Image processing method, image processing device and electronic device for network graph

Abstract

The present application discloses an image processing method, an image processing device and an electronic device for a network diagram. The method comprises: processing an acquired network diagram to generate target transformation parameters of the network diagram, wherein the target transformation parameters include translation parameters and scaling parameters; generating a PID controller based on the target transformation parameters; updating the target transformation parameters of the network diagram based on the PID controller; and, in response to the PID controller determining that the limit value of the error is within a preset range, stopping updating the target transformation parameters of the network diagram. In this way, the network diagram is subjected to image transformation processing based on the PID control theory to make the image transformation smoother.

Description

Technical Field

[0001] The present application relates to the field of image processing, and more specifically to an image processing method, an image processing device and an electronic device for a network graph. Background Art

[0002] In recent years, big data, artificial intelligence and computing technologies have flourished. For example, social network analysis, one of the applications of the above technologies, has become very mature and has been widely used in areas such as social figure influence calculation, friend and product recommendations, and social circle analysis. In recent years, the application of social network analysis algorithms has continued to expand and has begun to be applied to various anti-fraud fields such as finance and insurance, with good results.

[0003] In network analysis techniques such as social network analysis, network graphs are widely used as a graphical model. For example, in the field of anti-fraud, when using social network analysis for community analysis, faction graphs (network graphs) can reflect a lot of valuable information. Behind this kind of graph is usually a group of people working together, and the communication between two people indicates that they know each other. The purpose behind this is often to collude with each other and falsify information to meet the requirements of financial loan review. In addition, this kind of community often has internal and external collusion, which requires special attention.

[0004] However, in the process of displaying the network diagram, as the number of related nodes in the network increases, the display and transformation operations of the image will become messy and stuck, affecting the user experience.

[0005] Therefore, a new image processing scheme for network graphs is needed. Summary of the invention

[0006] In order to solve the above technical problems, this application is proposed. The embodiments of this application provide an image processing method, an image processing device and an electronic device for a network graph, which perform image transformation processing on the network graph based on PID control theory to make the image transformation smoother and avoid undesirable phenomena such as freezing and jumping.

[0007] According to one aspect of the present application, there is provided an image processing method for a network graph, comprising:

[0008] Processing the acquired network graph to generate target transformation parameters of the network graph, wherein the target transformation parameters include translation parameters and scaling parameters;

[0009] generating a PID controller based on the target transformation parameter;

[0010] Based on the PID controller, updating the target transformation parameters of the network diagram; and

[0011] In response to the PID controller determining that the limit value of the error is within a preset range, updating of the target transformation parameter of the network diagram is stopped.

[0012] In the above-mentioned image processing method, the acquired network graph is processed to generate target transformation parameters of the network graph, including: determining the target transformation parameters of the network graph based on the nodes and the relationships between the nodes in the network graph.

[0013] In the above-mentioned image processing method, the target transformation parameters of the network diagram are determined based on the relationship between the nodes in the network diagram, including: determining the center point of the network diagram; determining the relative position relationship between other nodes and the center point based on the center point; and determining the target transformation parameters of the network diagram based on the relative position relationship between the center point and other nodes and the center point.

[0014] In the above image processing method, the translation parameters include the coordinates of the center point and other nodes of the network diagram on the X-axis and the coordinates on the Y-axis.

[0015] In the above-mentioned image processing method, a PID controller is generated based on the target transformation parameters, including: determining an initial proportional coefficient and an initial integral coefficient of the PI controller based on the target transformation parameters; determining an initial differential coefficient of the PID controller based on an adjustment result of the PI controller; and determining a proportional coefficient, an integral coefficient and a differential coefficient of the PID controller based on the adjustment result of the PID controller.

[0016] In the above image processing method, the initial proportional coefficient of the PI controller does not exceed the first preset threshold, and the initial integral coefficient is not lower than the second preset threshold.

