A control method, device, equipment and medium based on LED power supply test system

Abstract

The present application relates to a control method, device, equipment and medium based on an LED power supply test system. A control method based on an LED power supply test system includes determining the hysteresis interval of a temperature sensing analysis system, obtaining the current load state of the LED power supply test system in real time, and determining the corresponding first adjustment weight if the current load state is greater than a predetermined preset state threshold; obtaining the coolant flow data of the cooling system, and the coolant flow data falls into a predetermined preset flow interval, then re-determining the interval range of the hysteresis interval based on the first adjustment weight; if it does not fall into the predetermined preset flow interval, then determining the corresponding second adjustment weight, and re-determining the interval range of the hysteresis interval based on the first adjustment weight and the second adjustment weight. Effectively reducing the problem of frequent start and stop of the cooling system due to slight temperature fluctuations, thereby improving the operating stability of the system and avoiding the loss of the solenoid valve caused by frequent switching.

Description

Technical Field

[0001] The present invention relates to the technical field of test circuit control, and in particular to a control method, device, equipment and medium based on an LED power supply test system. Background Art

[0002] At present, the traditional LED power supply test system usually relies on fixed temperature trigger thresholds for simple cooling control in temperature control management. When the system temperature reaches the set high or low temperature threshold, the cooling operation is triggered or stopped. However, in actual applications, the system temperature during the LED power supply test often frequently approaches these trigger thresholds due to fluctuations in the external environment and power load. Due to the lack of a hysteresis control mechanism, the cooling system will be repeatedly triggered when the temperature fluctuates slightly, resulting in frequent start and stop of the solenoid valve and the cooling system, increasing the mechanical wear of the equipment and reducing the service life of the cooling system and the solenoid valve. In addition, frequent switching actions will increase the energy consumption of the system, reduce the cooling efficiency, and affect the stability of the overall operation. Therefore, the traditional temperature control design has obvious shortcomings in dealing with temperature fluctuations, and the frequent fluctuations in cooling control need to be improved. Summary of the invention

[0003] In order to solve the problem that frequent fluctuations in temperature control in traditional LED power supply test systems lead to increased wear of cooling systems and solenoid valves, shortened service life and increased energy consumption, the present application provides a management and control method, device, equipment and medium based on an LED power supply test system.

[0004] A control method based on an LED power supply test system, the control method based on an LED power supply test system comprising: Determine a hysteresis interval of a temperature sensing and analysis system, wherein the temperature sensing and analysis system is used to collect temperature data of the LED power supply test system during the test process and compare the temperature data with the hysteresis interval to generate a corresponding comparison result; Acquire the current load state of the LED power supply test system in real time, judge whether the current load state is greater than a preset state threshold, if not greater than the preset state threshold, continue to judge, if greater than the preset state threshold, calculate the state difference between the current load state and the preset state threshold, and determine the corresponding first adjustment weight according to the state difference; Acquire coolant flow data of a cooling system, wherein the cooling system is used to determine whether to cool the LED power supply test system according to the comparison result; Determining whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, re-determining the interval range of the hysteresis interval based on the first adjustment weight; If it does not fall within the determined preset flow interval, the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval is calculated, and the corresponding second adjustment weight is determined based on the flow difference, and based on the first adjustment weight and the second adjustment weight, the interval range of the hysteresis interval is redetermined.

[0005] By adopting the above technical solution, the hysteresis interval of the temperature perception analysis system can be reasonably set during the control process, and the temperature data of the LED power supply test system can be collected in real time through the temperature perception analysis system, and the temperature data can be compared with the set hysteresis interval, so as to generate a clear temperature control comparison result, so that the cooling system can judge whether cooling is needed based on the comparison result. At the same time, the system can obtain the load state of the LED power supply test system in real time. When the load state exceeds the preset threshold, the load state difference is calculated and the corresponding first adjustment weight is generated, and the range of the hysteresis interval is further optimized according to the coolant flow data. Through this dynamic adjustment method, the problem of frequent start and stop of the cooling system due to slight temperature fluctuations can be effectively reduced, thereby improving the operating stability of the system, avoiding the loss of the solenoid valve caused by frequent switching, and extending the service life of the equipment.

[0006] Preferably, the step of determining the corresponding first adjustment weight according to the state difference includes: Based on the weight definition algorithm model, a first adjustment formula and a multi-level first priority queue are defined; Substituting the state difference into a first adjustment formula to generate a corresponding first weight value; Matching a first priority queue corresponding to the first weight value, generating a corresponding first matching result, and determining a first weight coefficient of the first weight value according to the first matching result; A corresponding first adjustment weight is determined according to the first weight value and the first weight coefficient.

[0007] By adopting the above technical solution, the first adjustment weight value can be generated according to the load state difference in combination with the weight definition algorithm model. By defining the first adjustment formula and the multi-level priority queue, the system can substitute the state difference into the formula to calculate the first weight value, and match the first weight value with the multi-level priority queue to obtain a first matching result, and determine the first weight coefficient based on the result. Furthermore, the first adjustment weight is finally generated based on the first weight value and the first weight coefficient, which enables the system to obtain the corresponding adjustment force when the load state changes, thereby dynamically adapting to the needs of load changes, effectively avoiding the problem of untimely temperature control caused by load increase or decrease, thereby improving the cooling response efficiency and accuracy of the system under different load states.

[0008] Preferably, the step of determining the corresponding second adjustment weight according to the flow difference includes: Based on the weight definition algorithm model, a second adjustment formula is defined, wherein the second adjustment formula at least includes a positive state formula and a negative state formula, and a multi-level second priority queue corresponding to the positive state formula and a multi-level third priority queue corresponding to the negative state formula are defined; Determining a difference state of the flow difference; If the difference state is a positive state, the flow difference is substituted into the positive state formula to generate a corresponding second weight value; Matching a second priority queue corresponding to the second weight value, generating a corresponding second matching result, and determining a second weight coefficient of the second weight value according to the second matching result; If the difference state is a negative state, the flow difference is input into the negative state formula to generate a corresponding third weight value; Matching a third priority queue corresponding to the third weight value, generating a corresponding third matching result, and determining a third weight coefficient of the third weight value according to the third matching result; A corresponding second adjustment weight is determined according to the second weight value and the second weight coefficient, or according to the third weight value and the third weight coefficient.

[0009] By adopting the above technical solution, the second adjustment weight can be dynamically generated according to the change of the coolant flow rate. By using the second adjustment formula of the weight definition algorithm model, when the flow difference is in a positive state, the system substitutes the flow difference into the positive state formula to generate a second weight value, and matches it with the second priority queue to determine the second weight coefficient; and when the flow difference is in a negative state, the flow difference is substituted into the negative state formula to generate a third weight value, and matches it with the third priority queue to determine the third weight coefficient. In this way, according to the generated second or third weight value and its corresponding weight coefficient, the system can generate the corresponding second adjustment weight. This method can accurately adjust the cooling response according to the positive and negative state changes of the coolant flow, ensuring that the hysteresis interval can be quickly adjusted when the coolant flow is insufficient or excessive, thereby avoiding the situation of over-response or under-response of the cooling system, and improving the stability and responsiveness of the cooling effect.

[0010] Preferably, after the step of re-determining the interval range of the hysteresis interval, the method further comprises: Acquire temperature fluctuation feedback data according to a preset collection cycle, wherein the temperature fluctuation feedback data at least includes temperature fluctuation frequency and temperature fluctuation amplitude; Substituting the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment into the weight definition algorithm model to analyze the change trend between the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment; According to the change trend, the weight definition algorithm model is optimized.

[0011] By adopting the above technical solution, after re-determining the hysteresis interval range, the temperature fluctuation feedback data can be obtained in real time according to the preset collection cycle, and the temperature fluctuation feedback data in the current cycle can be compared with the temperature fluctuation data before adjustment. In this way, the system can analyze the changing trend of the temperature fluctuation data based on the comparison of the fluctuation frequency and the fluctuation amplitude. Based on this changing trend, the system can optimize the weight definition algorithm model to make the adjustment of the temperature control system more accurate, so that when the temperature fluctuation increases, the hysteresis interval can be effectively reduced, the sensitivity of the cooling response can be improved, and when the temperature fluctuation tends to be stable, a larger hysteresis interval can be maintained to reduce the frequent start and stop of the cooling system. This process can dynamically optimize and adjust the weight model according to the temperature fluctuation situation, improve the stability of the system operation and the accuracy of the cooling operation.

