties of the final product such as density, strength and structure uniformity, and is widely used in the production of graphite products.

[0003] The main process of the compression process can be divided into raw material preparation, mixing and kneading, compression and other links. However, the current compression process has the following problems. First, there are many process parameters in each link, and there is a lack of unified coupling mechanism between parameters in each link, resulting in loss of control of process stability. Second, there is a lack of parameter cooperation compensation mechanism in each link. Third, real-time monitoring data in each link cannot be fed back in time. The existence of the above problems makes it impossible to guarantee the quality of the paste, the forming process and batch consistency, and ultimately results in defects such as cracks, size deviation, out-of-roundness and quality fluctuations, which seriously affect the quality of graphite products. SUMMARY

[0004] Therefore, the present application provides a carbon graphite product compression control system and method based on multi-parameter cooperation to solve the problems in the prior art.

[0005] The first aspect of the present application provides a carbon graphite product compression control system based on multi-parameter cooperation, comprising: A process parameter setting unit sets corresponding initial process parameters for each processing link in the carbon graphite product compression process; A real-time monitoring unit sets corresponding monitoring equipment in each processing link and simultaneously acquires real-time monitoring data; A feedback compensation unit adjusts the corresponding initial process parameters in each processing link based on the real-time monitoring data to obtain real-time process parameters; A process parameter adjustment unit determines whether there is a change ratio value between the real-time process parameters and the corresponding initial process parameters in a single processing link that exceeds a set value based on the real-time process parameters and the corresponding initial process parameters in each processing link. If so, the corresponding process parameters are recorded as master process parameters, and the remaining process parameters are recorded as slave process parameters. Each master process parameter is used to adjust all slave process parameters in turn. If not, no action is taken. The process parameter coupling unit: based on the processing sequence of the processing link, obtaining the adjusted real-time process parameters in the initial processing link and performing integrated processing to obtain an initial processing result, and based on the initial processing result, setting different influence coefficients for all subsequent processing links; In any processing link selected from all subsequent processing links except the last processing link, denoted as an intermediate processing link, the adjusted real-time process parameters in the intermediate processing link are obtained, and the influence coefficients transmitted by all upstream processing links of the intermediate processing link are combined for integrated processing to obtain an intermediate processing result, and based on the intermediate processing result, different influence coefficients are set for all downstream processing links of the intermediate processing link. All adjusted real-time process parameters in the processing link are combined with all received influence coefficients for dynamic adjustment.

[0006] In a possible implementation manner of the first aspect, the adjusting of the corresponding initial process parameters in each processing link based on the real-time monitoring data to obtain the real-time process parameters comprises: All real-time monitoring data in a first time interval are obtained, and a curve graph of the real-time monitoring data changing over time is constructed; The curve graph is fitted to obtain a change curvature value of the corresponding real-time monitoring data in each first time interval; Based on the change curvature value, the process parameters corresponding to the real-time monitoring data are adjusted to obtain the real-time process parameters.

[0007] In a possible implementation manner of the first aspect, the judging of whether the change ratio value between the real-time process parameter and the corresponding initial process parameter in a single processing link exceeds a set value comprises: The change ratio value between the real-time process parameter and the corresponding initial process parameter is calculated by using the following formula, specifically: The change ratio value is denoted as R, the real-time process parameter is denoted as P, and the corresponding initial process parameter is denoted as P0. The real-time process parameter is denoted as P. The corresponding initial process parameter is denoted as P0.

[0008] In a possible implementation manner of the first aspect, the adjusting of all slave process parameters by using each master process parameter in sequence comprises: Any processing link is obtained, denoted as a first processing link, and the historical data of all process parameters in the first processing link are obtained synchronously; Based on the historical data and prior data of the first processing link, an association model between a single process parameter and other process parameters is constructed. obtain a correlation model corresponding to the main process parameter in the first processing link, and obtain a corresponding main process parameter, denoted as a first main process parameter; input the first main process parameter and the remaining slave process parameters into the corresponding correlation model, output the adjusted slave process parameters after adjustment, and complete the adjustment of the remaining slave process parameters.