[0017] In the above-mentioned image processing method, based on the adjustment result of the PI controller, the initial differential coefficient of the PID controller is determined, including: in response to the overshoot of the step response exceeding the third preset threshold, reducing the proportional coefficient and increasing the integral coefficient; in response to the step response having no overshoot and the rising rate of the controlled quantity being lower than the fourth preset threshold and the transition process time exceeding the fifth preset threshold, increasing the proportional coefficient and reducing the integral coefficient; in response to the speed of eliminating errors being lower than the sixth threshold, increasing the integral coefficient; and, in response to the overshoot still exceeding the seventh preset threshold after the proportional coefficient and the integral coefficient are adjusted for a preset number of times, determining the initial differential coefficient of the PID controller.

[0018] In the above image processing method, the network diagram is a network diagram used to display social network analysis results.

[0019] In the above image processing method, the proportional coefficient of the PID controller is 0.35, the integral coefficient is 0.002, and the differential coefficient is 0.05.

[0020] According to another aspect of the present application, there is provided an image processing device, comprising:

[0021] A target parameter acquisition unit, used for processing the acquired network diagram to generate target transformation parameters of the network diagram, wherein the target transformation parameters include translation parameters and scaling parameters;

[0022] A controller generating unit, used for generating a PID controller based on the target transformation parameter;

[0023] an updating unit, configured to update a target transformation parameter of the network diagram based on the PID controller; and

[0024] A stopping unit stops updating the target transformation parameter of the network diagram in response to the PID controller determining that the limit value of the error is within a preset range.

[0025] In the above-mentioned image processing device, the target parameter acquisition unit is further used to: determine the target transformation parameters of the network graph based on the relationship between nodes in the network graph.

[0026] In the above-mentioned image processing device, the target parameter acquisition unit is further used to: determine the center point of the network diagram; determine the relative position relationship between other nodes and the center point based on the center point; and determine the target transformation parameters of the network diagram based on the relative position relationship between the center point and other nodes and the center point.

[0027] In the above-mentioned image processing device, the translation parameters include the coordinates of the center point and other nodes of the network diagram on the X-axis and the coordinates on the Y-axis.

[0028] In the above-mentioned image processing device, the control generation unit is further used to: determine the initial proportional coefficient and initial integral coefficient of the PI controller based on the target transformation parameter; determine the initial differential coefficient of the PID controller based on the adjustment result of the PI controller; and determine the proportional coefficient, integral coefficient and differential coefficient of the PID controller based on the adjustment result of the PID controller.

[0029] In the above-mentioned image processing device, the initial proportional coefficient of the PI controller does not exceed the first preset threshold value, and the initial integral coefficient is not lower than the second preset threshold value.

[0030] In the above-mentioned image processing device, the control generation unit is further used to: in response to the overshoot of the step response exceeding the third preset threshold, reduce the proportional coefficient and increase the integral coefficient; in response to the step response having no overshoot and the rising rate of the controlled quantity being lower than the fourth preset threshold and the transition process time exceeding the fifth preset threshold, increase the proportional coefficient and reduce the integral coefficient; in response to the speed of eliminating errors being lower than the sixth threshold, increase the integral coefficient; and, in response to the overshoot still exceeding the seventh preset threshold after the proportional coefficient and the integral coefficient are adjusted for a preset number of times, determine the initial differential coefficient of the PID controller.

[0031] In the above-mentioned image processing device, the network diagram is a network diagram used to display social network analysis results.

[0032] In the above image processing device, the proportional coefficient of the PID controller is 0.35, the integral coefficient is 0.002, and the differential coefficient is 0.05.

[0033] According to another aspect of the present application, an electronic device is provided, comprising: a processor; and a memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the image processing method as described above.

[0034] According to another aspect of the present application, a computer-readable medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor is enabled to perform the image processing method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0036] Figure 1 The figure illustrates a flow chart of an image processing method according to an embodiment of the present application.

[0037] Figure 2 The figure shows a schematic diagram of a PID controller according to an embodiment of the present application.

[0038] Figure 3 The diagram shows a schematic diagram of the change of the controlled variable with respect to time under different proportional coefficients according to an embodiment of the present application.

