Automobile bus load balancing method and device and computer program product

Abstract

The invention discloses an automobile bus load balancing method and device and a computer program product, and the method comprises the steps: recognizing an actuator, a sensor and a controller in a target adjustment range, and the communication relation among the actuators, the sensor and the controller, and obtaining the feature data of a function message group; based on the function message group feature data, calculating a preliminary optimal I / O allocation scheme by adopting a linear programming algorithm and taking bus load rate balance as a target; and based on the preliminary optimal signal distribution scheme, in combination with the selected related influence factors, determining optimal I / O distribution schemes in different scenes through a load balancing evaluation model. According to the embodiment of the invention, bus signal distribution can be dynamically optimized, the bus load rate is remarkably reduced, signal transmission delay or loss is avoided, and the performance of vehicle power control, a safety system and comfort adjustment is improved; meanwhile, by comprehensively considering all influence factors, high efficiency and practicability of the scheme are ensured, and the problems of high cost, low efficiency, dependence on manpower and the like in the prior art are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent connected vehicles, and particularly relates to an automotive bus load balancing method, device and computer program product. Background Art

[0002] With the increasing complexity of automotive electronic systems, the amount of information carried by the bus network has increased sharply, resulting in too high a load rate on some bus segments, which has become a bottleneck restricting system performance. The high load state will not only cause significant delays in signal transmission, but may even cause key signals to be lost during transmission, directly affecting multiple aspects such as vehicle power control, safety systems, and comfort adjustment. For example, in the case of emergency braking, if the control signal of the ABS system is delayed or lost due to bus congestion, it will seriously affect the braking performance of the vehicle and the safety of passengers.

[0003] When the prior art performs load balancing optimization, it mainly adopts the following two types of methods:

[0004] 1. Upgrade the CAN bus itself: For example, increase the communication segment or change the network type. Although this type of method can alleviate the bus load problem to a certain extent, it will significantly increase the system cost.

[0005] 2. Optimize the communication signals: For example, increase the priority of important signals. However, this type of method is difficult to avoid the delay or loss of signals with lower priority, and relies heavily on manual allocation, and cannot achieve more efficient and optimal bus load balancing through a mathematical model. Summary of the Invention

[0006] The technical problem to be solved by the embodiments of the present invention is to provide an automotive bus load balancing method, device and computer program product to effectively balance the bus load and ensure timely and accurate signal transmission.

[0007] To solve the above technical problem, the present invention provides an automotive bus load balancing method, including the following steps:

[0008] Identify the actuators, sensors and controllers within the target adjustment range and their communication relationships, and obtain the feature data of the functional message group;

[0009] Based on the feature data of the functional message group, adopt a linear programming algorithm, with the goal of balancing the bus load rate, and calculate a preliminary optimal I / O allocation plan;

[0010] Based on the preliminary optimal signal allocation plan, combined with the selected relevant influencing factors, determine the optimal I / O allocation plan in different scenarios through a load balancing evaluation model.

[0011] Preferably, identify the actuators, sensors, and controllers within the target adjustment range and their communication relationships, and obtain the characteristic data of the functional message group, specifically including:

[0012] Identify the actuators, sensors, and controllers within the target adjustment range and their communication relationships;

[0013] Define the upstream and downstream signals between the upper and lower level controllers that achieve the same function as the same functional message group;

[0014] For the control system that needs to reallocate signals within the target adjustment range, divide it into multiple functional message groups.

[0015] Preferably, based on the characteristic data of the functional message group, use the linear programming algorithm to calculate the preliminary optimal I / O allocation scheme with the goal of balancing the bus load rate, specifically including:

[0016] Calculate the bus load rate of a certain message i

[0017] Calculate the bus load rate of the functional message group j

[0018] Calculate the bus load rate W of a certain network segment k k ;

[0019] Define the objective function with the goal of minimizing the standard deviation S of the network segment load rate;

[0020] Define the constraint conditions with the best load rate range of the bus as the constraint;

[0021] Use the linear programming algorithm to substitute the bus load rate of a certain message i the bus load rate of the functional message group j and the bus load rate W of a certain network segment k k into the objective function and constraint conditions for calculation, and solve to obtain the preliminary optimal I / O allocation scheme, where the preliminary optimal I / O allocation scheme includes the bus load rate of each network segment and the corresponding signal allocation method.

