Park operation cost determination method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a park operation cost determination method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of initial decision reference values respectively corresponding to each user in a park in a power utilization process; for each user, determining the power utilization fitness of the user based on each initial decision reference value corresponding to the user; the power utilization fitness is in inverse correlation with theoretical power consumption in the power utilization process and is in inverse correlation with theoretical carbon emission in the power utilization process; according to an updating mode matched with the power utilization fitness, performing updating processing on each initial decision reference value, and determining an updated decision reference value corresponding to each initial decision reference value; and determining the operation cost of the park based on each updated decision reference value. By adopting the method, the accuracy of park operation cost optimization can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for determining park operation costs. Background Art

[0002] With the continuous development of power technology, energy conservation and emission reduction have become a critical measure in the development of power technology to achieve the green transformation of industrial production capacity. Deeply exploring the system role and integrated effect of green construction and development of the park will make a huge contribution to achieving the overall carbon peak and carbon neutrality goals.

[0003] In order to achieve energy conservation and emission reduction, the operation cost of the park needs to be optimized. The traditional method of determining the operation cost of the park is generally to adjust the operation strategy of the park based on personal experience. However, the operation process of the park is relatively complicated, and there is an inaccuracy problem in adjusting the operation strategy of the park based only on personal experience. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining park operating costs that can improve the accuracy of park operating cost optimization in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining park operation costs, comprising:

[0006] Obtain multiple initial decision reference values ​​corresponding to each user in the park during the electricity consumption process;

[0007] For each of the users, based on the initial decision reference values ​​corresponding to the users, determining the user's power usage adaptability; the power usage adaptability is inversely correlated with the theoretical power consumption during the power usage process, and is inversely correlated with the theoretical carbon emissions during the power usage process;

[0008] According to the updating method matching the electricity usage adaptability, each of the initial decision reference values ​​is updated respectively to determine the updated decision reference value corresponding to each of the initial decision reference values;

[0009] Based on each of the update decision reference values, the operation cost of the park is determined.

[0010] In one of the embodiments, determining the user's electricity usage adaptability based on each of the initial decision reference values ​​corresponding to the user includes: determining the user's electricity cost and carbon emission cost based on each of the initial decision reference values ​​corresponding to the user;

[0011] Determining respective weights corresponding to the electricity cost and the carbon emission cost;

[0012] The electricity cost and the carbon emission cost are calculated based on the weights to obtain the electricity usage adaptability of the user.

[0013] In one embodiment, the method for determining the park operation cost further includes:

[0014] Arrange the electricity adaptability in descending order;

[0015] Based on the preset update mode ratio, the update mode that matches each of the power usage adaptability is determined.

[0016] In one embodiment, the updating method includes a first updating method; the updating method matching the power usage adaptability is used to update each of the initial decision reference values, and the updating decision reference values ​​corresponding to each of the initial decision reference values ​​are determined, including:

[0017] For each of the initial decision reference values ​​of the user, determining a warning value and a safety value corresponding to the initial decision reference value;

[0018] Determining a first updating method of the initial decision reference value based on a numerical comparison relationship between the warning value and the safety value;

[0019] The initial decision reference value is updated based on the first updating method to obtain an updated decision reference value.

[0020] In one embodiment, the updating method includes a second updating method; the updating method matching the power usage adaptability is used to update each of the initial decision reference values, and the updated decision reference values ​​corresponding to each of the initial decision reference values ​​are determined, including:

[0021] For each of the power usage adaptability matching the second update mode, performing a fitness value judgment on the power usage adaptability, and determining a plurality of priority initial reference values ​​that meet the fitness priority condition, and a plurality of common initial reference values ​​that do not meet the fitness priority condition;

[0022] For each of the priority initial reference values, updating the priority initial reference value in an updating manner matching the priority initial reference value to obtain an updated decision reference value;

[0023] For each of the common initial reference values, the common initial reference value is updated in an updating manner that matches the common initial reference value to obtain an updated decision reference value.

[0024] In one embodiment, determining the operation cost of the park based on each of the update decision reference values ​​includes:

[0025] Determining a candidate operating cost of the park based on each of the update decision reference values;

[0026] Taking the updated decision reference value as the initial decision reference value, returning to the step of determining the power usage adaptability of each user based on the initial decision reference values ​​corresponding to the user for each user;

[0027] When the number of determined candidate operating costs reaches a set number, determining a target operating cost with the smallest value from among the candidate operating costs;

[0028] Each updated decision reference value corresponding to the target operating cost is determined as a recommended reference value for the park.

[0029] In a second aspect, the present application also provides a device for determining park operation costs, comprising:

[0030] An initial decision reference value acquisition module is used to obtain a plurality of initial decision reference values ​​corresponding to each user in the park during the electricity use process;

[0031] A power usage adaptability determination module, configured to determine the power usage adaptability of each user based on the initial decision reference values ​​corresponding to the user; the power usage adaptability is inversely correlated with the theoretical power consumption in the power usage process, and is inversely correlated with the theoretical carbon emissions in the power usage process;

[0032] An update processing module, used to update each of the initial decision reference values ​​according to an update method matching the power usage adaptability, and determine an updated decision reference value corresponding to each of the initial decision reference values;

[0033] An operation cost determination module is used to determine the operation cost of the park based on each of the update decision reference values.