[0039] Figure 4The diagram shows a schematic diagram of the change of the controlled coefficient over time under different integral coefficients according to an embodiment of the present application.

[0040] Figure 5 The diagram shows a schematic diagram of the change of the controlled variable over time under different differential coefficients according to an embodiment of the present application.

[0041] Figure 6 The figure shows a block diagram of an image processing apparatus according to an embodiment of the present application.

[0042] Figure 7 A block diagram of an electronic device according to an embodiment of the present application is illustrated. DETAILED DESCRIPTION

[0043] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0044] Application Overview

[0045] As mentioned above, in the process of displaying a network diagram, as the number of nodes associated in the network increases, the display and transformation of the image will become messy and jerky. In order to facilitate the operation and view the information displayed by the network diagram, such as the relationship between nodes, it is necessary to solve the problem of jerking and jumping during the transformation of the network diagram (especially during zooming).

[0046] PID (Proportion Integration Differentiation) control theory is the most widely used control theory in engineering practice. Its controller has become one of the main technologies of industrial control due to its simple structure, good stability, reliable operation and convenient adjustment. In particular, when the structure and parameters of the controlled object cannot be fully understood (or when an accurate mathematical model cannot be obtained), the PID controller is particularly suitable.

[0047] How to apply the traditional control concept of PID to the image processing scheme of network graphs to make the transformation of network graphs smoother is the research and development idea of ​​the inventor of this application.

[0048] Accordingly, the basic concept of the present application is to perform image transformation processing on the network diagram based on the PID control theory to make the image transformation smoother and avoid undesirable phenomena such as freezing and jumping.

[0049] Based on this, the present application proposes an image processing method for a network diagram, which first processes the acquired network diagram to generate target transformation parameters of the network diagram, wherein the target transformation parameters include translation parameters and scaling parameters; then generates a PID controller based on the target transformation parameters; then updates the target transformation parameters of the network diagram based on the PID controller; and then stops updating the target transformation parameters of the network diagram in response to the PID controller determining that the limit value of the error is within a preset range. In this way, the network diagram is subjected to image transformation processing based on the PID control theory, so that the image transformation is smoother and undesirable phenomena such as freezing and jumping are avoided.

[0050] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0051] Exemplary Methods

[0052] Figure 1 FIG. 1 is a flowchart of an image processing method according to an embodiment of the present application. Figure 1 As shown, the image processing method according to the embodiment of the present application includes: S110, processing the acquired network diagram to generate target transformation parameters of the network diagram, the target transformation parameters including translation parameters and scaling parameters; S120, generating a PID controller based on the target transformation parameters; S130, updating the target transformation parameters of the network diagram based on the PID controller; and, S140, in response to the PID controller determining that the limit value of the error is within a preset range, stopping updating the target transformation parameters of the network diagram.

[0053] In step S110, the acquired network graph is processed to generate target transformation parameters of the network graph, and the target transformation parameters include translation parameters and scaling parameters. Here, in the embodiment of the present application, the network graph can be implemented as a network graph for displaying social network analysis results. Of course, the network graph can also be other network graphs for displaying network analysis results, such as legal knowledge graphs, Baidu Encyclopedia knowledge graphs, etc., which are not limited to the present application. The target transformation parameters of the network graph represent the parameters of the image transformation operation performed on the network graph. In particular, in the embodiment of the present application, since the main problem to be solved is the display problem of the network graph and the network graph is usually a two-dimensional image, in the embodiment of the present application, the target transformation parameters only include translation parameters and scaling parameters. A person of ordinary skill in the art should understand that, from the perspective of image affine transformation, image transformation should include parameters such as translation, scaling, and rotation. That is to say, in other examples of the present application, when the network graph needs to be rotated, the target transformation parameters of the network graph may also include rotation transformation parameters, which are not limited to the present application.