[0009] In a possible implementation manner of the first aspect, processing the initial processing result includes: obtain all adjusted real-time process parameters of the initial processing link, denoted as first real-time process parameters; normalize all first real-time process parameters under the same data protocol and place them in a preset data set, corresponding to a multi-parameter feature set; obtain all multi-parameter feature sets and corresponding processing quality index data of the initial processing link in a second time interval, and extract distribution features of the multi-parameter feature sets; establish a mapping relationship table between the distribution features of the multi-parameter feature sets and the corresponding processing quality index data; use the adjusted real-time process parameters of the initial processing link and the mapping relationship table to output the corresponding processing quality index data of the initial processing link as the initial processing result.

[0010] In a possible implementation manner of the first aspect, different influence coefficients are set for all subsequent processing links. obtain corresponding processing quality index data based on the initial processing result, denoted as first processing quality index data; based on all first processing quality index data in a third time interval and corresponding processing quality index data of the remaining processing links, construct a quantitative model for adjusting the corresponding process parameters of the remaining processing links and train the quantitative model; obtain the first processing quality index data of the initial processing link and the real-time process parameters of the remaining processing links, and input them into the trained quantitative model to output the influence coefficient of the remaining processing links.

[0011] In a possible implementation manner of the first aspect, processing the intermediate processing result includes: obtain the adjusted real-time process parameters of the intermediate processing link and multiply them by the influence coefficients corresponding to all upstream processing links to obtain corrected real-time process parameters; use the corrected real-time process parameters of the intermediate processing link and the corresponding mapping relationship table to output the corresponding processing quality index data of the intermediate processing link as the intermediate processing result.

[0012] In a possible implementation manner of the first aspect, the setting different influence coefficients for all downstream processing links located after the intermediate processing link based on the intermediate processing result comprises: obtaining a quantization model corresponding to the intermediate processing link, denoted as a first quantization model, and training the first quantization model; inputting the processing quality index data corresponding to the intermediate processing link and the real-time process parameters of the remaining processing links into the trained first quantization model, and outputting the influence coefficients for all downstream processing links located after the intermediate processing link.

[0013] In a possible implementation manner of the first aspect, the dynamic adjustment of the adjusted real-time process parameters of all processing links in combination with all received influence coefficients comprises: calculating the product of the adjusted real-time process parameters of any processing link and all received influence coefficients to obtain a product result; based on the product result, dynamically adjusting the real-time process parameters of the corresponding processing link.

[0014] The second aspect of the present application provides a carbon graphite product compression control method based on multi-parameter cooperation, comprising: setting corresponding initial process parameters for each processing link in the carbon graphite product compression process; setting corresponding monitoring equipment for each processing link, and simultaneously obtaining real-time monitoring data; based on the real-time monitoring data, adjusting the corresponding initial process parameters of each processing link to obtain real-time process parameters; based on the real-time process parameters and the corresponding initial process parameters of each processing link, determining whether the change ratio value between the real-time process parameters and the corresponding initial process parameters in a single processing link exceeds a set value, if yes, recording the corresponding process parameters as master process parameters, and recording the remaining process parameters as slave process parameters, and adjusting all slave process parameters in turn using each master process parameter, if no, then no action is taken; based on the processing sequence of the processing links, obtaining the adjusted real-time process parameters of the initial processing link and performing integration processing to obtain an initial processing result, and based on the initial processing result, setting different influence coefficients for all subsequent processing links; selecting any processing link from the subsequent processing links except the last processing link, denoted as an intermediate processing link, obtaining the adjusted real-time process parameters of the intermediate processing link, and combining the influence coefficients transmitted by all upstream processing links located before the intermediate processing link to perform integration processing to obtain an intermediate processing result, and based on the intermediate processing result, setting different influence coefficients for all downstream processing links located after the intermediate processing link; All processing links are adjusted in real time, and the real-time process parameters are combined with all received influence coefficients for dynamic adjustment.