[0022] Preferably, the method for calculating the bus load rate of the functional message group j is as follows:

[0023]

[0024] where n j is the number of messages in the j-th functional message group;

[0025] Calculate the bus load rate W of a certain network segment k k is as follows:

[0026]

[0027] Among them, n k is the number of network segments; is the load rate generated by the repeated messages of a certain network segment k, and is defined as follows:

[0028]

[0029] Among them, n r is the number of repeated messages of the k-th network segment, is the number of repetitions of the i-th message in the functional message group, is the bus load rate of the i-th message.

[0030] Preferably, the objective function is as follows:

[0031]

[0032] Among them, is the average value of the bus load rate W k of a certain network segment k;

[0033] The constraint conditions are as follows:

[0034]

[0035] Among them, a1 is the upper limit of the optimal load rate range of the bus, and a2 is the lower limit of the optimal load rate range of the bus.

[0036] Preferably, the relevant influencing factors specifically include the load balancing rate S, the software development technical difficulty E 1 , the harness adjustment difficulty E 2 , the vehicle energy consumption E 3 , and the optimal I / O allocation scheme under different scenarios is determined through the load balancing evaluation model, specifically including:

[0037] The expert scoring method is used to score each relevant influencing factor, and the total score G is statistically calculated by the weighted summation method:

[0038] G = α 0 S + α 1 E 1 + α 2 E 2 + α 3 E 3

[0039] Among them, α 0 , α 1 , α 2 , α 3 are weight coefficients, satisfying α 0 + α 1 + α2 +α 3 = 1 and α 0 > 0.5;

[0040] Determine the initially optimal I / O allocation scheme with a higher score as the final optimal I / O allocation scheme.

[0041] Preferably, when scoring each relevant influencing factor, a simple difficulty is scored 3 points, a medium difficulty is scored 2 points, and a high difficulty is scored 1 point.

[0042] The present invention also provides an automotive bus load balancing device, comprising:

[0043] A feature data acquisition module, configured to identify actuators, sensors, and controllers within a target adjustment range and their communication relationships therebetween, and acquire feature data of a functional message group;

[0044] An initial scheme calculation module, configured to calculate an initially optimal I / O allocation scheme based on the feature data of the functional message group by using a linear programming algorithm with the goal of balancing the bus load rate;

[0045] A final scheme determination module, configured to determine the optimal I / O allocation scheme in different scenarios based on the initially optimal signal allocation scheme, in combination with selected relevant influencing factors, through a load balancing evaluation model.

[0046] The present invention also provides an automotive bus load balancing device, comprising:

[0047] One or more processors;

[0048] A memory;

[0049] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to execute the automotive bus load balancing method described above.

[0050] The present invention also provides a computer program product, comprising computer instructions, and the computer instructions instruct a computer device to perform the operations corresponding to the method.

[0051] Implementing the present invention has the following beneficial effects: By optimizing the vehicle network signal transmission path through intelligent I / O allocation, balancing the bus load rate of each network segment, significantly reducing signal transmission delays and packet loss phenomena, remarkably enhancing the real-time performance and reliability of the vehicle network, ensuring the timely and accurate transmission of key function signals, enhancing the vehicle's controllability and safety under various working conditions, and providing a more comfortable and safe experience for the driver and passengers. At the same time, the refined signal allocation strategy can optimize network resource allocation, maintain the network load rate within an ideal range, reduce system energy consumption and costs, and improve resource utilization efficiency. In addition, when considering load balancing, the present invention takes into account various actual engineering influencing factors such as the technical difficulty of software development and the difficulty of harness adjustment, avoids cost increases and reduced feasibility caused by single problems, can flexibly select appropriate optimization solutions according to different scenarios, and has extremely high engineering practicability and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0053] Figure 1 FIG. is a schematic flowchart of a method for balancing the load of an automotive bus according to Embodiment 1 of the present invention.