[0054] Specifically, in an embodiment of the present application, the target transformation parameters of the network diagram are determined based on the relationship between nodes in the network diagram. Specifically, the calculation process of the target transformation parameters includes: first determining the center point of the network diagram; then, determining the relative position relationship between other nodes and the center point based on the center point; then, determining the target transformation parameters of the network diagram based on the relative position relationship between the center point and other nodes and the center point. In particular, in an embodiment of the present application, the translation parameters are expressed based on the coordinates of the center point and other nodes of the network diagram on the X-axis and on the Y-axis.

[0055] In step S120, a PID controller is generated based on the target transformation parameter. PID is the abbreviation of Proportion, Integration, and Differentiation, which represent three control algorithms respectively. The combination of these three algorithms can effectively correct the deviation of the controlled object, so that it reaches a stable state. Figure 2 FIG. 1 is a schematic diagram of a PID controller according to an embodiment of the present application. Figure 2 As shown, the PID algorithm controls the controlled quantity through the error signal, and the controller itself is the sum of the three links of proportion, integration and differentiation. Accordingly, at time t:

[0056] 1. The input is i (t)

[0057] 2. Output is o (t)

[0058] 3. The deviation is err (t) =i (t) -o (t)

[0059]

[0060] The control concept of PID is very simple. Its main problems and difficulties lie in the parameter setting process of proportional, integral and differential links. In the embodiment of the present application, a patchwork method is used to perform experimental setting to determine the parameters of the PID controller. That is, when setting the parameters of the PID controller, the parameters of the controller are adjusted by experimental methods based on the qualitative relationship between the parameters of the controller and the dynamic performance and steady-state performance of the system.

[0061] In the embodiment of the present application, expert PID or fuzzy PID can be used to meet this demand. Specifically, both expert algorithms and fuzzy algorithms belong to the category of intelligent algorithms. The biggest advantage of intelligent algorithms is that they can control the model when the control model is unknown. It should be noted here that in the embodiment of the present application, whether it is expert PID or fuzzy PID, it is not a simple addition of an expert system or a fuzzy algorithm and a PID control algorithm. It is the application of an expert system or a fuzzy algorithm in the parameter setting of a PID controller. In other words, the intelligent algorithm is a means to assist PID in parameter setting.

[0062] The most important issue in debugging is to know which parameter should be adjusted when the system performance is not satisfactory, and whether the parameter should be increased or decreased. Whether it is an expert PID or a fuzzy PID algorithm, it is necessary to determine the basis for parameter adjustment, that is, there must be a basis for what value to adjust under what circumstances. This basis is a combination of some logics. As long as the logical combination relationship is found, these bases cannot be more obvious.

[0063] The following is an explanation of the specific implementation of expert PID. As mentioned above, in the process of parameter tuning, it is necessary to find some basis for tuning, which starts from the PID coefficient itself. In the PID controller,

[0064] (1) Proportional coefficient K p The function of K is to speed up the response speed of the system and improve the adjustment accuracy of the system. p The larger the value, the faster the system responds and the higher the adjustment accuracy of the system, but it is easy to produce overshoot and even make the system unstable. p If the value is too small, the adjustment accuracy will be reduced, the response speed will be slow, and the adjustment time will be prolonged, which will deteriorate the static and dynamic characteristics of the system.

[0065] (2) Integral action coefficient K i The function of K is to eliminate the steady-state error of the system. i The larger the K is, the faster the static error of the system is eliminated, but i If K is too large, integral saturation will occur in the early stage of the response process, causing a large overshoot in the response process. i If it is too small, it will make it difficult to eliminate the static error of the system and affect the adjustment accuracy of the system.

[0066] (3) Differential coefficient K d The role of K is to improve the dynamic characteristics of the system. Its main function is to suppress the change of deviation in any direction during the response process and to predict the change of deviation in advance. d If it is too large, the response process will be braked in advance, thereby extending the adjustment time and reducing the system's anti-interference ability.

