Resource configuration method and device of RF control system, storage medium and equipment

Abstract

The invention provides a resource allocation method and device for an RF control system, a storage medium and equipment, and the method comprises the steps: obtaining a plurality of performance indexes biased by a user and a pre-constructed first relation model which is used for representing the influence relation between each resource item and each performance index of the RF control system; an initial population containing a plurality of individuals is generated, an evaluation result of any individual under a plurality of performance indexes is determined based on the first relation model, one individual represents one resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations; selecting individuals from the initial population based on an evaluation result for variation to realize population iterative optimization to obtain an optimized population, and selecting a high-quality population from the optimized population; and configuring system resources of the RF control system based on the high-quality individuals in the high-quality population. According to the invention, the resource configuration of the RF control system can meet the personalized system performance requirement of a user, and a plurality of performance indexes can be balanced.

Description

Technical Field

[0001] The present invention relates to the field of wireless technology, and in particular to a resource configuration method, device, storage medium and equipment for an RF control system. Background Art

[0002] With the continuous development of wireless or radio frequency (RF) control technology, more and more RF control systems have emerged. RF control systems are key systems for controlling controlled models such as model aircraft, drones, and unmanned vehicles. The performance of RF control systems is crucial to the safety and controllability of controlled models, and the system performance of RF control systems is inseparable from the configuration of system resources.

[0003] Currently, a fixed, single resource allocation strategy is commonly used to configure RF control system resources. However, RF control systems have a wide range of performance requirements, and each user has different preferences for the performance of each system. If a fixed, single resource allocation strategy is used, the performance preferences of the RF control system will also be fixed, resulting in the RF control system's resource allocation failing to meet the user's personalized system performance requirements. Therefore, ensuring that the RF control system's system resource allocation meets the user's personalized system performance requirements is a pressing technical issue in the field of RF control technology. Summary of the Invention

[0004] The main purpose of the present invention is to provide a resource configuration method, device, storage medium and equipment based on an RF control system, aiming to solve the technical problem in the prior art that the system resource configuration of the RF control system cannot meet the user's personalized system performance requirements.

[0005] To achieve the above object, the present invention provides a resource configuration method for an RF control system, the method comprising: Acquire multiple performance indicators preferred by the user and a pre-built first relationship model, where the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; generating an initial population comprising a plurality of individuals, and determining an evaluation result of each individual under a plurality of performance indicators based on the first relationship model and each resource item corresponding to each individual, wherein each individual represents a resource allocation strategy, and different resource allocation strategies correspond to different resource item combinations; Based on the evaluation results, individuals are selected from the initial population to perform mutation to achieve iterative population optimization, thereby obtaining an optimized population, and a high-quality population is selected from the optimized population, wherein any individual in the high-quality population satisfies multiple performance indicators; System resources of the RF control system are configured based on the high-quality individuals in the high-quality population.

[0006] To achieve the above object, the present invention further provides a resource configuration device for an RF control system, the device comprising: an acquiring unit, configured to acquire a plurality of performance indicators preferred by a user, and a pre-built first relationship model, wherein the first relationship model is configured to represent an influence relationship between various resource items and various performance indicators of the RF control system; A generation unit, used to generate an initial population comprising a plurality of individuals; a determining unit, configured to determine an evaluation result of any individual under multiple performance indicators based on the first relationship model and each resource item corresponding to any individual, wherein each individual represents a resource allocation strategy, and different resource allocation strategies correspond to different resource item combinations; An optimization unit, configured to select individuals from the initial population based on the evaluation results to perform mutation to achieve iterative population optimization, thereby obtaining an optimized population, and to select a high-quality population from the optimized population; The configuration unit is used to configure system resources of the RF control system based on the high-quality individuals in the high-quality population.

[0007] To achieve the above-mentioned objectives, the present invention provides a resource configuration device for an RF control system, wherein the resource configuration device comprises: a memory and a processor, wherein the memory stores a resource configuration program for the RF control system, and when the processor executes the resource configuration program for the RF control system, the steps of the resource configuration method for the RF control system as described above are implemented.

[0008] The present invention provides a resource configuration method, apparatus, storage medium and equipment for an RF control system. The present invention can obtain multiple performance indicators preferred by users, as well as a pre-constructed first relationship model, wherein the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; an initial population comprising multiple individuals can be generated, and based on the first relationship model and each resource item corresponding to any individual, an evaluation result of any individual under multiple performance indicators can be determined, wherein each individual represents a resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations. At the same time, based on the evaluation results, the present invention can select individuals from the initial population for mutation to achieve iterative optimization of the population, thereby obtaining an optimized population. Any individual in the high-quality population satisfies multiple performance indicators, and system resources of the RF control system are configured based on high-quality individuals in the high-quality population. Compared to existing technologies, the present invention supports users setting or selecting their preferred performance indicators, meeting the diverse user preferences for multiple performance indicators. It pre-constructs a first relationship model and then, based on the first relationship model, continuously iteratively optimizes a population of multiple resource allocation strategies, targeting the user's preferred system performance indicators. This yields an optimized population. Based on the scoring results, a high-quality population (any individual that simultaneously meets the user's preferred performance indicators) is selected from the optimized population, and system resource allocation is performed based on the high-quality individuals within the high-quality population. In other words, the present invention generates high-quality resource allocation strategies that simultaneously meet the user's preferred performance indicators. This allows the RF control system to simultaneously meet the user's personalized preferences for system performance indicators while balancing multiple performance indicators during resource allocation. Furthermore, in embodiments of the present invention, high-quality resource allocation strategies can be obtained without requiring technicians to manually test, verify, and fine-tune resource items. This not only avoids inconsistent resource allocation results due to different understandings among technicians, but also prevents the overall system performance of the RF control system from being affected by improper resource item settings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the solutions in the present invention, a brief introduction is given below to the drawings required for use in describing the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0010] Figure 1 is a flow chart of a resource configuration method of an RF control system provided by an embodiment of the present invention; Figure 2 is a flow chart of another resource configuration method of an RF control system provided by an embodiment of the present invention; Figure 31 is a schematic diagram of the structure of a resource configuration device of an RF control system provided by an embodiment of the present invention; Figure 4 This is a basic structural block diagram of a resource configuration device of an RF control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The resource configuration method for an RF control system provided in an embodiment of the present invention is applicable to a resource configuration method apparatus for an RF control system. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this invention belongs. The terms used in the specification and application description herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The terms "including" and "having," as well as any variations thereof, in the specification and claims of the present invention and the accompanying drawings, are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the specification and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order.

[0012] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0013] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0014] With the continuous development of wireless or radio frequency (RF) control technology, more and more RF control systems have emerged. RF control systems are key systems for controlling controlled models such as model aircraft, drones, and unmanned vehicles. The system performance of RF control systems is crucial to the safety and controllability of controlled models, and the system performance of RF control systems is inseparable from resource allocation.

[0015] At present, in order to ensure that the system performance of the RF control system reaches the expected performance, a fixed and single resource configuration strategy is usually adopted to configure the resources of the RF control system. However, the system performance of the RF control system is relatively large, and the RF control system faces different users, and different users have different preferences for the system performance of the RF control system. If a fixed and single resource configuration strategy is adopted, the system performance preference of the RF control system will also be fixed and single, which will cause the system resource configuration of the RF control system to be unable to meet the user's personalized system performance requirements. Therefore, how to ensure that the system resource configuration of the RF control system meets the user's personalized system performance requirements is a technical problem that needs to be solved urgently in the field of RF control technology. In addition, it is impossible to balance multiple performance indicators at the same time. Therefore, how to ensure that the system resource configuration of the RF control system can balance multiple performance indicators that meet the user's preferences is also a technical problem that needs to be solved urgently in the field of RF control technology.