[0054] Figure 2 FIG. is a schematic flowchart of performing intelligent I / O allocation in an embodiment of the present invention.

[0055] Figure 3 FIG. is a schematic flowchart of performing multi-scheme selection in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following descriptions of the embodiments are made with reference to the drawings to illustrate specific embodiments in which the present invention can be implemented.

[0057] Please refer to Figure 1 as shown, Embodiment 1 of the present invention provides a method for balancing the load of an automotive bus, including the following steps:

[0058] Step S1, identify the actuators, sensors, and controllers within the target adjustment range and their communication relationships, and obtain the characteristic data of the function message group;

[0059] Step S2, based on the characteristic data of the function message group, use the linear programming algorithm to calculate a preliminary optimal I / O allocation scheme with the goal of balancing the bus load rate;

[0060] Step S3: Based on the preliminary optimal signal allocation scheme, combined with the selected relevant influencing factors, determine the optimal I / O allocation scheme under different scenarios through the load balancing evaluation model.

[0061] As can be seen from the above steps, the embodiments of the present invention can dynamically optimize the bus signal allocation, significantly reduce the bus load rate, avoid signal transmission delay or loss, and improve the performance of vehicle power control, safety systems, and comfort adjustment; at the same time, by comprehensively considering various influencing factors, ensure the efficiency and practicality of the scheme, effectively solve the problems of high cost, low efficiency, and dependence on manual labor in the prior art, and provide a reliable guarantee for the stable operation of automotive electronic systems.

[0062] Specifically, in step S1 of the embodiments of the present invention, signal identification and classification are performed.

[0063] First, comprehensively identify the actuators, sensors, and controllers within the target adjustment range, as well as the communication relationships between them.

[0064] In particular, it is necessary to identify and classify the up and down signals responsible for the same function between the upper and lower level controllers. These signals are logically closely connected, and it is necessary to ensure their mutual transmission between two fixed ECUs when performing I / O allocation. For example, to achieve the function of raising the window, ECU1 sends a window raising instruction to ECU2, and ECU2 then transmits the window action status back to ECU1. It is impossible for the down signal to be transmitted from ECU1 to ECU2, while the up signal is transmitted back from ECU3 to ECU1.

[0065] Therefore, the embodiments of the present invention define the up and down signals between the upper and lower level controllers that achieve the same function as the same functional message group. Due to the strong correlation between the down and up signals, all signals within the same functional message group need to be on the same network segment and need to be bound during allocation, that is, the functional message group W f is the smallest unit of I / O allocation.

[0066] For the control system that needs to reallocate signals, multiple functional message groups can be divided as the basic data for intelligent I / O allocation in the subsequent step S2.

[0067] Thus, step S1 specifically includes:

[0068] Step S11: Identify the actuators, sensors, and controllers within the target adjustment range and their communication relationships;

[0069] Step S12: Define the up and down signals between the upper and lower level controllers that achieve the same function as the same functional message group;

[0070] Step S13: For the control system within the target adjustment range that needs to reallocate signals, divide multiple functional message groups.

[0071] In step S2, an intelligent I / O allocation model based on a linear programming algorithm is adopted for intelligent I / O allocation. This model can calculate an I / O allocation scheme for achieving optimal load balancing through the linear programming algorithm, and the output scheme specifically includes the allocation method of signal groups in each network segment and the corresponding load rate of each network segment.

[0072] Please refer to Figure 2 As shown, specifically, with the goal of minimizing the standard deviation of the bus load rate of each network segment and with the acceptable load rate range of each network segment as the constraint condition, signals are allocated through the linear programming algorithm. The steps are as follows:

[0073] Step S21: Calculate the bus load rate of a certain message i

[0074] The bus load rate generated for a single message i is:

[0075]

[0076] Among them, is the signal length (bit) of message i, is the signal period (ms) of message i, and B is the bus baud rate (kbps).