[0015] The beneficial effects are that the application discloses a carbon graphite product pressing control system and method based on multi-parameter cooperation, initial process parameters are set for each processing link by a process parameter setting unit, a real-time monitoring unit and a feedback compensation unit are used to establish a feedback mechanism for adjusting the initial process parameters by using real-time data, and primary adjustment of the process parameters is realized; then, by using the correlation between different process parameters in a single processing link and combining with process parameters with large adjustment range, adaptive adjustment is made on other parameters, secondary adjustment of the process parameters is realized; finally, by using a process parameter coupling unit, the coupling of multi-process parameters between different links is analyzed, real-time process parameters of a previous processing link are used to adaptively adjust process parameters of a subsequent processing link, and tertiary adjustment of the process parameters is completed. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0017] Figure 1 is a carbon graphite product pressing control system based on multi-parameter cooperation provided by the application, and is a component schematic diagram of the system; Figure 2 is a carbon graphite product pressing control method based on multi-parameter cooperation provided by the application, and is a flowchart of the method. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0019] In this application, the relational terms, such as first and second, and the like, are used solely to distinguish one from another entity or action, without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a", "comprising", or "includes... a" does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0020] Embodiment one In the prior art, the main process of the profiling process can be divided into raw material preparation, mixing, profiling and other links, but the current profiling process has the following problems, first, there are many process parameters in each link, and there is a lack of unified coupling mechanism between parameters in each link, resulting in loss of control of process stability; second, there is a lack of parameter coordination compensation mechanism in each link; third, real-time monitoring data in each link cannot be fed back in time.

[0021] Therefore, the present application provides a carbon graphite product profiling control system based on multi-parameter coordination, as shown in Figure 1 The profiling control system comprises: A process parameter setting unit sets corresponding initial process parameters for each processing link in the profiling process of carbon graphite products; A real-time monitoring unit sets corresponding monitoring equipment in each processing link and simultaneously acquires real-time monitoring data; A feedback compensation unit adjusts the corresponding initial process parameters in each processing link based on the real-time monitoring data to obtain real-time process parameters; A process parameter adjustment unit determines whether there is a change ratio value between the real-time process parameters and the corresponding initial process parameters in a single processing link that exceeds a set value based on the real-time process parameters and the corresponding initial process parameters in each processing link, if so, records the corresponding process parameters as master process parameters, records the remaining process parameters as slave process parameters, and adjusts all slave process parameters in turn using each master process parameter, if not, does not act; A process parameter coupling unit acquires and integrates the adjusted real-time process parameters in the initial processing link to obtain an initial processing result based on the processing sequence of the processing links, and sets different influence coefficients for all subsequent processing links based on the initial processing result. In all subsequent processing links, select any processing link as an intermediate processing link, obtain the adjusted real-time process parameters in the intermediate processing link, and combine the influence coefficients transmitted by all upstream processing links in the intermediate processing link for integrated processing to obtain an intermediate processing result. Based on the intermediate processing result, different influence coefficients are set for all downstream processing links in the intermediate processing link. All adjusted real-time process parameters in all processing links are combined with all received influence coefficients for dynamic adjustment.

[0022] In the carbon graphite product compression process, initial process parameters are set for each processing link, including raw material preparation, raw material mixing, compression processing, and green body processing. Initial process parameters such as conveyor belt conveying speed, mixing machine processing power, mold cavity temperature, mold cavity pressure, and mold preheating power are set for each processing link. The setting logic uses prior data for setting.