[0012] Preferably, the step of optimizing the weight definition algorithm model according to the change trend includes: Analyze the change trend to determine the corresponding trend direction and trend slope; According to the trend direction and the trend slope, definition parameters corresponding to the weight definition algorithm model are determined, and the definition parameters are used to define the first adjustment formula and / or the second adjustment formula.

[0013] By adopting the above technical solution, after analyzing the change trend, the weight definition algorithm model can be further optimized by determining the trend direction and trend slope. By analyzing the change trend, the system can determine whether the temperature fluctuation is rising or falling, as well as the speed of change of the fluctuation amplitude, thereby generating the corresponding trend direction and trend slope. According to different trend directions and slopes, the system can optimize the definition parameters of the first adjustment formula or the second adjustment formula so that the weight definition model can flexibly adapt to temperature fluctuations. For example, when the temperature fluctuation trend rises and the amplitude of the change is large, the system can increase the first adjustment weight and reduce the hysteresis interval to speed up the cooling response speed; when the temperature fluctuation decreases, the weight adjustment amplitude is appropriately reduced, the hysteresis interval is expanded to reduce the cooling frequency, thereby improving the operating stability and durability of the cooling system.

[0014] Preferably, before the step of determining the hysteresis interval of the temperature sensing analysis system, the method further includes: Obtaining the number of starts and stops of the solenoid valve, wherein the solenoid valve is used to control the circulation flow of the coolant in the cooling system; Determine whether the start and stop times of the solenoid valve exceed a preset start and stop times threshold, and if not, continue to determine; If exceeded, the preset temperature threshold of the temperature sensing analysis system is obtained; A mapping relationship is established based on the hysteresis interval of the number of starts and stops of the solenoid valve, and an upper limit value and a lower limit value corresponding to the preset temperature threshold are determined. The upper limit value and the lower limit value are used to determine the hysteresis interval with the preset temperature threshold as the intermediate value.

[0015] By adopting the above technical solution, the upper and lower limits of the hysteresis interval can be dynamically adjusted based on the changes in the number of starts and stops of the solenoid valve before the range of the hysteresis interval is re-determined. When the number of starts and stops of the solenoid valve exceeds the preset start and stop threshold, the system obtains the preset temperature threshold of the temperature sensing analysis system, and establishes a mapping relationship of the hysteresis interval according to the number of starts and stops of the solenoid valve, and dynamically sets the upper and lower limits of the hysteresis interval with the preset temperature threshold as the middle value. This method can expand the hysteresis interval when the solenoid valve is frequently started and stopped, thereby effectively reducing the excessive response of the cooling system, reducing the wear of the solenoid valve and extending the service life of the equipment, so that the temperature control of the system has higher stability and reliability while maintaining responsiveness.

[0016] The second object of the invention is achieved by the following technical solutions: A first determination module is used to determine a hysteresis interval of a temperature perception analysis system, wherein the temperature perception analysis system is used to collect temperature data of the LED power supply test system during the test process, and to compare the temperature data with the hysteresis interval to generate a corresponding comparison result; A first judgment module is used to obtain the current load state of the LED power supply test system in real time, judge whether the current load state is greater than a predetermined preset state threshold, and if not, continue to judge; if greater than the predetermined preset state threshold, calculate the state difference between the current load state and the preset state threshold, and determine the corresponding first adjustment weight according to the state difference; A first acquisition module, used for acquiring coolant flow data of a cooling system, wherein the cooling system is used for determining whether to cool the LED power supply test system according to the comparison result; a second judgment module, configured to judge whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, to re-determine the interval range of the hysteresis interval based on the first adjustment weight; A calculation module is used to calculate the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval if it does not fall into the determined preset flow interval, determine the corresponding second adjustment weight according to the flow difference, and redetermine the interval range of the hysteresis interval based on the first adjustment weight and the second adjustment weight.

[0017] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned control method based on an LED power supply test system when executing the computer program.

[0018] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned control method based on an LED power supply test system are implemented.

[0019] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application sets a hysteresis interval in the management and control method so that the temperature control logic no longer directly relies on a single temperature trigger threshold, but triggers the cooling operation only when the temperature is significantly higher or lower than the set value. When the load state of the LED power supply test system exceeds the preset threshold, the system will calculate the first adjustment weight based on the load difference, and further optimize the range of the hysteresis interval based on whether the coolant flow data is within the preset flow range. This design effectively reduces the frequent switching of the cooling system due to small temperature fluctuations, reduces the mechanical wear of the solenoid valve and the cooling system, and significantly improves the operating stability of the system; 2. By acquiring the load status and coolant flow data of the LED power supply test system in real time, the present application can adjust the hysteresis interval range according to the dynamic changes of these data, thereby flexibly controlling the response of the cooling system. When the coolant flow data does not reach the preset flow interval, the system will also calculate the second adjustment weight based on the flow difference, and further optimize the hysteresis interval in combination with the first adjustment weight, so that the cooling system can respond more accurately to actual temperature change requirements. This design not only reduces the energy consumption of the system, but also extends the service life of the cooling system and solenoid valve, and improves the reliability and durability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a control method based on an LED power supply test system in an embodiment of the present application.

[0021] Figure 2This is a flowchart for implementing step S20 in a control method based on an LED power supply test system in an embodiment of the present application; Figure 3 This is a flowchart for implementing step S50 in a control method based on an LED power supply test system in an embodiment of the present application; Figure 4 This is another implementation flow chart of a control method based on an LED power supply test system in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S63 in a control method based on an LED power supply test system in one embodiment of the present application; Figure 6 This is another implementation flow chart of a control method based on an LED power supply test system in one embodiment of the present application; Figure 7 This is a principle block diagram of a control device based on an LED power supply test system in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The present application is further described in detail below in conjunction with the accompanying drawings.

[0023] In one embodiment, if Figure 1 As shown, the present application discloses a control method based on an LED power supply test system, which specifically includes the following steps: S10, determining the hysteresis interval of the temperature perception analysis system. The temperature perception analysis system is used to collect temperature data of the LED power supply test system during the test process on the one hand, and to compare the temperature data with the hysteresis interval on the other hand to generate a corresponding comparison result. In this embodiment, determining the hysteresis interval of the temperature perception analysis system refers to presetting a temperature range with upper and lower limits during the temperature control process, so that the system can start cooling when the temperature exceeds a certain value, but stop cooling when the temperature drops to a lower range, thereby avoiding frequent start and stop of the cooling system due to small temperature fluctuations. The temperature perception analysis system collects the temperature data of the LED power supply test system in real time during the test process, and compares the collected temperature data with the pre-set hysteresis interval to generate a corresponding comparison result. If the temperature is higher than the upper limit of the hysteresis interval, the comparison result will trigger the cooling system to start; if the temperature is lower than the lower limit of the hysteresis interval, the comparison result will trigger the cooling system to stop. In this way, the system can maintain stable cooling control under temperature fluctuations, reduce the frequent switching of the cooling system, and improve the operating stability and service life of the equipment.

[0024] S20, real-time acquisition of the current load state of the LED power supply test system, determine whether the current load state is greater than the determined preset state threshold, if not greater than the determined preset state threshold, then continue to judge, if greater than the determined preset state threshold, then calculate the state difference between the current load state and the preset state threshold, and determine the corresponding first adjustment weight according to the state difference; in this embodiment, real-time acquisition of the current load state of the LED power supply test system means that the system monitors the working load of the LED power supply in real time through a sensor or a data acquisition module to ensure that corresponding adjustments can be made when the load changes. When the acquired load state is less than the preset state threshold, the system will maintain the monitoring state and continue to determine whether it exceeds the threshold when the load continues to change; if it is detected that the load state is greater than the set preset threshold, the system will obtain the "state difference" by calculating the difference between the current load state and the preset threshold. The state difference can reflect the degree of deviation of the load, and the system can generate the first adjustment weight according to this difference. The first adjustment weight is used to further control the size of the hysteresis interval, so that when the load increases significantly, the system can adjust the sensitivity of the cooling response in time to ensure that it enters the cooling state faster when the load is large, thereby effectively improving the response efficiency of the temperature control system.

[0025] S30. Obtain the coolant flow data of the cooling system. The cooling system is used to determine whether to cool the LED power supply test system according to the comparison results. In this embodiment, obtaining the coolant flow data of the cooling system means that the system obtains the current coolant flow value from the flow sensor in the cooling system, which is used to determine whether the supply of coolant meets the cooling demand. The stability of the coolant flow directly affects the overall cooling effect of the cooling system and the reliability of temperature control. A lower coolant flow will lead to a poor cooling effect, while an excessively high flow will lead to an increase in cooling energy consumption. The cooling system determines whether to start cooling based on the comparison results generated by the temperature perception analysis system. For example, when the comparison results of the temperature perception analysis system show that the temperature is higher than the upper limit of the hysteresis interval and the coolant flow is sufficient, the cooling system starts to work, so that the temperature of the LED power supply test system can quickly return to the set range to ensure the stable operation of the system.