[0067] Moreover, two parameters reflecting the performance of the reaction system are the system error e and the error change law e c . For the sake of convenience in explanation, a limit value of the error is defined, assumed to be Mmax; a relatively large value of the error is defined, assumed to be Mmid; a relatively small value of the error is defined, assumed to be Mmin;

[0068] When abs(e)>Mmax, it indicates that the absolute value of the system error is already very large. Regardless of the trend of the error change, it should be considered that the input of the controller should output at the maximum (or minimum) to achieve the effect of quickly adjusting the error and reducing the absolute value of the error at the maximum speed. At this time, it is equivalent to implementing open-loop control.

[0069] When e*e c >0, it indicates that the error is changing in the direction of increasing the absolute value of the error. At this time, if abs(e)>Mmid, it indicates that the error is also relatively large, and it can be considered that the controller implements a strong control action to reverse the change of the absolute value of the error to the decreasing direction and quickly reduce the absolute value of the error. At this time, if abs(e)<Mmid, it indicates that although the error is changing in the direction of increasing the absolute value, the absolute value of the error itself is not very large, and it can be considered that the controller implements a general control action and only needs to reverse the trend of the error change to make it change in the direction of decreasing the absolute value of the error.

[0070] When e*err (t) <0 and e*err (t-1) >0 or e = 0, it indicates that the absolute value of the error is changing in the decreasing direction or has reached the equilibrium state. At this time, the output of the controller can be kept unchanged.

[0071] When e*err (t) <0 and e*err (t-1) <0, it indicates that the error is in the limit state. If the absolute value of the error is relatively large and greater than Mmin at this time, a strong control action can be considered. If the absolute value of the error is relatively small at this time, a weak control action can be considered.

[0072] When abs(e)<Mmin, it indicates that the absolute value of the error is very small. At this time, integral is added to reduce the static error.

[0073] The above logical judgment process is actually an expert judgment process for the PID control system. Therefore, the above PID control idea is called expert PID.

[0074] Correspondingly, the fuzzy PID algorithm is an intelligent algorithm, which includes expert systems, fuzzy algorithms, genetic algorithms, neural network algorithms, etc. In fact, any of these algorithms can be combined with PID, and the key to the choice is that the real-time performance of the processing cannot be met. When the processor speed is fast enough, a more complex and more accurate algorithm can be selected. In addition, cost is the most fundamental reason for limiting the speed of the processor. There is no problem for fuzzy PID to adapt to general control systems.

[0075] Based on the above theoretical guidance, in an embodiment of the present application, a PID controller is generated based on the target transformation parameters, including: first, based on the target transformation parameters, an initial proportional coefficient and an initial integral coefficient of the PI controller are determined; then, based on the adjustment result of the PI controller, an initial differential coefficient of the PID controller is determined; then, based on the adjustment result of the PID controller, a proportional coefficient, an integral coefficient and a differential coefficient of the PID controller are determined.

[0076] Preferably, in the embodiment of the present application, the initial proportional coefficient of the PI controller does not exceed the first preset threshold value, and the initial integral coefficient is not lower than the second preset threshold value. That is to say, in order to reduce the parameters that need to be adjusted, in the embodiment of the present application, the PI controller can be used first. Preferably, more conservative parameters should be set at the beginning of debugging, such as the proportional coefficient should not be too large, and the integral time should not be too small, so as to avoid abnormal situations such as system instability or excessive overshoot. Figure 3 The diagram shows a schematic diagram of the change of the controlled variable with respect to time under different proportional coefficients according to an embodiment of the present application, and, Figure 4 The diagram shows a schematic diagram of the change of the controlled coefficient over time under different integral coefficients according to an embodiment of the present application.

[0077] Further, in the implementation of the present application, based on the adjustment result of the PI controller, the initial differential coefficient of the PID controller is determined, including: in response to the overshoot of the step response exceeding the third preset threshold, reducing the proportional coefficient and increasing the integral coefficient; in response to the step response having no overshoot and the rising rate of the controlled quantity being lower than the fourth preset threshold and the transition process time exceeding the fifth preset threshold, increasing the proportional coefficient and reducing the integral coefficient; in response to the speed of eliminating errors being lower than the sixth threshold, increasing the integral coefficient; and, in response to the overshoot still exceeding the seventh preset threshold after the proportional coefficient and the integral coefficient are adjusted for a preset number of times, determining the initial differential coefficient of the PID controller.