[0023] In each processing link, a corresponding monitoring device is provided, and real-time monitoring data is obtained. The monitoring data is used to adjust the corresponding initial process parameters in each processing link, i.e., a feedback mechanism is established to adjust the initial process parameters using real-time monitoring data. For example, a laser particle size analyzer is used to monitor the particle size distribution of raw materials in real time, and the conveyor belt speed and weighing sensor sampling frequency are adjusted. The environmental humidity sensor is used to monitor the humidity data in real time, and the amount of binder (coal tar) is adjusted. A multi-point infrared temperature sensor is deployed on the inner wall of the mixing machine to monitor the internal temperature data in real time, and the heating partition power is adjusted. An online viscosity counter is used to monitor the viscosity data in real time, and the mixing time is adjusted to prevent insufficient plasticization. A thermocouple and a temperature sensor are implanted at key points of the mold, and the initial temperature is automatically set. The thermocouple and temperature sensor are used to stabilize the mold cavity temperature within a controllable range. A pressure sensor is used to monitor the pressure of each region of the mold cavity, and the partition pressure of the hydraulic cylinder is adjusted. A laser displacement sensor is used to detect the height of the green body, and the correction pressure holding time is adjusted. A micro-strain gauge is embedded in the key contact surface of the mold head to compensate for the displacement amount of the compression of the mold head. An outdoor temperature sensor is used to monitor the working environment data, and the mold preheating time is adjusted.

[0024] Based on the real-time monitoring data, the corresponding initial process parameters in each processing link are adjusted. The adjustment logic of this embodiment is as follows: all real-time monitoring data in a certain time interval is obtained, a curve graph of the real-time monitoring data with respect to time is constructed and fitted to obtain the change curvature value of the corresponding real-time monitoring data, and the process parameters are adjusted. That is, by establishing a feedback adjustment model, the change curvature of the real-time monitoring data and the corresponding process parameters are trained to adjust the process parameters.

[0025] Among them, the embodiment sets a unified feedback adjustment logic for the initial process parameters according to the real-time monitoring data, and can also set specific feedback adjustment logic according to different real-time monitoring data; For example, a material density-particle size feedback model is constructed. The raw material particle size distribution data monitored by the laser particle size analyzer in real time is input into the model, and the conveying belt speed and the sampling frequency of the weighing sensor are automatically adjusted. Since the particle size distribution of the raw material directly affects the bulk density, for example, the smaller the particle size, the greater the bulk density; the larger the particle size, the smaller the bulk density. When the particle size is small and the bulk density is large, the weight of the material passing through the weighing area per unit time exceeds the standard at the same conveying speed, which may cause the weighing sensor to overload or the data to jump, so it is necessary to reduce the conveying belt speed and increase the sampling frequency of the weighing sensor. Similarly, for raw materials with large particle size, it is necessary to increase the conveying belt speed and reduce the sampling frequency of the weighing sensor.

[0026] For example, based on the environmental humidity sensor data, the amount of binder added is dynamically corrected. The correction algorithm uses: , is the reference addition amount, is the humidity deviation data, is the humidity coefficient, is the adjusted addition amount.

[0027] For example, the height of the blank is monitored by a laser displacement sensor, and the blank correction pressure holding time is adjusted. The adjustment algorithm uses: , is the height correction coefficient, is the height deviation, is the reference pressure holding time, is the adjusted pressure holding time.

[0028] For example, micro strain gauges are embedded in the key contact surfaces of the mold to automatically compensate for the molding displacement. The adjustment logic is to calculate the wear coefficient and calculate the compensation molding displacement, , is the wear coefficient, is the compensation amount.