[0016] Alternatively, to meet personalized system performance requirements, users can be allowed to modify some RF control system resource items. Based on the user's selected or set resource items, the resource allocation policy is then adjusted to manage the RF control system's resource allocation. However, this approach results in resource allocation policies that are limited and not based on a holistic system plan. This can lead to inability to balance multiple performance metrics and the risk of system anomalies. For example, if a resource item is adjusted too high, excessive resource consumption for a single module may occur, preventing the system from processing other modules.

[0017] Furthermore, a fixed, single resource allocation strategy is typically pre-configured by technicians based on their personal technical understanding. This means they pre-allocate system resources, then test and verify system stability and effectiveness before fine-tuning. However, this approach to resource allocation results in a time-consuming and unstable system resource allocation, and significant differences in allocation results between different individuals. This leads to poor efficiency and uniformity in resource allocation within the RF control system.

[0018] To achieve the above objectives, an embodiment of the present invention provides a resource configuration method for an RF control system, which is applied to a resource configuration device of an RF control system, such as Figure 1 As shown, the method includes: Step 101: Acquire multiple performance indicators preferred by a user and a pre-built first relationship model, where the first relationship model is used to represent the influence relationship between various resource items and various performance indicators of the RF control system.

[0019] In an optional embodiment of the present invention, the step of obtaining multiple performance indicators preferred by the user may specifically include: displaying a configuration interface so that the user can set or select multiple performance indicators preferred by the user on the configuration interface, and triggering the generation of configuration instructions; in response to the generation of configuration instructions, obtaining multiple performance indicators preferred by the user. Specifically, an input box may be set in the configuration interface so that the user can input indicator values ​​corresponding to multiple performance indicators; any multiple performance indicators corresponding to indicator value constraints, when allowing the user to set their preferred performance indicators in the form of an input box, it may be detected whether the performance indicator value entered by the user exceeds the constraint conditions, and if so, the value is cleared and the user is reminded to re-enter. A slider or a drop-down box may also be set in the configuration interface, and the user may select a performance indicator by pulling the slider or from the drop-down box.

[0020] In another optional embodiment of the present invention, the resource items of the RF control system may include: system operating frequency A1, timing allocation B1, module component control C1. Specifically, the timing allocation B1 may include fan cooling time slot ratio B11 and control function time slot ratio B12; module component control C1 may include: radio frequency setting C11, operation module C12, screen module C13, vibration module C14, speaker assembly C15, lamp assembly C16, fan cooling assembly C17. The performance indicators of the RF control system may include: power consumption D1, battery life E1, temperature E2, number of control functions E3, radio frequency performance E4, system stability E5, system operation experience F1, control operation experience F2, etc. Radio frequency performance E4 may specifically include: control distance E41, anti-interference performance E42, delay performance E43. The first relationship model may be in the form of a formula, a function, a mapping table, or a matrix mapping form, which is not limited in the embodiment of the present invention. For example, the resource items of the RF control system are ; The performance indicators of the RF control system are , the first relational model can ; In order to better explain the embodiments of the present invention, the embodiments of the present invention provide Table 1 here to explain the resource items of the RF control system. It should be emphasized that Table 1 is only for explaining the resource items and facilitating the subsequent example explanation of the resource configuration method of the embodiments of the present invention, and does not limit the resource items involved in the RF control system.

[0021] Table 1

[0022] To better explain the embodiments of the present invention, Table 2 is provided herein to illustrate the performance indicators of the RF control system. It should be emphasized that Table 2 is provided solely for the purpose of explaining the performance indicators and facilitating the subsequent illustration of the resource configuration method of the embodiments of the present invention, and does not limit the performance indicators involved in the RF control system.

[0023] Table 2

[0024] To better illustrate the first relationship model, an example is given here. For example, the relationship between power consumption D1 and system operating frequency A1 can be: , where the basic power consumption is 20W, and the power consumption D1 increases with each 1GHz increase in the system operating frequency A1. For another example, the relationship between system stability E5, computing module C12, and fan cooling component C17 can be: The basic stability is 90%. For each additional computing unit of computing module C12, the system stability increases by E5+2%. For each additional gear of fan cooling component C17, the system stability increases by E5+1%. In the first relationship model of this embodiment of the present invention, different performance indicators can be affected by different resource items or the same resource item, or by one resource item or multiple resource items. The same resource item can have different effects on different system performance indicators. This embodiment of the present invention does not enumerate and illustrate the first relationship model.

[0025] In an embodiment of the present invention, the first relationship model is used to represent the influence relationship between each resource item and each performance indicator. The resource configuration method of the RF control system further includes: pre-constructing the first relationship model. The step of constructing the first relationship model may specifically include: obtaining the value range of each resource item, and performing a performance control variable experiment on each resource item to obtain test values ​​of each performance indicator of each resource item under different values; analyzing the test values ​​of each performance indicator of each resource item under different values ​​to obtain the influence relationship between each resource item and each performance indicator; and constructing the first relationship model based on the influence relationship obtained by the analysis.

[0026] It should be noted that when conducting performance control variable experiments on each resource item, in order to ensure the accuracy of each resource item and the test values ​​of each performance indicator of each resource item under different values ​​and not affect the normal use of the RF control system, it is necessary to repeatedly perform product verification on the test values ​​of each resource item and each performance indicator. After confirming that the verification is correct, the test values ​​of each performance indicator of each resource item under different values ​​are analyzed, and a first relationship model is constructed.

[0027] Step 102: Generate an initial population including multiple individuals. Based on the first relationship model and the resource items corresponding to any individual, determine the evaluation result of any individual under multiple performance indicators. One individual represents a resource allocation strategy, and different resource allocation strategies correspond to different resource item combinations.

[0028] In an optional embodiment of the present invention, the evaluation results can be expressed as fitness or a score. For RF control systems, different performance indicators may have different units or physical quantities (such as time, temperature, and quantity). The first relational model may also include rules for normalizing the physical quantities of the performance indicators or scoring rules, so that each performance indicator can be evaluated using the same evaluation system. If the first relational model does not include rules for normalizing the physical quantities of the performance indicators or scoring rules, the physical quantity of any individual under any user-set performance indicator can be calculated based on the first relational model. Then, based on a preset normalization rule or scoring rule, the physical quantity under each performance indicator is converted into a score, and the individuals in the initial population are evaluated to obtain an evaluation result. For example, if the generated initial population contains 100 individuals, i.e., 100 resource allocation strategies, a score can be calculated for each resource allocation strategy under any system performance.

[0029] Step 103: Based on the evaluation results, individuals are selected from the initial population to perform mutation to achieve iterative population optimization, thereby obtaining an optimized population, and a high-quality population is selected from the optimized population, where any individual in the high-quality population meets multiple performance indicators.

[0030] In the embodiment of the present invention, based on the evaluation results, individuals are selected from the initial population for mutation to achieve iterative population optimization, and the specific process of obtaining the optimized population may include: 1. Select the individuals with the highest scores (e.g., the top 20%) from the initial pre-population. These individuals are called "selected individuals." These individuals represent the solutions in the current population that are most likely to optimize multiple objectives and should be retained for the next generation. For example, if the population has 100 individuals, select the top 20 individuals as selected individuals.