[0034] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0036] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0037] The above-mentioned park operation cost determination method, device, computer equipment, computer-readable storage medium and computer program product obtain multiple initial decision reference values ​​corresponding to each user in the park during the electricity usage process, so as to determine the electricity usage of each user. For each user, based on the initial decision reference values ​​corresponding to the user, the user's electricity usage adaptability is determined, wherein the electricity usage adaptability is negatively correlated with the theoretical power consumption in the electricity usage process, and negatively correlated with the theoretical carbon emissions in the electricity usage process, thereby determining the energy saving degree of each user in the electricity usage process, and updating each initial decision reference value in accordance with the update method matching the electricity usage adaptability, and determining the updated decision reference value corresponding to each initial decision reference value. Based on different energy saving degrees, the decision reference value update method suitable for the energy saving degree can be matched. Finally, based on each updated decision reference value, the operation cost of the park is determined, and the reasonable optimization of the initial decision reference value can be achieved, thereby improving the accuracy of the park operation cost optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 An application environment diagram of a method for determining park operation costs in an embodiment;

[0040] Figure 2 A schematic diagram of a flow chart of a method for determining park operation costs in one embodiment;

[0041] Figure 3 A schematic diagram of a flow chart of a method for determining park operation costs in another embodiment;

[0042] Figure 4 It is a structural block diagram of a device for determining park operation cost in one embodiment;

[0043] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] The method for determining the park operation cost provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the collection device 104 through the network. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The collection device 104 can be, for example, a sensor. Specifically, in the process of determining the operating cost of the park, the terminal first obtains multiple initial decision reference values ​​corresponding to each user in the park during the electricity usage process collected by the collection device 104, and then for each user, based on the initial decision reference values ​​corresponding to the user, determines the user's electricity usage adaptability. The electricity usage adaptability is inversely correlated with the theoretical power consumption in the electricity usage process, and is inversely correlated with the theoretical carbon emissions in the electricity usage process. According to the update method matching the electricity usage adaptability, each initial decision reference value is updated separately to determine the updated decision reference value corresponding to each initial decision reference value. Based on each updated decision reference value, the operating cost of the park is determined.

[0046] In an exemplary embodiment, Figure 2 As shown, a method for determining the park operation cost is provided, and the method is applied to Figure 1 The following is an example of a terminal in the example, including:

[0047] Step S202, obtaining a plurality of initial decision reference values ​​corresponding to each user in the park during the electricity usage process.

[0048] Among them, the park is a specific area. In this application, the park refers to a specific industrial park area, which includes multiple electricity users, and each user will use multiple electricity loads, such as photovoltaic loads and wind turbine loads. The initial decision reference value refers to the user's electricity decision reference value in the initial state. For example, the initial decision reference value may include the power output of the photovoltaic load, the power output of the wind turbine load, the user's electricity power, the photovoltaic load switch control strategy, the wind turbine load switch control strategy, the user's electricity power switch control strategy, the photovoltaic load working time, the wind turbine load working time, and the user's electricity working time.

[0049] Specifically, in order to optimize the operation cost of the park, it is necessary to determine the initial decision reference value of each user in the park during the electricity use process, that is, it is necessary to obtain multiple initial decision reference values ​​corresponding to each user in the park during the electricity use process. For example, the terminal needs to obtain the power output of the photovoltaic load, the power output of the fan load, the user's power consumption, the switch control strategy of the photovoltaic load, the switch control strategy of the fan load, the switch control strategy of the user's power consumption, the working time of the photovoltaic load, the working time of the fan load, and the working time of the user's electricity consumption. Exemplarily, the initial decision reference value can be randomly set, or it can be obtained by obtaining the initial decision reference value of each user at a certain actual moment, which is not limited here.

[0050] Step S204: for each user, determine the user's electricity usage adaptability based on the initial decision reference values ​​corresponding to the user.

[0051] Among them, electricity adaptability is negatively correlated with the theoretical electricity consumption in the electricity consumption process, and is negatively correlated with the theoretical carbon emissions in the electricity consumption process.

[0052] Specifically, since the power adaptability is inversely correlated with the theoretical power consumption in the power consumption process, and is inversely correlated with the theoretical carbon emissions in the power consumption process, and the power adaptability of the user is different, the subsequent update method is also different. Therefore, in order to facilitate the subsequent matching of the appropriate update method for each user, the terminal needs to first determine the user's power adaptability, that is, it is necessary to first determine the user's power consumption under the initial decision reference value, and for each user, based on the initial decision reference values ​​corresponding to the user, determine the user's power adaptability. In a specific embodiment, a power adaptability determination model can be established, and the initial decision reference values ​​corresponding to the user are input into the power adaptability determination model, and the user's power adaptability is obtained. In another specific embodiment, the terminal can also determine the user's power cost and carbon emission cost based on the initial decision reference values ​​corresponding to the user, determine the weights corresponding to the power cost and the carbon emission cost, and calculate the power cost and the carbon emission cost based on the weights to obtain the user's power adaptability.

[0053] Step S206: updating each initial decision reference value in accordance with an updating method that matches the power usage adaptability, and determining an updated decision reference value corresponding to each initial decision reference value.

[0054] The updating method refers to the updating method of the initial decision reference value, and the initial decision reference value can be adaptively updated to obtain an updated decision reference value.