[0078] That is to say, during the tuning process, the PID parameters should be repeatedly adjusted based on the relationship between the PID parameters and the system performance (including system errors and error change rates). Specifically, if the overshoot of the step response is too large, it takes many oscillations to stabilize or it is not stable at all, the proportional coefficient should be reduced and the integral time should be increased. If there is no overshoot in the step response, but the controlled quantity rises too slowly and the transition time is too long, the parameters should be adjusted in the opposite direction. If the error elimination speed is slow, the integral time can be appropriately reduced to enhance the integral effect. If the proportional coefficient and integral time are repeatedly adjusted more than the preset number of times, if the overshoot is still large, differential control can be added. Preferably, the differential time is gradually increased from 0, and the parameters of the proportional, integral and differential parts of the controller are repeatedly adjusted. Figure 5 The diagram shows a schematic diagram of the change of the controlled variable over time under different differential coefficients according to an embodiment of the present application.

[0079] From the above, it can be seen that the debugging and adjustment of PID parameters is a comprehensive process in which various parameters influence each other. Multiple attempts in the actual debugging and adjustment process are very important and necessary.

[0080] Specifically, in the implementation of this application, after multiple tests, the parameters of the PID control for the network diagram used to display the social network analysis results are finally determined to be: the proportional coefficient is 0.35, the integral coefficient is 0.002, and the differential coefficient is 0.05. It should be understood by those skilled in the art that the parameters given above are only used as a specific example and are not limited to this application.

[0081] In step S130, the target transformation parameters of the network diagram are updated based on the PID controller. Accordingly, after the parameters of the PID controller are obtained through experimental tuning using the above patchwork method, the parameters of the network diagram in the image transformation process (e.g., the scaling process) are adjusted in real time based on the PID controller, so that the transformation process of the network diagram can be smoother.

[0082] The following is an example of a partial code of an algorithm for updating the target transformation parameters of the network diagram based on a PID controller, which includes:

[0083]

[0084] It should be understood that the code examples given above are only examples and are not limited to the present application.

[0085] In step S140, in response to the PID controller determining that the limit value of the error is within the preset range, the target transformation parameter of the network diagram is stopped from being updated. That is, in response to the PID controller determining that the limit value of the system error is within the preset range, the network diagram reaches a stable state to complete the image transformation process.

[0086] In summary, the image processing method based on the embodiment of the present application is explained, which performs image transformation processing on the network diagram based on the PID control theory to make the image transformation smoother and avoid undesirable phenomena such as stuttering and jumping.

[0087] Although the basic concept of the present application is used as an example to illustrate the network diagram of the social network analysis results, ordinary technicians in this field should know that the above basic concept of the present application can also be applied to other network diagrams for displaying network analysis results, such as legal knowledge graphs, Baidu Encyclopedia knowledge graphs, etc., and this is not limited to the application.

[0088] Exemplary Devices

[0089] Figure 6 The figure shows a block diagram of an image processing apparatus according to an embodiment of the present application.

[0090] like Figure 6 As shown, the image processing device 600 implemented according to the present application includes: a target parameter acquisition unit 610, used to process the acquired network diagram to generate target transformation parameters of the network diagram, and the target transformation parameters include translation parameters and scaling parameters; a controller generation unit 620, used to generate a PID controller based on the target transformation parameters; an updating unit 630, used to update the target transformation parameters of the network diagram based on the PID controller; and a stopping unit 640, in response to the PID controller determining that the limit value of the error is within a preset range, stops updating the target transformation parameters of the network diagram.

[0091] In one example, in the above-mentioned image processing apparatus 600, the target parameter acquisition unit 610 is further used to: determine the target transformation parameters of the network graph based on the relationship between nodes in the network graph.