[0029] Among them, based on the real-time process parameters in each processing link and the corresponding initial process parameters, it is judged whether there is a change ratio value between the real-time process parameters and the corresponding initial process parameters in a single processing link that exceeds a set value. Specifically: is the change ratio value, is the real-time process parameter, is the corresponding initial process parameter; If there is a real-time process parameter change ratio value exceeding the set value, it means that the adjustment range of the corresponding real-time process parameter is large. For a single processing link, the adjustment range of a single process parameter is large, which will affect other process parameters. For example, in the raw material pretreatment link, if the adjustment range of the conveying belt speed and the weighing sensor sampling frequency is large, it means that the particle size of the raw material is too large or too small. Therefore, the amount of binder added per unit volume and the processing temperature of the paste need to be adjusted accordingly. At this time, the conveying belt speed and the weighing sensor sampling frequency are the master process parameters, and the amount of binder added per unit volume and the processing temperature of the paste are the slave process parameters. It should be noted that the master process parameters and the slave process parameters in a single processing link are only differentiated by the adjustment range, not fixed parameters.

[0030] Among them, the master process parameter in a single processing link is used to adjust all slave process parameters, specifically: using historical data and prior data to build a correlation model between a single process parameter and other process parameters. Because the master process parameter and the slave process parameter in a single processing link both change, there are multiple correlation models in a single processing link. Any master process parameter and the corresponding correlation model are obtained to adjust the remaining slave process parameters.

[0031] Among them, based on the processing order of the processing link, the processing link is processed in turn. For example, there are processing link B, processing link C, processing link D and processing link E. Each processing link obtains the adjusted real-time process parameters. It should be noted that the adjusted real-time process parameters refer to the process parameters after one-level adjustment by real-time monitoring data and two-level adjustment by master process parameters.

[0032] Processing link B as the initial processing link, obtains all adjusted real-time process parameters of processing link B, and normalizes all real-time process parameters under the same data protocol to obtain a multi-parameter feature set, which eliminates the dimensional difference and order of magnitude difference between parameters and provides a unified analysis benchmark for extracting single processing link parameter distribution characteristics. Obtain all multi-parameter feature sets of processing link B in the second time interval (such as three months) and the corresponding processing quality index data of the processing link, and simultaneously extract the distribution characteristics of the parameters; establish a mapping relationship table between the distribution characteristics of the parameters and the corresponding processing quality index data, and use the mapping relationship table and the adjusted real-time process parameters to output the corresponding processing quality index data. The above processing logic is to normalize the real-time process parameters of the processing link, extract the parameter distribution characteristics, and establish a mapping relationship table with the prior processing quality index data. In this way, after obtaining the adjusted real-time process parameters, the mapping relationship table can be used to directly output the corresponding processing quality index data as the distribution characteristics of the real-time process parameters.

[0033] The processing quality index data of the processing link B in the third time interval and the processing quality index data of the remaining processing links are acquired, a quantitative model about the corresponding process parameter adjustment of the remaining processing links is constructed and trained, and the trained quantitative model is used to output the influence coefficient of the remaining processing links, such as setting the influence coefficient of the processing link C , setting the influence coefficient of the processing link , setting the influence coefficient of the processing link .

[0034] Similarly, the remaining processing link C, the processing link D and the processing link E are processed in turn, and the processing link C and the processing link D are intermediate processing links, and their processing logic is consistent with that of the intermediate processing link. The adjusted real-time process parameters of the intermediate processing link are acquired and multiplied by the influence coefficient transmitted by the upstream processing link to obtain the corrected real-time process parameters, and then the processing quality index data of the intermediate processing link is output in combination with the corresponding mapping relationship table. The influence coefficient of the processing link located downstream of the intermediate processing link is set through the quantitative model of the intermediate processing link. For example, based on the intermediate processing result of the processing link C, the influence coefficient of the processing link D is set as , the influence coefficient of the processing link E is set as ; based on the intermediate processing result of the processing link D, the influence coefficient of the processing link E is set as . Finally, the adjusted real-time process parameters of all processing links are combined with all received influence coefficients for dynamic adjustment. Specifically, the processing link B does not act on the acquired adjusted real-time process parameters, the processing link C acquires the adjusted real-time process parameters as , so the adjusted real-time process parameters should be ; the processing link D acquires the adjusted real-time process parameters as , so the adjusted real-time process parameters should be ; the processing link E acquires the adjusted real-time process parameters as , so the adjusted real-time process parameters should be The coupling logic of the multi-parameter coupling of each processing link in this embodiment is to convert the processing parameter distribution characteristics of the previous processing link into corresponding processing quality index data, set the influence coefficient of the influence degree of the subsequent processing link by using the processing quality index data, superimpose the influence coefficients transmitted by all upstream processing links of the subsequent processing link, and then adjust the real-time process parameters to realize the coupling of the multi-parameters among the processing links.