[0031] 2. Mutation (Generation of New Individuals): Based on the selected individual, a new individual (new solution) is generated through "mutation." Mutation involves making small, random adjustments to the parameters of the selected individual (e.g., system operating frequency ±0.1 GHz, time slot ratio ±5%). This approach preserves the selected individual's strengths while introducing new possibilities. For example, after mutating the selected individual (1.2 GHz, 15%, 30%), the resulting solution might be (1.1 GHz, 17%, 28%), which is close to the original solution while also incorporating new variations.

[0032] 3. Forming a descendant population: The descendant population consists of the selected individuals plus the new individuals generated by mutation, ensuring that the population size remains constant (e.g., 100 individuals). By repeating the "evaluation-selection-mutation" process, the average fitness of each generation of the population will gradually increase (i.e., the overall solution becomes increasingly optimal).

[0033] 4. High-Quality Population: Screening for the Optimal Solution that Meets Multiple Objectives: After multiple iterations (e.g., 50-100 generations), the individuals in the population are fully optimized. From this final population, the highest-scoring individuals (e.g., the top 10%) are selected to form the "high-quality population." Individuals in the high-quality population meet the following criteria: Each individual's performance metrics are at a high level (e.g., system operation experience F1 > 90, control operation experience F2 > 85, D1 < 35W, E4 > 95%), and they balance conflicting objectives (e.g., neither excessively sacrificing power consumption for high RF performance nor sacrificing basic control experience for low power). For example, after 100 iterations, a typical individual in the high-quality population is: (1.3 GHz, 20%, 35%). Its corresponding system performance metrics are: system operation experience F1 = 92 (smooth system operation), control operation experience F2 = 88 (timely control response), power consumption D1 = 32W (moderate power consumption), and RF performance E4 = 96% (stable RF signal).

[0034] Step 104: Based on the high-quality individuals in the high-quality population, configure system resources of the RF control system.

[0035] In this embodiment of the present invention, users can set different system performance parameters for different RF control system usage scenarios. Based on steps 101 to 104 above, an optimal resource allocation strategy can be generated for each usage scenario. This can also balance multiple system performance indicators, avoiding sacrificing other indicators to improve one.

[0036] For example, in scenario 1, users prefer to balance stability and control accuracy: Based on the system performance indicators of user preference, population optimization and screening are performed, and the selected high-quality individuals 1 can be: System operating frequency A1: 1.5GHz (balancing computing power and heat dissipation); Timing allocation B1: Fan cooling time slot ratio B11: 25%; Control function time slot ratio B12: 40% (prioritizes control response); Module component control C1: RF setting C11: High gain mode (improves signal penetration); Computing module C12: 4-core operation (quickly processes multi-device control instructions); Fan cooling component C17: 3 gears (strong heat dissipation, maintaining equipment stability); The system performance corresponding to the above-mentioned high-quality individual 1 can be expressed as follows: Power consumption D1 = 38W (acceptable range), temperature E2 = 50°C (below the safety threshold), RF performance E4 = 98% (no signal loss), system stability E5 = 97% (continuous operation without faults), control operation experience F2 = 92 points (command response delay <100ms).

[0037] For scenario 2, outdoor mobile device control (users prefer to prioritize battery life) Based on the system performance indicators and the first relationship model of user preference, population optimization and screening are performed to obtain a high-quality population. The resource allocation details corresponding to the high-quality individual 2 are selected from the high-quality population: System operating frequency A1: 1.0GHz (reduces energy consumption); Timing allocation B1: Fan cooling time slot ratio B11: 10%; Control function time slot ratio B12: 25% (reducing unnecessary energy consumption); Module component control C1: RF setting C11: Medium gain mode (balanced signal and power consumption); Computing module C12: 2-core operation (meeting basic control requirements); Screen module C13: low brightness (reduces screen power consumption); Fan cooling assembly C17: 1st gear (quiet + low power consumption); The corresponding performance of the above-mentioned high-quality individual 2 is: Power consumption D1 = 22W (significantly reduced), battery life E1 = 12 hours (meeting all-day outdoor work), temperature E2 = 45°C (natural heat dissipation is sufficient), control operation experience F2 = 85 points (basic functions respond smoothly).

[0038] In an embodiment of the present invention, all resource items that can affect performance indicators can also be displayed on the configuration interface, allowing users to adjust resource configuration policies by setting the resource values ​​corresponding to each resource item. When users change or adjust the resource values ​​corresponding to resource items, the performance sub-indicators or performance indicator values ​​of the RF control system will also change and adjust accordingly, and the resource configuration policy will also change accordingly. Compared to adjusting resource configuration policies by displaying only some resource items, by displaying all resource items that can affect performance indicators, resource configuration policies can be adjusted globally across the system, avoiding conflicts between different performance indicators and preventing system anomalies.

[0039] An embodiment of the present invention provides a resource configuration method for an RF control system. The present invention can obtain multiple performance indicators preferred by users, as well as a pre-constructed first relationship model, wherein the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; it can generate an initial population comprising multiple individuals, and based on the first relationship model and each resource item corresponding to any individual, determine the evaluation result of any individual under multiple performance indicators, wherein one individual represents a resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations. At the same time, the embodiment of the present invention can select individuals from the initial population based on the evaluation results to perform mutations to achieve iterative optimization of the population, thereby obtaining an optimized population, wherein any individual in the high-quality population satisfies multiple performance indicators, and based on the high-quality individuals in the high-quality population, the system resources of the RF control system are configured. Compared to existing technologies, embodiments of the present invention support users setting or selecting their preferred performance indicators, meeting the diverse user preferences for multiple performance indicators. A first relationship model is pre-built. Based on the first relationship model, a population of multiple resource allocation strategies is iteratively optimized, targeting the user's preferred system performance indicators. This results in an optimized population. High-quality populations (where any individual simultaneously meets the user's preferred performance indicators) are selected from the optimized population based on the scoring results for system resource allocation. System resource allocation is then performed based on high-quality individuals within the high-quality population. In other words, the present invention generates high-quality resource allocation strategies that simultaneously meet the user's preferred performance indicators. This allows the RF control system to simultaneously meet the user's personalized preferences for system performance indicators while balancing multiple performance indicators during resource allocation. Furthermore, embodiments of the present invention eliminate the need for technicians to manually test, verify, and fine-tune resource items to obtain high-quality resource allocation strategies. This not only avoids inconsistent resource allocation results due to different understandings among technicians, but also prevents the overall system performance of the RF control system from being affected by improper resource item settings.

[0040] To achieve the above object, an embodiment of the present invention provides another resource configuration method for an RF control system, which is applied to a resource configuration device of an RF control system, such as Figure 2 As shown, the method includes: Step 201: Obtain the value range of each resource item, and conduct a performance control variable experiment on each resource item to obtain the test value of each performance sub-indicator under different values ​​of each resource item, as well as the test values ​​of each performance sub-indicator and each performance indicator.

[0041] Step 202: Analyze the test values ​​of each performance sub-indicator and each performance indicator under different values ​​of each resource item to obtain the influence relationship between each resource item and each performance sub-indicator, and the influence relationship between each resource item and each performance indicator.

[0042] Step 203: Based on the influence relationships obtained through analysis, a first relationship model is constructed. The first relationship model is used to represent the influence relationships between each resource item and each performance sub-indicator, and the influence relationships between each performance sub-indicator and each performance indicator.