[0077] Step S22: Calculate the bus load rate of the functional message group j

[0078] is the bus load rate generated for a group of functional message groups. According to the functional cooperation characteristics, functional message groups are combined, and based on it is further calculated as:

[0079]

[0080] Among them, n j is the number of messages in the jth functional message group.

[0081] Step S23: Calculate the bus load rate W of a certain network segment k k

[0082] The bus load rate is the sum of the load rates of all messages, but not the sum of the load rates of all functional message groups, because a certain signal may be associated with multiple functions, so a single signal may exist in multiple functional message groups. When calculating the network segment load rate W based on the load rate of the functional message group k it is necessary to identify and remove the duplicate part, and define the load rate generated by the duplicate messages of a certain network segment k as the duplicate load rate

[0083]

[0084] where n r is the number of duplicate messages in the k-th network segment, is the number of repetitions of the i-th message in the functional message group ( is an integer greater than or equal to 0), is the bus load rate of the i-th message.

[0085] Furthermore, the bus load rate W of a certain network segment k k is:

[0086]

[0087] where n k is the number of network segments.

[0088] Step S24: Define the objective function

[0089] To achieve the optimal signal transmission effect, with the bus load balance of each network segment as the objective function and using the standard deviation to characterize the difference degree of the network segment load rate, the load balance can be transformed into minimizing the standard deviation S of the network segment load rate. Define S as the load balance rate:

[0090]

[0091] where is the mean value of W k of.

[0092] Step S25: Define the constraint conditions

[0093] The constraint conditions are the optimal load rate range of the bus. On the one hand, to avoid signal congestion resulting in transmission delay or loss, define the upper limit of the load rate constraint; on the other hand, to improve the bus utilization rate, define the lower limit of the load rate constraint:

[0094]

[0095] where a1 is the upper limit of the optimal load rate range of the bus, and a2 is the lower limit of the optimal load rate range of the bus. As an example, a1 can be set to 50%, a1 can be set to 30%, and it can be adjusted according to the actual situation. Generally, the upper limit a1 should not be higher than 60%.

[0096] Step S26: Solve the optimization problem based on the linear programming algorithm

[0097] Based on the above variable definitions, objective function, and constraint conditions, use the linear programming algorithm to solve the above model. Transform the objective function into a population fitness function, define the network segment where the functional message group is located as an individual, and map it to the bus load rate generated by each functional message group. Call the Python solution function to calculate the preliminary optimal I / O allocation scheme.

[0098] The algorithm output results include the bus load rates of each network segment and the corresponding signal allocation methods.

[0099] After obtaining multiple preliminary optimal I / O allocation schemes through the linear programming algorithm in step S2, in order to further select the most suitable scheme for actual applications in practical engineering, improve the flexibility of the optimization scheme and its adaptability to different scenarios, other influencing factors besides the load balancing rate need to be comprehensively considered, such as the technical difficulty of software development, the difficulty of harness adjustment, and the vehicle energy consumption.

[0100] As Figure 3 shown, the embodiment of the present invention performs multi-scheme selection through step S3. The specific influencing factors considered during scheme selection include the load balancing rate S, the technical difficulty of software development E 1 , the difficulty of harness adjustment E 2 , and the vehicle energy consumption E 3 , which are specifically described as follows:

[0101] (1) Load balancing rate S: Measures the difference in load rates of each network segment. The smaller this value, the better the load balancing effect;

[0102] (2) Technical difficulty of software development E 1 : Scored according to the development difficulty. If the software development difficulty is high, the cost and development cycle will increase, and the practical feasibility will decrease;

[0103] (3) Difficulty of harness adjustment E 2 : If the I / O allocation changes, the harness connections need to be correspondingly matched to ensure the normal transmission of information between nodes. If the difficulty of harness adjustment is high, the development cycle will be prolonged and the practical feasibility will decrease;

[0104] (4) Vehicle energy consumption E 3 : If the signal transmission path in the network segment changes, it may lead to a change in the network segment wake-up method. If the wake-up is more frequent, it is likely to cause an increase in vehicle energy consumption and a decrease in practical feasibility.