[0055] Specifically, the update method corresponding to the initial decision reference value is different for different power adaptability. The power adaptability of each initial decision reference value can be sorted in order from large to small, and the power adaptability can be divided into several parts. Each part corresponds to a different update method. According to the corresponding update method, each initial decision reference value is updated and processed respectively to determine the updated decision reference value corresponding to each initial decision reference value. Exemplarily, users can be divided into three parts according to the order of power adaptability. The users in the first half of the order are regarded as discoverers, the 30% of users in the middle are regarded as joiners, and the remaining 20% ​​of users are regarded as users who are aware of the dangerous state, because their power adaptability is relatively poor and they are often in a dangerous avoidance state. In a specific embodiment, the update method includes a first update method. The terminal can determine the warning value and safety value corresponding to the initial decision reference value for each initial decision reference value of the user, and determine the first update method of the initial decision reference value based on the numerical comparison relationship between the warning value and the safety value. The initial decision reference value is updated based on the first update method to obtain the updated decision reference value. In another specific embodiment, for users who are aware of the dangerous state, the initial decision reference value can be updated by the following update method:

[0056]

[0057] in, is the global optimal reference value in the tth iteration. β, as a step size control parameter, is a random number that follows a normal distribution with a mean of 0 and a variance of 1. K∈[-1,1] is a random number, and fi is the current user's electricity fitness value. fg and fw are the current global best and worst electricity fitness values, respectively. ε is the smallest constant to avoid zero in the denominator. In this application, fg represents the optimal objective function value of the currently found park scheduling scheme (scheduling plan for photovoltaic load, wind turbine load and user electricity load) among all alternative schemes. It reflects the currently known best scheduling strategy, which can achieve the lowest sum of electricity cost and carbon emission cost. That is, fg represents the solution with the lowest cost found so far in the optimization process. Conversely, fw represents the worst objective function value in the current park scheduling scheme. This represents a solution with the highest sum of electricity cost and carbon emission cost in the existing scheduling strategy. fw reflects the most undesirable scheduling scheme, which may correspond to the highest electricity consumption and carbon emissions, or the most unfavorable combination of electricity price and carbon emission price.

[0058] Step S208: determining the operation cost of the park based on each update decision reference value.

[0059] Among them, operating cost refers to the cost incurred when the park operates according to the decision reference value of each user.

[0060] Specifically, after obtaining the updated update decision reference value, the optimized operation cost of the park can be determined. Exemplarily, the objective function for determining the operation cost of the park is as follows:

[0061]

[0062] in, represents the electricity cost function; represents the carbon emission cost function; Indicates the duration of the power consumption process; Indicates the switch control strategy of users, photovoltaic loads, and wind turbine loads; Indicates the park The power consumption of the user in period t; and is the power output of the mth PV load and wind turbine load in the tth period; is the carbon emission factor value of the power distribution network node n connected to the park in the tth period; The price of electricity in time period t; is the carbon emission price in the tth time period; J is the total number of photovoltaic load clusters; M is the total number of wind turbine load clusters; T represents the total number of hours in a day; I represents the total number of users in the park.

[0063] The user load, photovoltaic load and wind turbine load clusters need to meet the constraints of switch duration, number, power, etc., as shown below:

[0064]

[0065] Where: and Each represents the start time and end time of electricity production by the photovoltaic load; and Each represents the start time and end time of electricity production by the wind turbine load; and is the minimum power output of photovoltaic and wind turbines; and It is the maximum power output of photovoltaic and wind turbine.

[0066] In a specific embodiment, the terminal can directly determine each updated decision reference value after one update as the recommended reference value of the park, and then determine the operation cost of the park. In another specific embodiment, the terminal can also determine the candidate operation cost of the park based on each updated decision reference value, use the updated decision reference value as the initial decision reference value, return to the step of determining the user's electricity adaptability based on each initial decision reference value corresponding to each user, and when the number of determined candidate operation costs reaches a set number, determine the target operation cost with the smallest value from each candidate operation cost, and determine each updated decision reference value corresponding to the target operation cost as the recommended reference value of the park.

[0067] The above-mentioned method for determining the operating cost of the park obtains multiple initial decision reference values ​​corresponding to each user in the park during the electricity usage process, so as to determine the electricity usage of each user. For each user, based on the initial decision reference values ​​corresponding to the user, the user's electricity usage adaptability is determined, wherein the electricity usage adaptability is negatively correlated with the theoretical power consumption in the electricity usage process, and negatively correlated with the theoretical carbon emissions in the electricity usage process, so as to determine the energy saving degree of each user in the electricity usage process, and update the initial decision reference values ​​according to the update method matching the electricity usage adaptability, and determine the updated decision reference value corresponding to each initial decision reference value. Based on different energy saving degrees, the decision reference value update method suitable for the energy saving degree can be matched. Finally, based on the updated decision reference values, the operating cost of the park is determined, and the reasonable optimization of the initial decision reference values ​​can be achieved, thereby improving the accuracy of the optimization of the park operating cost.

[0068] In an exemplary embodiment, the user's electricity usage adaptability is determined based on the initial decision reference values ​​corresponding to the user, including: determining the user's electricity cost and carbon emission cost based on the initial decision reference values ​​corresponding to the user; determining the weights corresponding to the electricity cost and the carbon emission cost respectively; and calculating the electricity cost and the carbon emission cost based on the weights to obtain the user's electricity usage adaptability.

[0069] Among them, electricity cost refers to all costs incurred in the process of producing, transmitting, distributing and using electricity. Carbon emission cost refers to the costs associated with greenhouse gas emissions, especially carbon dioxide emissions. Weight is a numerical value that expresses relative importance. It is used to assign different influences or priorities in decision-making, evaluation, calculation and modeling.