[0043] It should be noted that when conducting performance control variable experiments on each resource item, in order to ensure the accuracy of each resource item, and the test values ​​of each performance sub-indicator of each resource item at different values, as well as the test values ​​of each performance sub-indicator and each performance indicator, and not affecting the normal use of the RF control system, it is necessary to repeatedly conduct product verification on each resource item, the test values ​​of each performance sub-indicator of each resource item at different values, as well as the test values ​​of each performance sub-indicator and each performance indicator. After confirming that the verification is correct, the test values ​​of each performance sub-indicator and the test values ​​of each performance indicator of each resource item at different values ​​are analyzed, and a first relationship model is constructed.

[0044] In an optional embodiment of the present invention, to simplify the computational workload and workload of selecting the next generation population for the next iteration, the present invention can also redefine the performance indicators of the RF control system. Specifically, the performance indicators are defined in a hierarchical manner, with some performance indicators separated into performance sub-indicators. These sub-indicators are not open to user configuration during the policy optimization and generation phases, while other performance indicators are open to user configuration and adjustment. For example, indicators representing user experience (user experience indicators) and indicators representing system performance (system performance indicators) can be defined in a hierarchical manner. The user experience indicators can be defined as performance indicators open to user configuration and adjustment, while the system performance indicators can be defined as performance sub-indicators. Therefore, during the model construction phase, a fitting analysis can be performed to analyze the impact of various resource items on various system performance indicators (performance sub-indicators), as well as the impact of various system performance indicators (performance sub-indicators) on the user experience indicators (performance indicators). Specifically, the performance indicators of the RF control system can be defined to include system operation experience and control operation experience, and the performance sub-indicators of the RF control system can be defined to include power consumption, battery life, temperature, number of control functions, RF performance, and system stability.

[0045] To better explain the embodiments of the present invention, Table 3 is provided herein to explain the performance sub-indicators and performance indicators. It should be emphasized that Table 3 is only provided to illustrate the resource configuration method of the embodiments of the present invention, and does not limit the performance sub-indicators and performance indicators involved in the RF control system.

[0046] Table 3

[0047] It should be noted that, in order to better understand the embodiments of the present invention, examples are given for each resource item, each performance sub-indicator and performance indicator respectively. The resource items of the RF control system may include: system operating frequency A1, timing allocation B1, module component control C1. Specifically, the timing allocation B1 may include fan cooling time slot ratio B11, control function timing ratio B12; module component control C1 may include: radio frequency setting C11, operation module C12, screen module C13, vibration module C14, speaker assembly C15, lamp assembly C16, fan cooling assembly C17. The performance sub-indicators of the RF control system may include: power consumption D1, battery life E1, temperature E2, number of control functions E3, radio frequency performance E4, system stability E5. The performance indicators of the RF control system may include: system operation experience F1, control operation experience F2. The resource items of the RF control system are , the performance sub-indicators of the RF control system are , the performance indicators of the RF control system are , the first relational model can be , in, Used to express the influence relationship between each performance sub-indicator and each performance indicator. Used to indicate the impact relationship between each resource item and each performance sub-indicator.

[0048] For example, if the performance indicator is system operation experience F1, that is, the system operation experience F1 is affected by the performance sub-indicators of battery life E1, temperature E2, and system stability E5. Specifically: Battery Life (E1): The longer the battery life (E1), the less frequent charging required, and the better the control experience (F1). For example, if the battery life (E1) increases from 5 hours to 10 hours, the control experience (F1) may improve from 70 to 85.

[0049] Temperature E2: Excessively high temperature E2 can cause system throttling or freezing, affecting operational fluency. For example, a temperature drop from 60°C to 45°C can improve the control experience F1 by 10-15 points.

[0050] System Stability E5: The higher the system stability E5 (e.g., an increase in the percentage of trouble-free operation time), the more reliable the control and operation experience F1. For example, if the system stability E5 increases from 90% to 98%, the control and operation experience F1 may increase by 5-8 points.

[0051] For another example, if the performance indicator is the control operation experience F2, the control operation experience F2 is affected by the performance sub-indicators of battery life E1, temperature E2, number of control functions E3, and radio frequency performance E4. Specifically: Battery Life E1: The longer the battery life, the less frequent charging required, and the better the control experience. For example, if the battery life increases from 5 hours to 10 hours, the control experience F2 score may increase from 70 to 85.

[0052] Number of control functions (E3): The richer the function, the more tasks users can complete, and the higher the F2 score. For example, if the number of control functions increases from 5 to 10, the control operation experience (F2) score may increase from 75 to 90.

[0053] Radio Frequency Performance E4: Better radio frequency signal quality means more timely control command transmission, resulting in a higher control experience (F2). For example, if radio frequency performance E4 improves from 90% to 99%, the control experience (F2) may improve by 8 to 12 points.

[0054] Temperature E2: Excessively high temperatures can cause system throttling or freezing, leading to command execution delays. For example, if the temperature E2 drops from 60°C to 45°C, the control experience F2 score may improve by 10-12 points.

[0055] Step 204: Acquire multiple performance indicators preferred by the user and a pre-built first relationship model.

[0056] Step 205: Generate an initial population including multiple individuals. Based on the first relationship model and the resource items corresponding to any individual, determine the evaluation result of any individual under multiple performance indicators. One individual represents a resource allocation strategy, and different resource allocation strategies correspond to different resource item combinations.

[0057] In another embodiment of the present disclosure, the first relationship model includes: a first relationship sub-model and a second relationship sub-model, and the step of determining the evaluation result of any individual under the multiple performance indicators based on the first relationship model and the various resource items corresponding to any individual may specifically include: determining the multiple performance sub-indicators corresponding to any individual based on the first relationship sub-model and the various resource items corresponding to any individual, the first relationship sub-model is used to represent the influence relationship between the various resource items and the various performance sub-indicators of the RF control system; determining the evaluation result of any individual under the multiple performance indicators based on the second relationship sub-model and the multiple performance sub-indicators corresponding to any individual, the second relationship sub-model is used to represent the influence relationship between the various performance sub-indicators and the various performance indicators.

[0058] For example, resource allocation strategy 1 in the initial population includes the following resource items: system operating frequency A1 (1.2 GHz), fan cooling time slot ratio B11 (20%), control function time slot ratio B12 (35%), RF setting C11 (standard gain mode), and computing module C12 (1-core operation).

[0059] Based on the first relationship sub-model and the above resource items, the performance sub-indicators can be calculated: battery life E1 (8 hours), temperature E2 (48°C), number of control functions E3 (8 items), RF performance E4 (93%), and system stability (E5) 85%.

[0060] Based on the second relationship sub-model and the above performance sub-indicators, the performance indicators can be calculated: system operation experience F1 (82 points); control operation experience F2 (85 points).

[0061] Similarly, the scores of all resource allocation strategies in the initial population under various performance indicators can be calculated.

[0062] In one embodiment of the present invention, the step of generating an initial population comprising multiple individuals may specifically include: randomly generating multiple first resource items; generating multiple second resource items based on the multiple first resource items and a pre-constructed second relationship model, wherein the second relationship model is used to represent the influence relationship between the first resource items and the second resource items; or randomly generating multiple first resource items and multiple second resource items; and constructing the initial population based on the multiple first resource items and the multiple second resource items. In other words, a controlled variable experiment can be used to determine whether an influence relationship exists between the resource items. If an influence relationship exists, a second relationship model can be constructed, and then the second resource items can be generated based on the randomly generated first resource items to ensure the accuracy of the second resource item generation. If no influence relationship exists, the first resource items and the second resource items are independent of each other and can also be randomly generated. For example, if the timing allocation B1 is affected by the system operating frequency A1, when generating the timing allocation B1, the influence relationship between the timing allocation B1 and the system operating frequency A1 can be considered to ensure the accuracy of the second resource item generation.