[0105] The above influencing factors are scored using the expert scoring method. A simple difficulty is scored 3 points, a medium difficulty is scored 2 points, and a high difficulty is scored 1 point. The total score G is statistically calculated using the weighted summation method. The scheme with a higher score is better:

[0106] G = α 0 S + α 1 E 1 + α 2 E 2 + α 3 E 3

[0107] Among them, α 0 , α 1 , α2 , α 3 is a weight coefficient, satisfying α 0 + α 1 + α 2 + α 3 = 1. The specific values of each coefficient can be adjusted according to the actual situation. To achieve the goal of load balancing, generally α 0 needs to be greater than 0.5.

[0108] According to the solution results of the above intelligent I / O allocation model and multi-scheme selection model, adjust the signal allocation of each network segment, so as to achieve the balance of the bus load rate of each network segment on the basis of practicability.

[0109] Corresponding to the automotive bus load balancing method described in the foregoing Embodiment 1 of the present invention, Embodiment 2 of the present invention further provides an automotive bus load balancing device, including:

[0110] A feature data acquisition module, configured to identify actuators, sensors, and controllers within a target adjustment range and their communication relationships, and acquire functional message group feature data;

[0111] A preliminary scheme calculation module, configured to calculate a preliminary optimal I / O allocation scheme based on the functional message group feature data by using a linear programming algorithm with the goal of bus load rate balancing;

[0112] A final scheme determination module, configured to determine an optimal I / O allocation scheme in different scenarios based on the preliminary optimal signal allocation scheme and in combination with selected relevant influencing factors through a load balancing evaluation model.

[0113] Corresponding to the automotive bus load balancing method described in the foregoing Embodiment 1 of the present invention, Embodiment 3 of the present invention further provides an automotive bus load balancing device, including:

[0114] One or more processors;

[0115] A memory;

[0116] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the automotive bus load balancing method described in the foregoing Embodiment 1 of the present invention.

[0117] Corresponding to the automotive bus load balancing method described in the foregoing Embodiment 1 of the present invention, Embodiment 4 of the present invention further provides a computer program product, including computer instructions, and the computer instructions direct a computer device to execute the operations corresponding to the automotive bus load balancing method described in the foregoing Embodiment 1 of the present invention.

[0118] Preferably, the processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor. The processor is the control center of the device, connecting various parts of the device through various interfaces and circuits.

[0119] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory may be a high-speed random access memory, or may also be a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory may also be other volatile solid-state storage devices.

[0120] It should be noted that the above device may include, but is not limited to, a processor and a memory, which can be understood by those skilled in the art.

[0121] Regarding the working principle and process of the above embodiments, refer to the description of Embodiment 1 of the present invention above, and details will not be repeated here.

[0122] From the above description, it can be seen that compared with the prior art, the beneficial effects of the present invention are as follows: By optimizing the vehicle network signal transmission path through intelligent I / O allocation, balancing the bus load rate of each network segment, significantly reducing signal transmission delays and packet loss phenomena, significantly improving the real-time performance and reliability of the vehicle network, ensuring the timely and accurate transmission of key function signals, enhancing the controllability and safety of the vehicle under various working conditions, and providing a more comfortable and safe experience for the driver and passengers. At the same time, the refined signal allocation strategy can optimize network resource allocation, keep the network load rate within an ideal range, reduce system energy consumption and costs, and improve resource utilization efficiency. In addition, when considering load balancing, the present invention takes into account various actual engineering influencing factors such as the technical difficulty of software development and the difficulty of wiring harness adjustment, avoids cost increases and reduced feasibility caused by single problems, and can flexibly select appropriate optimization solutions according to different scenarios, with extremely high engineering practicability and flexibility.