[0070] Specifically, after determining the initial decision reference value of each user, it is necessary to calculate the corresponding electricity cost and carbon emission cost for each user based on the initial decision reference value of the user, and obtain the weights corresponding to the electricity cost and carbon emission cost respectively, and finally calculate the electricity cost and carbon emission cost based on each weight to obtain the user's electricity use adaptability. Among them, the weights corresponding to the electricity cost and carbon emission cost respectively can be set in advance, or can be set in real time based on the electricity use of each user, which is not limited here.

[0071] In a specific embodiment, the electricity adaptability can be defined as:

[0072]

[0073] Electricity costs , this part calculates the cost of electricity consumption. represents the power output of the photovoltaic load, is the power output of the fan load, It is the power consumed by the user.

[0074] The electricity cost calculation can be a weighted sum of the power output of each load:

[0075]

[0076] here is the corresponding unit electricity cost coefficient.

[0077] Carbon emissions costs , this part calculates the carbon emissions of each load and calculates the corresponding cost. The carbon emission cost can be based on the type of load and power output, such as:

[0078]

[0079] here It is the carbon emission coefficient related to each load, reflecting the carbon emission intensity per unit power, and is the same value in this application.

[0080] In this embodiment, the electricity cost and the carbon emission cost are determined respectively by each initial decision reference value, and the electricity adaptability is determined based on their respective weights, which can ensure the accuracy of the determination of the electricity adaptability.

[0081] In an exemplary embodiment, the method for determining the park operation cost also includes: sorting each power usage adaptability in order from large to small; and determining the update method that matches each power usage adaptability based on a preset update method ratio.

[0082] Among them, the proportion of update methods refers to the proportion of update methods. Specifically for this application, different update methods have different proportions in electricity adaptability.

[0083] Specifically, the terminal can sort the electricity adaptability in order from large to small, and divide the users into three parts according to the size of the electricity adaptability. According to the proportion of update methods, the users in the first half of the ranking are regarded as discoverers, the middle 30% of users are regarded as joiners, and the remaining 20% ​​of users are regarded as users who are aware of the dangerous state. Because their electricity adaptability is relatively poor, they are often in a danger avoidance state. The update methods corresponding to discoverers, joiners and users who are aware of the dangerous state are different.

[0084] In this embodiment, different sizes of power usage adaptability are divided based on the preset update mode ratio, and the update mode matched with each power usage adaptability is finally determined, which can improve the accuracy of power usage adaptability determination.

[0085] In an exemplary embodiment, the update method includes a first update method; each initial decision reference value is updated separately according to the update method matching the electricity usage adaptability to determine the updated decision reference value corresponding to each initial decision reference value, including: for each initial decision reference value of the user, determining the warning value and safety value corresponding to the initial decision reference value; based on the numerical comparison relationship between the warning value and the safety value, determining the first update method of the initial decision reference value; based on the first update method, updating the initial decision reference value to obtain an updated decision reference value.

[0086] The first update mode refers to the update mode corresponding to the discoverer. The warning value can be used to reflect the warning situation of the initial decision reference value. The safety value can be used to reflect the safety situation of the initial decision reference value. The numerical comparison relationship refers to the ratio relationship between the numerical values ​​of the warning value and the safety value.

[0087] Specifically, when the update method is the first update method, for each initial decision reference value of the user, it is necessary to perform standardization processing on the initial decision reference value to obtain the warning value corresponding to the initial decision reference value, and obtain the safety value corresponding to the initial decision reference value, and determine the first update method of the initial decision reference value based on the numerical comparison relationship between the warning value and the safety value, and update the initial decision reference value based on the first update method to obtain the updated decision reference value. Exemplarily, the first update method is represented by the following formula:

[0088]

[0089] Where t represents the current iteration number and j = 1, 2, 3, …, d. Is a constant indicating the maximum number of iterations. represents the initial decision reference value of the i-th user in the j-th dimension. α∈(0,1] is a random number. ( ∈[0,1]) and ( ∈[0.5,1]) represent the warning value and safety value respectively. Q is a random number that follows a normal distribution. L represents a 1×d matrix, where all elements in the matrix are 1. It should be noted that the switch control strategy is updated when , using exponential decay update strategy , which is the current switch strategy value multiplied by a decay factor. This method is usually used to "fine-tune" the strategy value; when , using a linear update strategy , that is, the current switch strategy value increases by a value related to the random factor Q and the learning factor L. This approach is more radical and may lead to a large change in the strategy. The actual impact on the switch control strategy is that the switch state value is initially 1 (indicating "open"), and by updating the formula, the value is reduced to close to 0.5, which may reflect that the control strategy is gradually tending to the "closed" state. The specific switching mechanism sets a threshold of 0.5, <0.5 means off; ≥0.5 means open, to clarify the numerical rule for updating the switch state. Therefore, the update of the switch control strategy is the change of the value calculated by the formula, and then judged according to the threshold, resulting in the state being updated from "open" to "closed" or vice versa.

[0090] In this embodiment, based on the numerical comparison relationship between the warning value and the safety value corresponding to the initial decision reference value, a first update method of the initial decision reference value is determined, and the initial decision reference value is updated based on the first update method to obtain an updated decision reference value, which can improve the accuracy of determining the updated decision reference value.