[0035] The embodiment further establishes an environment-initial process parameter database, switches process parameter modes according to different outdoor environment temperatures, and sets different initial process parameters correspondingly.

[0036] In some embodiments, based on the real-time monitoring data, the corresponding initial process parameters in each processing link are adjusted to obtain real-time process parameters. All real-time monitoring data in the first time interval are obtained, and a curve graph of the real-time monitoring data changing over time is constructed. The curve graph is fitted to obtain a change curvature value of the corresponding real-time monitoring data of each first time interval. Based on the change curvature value, the process parameters corresponding to the real-time monitoring data are adjusted to obtain real-time process parameters.

[0037] In some embodiments, determining whether the change ratio value between the real-time process parameters and the corresponding initial process parameters in a single processing link exceeds a set value includes: The change ratio value between the real-time process parameters and the corresponding initial process parameters is calculated using the following formula, specifically: The change ratio value is The real-time process parameter is The corresponding initial process parameter is

[0038] In some embodiments, the corresponding process parameters are recorded as main process parameters, and the remaining process parameters are recorded as slave process parameters, and each main process parameter is used to adjust all slave process parameters in turn, including: Any processing link is obtained, recorded as a first processing link, and the historical data of all process parameters in the first processing link are obtained synchronously; Based on the historical data and prior data of the first processing link, an association model between a single process parameter and other process parameters is constructed; The association model corresponding to the main process parameter in the first processing link is obtained, and the corresponding main process parameter is recorded as a first main process parameter; After the first main process parameter and the remaining slave process parameters are input into the corresponding association model, the adjusted slave process parameters are output, and the adjustment of the remaining slave process parameters is completed.

[0039] In some embodiments, the processing obtains an initial processing result, including: All adjusted real-time process parameters of the initial processing link are obtained, recorded as first real-time process parameters; normalizing all the first real-time process parameters under the same data protocol and placing them into a preset data set, corresponding to a multi-parameter feature set; obtaining all the multi-parameter feature sets and corresponding processing quality index data of the initial processing link in a second time interval, and extracting distribution features of the multi-parameter feature sets; establishing a mapping relationship table of the distribution features of the multi-parameter feature sets and the corresponding processing quality index data; using the adjusted real-time process parameters in the initial processing link and the mapping relationship table, outputting the processing quality index data corresponding to the initial processing link as the initial processing result.

[0040] In some embodiments, based on the initial processing result, different influence coefficients are set for all subsequent processing links, including: based on the initial processing result, obtaining corresponding processing quality index data, denoted as first processing quality index data; based on all the first processing quality index data in the third time interval and the corresponding processing quality index data of the remaining processing links, constructing a quantitative model for adjusting the process parameters of the remaining processing links and training the same; obtaining the first processing quality index data in the initial processing link and the real-time process parameters of the remaining processing links, and inputting them into the trained quantitative model to output the influence coefficient of the remaining processing links.

[0041] In some embodiments, the intermediate processing result is obtained by processing, including: obtaining the adjusted real-time process parameters in the intermediate processing link and multiplying them by the influence coefficients transmitted by all the upstream processing links to obtain corrected real-time process parameters; using the corrected real-time process parameters in the intermediate processing link and the corresponding mapping relationship table, outputting the processing quality index data corresponding to the intermediate processing link as the intermediate processing result.