[0063] In order to better generate resource allocation strategies that can meet the personalized needs of users and balance multiple performance goals at the same time, so that the embodiment of the present invention can generate high-quality resource allocation strategies in a more flexible manner, the embodiment of the present invention can use two methods to screen individuals entering the next round of population iteration.

[0064] In one embodiment of the present invention, target weights can be set for multiple performance indicators for a user. The step of determining the evaluation results of any individual under the multiple performance indicators based on the first relationship model and the resource items corresponding to any individual can specifically include: obtaining the target weights corresponding to the multiple performance indicators set by the user; and determining the evaluation results of any individual under the multiple performance indicators based on the first relationship model, the target weights, and the resource items corresponding to any individual. In this case, the evaluation results generated based on the target weights can be comprehensive evaluation results. That is, the target weights can be used to transform multiple objective functions into a single overall objective function. Based on the overall evaluation results, individuals can then be selected from the current population to enter the next round of population iteration. For example, overall performance F = weight 1 * system operation experience F1 + weight 2 * control operation experience F2. Thus, the overall performance score of all individuals can be obtained, and then they can be sorted according to the overall performance score. The top 20 individuals with the highest overall performance scores are selected as "selected individuals" to enter the next round of screening.

[0065] In another embodiment of the present disclosure, the step of determining the evaluation results of any individual under multiple performance indicators based on the first relationship model and the various resource items corresponding to any individual may specifically include: determining the basic evaluation results of any individual under multiple performance indicators based on the first relationship model and the various resource items corresponding to any individual; obtaining the first constraint conditions corresponding to each resource item and the second constraint conditions corresponding to each performance indicator; adjusting the basic evaluation results based on a preset reward and punishment mechanism, the first constraint conditions and the second constraint conditions to obtain the evaluation results of any individual under multiple performance indicators.

[0066] The preset reward and punishment mechanism includes both reward and penalty rules. The reward rule awards additional points for performance indicators that exceed performance constraints, while the penalty rule deducts points for performance indicators that violate performance constraints. For example, for every hour that the battery life (E1) exceeds the 7-hour constraint, the system operation experience (F1) will receive an additional 2 points; for every 1°C below the 55°C constraint for the temperature (E2), the system operation experience (F1) will receive an additional 1 point; for every 1% above the 95% constraint for the system stability (E5), the system operation experience (F1) will receive an additional 1.5 points; for every 1% above the 6% constraint for the number of control functions (E3), the control operation experience (F2) will receive an additional 3 points; and for every 1% above the 96% constraint for the RF performance (E4), the control operation experience (F2) will receive an additional 2 points. For every 0.1 hour below the 7-hour constraint for battery life (E1), 1 point will be deducted from the System Operation Experience (F1) score. For every 1°C above the 55°C constraint for temperature (E2), 2 points will be deducted from the System Operation Experience (F1) score. For every 1% below the 95% constraint for system stability (E5), 3 points will be deducted from the System Operation Experience (F1) score. For every 1 function below the 6-constraint constraint for the number of control functions (E3), 5 points will be deducted from the Control Operation Experience (F2) score. For every 1% below the 96% constraint for RF performance (E4), 4 points will be deducted from the Control Operation Experience (F2) score.

[0067] To better understand the embodiments of the present invention, an example is given to illustrate how to determine the evaluation results based on a preset reward and punishment mechanism and constraints. 1. Using the first relational model, convert individual resource items into specific values ​​for performance sub-indicators (E1-E5). For example, based on the relationship between system operating frequency A1, RF performance E4, and power consumption D1, the calculated RF performance E4 = 97% and power consumption D1 = 53W. 2. Combining the relationship between power consumption D1 and battery life E1 (E1 = 10-0.05 × D1), we can deduce that battery life E1 = 7.2 hours. Similarly, we can obtain temperature E2 = 52°C and system stability E5 = 97%; 3. Perform weighted calculation on the performance sub-indicators to obtain the basic score of the performance indicator: System Operation Experience F1 Basic Score = 0.3 × E1 + 0.3 × (100-E2) + 0.4 × E5 (for example, if battery life E1 = 7.2 hours, temperature E2 = 52°C, and system stability E5 = 97%, the System Operation Experience F1 Basic Score is 84.6 points); The basic score for control operation experience (F2) is 0.4 × E3 + 0.6 × E4. (For example, if the number of control functions (E3) is 7 and the RF performance (E4) is 97%, the basic score for control operation experience (F2) is 86.2. 4. Adjustments to the base score to incorporate constraints, penalties and rewards: The basic score is adjusted according to the preset reward and punishment mechanism. Reward items: If the temperature E2 = 52°C (3°C lower than the constraint), add 3 points; if the system stability E5 = 97% (2% higher than the constraint), add 3 points. The system operation experience F1 is revised to 84.6+3+3=90.6 points. Penalty items: If the number of control functions E3 = 5 (1 lower than the constraint), deduct 5 points. The control operation experience F2 is revised to 86.2-5=81.2 points.

[0068] Step 206: Based on the evaluation result of any individual in the initial population, select individuals from the initial population to form a selected population; perform a pairwise crossover operation on the individuals in the selected population to obtain an offspring population.

[0069] In embodiments of the present invention, the evaluation result can be the total evaluation result of multiple performance indicators (overall performance score) or the set of scoring results for a single performance indicator (single performance score set). Therefore, the evaluation results can be sorted by overall performance score, and the top 20 individuals with the overall performance score can be selected as "selected individuals." A selected population is formed based on these "selected individuals," and a pairwise crossover operation is performed to obtain a progeny population for the next round of screening. Alternatively, a non-dominated sorting algorithm can be used to select the top 20 individuals based on a single performance score set as "selected individuals." A selected population is formed based on these "selected individuals," and a pairwise crossover operation is performed to obtain a progeny population for the next round of screening. Individuals are sorted using the "dominance relationship" of the non-dominated sorting algorithm, ultimately finding a Pareto front optimal solution that balances all performance indicators (i.e., a solution that cannot improve one performance objective without compromising others).

[0070] Step 207: Perform mutation operation on individuals in the offspring population; if the mutated individuals do not meet the preset conditions, perform mutation operation again; determine the next generation population based on the mutated individuals that meet the preset conditions, and repeat the above operation to achieve iterative optimization of the population.

[0071] The preset condition may be that each resource item in the individual satisfies its corresponding constraint condition.

[0072] Step 208: When a preset iteration stopping condition is reached, the population iteration is stopped to obtain an optimized population.

[0073] In this embodiment of the present invention, the preset iteration stopping condition can be when the number of iterations reaches a predetermined threshold, or when the evaluation results corresponding to individuals in the population converge and no longer change. In this case, the resulting optimized population contains individuals that cannot simultaneously balance multiple performance indicators, as well as individuals that are designed to simultaneously balance multiple performance indicators. Therefore, the resulting optimized population needs to be further screened to obtain the optimal Pareto front solution set.

[0074] Step 209: Based on the first relationship model, determine the evaluation results of any individual in the optimized population under multiple performance indicators.