[0091] In an exemplary embodiment, the update method includes a second update method; each initial decision reference value is updated separately according to the update method that matches the power usage adaptability, and the updated decision reference value corresponding to each initial decision reference value is determined, including: for each power usage adaptability matching the second update method, the power usage adaptability is judged for the fitness value, and multiple priority initial reference values ​​that meet the fitness priority condition and multiple ordinary initial reference values ​​that do not meet the fitness priority condition are determined; for each priority initial reference value, the priority initial reference value is updated according to the update method that matches the priority initial reference value to obtain an updated decision reference value; for each ordinary initial reference value, the ordinary initial reference value is updated according to the update method that matches the ordinary initial reference value to obtain an updated decision reference value.

[0092] Among them, the second update method refers to the update method corresponding to the subscriber. The priority initial reference value refers to the initial decision reference value with priority on fitness, and the ordinary initial reference value refers to the initial decision reference value with relatively ordinary and lower fitness. The fitness priority condition refers to the judgment condition of whether fitness is prioritized. In this embodiment, the fitness priority condition means that the size of the electricity fitness is in the first half of the electricity fitness of the subscriber.

[0093] Specifically, for each electricity fitness that matches the second update method, a fitness value judgment is performed on the electricity fitness, and a plurality of priority initial reference values ​​that meet the fitness priority condition and a plurality of ordinary initial reference values ​​that do not meet the fitness priority condition are determined. That is, it is determined whether the numerical value of the electricity fitness is in the first half of the electricity fitness of the subscriber. When the numerical value of the electricity fitness is in the first half of the electricity fitness of the subscriber, the initial decision reference value corresponding to the electricity fitness is determined to be the priority initial reference value. For each priority initial reference value, the priority initial reference value is updated according to the update method that matches the priority initial reference value to obtain an updated decision reference value. When the numerical value of the electricity fitness is not in the first half of the electricity fitness of the subscriber, the initial decision reference value corresponding to the electricity fitness is determined to be the ordinary initial reference value. For each ordinary initial reference value, the ordinary initial reference value is updated according to the update method that matches the ordinary initial reference value to obtain an updated decision reference value.

[0094] In a specific embodiment, the process of determining the update decision reference value of the joiner is represented by the following formula:

[0095]

[0096] in, It is the optimal decision reference value currently occupied by the discoverer. represents the current worst global decision reference value. The current global refers to the initial decision reference value corresponding to the discoverer and the updated joiner during this update process. A represents a 1×d matrix, in which each element is randomly assigned 1 or -1, and In this application, i represents the number of the load cluster (photovoltaic load, wind turbine or user), and n represents the total number of load clusters. There may be multiple photovoltaic loads and wind turbines in the park of this application, and each photovoltaic load or wind turbine has its own unique operating parameters and status. By using i, these devices can be traversed to optimize the power output, switch status, working time, etc. of each device; the population is often divided into two groups. The first half (i.e. devices) generally represent devices with higher priority regulation, while the second half (i.e. The first half of the devices usually get better scheduling strategies, while the second half of the devices may be more inclined to perform exploratory operations to avoid local optimality.

[0097] In this embodiment, for subscribers of the second update method, the electricity usage adaptability is divided again, and the initial decision reference values ​​that meet the fitness priority condition and the initial decision reference values ​​that do not meet the fitness priority condition are updated according to different update methods, which can improve the accuracy of determining the updated decision reference values ​​of the subscriber users.

[0098] In an exemplary embodiment, the operating cost of the park is determined based on each updated decision reference value, including: determining the candidate operating costs of the park based on each updated decision reference value; using the updated decision reference value as the initial decision reference value, returning the step of determining the user's electricity adaptability for each user based on each initial decision reference value corresponding to the user; when the number of determined candidate operating costs reaches a set number, determining the target operating cost with the smallest value from each candidate operating cost; and determining each updated decision reference value corresponding to the target operating cost as the recommended reference value of the park.

[0099] Among them, the candidate operating cost refers to the operating cost waiting to be selected. The set number refers to the number of candidate operating costs that need to be determined in advance. The target operating cost is the operating cost finally selected as the park operating cost. The recommended reference value is the update decision reference value with the best optimization effect, the best energy conservation and emission reduction effect, and the lowest operating cost.

[0100] Specifically, after determining the updated decision reference value of each user, the candidate operating costs of the park can be determined based on each updated decision reference value, and the updated decision reference value can be used as the initial decision reference value. Return to the step of determining the user's electricity adaptability for each user based on the initial decision reference values ​​corresponding to the user, and perform the step of updating the decision reference value again. If the number of determined candidate operating costs does not reach the set number, repeat the above operation until the number of determined candidate operating costs reaches the set number, determine the target operating cost with the smallest value from each candidate operating cost, and determine the updated decision reference values ​​corresponding to the target operating costs as the recommended reference values ​​for the park.

[0101] In this embodiment, by optimizing and updating the decision reference value multiple times, the reference value solution with the lowest cost for the park can be accurately determined.

[0102] In a specific embodiment, a method for determining park operation costs is also provided, including:

[0103] This application aims to reduce electricity costs and carbon emission costs under the premise of meeting various constraints such as the switch status, switch duration, number, power, etc. of photovoltaic load and wind turbine load. Therefore, the objective function for determining the park operation cost is as follows:

[0104]

[0105] in, represents the electricity cost function; represents the carbon emission cost function; Indicates the duration of the power consumption process; Indicates the switch control strategy of users, photovoltaic loads, and wind turbine loads; Indicates the park The power consumption of the user in period t; and is the power output of the mth PV load and wind turbine load in the tth period; is the carbon emission factor value of the power distribution network node n connected to the park in the tth period; The price of electricity in time period t; is the carbon emission price in the tth time period; J is the total number of photovoltaic load clusters; M is the total number of wind turbine load clusters; T represents the total number of hours in a day; I represents the total number of users in the park.