[0092] In one example, in the above-mentioned image processing device 600, the target parameter acquisition unit 610 is further used to: determine the center point of the network diagram; determine the relative position relationship between other nodes and the center point based on the center point; and determine the target transformation parameters of the network diagram based on the relative position relationship between the center point and other nodes and the center point.

[0093] In one example, in the above-mentioned image processing apparatus 600, the translation parameters include the coordinates of the center point and other nodes of the network diagram on the X-axis and the coordinates on the Y-axis.

[0094] In one example, in the above-mentioned image processing device 600, the control generation unit 620 is further used to: determine the initial proportional coefficient and the initial integral coefficient of the PI controller based on the target transformation parameter; determine the initial differential coefficient of the PID controller based on the adjustment result of the PI controller; and determine the proportional coefficient, integral coefficient and differential coefficient of the PID controller based on the adjustment result of the PID controller.

[0095] In one example, in the above-mentioned image processing device 600, the initial proportional coefficient of the PI controller does not exceed the first preset threshold, and the initial integral coefficient is not lower than the second preset threshold.

[0096] In one example, in the above-mentioned image processing device 600, the control generation unit 620 is further used to: in response to the overshoot of the step response exceeding the third preset threshold, reduce the proportional coefficient and increase the integral coefficient; in response to the step response having no overshoot and the rising rate of the controlled quantity being lower than the fourth preset threshold and the transition process time exceeding the fifth preset threshold, increase the proportional coefficient and reduce the integral coefficient; in response to the speed of eliminating errors being lower than the sixth threshold, increase the integral coefficient; and, in response to the overshoot still exceeding the seventh preset threshold after the proportional coefficient and the integral coefficient are adjusted for a preset number of times, determine the initial differential coefficient of the PID controller.

[0097] In one example, in the above-mentioned image processing device 600, the network diagram is a network diagram for displaying social network analysis results.

[0098] In one example, in the above-mentioned image processing apparatus 600, the proportional coefficient of the PID controller is 0.35, the integral coefficient is 0.002, and the differential coefficient is 0.05.

[0099] Here, those skilled in the art will appreciate that the specific functions and operations of the various units and modules in the above-mentioned image processing device 600 have been described in detail above. Figures 1 to 5 The image processing method / process has been described in detail in the description of the image processing method / process, and therefore, its repeated description will be omitted.

[0100] As described above, the image processing device 600 according to the embodiment of the present application can be implemented in various terminal devices, such as a large-screen smart device, or a computer independent of the large-screen smart device. In one example, the image processing device 600 according to the embodiment of the present application can be integrated into the terminal device as a software module and / or a hardware module. For example, the image processing device 600 can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the image processing device 600 can also be one of the many hardware modules of the terminal device.

[0101] Alternatively, in another example, the image processing apparatus 600 and the terminal device may also be separate devices, and the image processing apparatus 600 may be connected to the terminal device via a wired and / or wireless network, and transmit the interactive information in accordance with an agreed data format.

[0102] Exemplary Electronic Devices

[0103] Below, reference Figure 7 To describe an electronic device according to an embodiment of the present application.

[0104] Figure 7 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0105] like Figure 7 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0106] The processor 13 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0107] The memory 12 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the image processing method of each embodiment of the present application described above and / or other desired functions. Various contents such as network diagrams may also be stored in the computer-readable storage medium.

[0108] In one example, the electronic device 10 may further include: an input device 13 and an output device 14, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0109] The input device 13 may include, for example, a keyboard, a mouse, etc.

[0110] The output device 14 can output various information to the outside, including network diagrams after image transformation, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0111] Of course, to simplify, Figure 7 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 10 may also include any other appropriate components.