[0075] It should be noted that the specific determination method of step 209 can refer to steps 102 and 105 and will not be described in detail here.

[0076] Step 210: Based on the evaluation result of any individual in the optimized population, select the Pareto front optimal solution set from the optimized population as the high-quality population.

[0077] The Pareto front optimal solution set is a set of optimal solutions that cannot improve one performance indicator without compromising other performance indicators among the multiple performance indicators preferred by the user. Specifically, referring to step 206, the Pareto front optimal solution set can be selected, and the individual solutions can be sorted using the "dominance relationship" of the non-dominated sorting algorithm to ultimately find a set of Pareto front optimal solutions that balance all performance indicators.

[0078] Step 211: Display each high-quality individual in the high-quality population and / or each corresponding resource item on the configuration interface.

[0079] For example, if the performance indicators set by the user are: system operation experience F1 (85 points) and control operation experience F2 (90 points), and the generated high-quality population has three high-quality individuals, the following content can be displayed on the configuration interface: Title - Recommended Resource Allocation Strategy; Prompt: Based on the system operation experience (F1: 85) and control operation experience (F2: 90) you set, the following optimization plan is generated: Solution 1: Balancing performance and power consumption System operating frequency (1.2 GHz), fan cooling time slot ratio (20%), control function time slot ratio (35%), RF setting (standard gain mode), computing module (2-core operation), screen module (low brightness), vibration module (off), speaker assembly (off), light assembly (off), fan cooling assembly (level 1 - silent). This solution optimizes power consumption while ensuring system performance and is suitable for long-term continuous working scenarios.

[0080] Option 2: Ultimate Control Experience System operating frequency (1.5 GHz), fan cooling time slot ratio (25%), control function time slot ratio (35%), RF settings (high gain mode), computing module (2-core operation), screen module (medium brightness), vibration module (on), speaker assembly (off), fan cooling assembly (2nd level - standard). This solution maximizes the number of control functions and RF performance and is suitable for scenarios requiring high control precision.

[0081] Option 3: Low power consumption and long battery life System operating frequency (1.0 GHz), fan cooling time slot ratio (15%), control function time slot ratio (25%), RF setting (low gain mode), computing module (1 core operation), display module (low brightness), vibration module (off), speaker assembly (off), fan cooling assembly (level 1 - silent). This solution focuses on optimizing power consumption and battery life and is suitable for mobile use or power-constrained scenarios.

[0082] The system performance indicators of the above three optimization solutions are shown in Table 4: Table 4

[0083] After the above information is displayed on the configuration interface, the user can select any optimization plan. When the user selects the optimization plan and clicks Apply this strategy, the various resource items of the RF control system are modified and adjusted to the resource items in the user-selected strategy.

[0084] Step 212: After receiving the high-quality individual selection instruction, determine the selected high-quality individuals.

[0085] Step 213: Based on the selected high-quality individuals, configure system resources of the RF control system.

[0086] In an embodiment of the present invention, all resource items that can affect user experience indicators can also be displayed on the configuration interface, allowing users to adjust resource configuration policies by setting the resource values ​​corresponding to each resource item. When users change or adjust the resource values ​​corresponding to resource items, the performance sub-indicators or performance indicator values ​​of the RF control system will also change and adjust accordingly, and the resource configuration policy will also change accordingly. Compared to adjusting resource configuration policies by displaying only some resource items, by displaying all resource items that can affect user experience indicators, resource configuration policies can be adjusted globally across the system, avoiding conflicts between different performance indicators and preventing system anomalies.

[0087] For the embodiments of the present invention, in order to ensure the accuracy of the resource configuration of the RF control system and to ensure that the generated resource configuration strategy can meet user needs, after the user selects a high-quality individual, a resource configuration simulation can be performed on the RF control system, that is, the system resources of the RF control system are simulated and configured in advance as the various resource items in the high-quality individuals. After the simulation configuration, the RF control system has no abnormal problems and can operate normally, and meets the performance indicators set by the user. Then, the system resources of the RF control system are actually configured based on the various resource items in the high-quality individuals.

[0088] It should be noted that, in order to further ensure the accuracy of the resource configuration of the RF control system and further ensure that the generated resource configuration strategy can meet user needs, the embodiment of the present invention also supports the continuous optimization and improvement of the first relationship model, so that the construction of the relationship model, the resource configuration strategy, and the configuration of the system resources of the RF control system form a complete optimization closed loop. Specifically, a large number of actually applied resource configuration strategies and the actual performance indicators corresponding to the applied resource configuration strategies can be collected; based on the various resource items in the applied resource configuration strategies and their corresponding actual performance indicators, the first relationship model can be optimized. Specifically, based on the theoretical performance indicators and actual performance indicators corresponding to each resource item, it can be determined whether there are problems with the first relationship model; if so, the first relationship model can be corrected based on the difference performance indicator between the theoretical performance indicator and the actual performance indicator.

[0089] An embodiment of the present invention provides a resource configuration method for an RF control system. The present invention can obtain multiple performance indicators preferred by users and a pre-constructed first relationship model, wherein the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; an initial population comprising multiple individuals can be generated, and based on the first relationship model and each resource item corresponding to any individual, an evaluation result of any individual under multiple performance indicators can be determined, wherein one individual represents a resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations. At the same time, the present invention can select individuals from the initial population based on the evaluation results to perform mutations to achieve iterative optimization of the population, thereby obtaining an optimized population. Any individual in the high-quality population satisfies multiple performance indicators, and system resources of the RF control system are configured based on high-quality individuals in the high-quality population. Compared to existing technologies, the present invention supports users setting or selecting their preferred performance indicators, meeting the diverse user preferences for multiple performance indicators. It pre-constructs a first relationship model and then, based on the first relationship model, iteratively optimizes a population of multiple resource allocation strategies, targeting the user's preferred system performance indicators. This results in an optimized population. High-quality populations (where any individual simultaneously meets the user's preferred performance indicators) are selected from the optimized population based on the scoring results for system resource allocation, and system resource allocation is then performed based on high-quality individuals within the high-quality population. In other words, the present invention generates high-quality resource allocation strategies that simultaneously meet the user's preferred performance indicators. This allows the RF control system to simultaneously meet the user's personalized preferences for system performance indicators while balancing multiple performance indicators during resource allocation. Furthermore, in embodiments of the present invention, high-quality resource allocation strategies can be obtained without requiring technicians to manually test, verify, and fine-tune resource items. This not only avoids inconsistent resource allocation results due to different understandings among technicians, but also prevents the overall system performance of the RF control system from being affected by improper resource item settings.

[0090] To achieve the above object, an embodiment of the present invention further provides a resource configuration device for an RF control system, which is applied to a resource configuration device of an RF control system, such as Figure 3 As shown, the resource configuration device of the RF control system includes: The acquisition unit 31 is configured to acquire a plurality of performance indicators preferred by the user and a pre-built first relationship model, wherein the first relationship model is configured to represent the influence relationship between various resource items and various performance indicators of the RF control system.

[0091] The generating unit 32 is configured to generate an initial population comprising a plurality of individuals.

[0092] The determination unit 33 is used to determine the evaluation result of any individual under multiple performance indicators based on the first relationship model and the resource items corresponding to any individual. One individual represents a resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations.

[0093] The optimization unit 34 is configured to select individuals from the initial population based on the evaluation results to perform mutation to achieve iterative population optimization, thereby obtaining an optimized population; and to select a high-quality population from the optimized population.

[0094] The configuration unit 35 is configured to configure system resources of the RF control system based on the high-quality population.