[0106] The user load, photovoltaic load and wind turbine load clusters need to meet the constraints of switch duration, number, power, etc., as shown below:

[0107]

[0108] Where: and Each represents the start time and end time of electricity production by the photovoltaic load; and Each represents the start time and end time of electricity production by the wind turbine load; and is the minimum power output of photovoltaic and wind turbines; and It is the maximum power output of photovoltaic and wind turbine.

[0109] In this scheme, reducing electricity costs and reducing carbon emission costs are taken as the optimal scheduling of the two main goals. It can be understood that each sparrow below represents each user by using the sparrow search algorithm for calculation.

[0110] First, virtual sparrows search for real objects. The population consisting of n sparrows can be expressed as follows:

[0111]

[0112] in is the n-dimensional decision vector, i.e., the problem search space, is the dimension of the variable to be optimized, is the number of sparrows. The physical quantities represented by x in this application scenario are the power output of photovoltaic load, the power output of wind turbine load, the power consumption of users, photovoltaic load, wind turbine load, user power switch control strategy, and working time. Each sparrow has a fitness value in the objective function. The fitness value is used to evaluate the quality of the solution of the sparrow. The higher the fitness value, the better the quality of the solution. The fitness value of the sparrow can be expressed as follows:

[0113] Electricity adaptability can be defined as:

[0114]

[0115] Electricity costs , this part calculates the cost of electricity consumption. represents the power output of the photovoltaic load, is the power output of the fan load, It is the power consumed by the user.

[0116] The electricity cost calculation can be a weighted sum of the power output of each load:

[0117]

[0118] here is the corresponding unit electricity cost coefficient.

[0119] Carbon emissions costs , this part calculates the carbon emissions of each load and calculates the corresponding cost. The carbon emission cost can be based on the type of load and power output, such as:

[0120]

[0121] here It is the carbon emission coefficient related to each load, reflecting the carbon emission intensity per unit power, and is the same value in this application.

[0122] In this algorithm, the finder sparrow with a high fitness value will get food first during the food search process. During each iteration, the position of the finder sparrow is updated as follows:

[0123]

[0124] Where t represents the current iteration number and j = 1, 2, 3, …, d. Is a constant indicating the maximum number of iterations. represents the initial decision reference value of the i-th user in the j-th dimension. α∈(0,1] is a random number. ( ∈[0,1]) and ( ∈[0.5,1]) represent the warning value and safety value respectively. Q is a random number that follows a normal distribution. L represents a 1×d matrix, where all elements in the matrix are 1. It should be noted that the switch control strategy is updated when , using exponential decay update strategy , which is the current switch strategy value multiplied by a decay factor. This method is usually used to "fine-tune" the strategy value; when , using a linear update strategy , that is, the current switch strategy value increases by a value related to the random factor Q and the learning factor L. This approach is more radical and may lead to a large change in the strategy. The actual impact on the switch control strategy is that the switch state value is initially 1 (indicating "open"), and by updating the formula, the value is reduced to close to 0.5, which may reflect that the control strategy is gradually tending to the "closed" state. The specific switching mechanism sets a threshold of 0.5, <0.5 means off; ≥0.5 means open, to clarify the numerical rule for updating the switch state. Therefore, the update of the switch control strategy is the change of the value calculated by the formula, and then judged according to the threshold, resulting in the state being updated from "open" to "closed" or vice versa.

[0125] In the sparrow group, except for the discoverer sparrow, the remaining sparrows are called joiner sparrows. During the foraging process, the joiner sparrows will always monitor the discoverer sparrows. Once they perceive that the discoverer sparrow has found a better food location, they will leave their current position to compete with them for a better food location. The process of determining the update decision reference value of the joiner is expressed by the following formula:

[0126]

[0127] in, It is the optimal decision reference value currently occupied by the discoverer. represents the current worst global decision reference value. The current global refers to the initial decision reference value corresponding to the discoverer and the updated joiner during this update process. A represents a 1×d matrix, in which each element is randomly assigned 1 or -1, and In this application, i represents the number of the load cluster (photovoltaic load, wind turbine or user), and n represents the total number of load clusters. There may be multiple photovoltaic loads and wind turbines in the park of this application, and each photovoltaic load or wind turbine has its own unique operating parameters and status. By using i, these devices can be traversed to optimize the power output, switch status, working time, etc. of each device; the population is often divided into two groups. The first half (i.e. devices) generally represent devices with higher priority regulation, while the second half (i.e. The first half of the devices usually get better scheduling strategies, while the second half of the devices may be more inclined to perform exploratory operations to avoid local optimality.