[0123] The above-disclosed is only the preferred embodiment of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for balancing vehicle bus load, characterized in that: The following steps are involved: Identify the actuators, sensors and controllers within the target adjustment range and their communication relationships, and obtain the functional message group feature data; Based on the functional message group characteristic data, a linear programming algorithm is used to calculate a preliminary optimal I / O allocation scheme with bus load rate balancing as the goal; Based on the preliminary optimal signal allocation scheme, combined with the selected relevant influencing factors, the optimal I / O allocation scheme under different scenarios is determined through the load balancing evaluation model.

2. The method according to claim 1, characterized in that: The identification of the actuators, sensors and controllers within the target adjustment range and the communication relationship between them, and obtaining the functional message group characteristic data specifically includes: Identify the actuators, sensors and controllers within the target adjustment range and their communication relationships; Define the uplink and downlink signals between the upper and lower level controllers that implement the same function as the same functional message group; For control systems that need to redistribute signals within the target adjustment range, multiple functional message groups are divided.

3. The method according to claim 1, characterized in that Based on the functional message group characteristic data, a linear programming algorithm is used to calculate the preliminary optimal I / O allocation scheme with bus load rate balancing as the goal, specifically including: Calculate the bus load rate of a message i Calculate bus load rate of function message group j Calculate the bus load rate W of a certain network segment k k ; Define an objective function that minimizes the standard deviation S of the network segment load rate; Define a constraint condition based on the optimal load rate range of the bus; Using linear programming algorithm, the bus load rate of a certain message i is Bus load rate of functional message group j and the bus load rate W of a certain network segment k k Substitute the objective function and constraint conditions into the calculation to obtain a preliminary optimal I / O allocation scheme, which includes the bus load rate of each network segment and the corresponding signal allocation method.

4. The method according to claim 3, characterized in that Calculate bus load rate of function message group j The way is as follows: Among them, n j is the number of messages in the jth functional message group; Calculate the bus load rate W of a network segment k k The way is as follows: Among them, n k is the number of network segments; The load rate generated by repeated messages in a network segment k is defined as follows: Among them, n r is the number of duplicate messages in the kth network segment, is the number of repetitions of the ith message in the functional message group, is the bus load rate of the i-th message.

5. The method according to claim 4, characterized in that The objective function is as follows: in, is the bus load rate W of a certain network segment k k The mean of The constraints are as follows: Wherein, a1 is the upper limit of the optimal load rate range of the bus, and a2 is the lower limit of the optimal load rate range of the bus.

6. The method according to claim 1, characterized in that The relevant influencing factors specifically include load balancing rate S, software development technology difficulty E1, wiring harness adjustment difficulty E2, and vehicle energy consumption E3. The optimal I / O allocation scheme under different scenarios is determined by the load balancing evaluation model, specifically including: The expert scoring method is used to score each relevant influencing factor, and the total score G is calculated by weighted summation method: G=α0S+α1E1+α2E2+α3E3 Among them, α0, α1, α2, and α3 are weight coefficients, satisfying α0+α1+α2+α3=1 and α0>0.5; The preliminary optimal I / O allocation scheme with a higher score is determined as the final optimal I / O allocation scheme.

7. The method according to claim 6, characterized in that When scoring each relevant influencing factor, 3 points are given for simple difficulty, 2 points for medium difficulty, and 1 point for high difficulty.

8. A vehicle bus load balancing device, characterized in that: include: A characteristic data acquisition module is used to identify the actuators, sensors and controllers within the target adjustment range and the communication relationship between them, and obtain the characteristic data of the functional message group; A preliminary scheme calculation module is used to calculate a preliminary optimal I / O allocation scheme based on the functional message group characteristic data, using a linear programming algorithm and taking bus load rate balancing as a goal; The final solution determination module is used to determine the optimal I / O allocation solution under different scenarios based on the preliminary optimal signal allocation solution and the selected relevant influencing factors through a load balancing evaluation model.

9. A vehicle bus load balancing device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the vehicle bus load balancing method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions instruct a computer device to execute operations corresponding to the method according to any one of claims 1 to 7.

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