[0026] S40, determine whether the coolant flow data falls into a predetermined preset flow interval. If it falls into the predetermined preset flow interval, then based on the first adjustment weight, redetermine the interval range of the hysteresis interval; in this embodiment, determining whether the coolant flow data falls into a predetermined preset flow interval means checking whether the current coolant flow value is within a reasonable range before the cooling system is started or during the cooling process. The preset flow interval represents the optimal operating flow range of the cooling system, which can meet the cooling requirements without causing excessive energy consumption. When the coolant flow is within the preset interval, the system will use the first adjustment weight calculated in the previous step to redetermine the range of the hysteresis interval, so that the hysteresis interval can be fine-tuned according to the current cooling state. For example, when the coolant flow is within the ideal interval and the first adjustment weight value is large, the hysteresis interval will be slightly reduced, so that the cooling system can respond faster to temperature changes, thereby improving the response sensitivity of the cooling system and ensuring that the system can achieve efficient cooling under reasonable coolant flow conditions.

[0027] S50, if it does not fall into the determined preset flow interval, the flow difference between the coolant flow data and the corresponding critical value of the preset flow interval is calculated, the corresponding second adjustment weight is determined according to the flow difference, and the interval range of the hysteresis interval is re-determined based on the first adjustment weight and the second adjustment weight. In this embodiment, if the coolant flow data does not fall into the preset flow interval, the system will further analyze the difference between the coolant flow data and the corresponding critical value of the preset flow interval, so as to calculate the "flow difference". The flow difference can reflect the current insufficient or excessive coolant flow. The system generates a second adjustment weight based on the flow difference to ensure that the cooling effect can be compensated when the flow is insufficient, and to avoid excessive reaction of the cooling system when the flow is too much. At this time, the system will optimize the hysteresis interval based on the first adjustment weight and the second adjustment weight, so that the hysteresis interval can be dynamically adjusted according to the actual load and flow. For example, when the flow is insufficient, the second adjustment weight will increase the hysteresis interval, extend the cooling working time, so that the temperature control of the system remains effective when the coolant flow is low, while reducing the system wear caused by frequent start and stop, and improving the operating efficiency and stability of the cooling system.

[0028] Furthermore, the load state and coolant flow data are selected as the weights for adjustment, mainly because these two factors have a direct and significant impact on the cooling demand and cooling capacity of the temperature control system. The load state directly affects the temperature generation of the LED power supply test system. The larger the LED load, the more heat the system generates, and the cooling demand increases accordingly; on the contrary, when the load is small, the temperature generation is small and the cooling demand decreases. Therefore, the load state reflects the degree of heat generation of the system and is an important basis for adjusting the cooling system. By introducing the load state as the adjustment weight, the system can dynamically adjust the range of the hysteresis interval according to the current load situation. For example, when the load state is higher than the set threshold, the system calculates the "state difference" of the load state and generates the first adjustment weight to reduce the hysteresis interval. This adjustment enables the cooling system to respond to temperature changes more sensitively at high loads, quickly start cooling, and avoid excessive system temperature due to excessive loads; while at low loads, the hysteresis interval is maintained or slightly increased, thereby avoiding frequent start and stop of the cooling system due to slight temperature fluctuations. By using the load state as a weight, the system can accurately match the cooling demand, thereby improving the cooling efficiency and extending the service life of the cooling equipment; and the coolant flow directly determines the cooling capacity of the cooling system. When the coolant flow rate is too low, the cooling effect will be greatly reduced and will not be able to meet the heat dissipation requirements of the system; while if the flow rate is too high, it will increase the energy consumption of the cooling system and lead to unnecessary cooling waste. Therefore, the coolant flow rate data can reflect the actual cooling capacity of the current cooling system and is an important parameter for system cooling regulation. Introducing the coolant flow rate data as an adjustment weight can enable the system to automatically adjust the hysteresis interval when the cooling capacity is insufficient or excessive to avoid excessive or insufficient cooling. For example, when the coolant flow rate falls into the preset ideal interval, the system can fine-tune the hysteresis interval range based on the first adjustment weight to ensure that the cooling system works at the optimal flow rate and achieve effective cooling control; when the coolant flow rate is insufficient or excessive, the system generates a second adjustment weight based on the flow difference, and optimizes the hysteresis interval in combination with the first adjustment weight to make up for the lack of cooling capacity or avoid waste. Through this mechanism, the cooling system can extend the cooling working time when the coolant flow rate is insufficient, and appropriately shorten the hysteresis interval when the flow rate is sufficient to avoid excessive cooling frequency, thereby optimizing the cooling efficiency and energy consumption performance of the system.

[0029] In summary, the hysteresis interval of the temperature perception analysis system can be reasonably set during the control process. The temperature data of the LED power supply test system can be collected in real time through the temperature perception analysis system, and the temperature data can be compared with the set hysteresis interval to generate a clear temperature control comparison result, so that the cooling system can judge whether cooling is needed based on the comparison result. At the same time, the system can obtain the load state of the LED power supply test system in real time. When the load state exceeds the preset threshold, the load state difference is calculated and the corresponding first adjustment weight is generated, and the range of the hysteresis interval is further optimized according to the coolant flow data. Through this dynamic adjustment method, the problem of frequent start and stop of the cooling system due to slight temperature fluctuations can be effectively reduced, thereby improving the operating stability of the system, avoiding the loss of the solenoid valve caused by frequent switching, and extending the service life of the equipment.

[0030] In one embodiment, if Figure 2 As shown, in step S20, i.e., the step of determining the corresponding first adjustment weight according to the state difference, it includes: S201. Define an algorithm model based on weights, define a first adjustment formula, and a multi-level first priority queue; in this embodiment, a calculation formula applicable to different state differences is constructed through the calculation rules of the algorithm model, and different calculation results are divided into multi-level priority queues to ensure that the system can manage the size of the load difference in a hierarchical manner. The first adjustment formula is used to convert the state difference into a controllable weight value, and the multi-level first priority queue classifies different load status conditions into different priorities according to the size of the weight value. For example, when the load difference is large, the system will put it into a high priority queue so that the cooling system can respond quickly; when the difference is small, it will be placed in a low priority queue, thereby reducing the cooling frequency and ensuring the flexibility and rationality of the response of the system under different load conditions.

[0031] S202. Substitute the state difference into the first adjustment formula to generate the corresponding first weight value; in this embodiment, the currently calculated load state difference is input into the previously defined first adjustment formula, and a value is calculated by the formula as the "first weight value". The size of the first weight value directly reflects the degree of cooling demand of the system under the current load state. If the first weight value is large, it means that the load deviation is large, and the cooling system should respond more sensitively to temperature changes; if the first weight value is small, it means that the current load state is closer to the ideal state, and the cooling system can appropriately reduce the response intensity. This step converts the state difference into the first weight value, thereby converting the complex load state into a weight value that can be used for control, which is convenient for subsequent priority matching and adjustment.

[0032] S203, matching the first priority queue corresponding to the first weight value, generating the corresponding first matching result, and determining the first weight coefficient of the first weight value according to the first matching result; in this embodiment, the system compares the calculated first weight value with the preset multi-level first priority queue to determine its priority level. The matching result is used to further optimize the sensitivity and response strength of the cooling control. For example, if the first weight value belongs to a high priority queue, the first matching result generated by the system will point to the cooling strategy in the high priority queue to ensure that the cooling system starts quickly to cope with a large load; if the first weight value belongs to a medium or low priority queue, the matching result points to a medium and low priority cooling strategy to avoid unnecessary cooling frequency. Through this matching process, the system can dynamically select the corresponding cooling priority according to the load conditions to improve the adaptability and response efficiency of the cooling system.

[0033] S204. Determine the corresponding first adjustment weight according to the first weight value and the first weight coefficient. In this embodiment, after generating the first matching result and determining the first weight coefficient, multiply the first weight value by the first weight coefficient to obtain the final first adjustment weight. The first adjustment weight is used to actually adjust the responsiveness of the cooling system. For example, if the first weight value is 5 and the first weight coefficient is 1.2, the first adjustment weight is 6, indicating that the cooling system needs to respond quickly to large temperature fluctuations; and if the first weight value is 3 and the first weight coefficient is 0.8, the first adjustment weight is 2.4, indicating that the cooling system can appropriately reduce the response strength. Through this adjustment method, the system can flexibly adjust the intensity of the cooling operation according to the current load and priority, achieve precise control of the temperature, and ensure that the cooling system avoids frequent start and stop while meeting the temperature control requirements, thereby improving the stability of the system operation and the cooling effect.