[0112] Exemplary computer program products and computer-readable storage media

[0113] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the image processing method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0114] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0115] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the image processing method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0116] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0117] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0118] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0119] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0120] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0121] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. An image processing method for a network graph, characterized in that: include: Processing the acquired network graph to generate target transformation parameters of the network graph, wherein the target transformation parameters include translation parameters and scaling parameters; generating a PID controller based on the target transformation parameter; Based on the PID controller, updating the target transformation parameters of the network diagram; as well as In response to the PID controller determining that the limit value of the error is within a preset range, stopping updating the target transformation parameter of the network diagram; Wherein, based on the target transformation parameter, a PID controller is generated, including: Based on the target transformation parameter, determining an initial proportional coefficient and an initial integral coefficient of the PI controller; based on the adjustment result of the PI controller, determining an initial differential coefficient of the PID controller; and based on the adjustment result of the PID controller, determining a proportional coefficient, an integral coefficient and a differential coefficient of the PID controller; Wherein, the initial proportional coefficient of the PI controller does not exceed a first preset threshold value, and the initial integral coefficient does not fall below a second preset threshold value; Based on the adjustment result of the PI controller, the initial differential coefficient of the PID controller is determined, including: In response to the overshoot of the step response exceeding a third preset threshold, reducing the proportional coefficient and increasing the integral coefficient; In response to the step response having no overshoot and the controlled variable rising rate being lower than a fourth preset threshold and the transition process time exceeding a fifth preset threshold, increasing the proportional coefficient and decreasing the integral coefficient; In response to the speed of eliminating errors being lower than a sixth threshold, increasing the integral coefficient; and In response to the overshoot still exceeding a seventh preset threshold after the proportional coefficient and the integral coefficient are adjusted for a preset number of times, an initial differential coefficient of the PID controller is determined.

2. The image processing method according to claim 1, wherein: Processing the acquired network graph to generate target transformation parameters of the network graph includes: Based on the nodes in the network graph and the relationships between the nodes, target transformation parameters of the network graph are determined.

3. The image processing method according to claim 2, wherein: Determining target transformation parameters of the network graph based on the relationship between nodes in the network graph includes: determining a center point of the network diagram; Determine the relative position relationship between other nodes and the central point based on the central point; and Based on the relative positional relationship between the central point and other nodes and the central point, target transformation parameters of the network graph are determined.

4. The image processing method according to claim 3, wherein: The translation parameters include the coordinates of the center point and other nodes of the network diagram on the X-axis and the coordinates on the Y-axis.

5. The image processing method according to any one of claims 1 to 4, wherein: The network diagram is a network diagram used to display social network analysis results.

6. The image processing method according to claim 1, wherein: The proportional coefficient of the PID controller is 0.35, the integral coefficient is 0.002, and the differential coefficient is 0.

05.

7. An image processing device, characterized in that: include: A target parameter acquisition unit, used for processing the acquired network diagram to generate target transformation parameters of the network diagram, wherein the target transformation parameters include translation parameters and scaling parameters; A controller generating unit, used to generate a PID controller based on the target transformation parameter; further used to: determine an initial proportional coefficient and an initial integral coefficient of the PI controller based on the target transformation parameter; determine an initial differential coefficient of the PID controller based on a regulation result of the PI controller; And, based on the adjustment result of the PID controller, determining the proportional coefficient, integral coefficient and differential coefficient of the PID controller; the initial proportional coefficient of the PI controller does not exceed the first preset threshold value, and the initial integral coefficient is not lower than the second preset threshold value; In response to the overshoot of the step response exceeding the third preset threshold, the proportional coefficient is reduced and the integral coefficient is increased; in response to the step response having no overshoot and the controlled quantity rising rate being lower than the fourth preset threshold and the transition process time exceeding the fifth preset threshold, the proportional coefficient is increased and the integral coefficient is reduced; in response to the error elimination speed being lower than the sixth threshold, the integral coefficient is increased; and in response to the overshoot still exceeding the seventh preset threshold after the proportional coefficient and the integral coefficient are adjusted for a preset number of times, the initial differential coefficient of the PID controller is determined; An updating unit, configured to update a target transformation parameter of the network diagram based on the PID controller; as well as A stopping unit stops updating the target transformation parameter of the network diagram in response to the PID controller determining that the limit value of the error is within a preset range.

8. An electronic device, characterized in that: include: processor; as well as A memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the image processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a computing device, they are operable to execute the image processing method according to any one of claims 1 to 6.

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