[0095] In an optional specific embodiment of the present invention, the determination unit 33 can be specifically used to determine multiple performance sub-indicators corresponding to any individual based on the first relationship sub-model and the various resource items corresponding to any individual, where the first relationship sub-model is used to represent the influence relationship between the various resource items and the various performance sub-indicators of the RF control system; and to determine the evaluation results of any individual under multiple performance indicators based on the second relationship sub-model and the multiple performance sub-indicators corresponding to any individual, where the second relationship sub-model is used to represent the influence relationship between the various performance sub-indicators of the RF control system and the various performance indicators.

[0096] In another optional specific embodiment of the present invention, the determination unit 33 can also be used to obtain the target weights corresponding to multiple performance indicators set by the user; based on the first relationship model, the target weights and the resource items corresponding to any individual, determine the evaluation results of any individual under multiple performance indicators.

[0097] In an optional embodiment of the present invention, the function of pre-building the first relationship model is supported, and the resource configuration device of the RF control system further includes: an experiment unit, an analysis unit, and a construction unit.

[0098] The acquisition unit 31 can be used to obtain the value range of each resource item;

[0099] The experimental unit can be used to conduct performance control variable experiments on each resource item, obtaining the test values ​​of each performance sub-indicator under different values ​​of each resource item, as well as the test values ​​of each performance sub-indicator and each performance indicator; The analysis unit can be used to analyze the test values ​​of each performance sub-indicator and each performance indicator under different values ​​of each resource item, and obtain the influence relationship between each resource item and each performance sub-indicator, and the influence relationship between each resource item and each performance indicator; The construction unit can be used to construct the first relationship model based on the influence relationship obtained through analysis.

[0100] In another optional specific embodiment of the present invention, the generation unit 32 can be specifically used to randomly generate multiple first resource items; generate multiple second resource items based on the multiple first resource items and a pre-constructed second relationship model, wherein the second relationship model is used to represent the influence relationship between the first resource items and the second resource items; or randomly generate multiple first resource items and multiple second resource items; and construct the initial population based on the multiple first resource items and the multiple second resource items.

[0101] In another optional specific embodiment of the present invention, the optimization unit 34 can be specifically used to select individuals from the initial population to form a selected population based on the evaluation result of any individual in the initial population; perform a pairwise crossover operation on the individuals in the selected population to obtain an offspring population; perform a mutation operation on the individuals in the offspring population; if the mutated individuals do not meet the preset conditions, the mutation operation is performed again; based on the mutated individuals that meet the preset conditions, the next generation population is determined, and the above operations are repeated to achieve iterative optimization of the population; when the preset iteration stopping condition is reached, the population iteration is stopped to obtain an optimized population; based on the first relationship model, the evaluation result of any individual in the optimized population under multiple performance indicators is determined; based on the evaluation result of any individual in the optimized population, the Pareto front optimal solution set is selected from the optimized population as the high-quality population.

[0102] Correspondingly, the configuration unit 35 can be specifically used to display each high-quality individual in the high-quality population and / or its corresponding resource items on the configuration interface; when a high-quality individual selection instruction is received, the selected high-quality individual is determined; and based on the selected high-quality individual, the system resources of the RF control system are configured.

[0103] An embodiment of the present invention provides a resource configuration device for an RF control system. The embodiment of the present invention can obtain multiple performance indicators preferred by users, as well as a pre-constructed first relationship model, wherein the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; it can generate an initial population including multiple individuals, and determine the evaluation results of any individual under multiple performance indicators based on the first relationship model and each resource item corresponding to any individual. One individual represents a resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations. At the same time, the embodiment of the present invention can select individuals from the initial population based on the evaluation results to perform mutations to achieve iterative optimization of the population, thereby obtaining an optimized population. Any individual in the high-quality population satisfies multiple performance indicators, and system resources of the RF control system are configured based on high-quality individuals in the high-quality population. Compared to existing technologies, embodiments of the present invention support users setting or selecting their preferred performance indicators, meeting the diverse user preferences for multiple performance indicators. A first relationship model is pre-built. Based on the first relationship model, a population of multiple resource allocation strategies is iteratively optimized, targeting the user's preferred system performance indicators. This results in an optimized population. High-quality populations (where any individual simultaneously meets the user's preferred performance indicators) are selected from the optimized population based on the scoring results for system resource allocation. System resource allocation is then performed based on high-quality individuals within the high-quality population. In other words, the present invention generates high-quality resource allocation strategies that simultaneously meet the user's preferred performance indicators. This allows the RF control system to simultaneously meet the user's personalized preferences for system performance indicators while balancing multiple performance indicators during resource allocation. Furthermore, embodiments of the present invention eliminate the need for technicians to manually test, verify, and fine-tune resource items to obtain high-quality resource allocation strategies. This not only avoids inconsistent resource allocation results due to different understandings among technicians, but also prevents the overall system performance of the RF control system from being affected by improper resource item settings.

[0104] To achieve the above objectives, an embodiment of the present invention provides a storage medium storing a resource configuration program for an RF control system. When the resource configuration program for the RF control system is executed by a processor, the steps of the resource configuration method for the RF control system described above are implemented.

[0105] To achieve the above-mentioned objectives, an embodiment of the present invention provides a resource configuration device for an RF control system, wherein the resource configuration device includes: a memory and a processor, wherein the memory stores a resource configuration program for the RF control system, and when the processor executes the resource configuration program for the RF control system, the steps of the resource configuration method for the RF control system as described above are implemented.

[0106] In order to solve the above technical problems, the embodiment of the present invention also provides a resource configuration device for an RF control system. Figure 4 , Figure 4 This is a basic structural block diagram of the resource configuration device of the RF control system of this embodiment.

[0107] The resource configuration device 4 of the RF control system includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows the resource configuration device 4 of the RF control system having components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art will understand that the resource configuration device of the RF control system here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0108] The memory 41 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the resource configuration device 4 of the RF control system, such as a hard disk or internal memory of the resource configuration device 4 of the RF control system. In other embodiments, the memory 41 may also be an external storage device of the resource configuration device 4 of the RF control system, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 41 may also include both the internal storage unit and an external storage device of the resource configuration device 4 of the RF control system. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed in the resource configuration device 4 of the RF control system, such as the program code of the resource configuration method of the RF control system. In addition, the memory 41 can also be used to temporarily store various data that has been output or is about to be output.

[0109] In some embodiments, processor 42 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. Processor 42 is typically used to control the overall operation of resource configuration device 4 of the RF control system. In this embodiment, processor 42 is used to execute program code stored in memory 41 or process data, such as executing program code for a resource configuration method for the RF control system.

[0110] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the resource configuration device 4 of the RF control system and other electronic devices.