[0034] Furthermore, assume that the current load state of the LED power supply test system is 120 watts, and the preset load state threshold is 100 watts. This means that the system load state is higher than the threshold of 20 watts, indicating a higher cooling demand. At this time, the first adjustment formula defined is: first weight value = state difference ÷ 10. The multi-level first priority queue is divided into low priority (weight value 0-2), medium priority (weight value 2-4) and high priority (weight value 4 and above) according to the weight value, so that the system can select the appropriate cooling response intensity under different load differences. In this way, when the load state deviates from the preset value by a large amount, the system will generate a higher weight value and match it to the high priority queue to ensure that the cooling system responds more quickly; when the deviation is small or close to the ideal state, it will match it to the low priority queue to reduce the response frequency of the cooling system and avoid unnecessary start and stop. The current load state difference is 20 watts, which is substituted into the first adjustment formula to calculate: first weight value = 20 ÷ 10 = 2. This first weight value reflects the degree to which the current load state deviates from the threshold. Since the first weight value is small, it means that although the current load is higher than the threshold, the deviation is not large, and the cooling system can respond moderately without a large adjustment. This weight value will enter the next priority queue matching to determine the response level of the system. According to the first weight value 2, it will be matched with the multi-level first priority queue. The first weight value 2 falls in the "medium priority" range (weight value 2-4), so the first matching result generated by the system is "medium priority". According to the cooling response requirement of medium priority, the system sets the first weight coefficient to 1.1. This weight coefficient is used to adjust the intensity of the cooling response, reflecting the sensitivity of the cooling system under medium cooling demand, ensuring that a reasonable cooling response frequency is maintained under temperature changes, and not frequently starting and stopping the cooling system. The first adjustment weight is calculated by multiplying the first weight value and the first weight coefficient. The first adjustment weight = first weight value × first weight coefficient = 2 × 1.1 = 2.2. This adjustment weight will be used to actually adjust the range of the hysteresis interval, thereby controlling the response of the cooling system. Since the calculated first adjustment weight is 2.2, the system will adopt a medium response intensity in this cooling operation to maintain a moderate cooling effect. This can not only meet the cooling needs of the current load status, but also avoid frequent switching of the cooling system, thereby improving the overall operational stability and energy-saving effect.

[0035] In summary, the first adjustment weight value can be generated according to the load state difference in combination with the weight definition algorithm model. By defining the first adjustment formula and the multi-level priority queue, the system can substitute the state difference into the formula to calculate the first weight value, and match the first weight value with the multi-level priority queue to obtain a first matching result, and determine the first weight coefficient based on the result. Furthermore, the first adjustment weight is finally generated based on the first weight value and the first weight coefficient, which enables the system to obtain the corresponding adjustment force when the load state changes, thereby dynamically adapting to the needs of load changes, effectively avoiding the problem of untimely temperature control caused by load increase or decrease, thereby improving the cooling response efficiency and accuracy of the system under different load states.

[0036] In one embodiment, if Figure 3 As shown, in step S50, i.e., the step of determining the corresponding second adjustment weight according to the flow difference, it includes: S501. Based on the weight definition algorithm model, define the second adjustment formula, wherein the second adjustment formula includes at least a positive state formula and a negative state formula, and define a multi-level second priority queue corresponding to the positive state formula, and a multi-level third priority queue corresponding to the negative state formula; in this embodiment, in order to cope with the fluctuation of the coolant flow, the system sets different adjustment formulas to handle the positive and negative states of the flow difference. Specifically, the positive state formula is used to handle the situation where the flow difference is positive, indicating that the coolant flow exceeds the set upper limit of the ideal flow interval; and the negative state formula is used to handle the situation where the flow difference is negative, indicating that the coolant flow is lower than the set lower limit of the ideal flow interval. To facilitate the processing of different flow difference states, the positive state formula corresponds to a multi-level second priority queue, and the negative state formula corresponds to a multi-level third priority queue. These priority queues are graded according to different flow difference levels, so that the system can flexibly adjust the intensity of the cooling response according to the size and direction of the flow difference. For example, when the flow difference is large, it is matched to the high-priority queue, and the cooling system will respond quickly to adapt to the cooling demand; if the difference is small, it is matched to the low-priority queue, reducing the response frequency, thereby ensuring the stability and efficiency of the system under different flow conditions.

[0037] S502, determine the difference state of the flow difference; in this embodiment, by comparing the coolant flow data with the preset flow interval, determine whether the flow difference is a positive state or a negative state. The positive state usually means that the current coolant flow is too high, exceeding the upper limit of the set ideal flow interval, which will cause unnecessary high energy consumption of the cooling system; while the negative state means that the coolant flow is insufficient, which is lower than the lower limit of the set interval, resulting in poor cooling effect. Therefore, by judging the difference state, the system can decide to use the positive state formula or the negative state formula to ensure that the cooling system can flexibly select the appropriate control strategy according to the flow conditions, thereby improving cooling efficiency and reducing resource waste.

[0038] S503. If the difference state is a positive state, the flow difference is substituted into the positive state formula to generate a corresponding second weight value; in this embodiment, when the coolant flow exceeds the set upper limit, the flow difference is substituted into the positive state formula for calculation to generate a second weight value. This second weight value reflects the degree of flow difference, that is, the degree of deviation of the coolant flow beyond the ideal range. The higher the weight value, the greater the flow difference, and the faster the cooling system responds, so as to quickly adjust to the situation of excessive flow. For example, when the flow difference is large, the second weight value will also increase, indicating that a stronger response force is needed to avoid waste of coolant, thereby achieving energy saving. This step converts the flow difference into a controllable weight value to facilitate subsequent priority matching.

[0039] S504, match the second priority queue corresponding to the second weight value, generate the corresponding second matching result, and determine the second weight coefficient of the second weight value according to the second matching result; in this embodiment, the system matches the second weight value with the multi-level second priority queue to determine the priority level of the weight value. The second priority queue is divided into three priorities: high, medium, and low, and each priority corresponds to a different weight coefficient. For example, when the second weight value is large, it is matched to the high priority queue, and the generated second matching result points to the high priority, thereby determining the corresponding high weight coefficient. Through this matching process, the system can automatically select the appropriate response intensity according to the size and positive value of the flow difference. This priority matching result is used for adjustments in subsequent steps to ensure that the cooling system can respond quickly when the flow exceeds the standard seriously, and maintain a lower response frequency when the flow exceeds the standard not seriously, thereby optimizing the system operation.

[0040] S505. If the difference state is a negative state, the flow difference is entered into the negative state formula to generate a corresponding third weight value; in this embodiment, when the flow difference is a negative state, it means that the coolant flow is insufficient and the cooling effect needs to be enhanced. At this time, the flow difference is substituted into the negative state formula for calculation to generate a third weight value. The third weight value indicates the degree of insufficient coolant flow. The larger the value, the more serious the insufficient flow, and the cooling system requires a stronger compensation response. For example, when the coolant flow is much lower, the generated third weight value is also larger, which means that the system needs to enter the cooling state faster to make up for the insufficient cooling effect. By converting the flow difference into the third weight value, the system can flexibly respond to insufficient coolant and provide a basis for subsequent priority matching.

[0041] S506, match the third priority queue corresponding to the third weight value, generate the corresponding third matching result, and determine the third weight coefficient of the third weight value according to the third matching result; in this embodiment, the system matches the third weight value with the multi-level third priority queue to determine the priority level of the weight value and the corresponding response strength. The third priority queue is divided into three priorities: high, medium, and low according to the weight value, and each priority corresponds to a different weight coefficient. For example, when the third weight value is large, the system matches the high priority queue, and the generated third matching result points to the high priority, and applies the corresponding high weight coefficient to strengthen the cooling response; when the third weight value is small, it matches the low priority, and the response strength will be low. Through this priority matching method, the system can flexibly adjust the intensity of the cooling response according to the insufficient coolant flow, ensure that the cooling effect of the system is compensated when the coolant flow is insufficient, and improve the reliability of the cooling system.