[0128] At the same time, the algorithm also sets up a class of sparrows that are aware of dangerous states, among which the joiner sparrows and the sparrows that are aware of dangerous states are further sorted based on fitness, excluding the top 50% of the discoverer sparrows, and then the remaining top 30% of the individuals (with higher fitness) are classified as "joiner sparrows" because they tend to approach the discoverer sparrows; the remaining 20% ​​of the individuals (with lower fitness) are classified as "sparrows that are aware of dangerous states" because they have relatively poor fitness and are often in a state of danger avoidance. For users who are aware of dangerous states, the initial decision reference value can be updated in the following update method:

[0129]

[0130] in, is the global optimal reference value in the tth iteration. β, as a step size control parameter, is a random number that follows a normal distribution with a mean of 0 and a variance of 1. K∈[-1,1] is a random number, and fi is the current user's electricity fitness value. fg and fw are the current global best and worst electricity fitness values, respectively. ε is the smallest constant to avoid zero in the denominator. In this application, fg represents the optimal objective function value of the currently found park scheduling scheme (scheduling plan for photovoltaic load, wind turbine load and user electricity load) among all alternative schemes. It reflects the currently known best scheduling strategy, which can achieve the lowest sum of electricity cost and carbon emission cost. That is, fg represents the solution with the lowest cost found so far in the optimization process. Conversely, fw represents the worst objective function value in the current park scheduling scheme. This represents a solution with the highest sum of electricity cost and carbon emission cost in the existing scheduling strategy. fw reflects the most undesirable scheduling scheme, which may correspond to the highest electricity consumption and carbon emissions, or the most unfavorable combination of electricity price and carbon emission price.

[0131] In a specific embodiment, Figure 3As shown, a method for determining the park operation cost is also provided, including:

[0132] Step S301, obtaining a plurality of initial decision reference values ​​corresponding to each user in the park during the electricity use process;

[0133] Step S302, for each user, based on each initial decision reference value corresponding to the user, determining the user's electricity cost and carbon emission cost;

[0134] Step S303, determining the weights corresponding to the electricity cost and the carbon emission cost respectively, and calculating the electricity cost and the carbon emission cost based on the weights to obtain the user's electricity use adaptability;

[0135] Among them, electricity adaptability is negatively correlated with the theoretical electricity consumption in the electricity use process, and negatively correlated with the theoretical carbon emissions in the electricity use process;

[0136] Step S304, sorting the power usage adaptability in descending order, and determining the update method matched with each power usage adaptability based on the preset update method proportion;

[0137] Wherein, the updating method includes a first updating method and a second updating method;

[0138] Step S305, for each initial decision reference value of the user, determining a warning value and a safety value corresponding to the initial decision reference value;

[0139] Step S306, based on the numerical comparison relationship between the warning value and the safety value, determine a first updating method of the initial decision reference value, and update the initial decision reference value based on the first updating method to obtain an updated decision reference value;

[0140] Step S307, for each power usage fitness matching the second update mode, performing fitness value judgment on the power usage fitness, and determining a plurality of priority initial reference values ​​that meet the fitness priority condition, and a plurality of common initial reference values ​​that do not meet the fitness priority condition;

[0141] Step S308, for each priority initial reference value, updating the priority initial reference value according to an update method matching the priority initial reference value to obtain an update decision reference value;

[0142] Step S309, for each common initial reference value, updating the common initial reference value in an updating manner matching the common initial reference value to obtain an updated decision reference value;

[0143] Step S310, determining the candidate operating cost of the park based on each updated decision reference value, taking the updated decision reference value as the initial decision reference value, and returning to step S302;

[0144] Step S311, when the number of determined candidate operating costs reaches a set number, determine the target operating cost with the smallest value from the candidate operating costs;

[0145] Step S312: determining each update decision reference value corresponding to the target operating cost as a recommended reference value for the park.

[0146] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0147] Based on the same inventive concept, the embodiment of the present application also provides a device for determining park operation costs for implementing the above-mentioned method for determining park operation costs. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for determining park operation costs provided below can refer to the limitations of the method for determining park operation costs above, and will not be repeated here.

[0148] In an exemplary embodiment, Figure 4 As shown, a park operation cost determination device 400 is provided, including: an initial decision reference value acquisition module 402, a power usage adaptability determination module 404, an update processing module 406 and an operation cost determination module 408, wherein:

[0149] The initial decision reference value acquisition module 402 is used to acquire a plurality of initial decision reference values ​​corresponding to each user in the park during the electricity use process;

[0150] The power usage adaptability determination module 404 is used to determine the power usage adaptability of each user based on the initial decision reference values ​​corresponding to the user; the power usage adaptability is inversely correlated with the theoretical power consumption in the power usage process, and is inversely correlated with the theoretical carbon emissions in the power usage process;

[0151] An update processing module 406 is used to update each of the initial decision reference values ​​according to an update method that matches the power usage adaptability, and determine an updated decision reference value corresponding to each of the initial decision reference values;

[0152] The operation cost determination module 408 is used to determine the operation cost of the park based on each of the update decision reference values.

[0153] In a specific embodiment, the electricity adaptability determination module 404 is specifically used to: determine the electricity cost and carbon emission cost of the user based on each of the initial decision reference values ​​corresponding to the user;

[0154] Determining respective weights corresponding to the electricity cost and the carbon emission cost;

[0155] The electricity cost and the carbon emission cost are calculated based on the weights to obtain the electricity usage adaptability of the user.

[0156] In a specific embodiment, the park operation cost determination device 400 further includes an update mode determination module, which is specifically used to: sort the electricity usage adaptability in descending order;

[0157] Based on the preset update mode ratio, the update mode that matches each of the power usage adaptability is determined.