[0111] An embodiment of the present invention provides a resource configuration device for an RF control system. The embodiment of the present invention can obtain multiple performance indicators preferred by users, as well as a pre-constructed first relationship model, wherein the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; it can generate an initial population including multiple individuals, and based on the first relationship model and each resource item corresponding to any individual, determine the evaluation result of any individual under multiple performance indicators, wherein one individual represents a resource configuration strategy, and different resource configuration strategies correspond to different resource item combinations. At the same time, the embodiment of the present invention can select individuals from the initial population based on the evaluation results to perform mutations to achieve iterative optimization of the population, thereby obtaining an optimized population. Any individual in the high-quality population satisfies multiple performance indicators, and system resources of the RF control system are configured based on high-quality individuals in the high-quality population. Compared to existing technologies, the embodiments of the present invention support users setting or selecting their preferred performance indicators, meeting the diverse user preferences for multiple performance indicators. A first relationship model is pre-built. Based on this first relationship model, a population of multiple resource allocation strategies is iteratively optimized, targeting the user's preferred system performance indicators. This results in an optimized population. High-quality populations (where any individual simultaneously meets the user's preferred performance indicators) are selected from the optimized population based on the scoring results for system resource allocation. In other words, the embodiments of the present invention enable resource allocation based on multiple resource allocation strategies that simultaneously meet the user's preferred performance indicators, thereby meeting the user's personalized preferences for system performance indicators while simultaneously balancing multiple performance indicators of the RF control system. The entire resource allocation process eliminates the need for manual testing, verification, and fine-tuning by technicians. This not only avoids inconsistent resource allocation results due to different understandings among technicians, but also prevents the overall performance of the RF control system from being affected by improper resource item settings.

[0112] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented using software plus the necessary general-purpose hardware online platform. Of course, hardware can also be used, but in many cases the former is a more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0113] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0114] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present invention specification and drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A resource configuration method for an RF control system, characterized in that: The method comprises: Acquire multiple performance indicators preferred by the user and a pre-built first relationship model, where the first relationship model is used to represent the influence relationship between each resource item and each performance indicator of the RF control system; generating an initial population comprising a plurality of individuals, and determining an evaluation result of each individual under a plurality of performance indicators based on the first relationship model and each resource item corresponding to each individual, wherein each individual represents a resource allocation strategy, and different resource allocation strategies correspond to different resource item combinations; Based on the evaluation results, individuals are selected from the initial population to perform mutation to achieve iterative population optimization, thereby obtaining an optimized population, and a high-quality population is selected from the optimized population, wherein any individual in the high-quality population satisfies multiple performance indicators; System resources of the RF control system are configured based on the high-quality individuals in the high-quality population.

2. The method according to claim 1, characterized in that The first relationship model includes: a first relationship sub-model and a second relationship sub-model. Determining the evaluation result of any individual under the multiple performance indicators based on the first relationship model and each resource item corresponding to any individual includes: Determining a plurality of performance sub-indicators corresponding to any individual based on the first relationship sub-model and each resource item corresponding to any individual, wherein the first relationship sub-model is used to represent an influence relationship between each resource item and each performance sub-indicator of the RF control system; Based on the second relationship sub-model and multiple performance sub-indicators corresponding to any individual, the evaluation result of any individual under multiple performance indicators is determined, and the second relationship sub-model is used to represent the influence relationship between each performance sub-indicator and each performance indicator of the RF control system.

3. The method according to claim 1, characterized in that The determining, based on the first relationship model and each resource item corresponding to each individual, an evaluation result of each individual under multiple performance indicators includes: Get the target weights corresponding to multiple performance indicators set by the user; Based on the first relationship model, the target weight and each resource item corresponding to any individual, an evaluation result of any individual under multiple performance indicators is determined.

4. The method according to claim 1, wherein The determining, based on the first relationship model and each resource item corresponding to each individual, an evaluation result of each individual under multiple performance indicators includes: Determine a basic evaluation result of any individual under multiple performance indicators based on the first relationship model and each resource item corresponding to any individual; Obtaining the first constraint condition corresponding to each resource item and the second constraint condition corresponding to each performance indicator; Based on the preset reward and punishment mechanism, the first constraint condition and the second constraint condition, the basic evaluation result is adjusted to obtain the evaluation results of any individual under multiple performance indicators.

5. The method according to any one of claims 1 to 4, characterized in that The first relationship model is used to represent the influence relationship between each resource item and each performance sub-indicator, and the influence relationship between each performance sub-indicator and each performance indicator. The method further includes: Obtain the value range of each resource item, and conduct performance control variable experiments on each resource item to obtain the test values ​​of each performance sub-indicator under different values ​​of each resource item, as well as the test values ​​of each performance sub-indicator and each performance indicator; Analyze the test values ​​of each performance sub-indicator and each performance indicator under different values ​​of each resource item to obtain the influence relationship between each resource item and each performance sub-indicator, and the influence relationship between each resource item and each performance indicator; Based on the influence relationship obtained by analysis, the first relationship model is constructed.

6. The method according to any one of claims 1 to 4, characterized in that The generating of an initial population comprising a plurality of individuals comprises: Randomly generate a plurality of first resource items; generate a plurality of second resource items based on the plurality of first resource items and a pre-constructed second relationship model, wherein the second relationship model is used to represent the influence relationship between the first resource items and the second resource items; or randomly generate a plurality of first resource items and a plurality of second resource items; The initial population is constructed based on the multiple first resource items and the multiple second resource items.

7. The method according to any one of claims 1 to 4, characterized in that The step of selecting individuals from the initial population based on the evaluation results to perform mutation to achieve iterative population optimization and obtain an optimized population includes: Based on the evaluation results of any individual in the initial population, individuals are selected from the initial population to form a selected population; a pairwise crossover operation is performed on the individuals in the selected population to obtain a progeny population; Perform mutation operations on individuals in the offspring population; if the mutated individuals do not meet the preset conditions, perform mutation operations again; determine the next generation population based on the mutated individuals that meet the preset conditions, and repeat the above operations to achieve iterative optimization of the population; when the preset iterative stopping conditions are reached, stop the population iteration to obtain the optimized population; The step of selecting a high-quality population from the optimized population comprises: Determining, based on the first relationship model, an evaluation result of any individual in the optimized population under multiple performance indicators; Based on the evaluation result of any individual in the optimized population, selecting the Pareto front optimal solution set from the optimized population as the high-quality population; The configuring of system resources of the RF control system based on high-quality individuals in the high-quality population includes: Displaying each high-quality individual in the high-quality population and / or its corresponding resource items on the configuration interface; upon receiving a high-quality individual selection instruction, determining the selected high-quality individual; Based on the selected high-quality individuals, the system resources of the RF control system are configured, and multiple system performance indicators corresponding to the high-quality individuals are displayed.

8. A resource configuration device for an RF control system, characterized in that: The device comprises: an acquiring unit, configured to acquire a plurality of performance indicators preferred by a user, and a pre-built first relationship model, wherein the first relationship model is configured to represent an influence relationship between various resource items and various performance indicators of the RF control system; A generation unit, used to generate an initial population comprising a plurality of individuals; a determining unit, configured to determine an evaluation result of any individual under multiple performance indicators based on the first relationship model and each resource item corresponding to any individual, wherein each individual represents a resource allocation strategy, and different resource allocation strategies correspond to different resource item combinations; An optimization unit, configured to select individuals from the initial population based on the evaluation results to perform mutation to achieve iterative population optimization, thereby obtaining an optimized population, and to select a high-quality population from the optimized population; The configuration unit is used to configure system resources of the RF control system based on the high-quality individuals in the high-quality population.

9. A storage medium, characterized in that: The storage medium stores a resource configuration program of the RF control system. When the resource configuration program of the RF control system is executed by the processor, the steps of the resource configuration method of the RF control system according to any one of claims 1 to 7 are implemented.

10. A resource configuration device for an RF control system, characterized in that: The resource configuration device includes: a memory and a processor, the memory stores a resource configuration program of the RF control system, and the processor implements the steps of the resource configuration method of the RF control system as described in any one of claims 1 to 7 when executing the resource configuration program of the RF control system.

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