[0042] S507. Determine the corresponding second adjustment weight according to the second weight value and the second weight coefficient, or according to the third weight value and the third weight coefficient. In this embodiment, the system calculates the final second adjustment weight according to the weight value and the corresponding weight coefficient obtained in the above steps. If the flow difference is in a positive state, the second adjustment weight is equal to the second weight value multiplied by the second weight coefficient; if the flow difference is in a negative state, the second adjustment weight is equal to the third weight value multiplied by the third weight coefficient. Through this adjustment weight, the system can further control the response strength of the cooling system. For example, when the coolant flow is insufficient, the product of the third weight value and the weight coefficient is large, and the generated second adjustment weight is high, which means that the cooling system will take stronger cooling measures; when the flow exceeds the standard, the second adjustment weight is generated in a way that ensures that the cooling intensity of the system is adjusted appropriately to avoid cooling waste. This design enables the cooling system to achieve flexible response to different flow states, thereby optimizing the cooling effect and resource utilization.

[0043] Furthermore, assume that the preset interval of the coolant flow of the LED power supply test system is 2.0 to 2.5 liters / minute, that is, when the coolant flow is within this interval, the system considers the flow to be appropriate and does not need to be adjusted. When the flow is less than 2.0 liters / minute, the flow difference is in a negative state, indicating that the coolant flow is insufficient; when the flow is greater than 2.5 liters / minute, it is in a positive state, indicating that the coolant flow is excessive. The current coolant flow data is 1.8 liters / minute, which is lower than the lower limit of the set interval (2.0 liters / minute), so it belongs to a negative state. In order to handle different flow conditions, the system defines a second adjustment formula, in which the positive state formula is used to deal with the situation where the coolant flow exceeds the upper limit, and the negative state formula is used to deal with the situation where the coolant flow is lower than the lower limit. Assume that the negative state formula is: the third weight value = |flow difference|÷0.2, the second priority queue is used to match the positive state, and the third priority queue is used to match the negative state. The third priority queue is divided into low priority (0-1), medium priority (1-2) and high priority (2 and above) according to the size of the weight value, which is used to adjust different degrees of cooling demand. Defining these priorities and formulas can make the system more flexible to deal with excessive or insufficient flow, thereby optimizing the cooling effect and system operation efficiency; the system compares the coolant flow data (1.8 liters / minute) with the lower limit of the preset flow range (2.0 liters / minute), and calculates the flow difference as 1.8-2.0=-0.2 liters / minute, so the difference state is negative. The negative state indicates that the coolant flow is insufficient, which will lead to reduced cooling effect. The system needs to enter the corresponding adjustment steps according to this state to improve the cooling response; the system substitutes the flow difference (-0.2) into the negative state formula: the third weight value = |flow difference|÷0.2. The calculated third weight value is |-0.2|÷0.2=1. This third weight value indicates the degree of insufficient coolant flow. The larger the weight value, the more serious the insufficient flow, and the cooling system needs to enter the cooling state faster. This step generates a weight value through the flow difference, so that the system can convert the flow deviation into a controllable value, which is convenient for subsequent priority matching. According to the third weight value 1, it is matched with the multi-level third priority queue. In the third priority queue, the weight value 1 corresponds to "medium priority", so the third matching result generated by the system is "medium priority". Based on this matching result, the system determines that the weight coefficient of the third weight value is 1.3. This weight coefficient is used to adjust the intensity of the cooling response. For example, when the coolant flow is slightly lower than the ideal range, the weight coefficient is moderately increased to enhance the cooling effect and compensate for the insufficient flow; the system calculates the final second adjustment weight based on the third weight value and the third weight coefficient. The second adjustment weight = the third weight value × the third weight coefficient = 1 × 1.3 = 1.3. This second adjustment weight will be used to adjust the range of the hysteresis interval so that the cooling system can respond more sensitively to temperature fluctuations when the coolant flow is insufficient.For example, the calculated second adjustment weight is 1.3, which means that the system needs to be cooled more frequently in this case to ensure that the temperature of the LED power supply test system remains within a safe operating range when the coolant flow is insufficient. In this way, by making reasonable use of the flow difference state, the system can dynamically adjust the cooling intensity to improve the cooling effect and the stability of system operation.

[0044] In summary, the second adjustment weight can be dynamically generated according to the change of the coolant flow rate. By using the second adjustment formula of the weight definition algorithm model, when the flow difference is in a positive state, the system substitutes the flow difference into the positive state formula to generate a second weight value, and matches it with the second priority queue to determine the second weight coefficient; and when the flow difference is in a negative state, the flow difference is substituted into the negative state formula to generate a third weight value, and matches it with the third priority queue to determine the third weight coefficient. In this way, according to the generated second or third weight value and its corresponding weight coefficient, the system can generate the corresponding second adjustment weight. This method can accurately adjust the cooling response according to the positive and negative state changes of the coolant flow, ensuring that the hysteresis interval can be quickly adjusted when the coolant flow is insufficient or excessive, thereby avoiding the situation of over-response or under-response of the cooling system, and improving the stability and responsiveness of the cooling effect.

[0045] In one embodiment, if Figure 4 As shown, after step S50, that is, after the step of re-determining the interval range of the hysteresis interval, the method further includes: S61. Acquire temperature fluctuation feedback data according to a preset acquisition cycle. The temperature fluctuation feedback data includes at least the temperature fluctuation frequency and the temperature fluctuation amplitude. In this embodiment, the system regularly collects temperature changes of the LED power supply test system within a certain acquisition cycle. The specific data includes the temperature fluctuation frequency and the temperature fluctuation amplitude. The temperature fluctuation frequency indicates the number of times the temperature exceeds the set threshold within the cycle, reflecting the frequency of temperature changes; the temperature fluctuation amplitude is the difference between the maximum and minimum values ​​of the temperature within the cycle, which is used to measure the severity of the temperature fluctuation. By acquiring these feedback data, the system can determine whether the control effect of the current cooling system is stable and whether there is an overly frequent cooling response, thereby providing a basis for subsequent weight optimization and hysteresis interval adjustment. For example, if a high temperature fluctuation frequency or a large fluctuation amplitude is detected, it means that the current cooling intensity is insufficient, and the control strategy needs to be optimized to increase the stability of the cooling response.

[0046] S62. Substitute the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment into the weight definition algorithm model to analyze the change trend between the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment; in this embodiment, after the current acquisition cycle ends, the system compares the temperature fluctuation feedback data just acquired with the previously recorded temperature fluctuation data to analyze the change trend between the two. By substituting these data into the weight definition algorithm model, the system can calculate the change in the current temperature fluctuation data relative to the previous cycle, such as whether the fluctuation frequency has increased, whether the fluctuation amplitude has expanded, etc. This analysis can reveal the changing trend of temperature fluctuations, thereby helping the system determine whether the cooling control needs further adjustment. For example, if the feedback data shows that the temperature fluctuation continues to increase in the near future, it means that the existing control strategy will not be able to fully cope with the current load demand, and the cooling intensity will need to be increased to ensure the smooth operation of the temperature control system.

[0047] S63. Optimize the weight definition algorithm model according to the change trend. In this embodiment, after analyzing the change trend of the temperature fluctuation data, the system optimizes the parameters in the weight definition model according to this trend, so that the weight model is more suitable for the current cooling needs. For example, when the change trend shows that the frequency and amplitude of temperature fluctuations are both increasing, the system will increase the adjustment range of the weight value to respond to temperature changes more quickly, reduce the lag interval, and increase the sensitivity of the cooling response; if the change trend shows that the temperature fluctuation tends to be stable, the system will appropriately reduce the adjustment range of the weight value to reduce the cooling response frequency, avoid frequent start and stop of the cooling system, and thus improve the stability of the system.

[0048] In summary, after re-determining the hysteresis interval range, the temperature fluctuation feedback data can be obtained in real time according to the preset acquisition cycle, and the temperature fluctuation feedback data in the current cycle can be compared with the temperature fluctuation data before adjustment. In this way, the system can analyze the changing trend of the temperature fluctuation data based on the comparison of the fluctuation frequency and the fluctuation amplitude. Based on this changing trend, the system can optimize the weight definition algorithm model to make the adjustment of the temperature control system more accurate, so that when the temperature fluctuation increases, the hysteresis interval can be effectively reduced, the sensitivity of the cooling response can be improved, and when the temperature fluctuation tends to be stable, a larger hysteresis interval can be maintained to reduce the frequent start and stop of the cooling system. This process can dynamically optimize and adjust the weight model according to the temperature fluctuation situation, improve the stability of the system operation and the accuracy of the cooling operation.