[0042] In some embodiments, based on the intermediate processing result, different influence coefficients are set for all downstream processing links of the intermediate processing link, including: obtaining a quantitative model corresponding to the intermediate processing link, denoted as a first quantitative model and training the same; inputting the processing quality index data corresponding to the intermediate processing link and the real-time process parameters of the remaining processing links into the trained first quantitative model to output the influence coefficient of all downstream processing links of the intermediate processing link.

[0043] In some embodiments, the adjusted real-time process parameters in all processing links are dynamically adjusted in combination with all received influence coefficients, including: The product of the adjusted real-time process parameter in any processing link and the corresponding received influence coefficient is calculated to obtain a product result. Based on the product result, the real-time process parameter of the corresponding processing link is dynamically adjusted.

[0044] Embodiment Two Based on the carbon graphite product compression control system provided in Embodiment One of the present application, correspondingly, Embodiment Two of the present application further provides a carbon graphite product compression control method based on multi-parameter cooperation, as shown in Figure 2 The method comprises the following steps: The initial process parameters of each processing link in the carbon graphite product compression process are set; The corresponding monitoring equipment is arranged in each processing link, and real-time monitoring data is obtained at the same time; The initial process parameters of each processing link are adjusted based on the real-time monitoring data to obtain real-time process parameters; Based on the real-time process parameters and the initial process parameters in each processing link, it is determined whether the change ratio value between the real-time process parameters and the initial process parameters in a single processing link exceeds a set value, if yes, the corresponding process parameter is recorded as a master process parameter, and the remaining process parameters are recorded as slave process parameters, and each master process parameter is used to adjust all slave process parameters in turn, if no, no action is taken; Based on the processing sequence of the processing links, the adjusted real-time process parameters in the initial processing link are obtained and integrated to obtain an initial processing result, and based on the initial processing result, different influence coefficients are set for all subsequent processing links; Any processing link in all subsequent processing links except the last processing link is selected as an intermediate processing link, the adjusted real-time process parameters in the intermediate processing link are obtained, and the influence coefficients transmitted by all upstream processing links of the intermediate processing link are integrated to obtain an intermediate processing result, and based on the intermediate processing result, different influence coefficients are set for all downstream processing links of the intermediate processing link; The adjusted real-time process parameters in all processing links are dynamically adjusted in combination with all received influence coefficients.

[0045] Those skilled in the art will further appreciate that the units and algorithms described in connection with the examples disclosed herein can be embodied in electronic hardware, software, or a combination of both, and that the described steps can be carried out by specific hardware or software components or modules or units, or combinations thereof. The foregoing disclosure is generally related to the various suitable components or modules which can carry out the steps of the described examples. To clearly illustrate this interchangeability of hardware and software, various components will be described generally in terms of their functionality, without reference to the particular

[0046] Although preferred embodiments of the application have been described herein, those skilled in the art will appreciate that other changes and modifications can be made to the described embodiments without departing from the spirit and scope of the application. It is therefore intended that the appended claims encompass all such changes and modifications as fall within the scope of the application.

[0047] It is to be understood that the application can be carried out by specifically different embodiments and equivalents without departing from the spirit and scope of the application. Consequently, particular embodiments of the application are not to be understood as limiting the scope of the application. On the contrary, it is the intention to cover all modifications, permutations and equivalents falling within the scope of the application including its appended claims.

Claims

1. A multi-parameter synergy based carbon graphite product compaction control system, characterized in that, The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method.

2. The multi-parameter synergy based carbon graphite product compression control system according to claim 1, wherein, The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method.

3. The multi-parameter synergy based carbon graphite product compression control system of claim 1, wherein, The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. for the varying ratio value, for the real-time process parameter, for the corresponding initial process parameter.