[0158] In a specific embodiment, the updating method includes a first updating method. In the case of this embodiment, the updating processing module 406 is used to: for each of the initial decision reference values ​​of the user, determine the warning value and the safety value corresponding to the initial decision reference value;

[0159] Determining a first updating method of the initial decision reference value based on a numerical comparison relationship between the warning value and the safety value;

[0160] The initial decision reference value is updated based on the first updating method to obtain an updated decision reference value.

[0161] In a specific embodiment, the update method includes a second update method. In the case of this embodiment, the update processing module 406 is further used to: for each of the power usage adaptability matching the second update method, perform a fitness value judgment on the power usage adaptability, and determine a plurality of priority initial reference values ​​that meet the fitness priority condition, and a plurality of common initial reference values ​​that do not meet the fitness priority condition;

[0162] For each of the priority initial reference values, updating the priority initial reference value in an updating manner matching the priority initial reference value to obtain an updated decision reference value;

[0163] For each of the common initial reference values, the common initial reference value is updated in an updating manner that matches the common initial reference value to obtain an updated decision reference value.

[0164] In a specific embodiment, the operation cost determination module 408 is specifically used to: determine the candidate operation cost of the park based on each of the update decision reference values;

[0165] Taking the updated decision reference value as the initial decision reference value, returning to the step of determining the power usage adaptability of each user based on the initial decision reference values ​​corresponding to the user for each user;

[0166] When the number of determined candidate operating costs reaches a set number, determining a target operating cost with the smallest value from among the candidate operating costs;

[0167] Each updated decision reference value corresponding to the target operating cost is determined as a recommended reference value for the park.

[0168] Each module in the above-mentioned park operation cost determination device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0169] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for determining the operating cost of a park is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0170] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0171] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above method when executing the computer program.

[0172] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0173] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0175] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0177] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining park operation costs, characterized in that: The method comprises: Obtain multiple initial decision reference values ​​corresponding to each user in the park during the electricity consumption process; For each of the users, based on the initial decision reference values ​​corresponding to the users, determining the user's power usage adaptability; the power usage adaptability is inversely correlated with the theoretical power consumption during the power usage process, and is inversely correlated with the theoretical carbon emissions during the power usage process; According to the updating method matching the electricity usage adaptability, each of the initial decision reference values ​​is updated respectively to determine the updated decision reference value corresponding to each of the initial decision reference values; Based on each of the update decision reference values, the operation cost of the park is determined.

2. The method according to claim 1, characterized in that The determining the power usage adaptability of the user based on each of the initial decision reference values ​​corresponding to the user includes: Determining the electricity cost and carbon emission cost of the user based on each of the initial decision reference values ​​corresponding to the user; Determining respective weights corresponding to the electricity cost and the carbon emission cost; The electricity cost and the carbon emission cost are calculated based on the weights to obtain the electricity usage adaptability of the user.

3. The method according to claim 1, characterized in that: The method further comprises: Arrange the electricity adaptability in descending order; Based on the preset update mode ratio, the update mode that matches each of the power usage adaptability is determined.

4. The method according to claim 1, characterized in that The updating method includes a first updating method; the updating method matching the power usage adaptability is used to update each of the initial decision reference values, and to determine the updated decision reference values ​​corresponding to each of the initial decision reference values, including: For each of the initial decision reference values ​​of the user, determining a warning value and a safety value corresponding to the initial decision reference value; Determining a first updating method of the initial decision reference value based on a numerical comparison relationship between the warning value and the safety value; The initial decision reference value is updated based on the first updating method to obtain an updated decision reference value.

5. The method according to claim 1, characterized in that The updating method includes a second updating method; the updating method matching the power usage adaptability is used to update each of the initial decision reference values, and to determine the updated decision reference values ​​corresponding to each of the initial decision reference values, including: For each of the power usage adaptability matching the second update mode, performing a fitness value judgment on the power usage adaptability, and determining a plurality of priority initial reference values ​​that meet the fitness priority condition, and a plurality of common initial reference values ​​that do not meet the fitness priority condition; For each of the priority initial reference values, updating the priority initial reference value in an updating manner matching the priority initial reference value to obtain an updated decision reference value; For each of the common initial reference values, the common initial reference value is updated in an updating manner that matches the common initial reference value to obtain an updated decision reference value.

6. The method according to any one of claims 1 to 5, characterized in that: The determining the operation cost of the park based on each of the update decision reference values ​​includes: Determining a candidate operating cost of the park based on each of the update decision reference values; Taking the updated decision reference value as the initial decision reference value, returning to the step of determining the power usage adaptability of each user based on the initial decision reference values ​​corresponding to the user for each user; When the number of determined candidate operating costs reaches a set number, determining a target operating cost with the smallest value from among the candidate operating costs; Each updated decision reference value corresponding to the target operating cost is determined as a recommended reference value for the park.

7. A device for determining park operation costs, characterized in that: The device comprises: An initial decision reference value acquisition module is used to obtain a plurality of initial decision reference values ​​corresponding to each user in the park during the electricity use process; A power usage adaptability determination module, configured to determine the power usage adaptability of each user based on the initial decision reference values ​​corresponding to the user; the power usage adaptability is inversely correlated with the theoretical power consumption in the power usage process, and is inversely correlated with the theoretical carbon emissions in the power usage process; An update processing module, used to update each of the initial decision reference values ​​according to an update method matching the power usage adaptability, and determine an updated decision reference value corresponding to each of the initial decision reference values; An operation cost determination module is used to determine the operation cost of the park based on each of the update decision reference values.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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