[0049] In one embodiment, if Figure 5 As shown, in step S63, that is, according to the change trend, the step of optimizing the weight definition algorithm model includes: S631, analyze the change trend, determine the corresponding trend direction and trend slope; in this embodiment, after obtaining the temperature fluctuation feedback data and the temperature fluctuation data before adjustment, the system analyzes the trend of temperature fluctuation by comparing the data changes between different acquisition cycles. The trend direction indicates the direction of increase or decrease of temperature fluctuation. For example, if the temperature fluctuation frequency and amplitude of the current cycle are higher than those of the previous cycle, the trend direction is "increasing", indicating that the temperature fluctuation increases; on the contrary, if the temperature fluctuation frequency and amplitude of the current cycle are lower than those of the previous cycle, the trend direction is "decreasing". The trend slope indicates the rate of increase or decrease of temperature fluctuation, that is, the severity of the increase or decrease. For example, if the fluctuation amplitude rises rapidly from 1°C to 5°C between two cycles, the slope is large, indicating that the fluctuation change is more severe; if the fluctuation amplitude does not change much in several consecutive cycles, the slope is small, indicating that the fluctuation is relatively stable. Through the analysis of the trend direction and slope, the system can accurately judge the change of temperature fluctuation and provide a basis for subsequent adjustments.

[0050] S632. According to the trend and the trend slope, determine the definition parameters corresponding to the weight definition algorithm model, and the definition parameters are used to define the first adjustment formula and / or the second adjustment formula. In this embodiment, the system uses the trend and the trend slope obtained in the previous step to dynamically adjust the parameter settings in the weight definition algorithm model so that the model is more in line with the current temperature control requirements. For example, if the trend is rising and the slope is large, it means that the temperature fluctuation increases and changes rapidly. The system will increase the weight value in the first adjustment formula or the second adjustment formula, so that the cooling system responds to temperature changes more sensitively and improves the cooling strength; if the trend is falling and the slope is small, it means that the temperature fluctuation tends to be stable, and the system appropriately reduces the weight value to reduce the cooling frequency and reduce the system energy consumption. The definition parameters are realized by adjusting the weight coefficient or reaction coefficient in the formula, so that the cooling control strategy of the system can flexibly adapt to the temperature change trend, thereby achieving a rapid cooling response when the temperature fluctuation is large, reducing frequent operations when the fluctuation tends to be stable, and improving the system stability and cooling effect.

[0051] Furthermore, suppose that the temperature fluctuation feedback data of the LED power supply test system in multiple acquisition cycles are as follows: in the previous cycle: the temperature fluctuation amplitude is 2°C, and the fluctuation frequency is 5 times; in the current cycle: the temperature fluctuation amplitude rises to 5°C, and the fluctuation frequency increases to 10 times. From the data, it can be seen that the amplitude and frequency of temperature fluctuations have increased significantly, so the system needs to adjust the cooling control strategy to more effectively cope with this change; The system first compares the temperature fluctuation data of the current cycle and the previous cycle, and analyzes the trend and slope of the temperature fluctuation. The temperature fluctuation amplitude of the current cycle increases from 2°C in the previous cycle to 5°C, and the fluctuation frequency increases from 5 times to 10 times. Based on this change, the system determines that the trend is rising, indicating that the temperature fluctuation is increasing. The system further calculates the trend slope: the fluctuation amplitude change rate is (5°C-2°C) / 2°C=1.5, and the fluctuation frequency change rate is (10-5) / 5=1. These two slope values ​​indicate that the temperature fluctuation increases rapidly, so the system needs to increase the cooling response to suppress the further increase in temperature; the system adjusts the relevant parameters in the weight definition algorithm model based on the upward trend and the larger slope value obtained from the analysis to enhance the sensitivity of the cooling system. For example, the system decides to increase the definition parameters of the weight coefficient in the first adjustment formula so that the cooling system can respond to temperature changes more sensitively. Assuming that the original weight coefficient is 1.0, based on the upward trend and the larger slope value, the system increases the weight coefficient to 1.5. This adjustment will increase the cooling response. For example, if the current first adjustment weight was 5 before, then after the new weight coefficient is calculated, the first adjustment weight will become 5×1.5=7.5. This adjustment will enable the cooling system to reduce the temperature of the LED power supply test system more quickly, effectively respond to rapidly increasing temperature fluctuations, and avoid system instability due to overheating. In summary, after analyzing the trend of change, the weight definition algorithm model can be further optimized by determining the trend direction and trend slope. By analyzing the trend of change, the system can determine whether the temperature fluctuation is rising or falling, as well as the speed of change of the fluctuation amplitude, thereby generating the corresponding trend direction and trend slope. According to different trend directions and slopes, the system can optimize the definition parameters of the first adjustment formula or the second adjustment formula so that the weight definition model can flexibly adapt to temperature fluctuations. For example, when the temperature fluctuation trend rises and the amplitude of change is large, the system can increase the first adjustment weight and reduce the hysteresis interval to speed up the cooling response speed; when the temperature fluctuation decreases, the weight adjustment amplitude is appropriately reduced, the hysteresis interval is expanded to reduce the cooling frequency, thereby improving the operating stability and durability of the cooling system.

[0052] In one embodiment, if Figure 6 As shown, before step S10, that is, before the step of determining the hysteresis interval of the temperature sensing analysis system, it also includes: S01. Obtain the number of starts and stops of the solenoid valve, which is used to control the circulation of coolant in the cooling system; in this embodiment, obtaining the number of starts and stops of the solenoid valve means that the system monitors the operating status of the solenoid valve and records the total number of times the solenoid valve is started and stopped during the cooling process. The solenoid valve is used to control the circulation of coolant in the cooling system, and the number of starts and stops can reflect the response frequency of the cooling system. When the solenoid valve starts and stops frequently, it indicates that the hysteresis interval setting of the cooling system is unreasonable, resulting in frequent inflow and stop of coolant, thereby increasing the mechanical wear of the solenoid valve and reducing the stability of the system. By obtaining the number of starts and stops of the solenoid valve, the system can determine whether it is necessary to adjust the cooling control strategy to reduce the impact of frequent starts and stops on the system.

[0053] S02. Determine whether the number of starts and stops of the solenoid valve exceeds the preset start and stop threshold. If not, continue to judge. In this embodiment, determining whether the number of starts and stops of the solenoid valve exceeds the preset start and stop threshold means that after obtaining the number of starts and stops of the solenoid valve, the system compares the number with the preset start and stop threshold. The preset start and stop threshold is an upper limit value determined based on the working life of the solenoid valve and the design requirements of the cooling system, and is used to limit the maximum number of starts and stops allowed by the solenoid valve within a set time. If the number of starts and stops of the solenoid valve does not exceed the preset threshold, the system believes that the operating frequency of the cooling system is within a reasonable range, the cooling effect is stable, and no adjustment is required, so the monitoring state continues to be maintained; if the number of starts and stops exceeds the threshold, it means that the cooling system will have the problem of over-frequency starts and stops, and the system will further adjust the hysteresis interval to extend the working time and stability of the solenoid valve.

[0054] S03. If exceeded, the preset temperature threshold of the temperature sensing and analysis system is obtained; in this embodiment, if the number of starts and stops of the solenoid valve exceeds the preset threshold, the system will obtain the preset temperature threshold of the temperature sensing and analysis system in order to reset the hysteresis interval. The preset temperature threshold refers to the reference temperature value for the system to start and stop cooling under normal working conditions. For example, if the preset temperature threshold is 50°C, the system starts cooling when the temperature is higher than 50°C and stops cooling when it is lower than 50°C. The purpose of obtaining the preset temperature threshold is to reset the hysteresis interval with this value as the center, so as to appropriately adjust the start and stop conditions of the cooling system in the case of high-frequency start and stop, so as to reduce unnecessary start and stop operations.

[0055] S04, establish a mapping relationship based on the hysteresis interval of the number of starts and stops of the solenoid valve, determine the upper limit and lower limit corresponding to the preset temperature threshold, and the upper limit and lower limit are used to determine the hysteresis interval with the preset temperature threshold as the intermediate value. In this embodiment, the mapping relationship is established based on the hysteresis interval of the number of starts and stops of the solenoid valve, which means that when the number of starts and stops of the solenoid valve exceeds the preset threshold, the system will determine a new hysteresis interval range according to the current number of starts and stops, so as to reduce the start and stop frequency of the solenoid valve. Specifically, the system takes the preset temperature threshold as the intermediate value, and sets the upper and lower limits according to the number of starts and stops. For example, when the number of starts and stops is high, the upper and lower limits of the hysteresis interval are appropriately expanded (for example, 50°C±3°C, i.e., 47°C to 53°C), so that the cooling system will not start and stop frequently when the temperature fluctuates slightly. Through the adjustment of this hysteresis interval, the system can effectively reduce the number of starts and stops of the solenoid valve, thereby extending the working life of the cooling system and improving the stability of operation. The establishment of this mapping relationship ensures the dynamic adjustment of the hysteresis interval, making the control of the cooling system more reasonable and durable.