4. The multi-parameter synergy based carbon graphite product compression control system of claim 1, wherein, The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. The application relates to a carbon graphite product forming process parameter setting method. 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5. The multi-parameter synergy based carbon graphite product compression control system of claim 1, wherein, The initial processing result includes: All adjusted real-time process parameters of the initial processing link are obtained and denoted as first real-time process parameters; All first real-time process parameters are normalized under the same data protocol and placed in a preset data set, and a multi-parameter feature set is obtained; All multi-parameter feature sets and corresponding processing quality index data of the initial processing link in a second time interval are obtained, and distribution characteristics of the multi-parameter feature sets are extracted; A mapping relationship table of the distribution characteristics of the multi-parameter feature sets and the corresponding processing quality index data is established; The corresponding processing quality index data of the initial processing link is output as the initial processing result by using the adjusted real-time process parameters of the initial processing link and the mapping relationship table.

6. The multi-parameter synergy based carbon graphite product compression control system of claim 5, wherein, Based on the initial processing result, different influence coefficients are set for all subsequent processing links, including: Based on the initial processing result, corresponding processing quality index data is obtained and denoted as first processing quality index data; A quantitative model for adjusting the corresponding process parameters of the remaining processing links is constructed based on all first processing quality index data in a third time interval and corresponding processing quality index data of the remaining processing links, and the quantitative model is trained; The first processing quality index data of the initial processing link and the real-time process parameters of the remaining processing links are input into the trained quantitative model, and the influence coefficients of the remaining processing links are output.

7. The multi-parameter synergy based carbon graphite product compression control system of claim 6, wherein, The intermediate processing result includes: The adjusted real-time process parameters of the intermediate processing link are multiplied by the influence coefficients transmitted by all upstream processing links to obtain corrected real-time process parameters; The corresponding processing quality index data of the intermediate processing link is output as the intermediate processing result by using the corrected real-time process parameters of the intermediate processing link and the corresponding mapping relationship table.

8. The multi-parameter synergy based carbon graphite product compression control system of claim 7, wherein, Based on the intermediate processing result, different influence coefficients are set for all downstream processing links of the intermediate processing link, including: The corresponding quantitative model of the intermediate processing link is obtained and denoted as a first quantitative model, and the first quantitative model is trained; The corresponding processing quality index data of the intermediate processing link and the real-time process parameters of the remaining processing links are input into the trained first quantitative model, and the influence coefficients of all downstream processing links of the intermediate processing link are output.

9. The multi-parameter synergy based carbon graphite product compaction control system of claim 1, wherein, The adjusted real-time process parameters of all processing links are combined with all received influence coefficients to perform dynamic adjustment, including: The product of the adjusted real-time process parameters of any processing link and the corresponding received influence coefficients is calculated to obtain a product result; Based on the product result, the real-time process parameters of the corresponding processing link are dynamically adjusted.

10. A method for controlling the molding of carbon graphite products based on multi-parameter synergy, characterized by, including: The initial process parameters are set for each processing link in the carbon graphite product pressing process; Corresponding monitoring equipment is set for each processing link, and real-time monitoring data is obtained simultaneously; Adjust the initial process parameters in each processing link based on the real-time monitoring data to obtain real-time process parameters; Determine whether the change ratio between the real-time process parameters and the initial process parameters in each processing link exceeds a set value, and if so, record the corresponding process parameters as master process parameters and the remaining process parameters as slave process parameters, and adjust each master process parameter to all slave process parameters in turn, and if not, do nothing; Based on the processing order of the processing links, obtain the adjusted real-time process parameters in the initial processing link and perform integration processing to obtain an initial processing result, and based on the initial processing result, set different influence coefficients for all subsequent processing links; Select any processing link in all subsequent processing links except the last processing link as an intermediate processing link, obtain the adjusted real-time process parameters in the intermediate processing link, and perform integration processing in combination with the influence coefficients transmitted by all upstream processing links of the intermediate processing link to obtain an intermediate processing result, and based on the intermediate processing result, set different influence coefficients for all downstream processing links of the intermediate processing link; The adjusted real-time process parameters in all processing links are combined with all received influence coefficients for dynamic adjustment.

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