[0056] Furthermore, during the LED test, the system will power on the LED power supply under specific load conditions to observe its working performance, heat dissipation effect and stability. However, since the LED power supply will generate a lot of heat during power-on operation, the temperature will gradually rise, especially under high load conditions. In order to prevent unstable performance or damage caused by excessive temperature, the system is equipped with a cooling system, which controls the circulation of coolant through solenoid valves to achieve cooling. The function of the solenoid valve is to accurately open or close the flow of coolant according to the monitoring results of the temperature sensor. When the temperature exceeds the set value, the coolant starts to flow in, helping the system to cool down quickly, ensuring that the LED remains within a safe temperature range during the test, thereby improving the accuracy of the test and the protection of the equipment.

[0057] In summary, before the hysteresis interval range is re-determined, the upper and lower limits of the hysteresis interval can be dynamically adjusted based on the change in the number of solenoid valve starts and stops. When the number of solenoid valve starts and stops exceeds the preset start and stop threshold, the system obtains the preset temperature threshold of the temperature sensing analysis system, and establishes a mapping relationship of the hysteresis interval according to the number of solenoid valve starts and stops. The upper and lower limits of the hysteresis interval are dynamically set with the preset temperature threshold as the middle value. This method can expand the hysteresis interval when the solenoid valve starts and stops frequently, thereby effectively reducing the excessive response of the cooling system, reducing the wear of the solenoid valve and extending the service life of the equipment, so that the temperature control of the system has higher stability and reliability while maintaining responsiveness.

[0058] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0059] In one embodiment, a control device based on an LED power supply test system is provided, and the control device based on an LED power supply test system corresponds one-to-one to a control method based on an LED power supply test system in the above embodiment. Figure 7 As shown, the control device based on the LED power supply test system includes a first determination module, a first judgment module, a first acquisition module, a second judgment module and a calculation module. The functional modules are described in detail as follows: A first determination module is used to determine a hysteresis interval of a temperature perception analysis system, wherein the temperature perception analysis system is used to collect temperature data of the LED power supply test system during the test process, and to compare the temperature data with the hysteresis interval to generate a corresponding comparison result; A first judgment module is used to obtain the current load state of the LED power supply test system in real time, judge whether the current load state is greater than a predetermined preset state threshold, and if not, continue to judge; if greater than the predetermined preset state threshold, calculate the state difference between the current load state and the preset state threshold, and determine the corresponding first adjustment weight according to the state difference; A first acquisition module, used for acquiring coolant flow data of a cooling system, wherein the cooling system is used for determining whether to cool the LED power supply test system according to the comparison result; a second judgment module, configured to judge whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, to re-determine the interval range of the hysteresis interval based on the first adjustment weight; A calculation module is used to calculate the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval if it does not fall into the determined preset flow interval, determine the corresponding second adjustment weight according to the flow difference, and redetermine the interval range of the hysteresis interval based on the first adjustment weight and the second adjustment weight.

[0060] Optionally, the first judgment module includes: A first definition unit, configured to define an algorithm model based on a weight, define a first adjustment formula, and a multi-level first priority queue; A first generating unit, used for substituting the state difference into a first adjustment formula to generate a corresponding first weight value; a first matching unit, configured to match a first priority queue corresponding to the first weight value, generate a corresponding first matching result, and determine a first weight coefficient of the first weight value according to the first matching result; a first determining unit, configured to determine a corresponding first adjustment weight according to the first weight value and the first weight coefficient; Optionally, the second judgment module includes: A second definition unit is used to define a second adjustment formula based on a weight definition algorithm model, wherein the second adjustment formula includes at least a positive state formula and a negative state formula, and defines a multi-level second priority queue corresponding to the positive state formula, and a multi-level third priority queue corresponding to the negative state formula; A second judgment unit, used for judging the difference state of the flow difference; a second generating unit, configured to substitute the flow difference into the positive state formula to generate a corresponding second weight value if the difference state is a positive state; A second matching unit, configured to match a second priority queue corresponding to the second weight value, generate a corresponding second matching result, and determine a second weight coefficient of the second weight value according to the second matching result; A third generating unit, configured to input the flow difference into the negative state formula to generate a corresponding third weight value if the difference state is a negative state; A third matching unit, configured to match a third priority queue corresponding to the third weight value, generate a corresponding third matching result, and determine a third weight coefficient of the third weight value according to the third matching result; a third determining unit, configured to determine a corresponding second adjustment weight according to the second weight value and the second weight coefficient, or according to the third weight value and the third weight coefficient; Optionally, the control device based on the LED power supply test system further includes: A second acquisition module, used to acquire temperature fluctuation feedback data according to a preset acquisition cycle, wherein the temperature fluctuation feedback data at least includes a temperature fluctuation frequency and a temperature fluctuation amplitude; An analysis module, used for substituting the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment into the weight definition algorithm model to analyze the change trend between the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment; An optimization module, used for optimizing the weight definition algorithm model according to the change trend; Optionally, the optimization module includes: An analysis unit, used to analyze the change trend and determine the corresponding trend direction and trend slope; a fourth determining unit, configured to determine, according to the trend direction and the trend slope, definition parameters corresponding to the weight definition algorithm model, wherein the definition parameters are used to define the first adjustment formula and / or the second adjustment formula; Optionally, the control device based on the LED power supply test system further includes: A third acquisition module is used to acquire the number of starts and stops of the solenoid valve, where the solenoid valve is used to control the circulation of coolant in the cooling system; A third judgment module is used to judge whether the start and stop times of the solenoid valve exceed a preset start and stop times threshold, and if not, continue to judge; A fourth acquisition module is used to obtain a preset temperature threshold of the temperature perception analysis system if it is exceeded; The second determination module is used to establish a mapping relationship based on the hysteresis interval of the number of starts and stops of the solenoid valve, and determine the upper limit value and the lower limit value corresponding to the preset temperature threshold. The upper limit value and the lower limit value are used to determine the hysteresis interval with the preset temperature threshold as the intermediate value.

[0061] The specific definition of a control device based on an LED power supply test system can be found in the definition of a control method based on an LED power supply test system mentioned above, which will not be repeated here. Each module in the above-mentioned control device based on an LED power supply test system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0062] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a control method based on an LED power supply test system is implemented.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S10, determining a hysteresis interval of a temperature sensing and analysis system, where the temperature sensing and analysis system is used to collect temperature data of the LED power supply test system during the test process, and to compare the temperature data with the hysteresis interval to generate a corresponding comparison result; S20, obtaining the current load state of the LED power supply test system in real time, judging whether the current load state is greater than a predetermined preset state threshold, and if not, continuing to judge, and if greater than the predetermined preset state threshold, calculating the state difference between the current load state and the preset state threshold, and determining the corresponding first adjustment weight according to the state difference; S30, obtaining coolant flow data of a cooling system, where the cooling system is used to determine whether to cool the LED power supply test system according to the comparison result; S40, determining whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, re-determining the interval range of the hysteresis interval based on the first adjustment weight; S50. If it does not fall into the determined preset flow interval, the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval is calculated, the corresponding second adjustment weight is determined according to the flow difference, and based on the first adjustment weight and the second adjustment weight, the interval range of the hysteresis interval is re-determined.

[0064] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: S10, determining a hysteresis interval of a temperature sensing and analysis system, where the temperature sensing and analysis system is used to collect temperature data of the LED power supply test system during the test process, and to compare the temperature data with the hysteresis interval to generate a corresponding comparison result; S20, obtaining the current load state of the LED power supply test system in real time, judging whether the current load state is greater than a predetermined preset state threshold, and if not, continuing to judge, and if greater than the predetermined preset state threshold, calculating the state difference between the current load state and the preset state threshold, and determining the corresponding first adjustment weight according to the state difference; S30, obtaining coolant flow data of a cooling system, where the cooling system is used to determine whether to cool the LED power supply test system according to the comparison result; S40, determining whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, re-determining the interval range of the hysteresis interval based on the first adjustment weight; S50. If it does not fall into the determined preset flow interval, the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval is calculated, the corresponding second adjustment weight is determined according to the flow difference, and based on the first adjustment weight and the second adjustment weight, the interval range of the hysteresis interval is re-determined.

[0065] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0066] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A control method based on an LED power supply test system, characterized in that: The control method based on the LED power supply test system includes: Determine a hysteresis interval of a temperature sensing and analysis system, wherein the temperature sensing and analysis system is used to collect temperature data of the LED power supply test system during the test process and compare the temperature data with the hysteresis interval to generate a corresponding comparison result; Acquire the current load state of the LED power supply test system in real time, judge whether the current load state is greater than a preset state threshold, if not greater than the preset state threshold, continue to judge, if greater than the preset state threshold, calculate the state difference between the current load state and the preset state threshold, and determine the corresponding first adjustment weight according to the state difference; Acquire coolant flow data of a cooling system, wherein the cooling system is used to determine whether to cool the LED power supply test system according to the comparison result; Determining whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, re-determining the interval range of the hysteresis interval based on the first adjustment weight; If it does not fall within the determined preset flow interval, the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval is calculated, and the corresponding second adjustment weight is determined based on the flow difference, and based on the first adjustment weight and the second adjustment weight, the interval range of the hysteresis interval is redetermined.

2. A control method based on an LED power supply test system according to claim 1, characterized in that: The step of determining the corresponding first adjustment weight according to the state difference includes: Based on the weight definition algorithm model, a first adjustment formula and a multi-level first priority queue are defined; Substituting the state difference into a first adjustment formula to generate a corresponding first weight value; Matching a first priority queue corresponding to the first weight value, generating a corresponding first matching result, and determining a first weight coefficient of the first weight value according to the first matching result; A corresponding first adjustment weight is determined according to the first weight value and the first weight coefficient.

3. A control method based on an LED power supply test system according to claim 2, characterized in that: The step of determining the corresponding second adjustment weight according to the flow difference includes: Based on the weight definition algorithm model, a second adjustment formula is defined, wherein the second adjustment formula includes at least a positive state formula and a negative state formula, and a multi-level second priority queue corresponding to the positive state formula and a multi-level third priority queue corresponding to the negative state formula are defined; Determining a difference state of the flow difference; If the difference state is a positive state, the flow difference is substituted into the positive state formula to generate a corresponding second weight value; Matching a second priority queue corresponding to the second weight value, generating a corresponding second matching result, and determining a second weight coefficient of the second weight value according to the second matching result; If the difference state is a negative state, the flow difference is input into the negative state formula to generate a corresponding third weight value; Matching a third priority queue corresponding to the third weight value, generating a corresponding third matching result, and determining a third weight coefficient of the third weight value according to the third matching result; A corresponding second adjustment weight is determined according to the second weight value and the second weight coefficient, or according to the third weight value and the third weight coefficient.

4. A control method based on an LED power supply test system according to claim 3, characterized in that: After the step of re-determining the interval range of the hysteresis interval, the method further includes: Acquire temperature fluctuation feedback data according to a preset collection cycle, wherein the temperature fluctuation feedback data at least includes temperature fluctuation frequency and temperature fluctuation amplitude; Substituting the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment into the weight definition algorithm model to analyze the change trend between the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment; According to the change trend, the weight definition algorithm model is optimized.

5. A control method based on an LED power supply test system according to claim 4, characterized in that: The step of optimizing the weight definition algorithm model according to the change trend includes: Analyze the change trend to determine the corresponding trend direction and trend slope; According to the trend direction and the trend slope, definition parameters corresponding to the weight definition algorithm model are determined, and the definition parameters are used to define the first adjustment formula and / or the second adjustment formula.

6. A control method based on an LED power supply test system according to claim 1, characterized in that: Before the step of determining the hysteresis interval of the temperature sensing analysis system, the method further includes: Obtaining the number of starts and stops of the solenoid valve, wherein the solenoid valve is used to control the circulation flow of the coolant in the cooling system; Determine whether the start and stop times of the solenoid valve exceed a preset start and stop times threshold, and if not, continue to determine; If exceeded, the preset temperature threshold of the temperature sensing analysis system is obtained; A mapping relationship is established based on the hysteresis interval of the number of starts and stops of the solenoid valve, and an upper limit value and a lower limit value corresponding to the preset temperature threshold are determined. The upper limit value and the lower limit value are used to determine the hysteresis interval with the preset temperature threshold as the intermediate value.

7. A control device based on an LED power supply test system, characterized in that: The control device based on the LED power supply test system comprises: A first determination module is used to determine a hysteresis interval of a temperature perception analysis system, wherein the temperature perception analysis system is used to collect temperature data of the LED power supply test system during the test process, and to compare the temperature data with the hysteresis interval to generate a corresponding comparison result; A first judgment module is used to obtain the current load state of the LED power supply test system in real time, judge whether the current load state is greater than a predetermined preset state threshold, and if not, continue to judge; if greater than the predetermined preset state threshold, calculate the state difference between the current load state and the preset state threshold, and determine the corresponding first adjustment weight according to the state difference; A first acquisition module, used for acquiring coolant flow data of a cooling system, wherein the cooling system is used for determining whether to cool the LED power supply test system according to the comparison result; a second judgment module, configured to judge whether the coolant flow rate data falls within a predetermined preset flow rate interval, and if so, to re-determine the interval range of the hysteresis interval based on the first adjustment weight; A calculation module is used to calculate the flow difference between the coolant flow data and the critical value corresponding to the preset flow interval if it does not fall into the determined preset flow interval, determine the corresponding second adjustment weight according to the flow difference, and redetermine the interval range of the hysteresis interval based on the first adjustment weight and the second adjustment weight.

8. The control device based on the LED power supply test system according to claim 7, characterized in that: The first judgment module includes: A first definition unit, configured to define an algorithm model based on a weight, define a first adjustment formula, and a multi-level first priority queue; A first generating unit, used for substituting the state difference into a first adjustment formula to generate a corresponding first weight value; a first matching unit, configured to match a first priority queue corresponding to the first weight value, generate a corresponding first matching result, and determine a first weight coefficient of the first weight value according to the first matching result; a first determining unit, configured to determine a corresponding first adjustment weight according to the first weight value and the first weight coefficient; The second judgment module includes: A second definition unit is used to define a second adjustment formula based on a weight definition algorithm model, wherein the second adjustment formula includes at least a positive state formula and a negative state formula, and defines a multi-level second priority queue corresponding to the positive state formula, and a multi-level third priority queue corresponding to the negative state formula; A second judgment unit, used for judging the difference state of the flow difference; a second generating unit, configured to substitute the flow difference into the positive state formula to generate a corresponding second weight value if the difference state is a positive state; A second matching unit, configured to match a second priority queue corresponding to the second weight value, generate a corresponding second matching result, and determine a second weight coefficient of the second weight value according to the second matching result; A third generating unit, configured to input the flow difference into the negative state formula to generate a corresponding third weight value if the difference state is a negative state; A third matching unit, configured to match a third priority queue corresponding to the third weight value, generate a corresponding third matching result, and determine a third weight coefficient of the third weight value according to the third matching result; a third determining unit, configured to determine a corresponding second adjustment weight according to the second weight value and the second weight coefficient, or according to the third weight value and the third weight coefficient; The control device based on the LED power supply test system also includes: A second acquisition module, used to acquire temperature fluctuation feedback data according to a preset acquisition cycle, wherein the temperature fluctuation feedback data at least includes a temperature fluctuation frequency and a temperature fluctuation amplitude; An analysis module, used for substituting the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment into the weight definition algorithm model to analyze the change trend between the temperature fluctuation feedback data and the pre-recorded temperature fluctuation data before adjustment; An optimization module, used for optimizing the weight definition algorithm model according to the change trend; The optimization module includes: An analysis unit, used to analyze the change trend and determine the corresponding trend direction and trend slope; a fourth determining unit, configured to determine, according to the trend direction and the trend slope, definition parameters corresponding to the weight definition algorithm model, wherein the definition parameters are used to define the first adjustment formula and / or the second adjustment formula; The control device based on the LED power supply test system also includes: A third acquisition module is used to acquire the number of starts and stops of the solenoid valve, where the solenoid valve is used to control the circulation of coolant in the cooling system; A third judgment module is used to judge whether the start and stop times of the solenoid valve exceed a preset start and stop times threshold, and if not, continue to judge; A fourth acquisition module is used to obtain a preset temperature threshold of the temperature perception analysis system if it is exceeded; The second determination module is used to establish a mapping relationship based on the hysteresis interval of the number of starts and stops of the solenoid valve, and determine the upper limit value and the lower limit value corresponding to the preset temperature threshold. The upper limit value and the lower limit value are used to determine the hysteresis interval with the preset temperature threshold as the intermediate value.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the control method based on the LED power supply test system as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a control method based on an LED power supply test system as claimed in any one of claims 1 to 6 are implemented.

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