Intelligent heat dissipation method and system for outdoor advertising machine

Abstract

The present invention relates to the field of electrical data processing technology, and specifically to an intelligent heat dissipation method and system for outdoor advertising machines. The method uses the data point with a downward trend in the power supply monitoring curve as the first analysis point; screens the maximum value points on the temperature monitoring curves of various heat sources to obtain the second analysis point; and obtains the first correction value based on the first analysis point. The possibility of the main heat source is obtained and the main heat source is screened out. The extreme point with obvious changes on the heat source temperature monitoring curve of the main heat source is used as the second analysis point for analysis to obtain the response delay. The state transfer matrix is ​​adjusted according to the final correction amount, and the internal temperature monitoring data is compensated by the Kalman filter algorithm to obtain an accurate heat dissipation strategy. The present invention effectively adjusts the state transfer matrix in the Kalman filter algorithm, and then accurately compensates the internal temperature data of the advertising machine to obtain an effective heat dissipation strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical data processing, and in particular to an intelligent heat dissipation method and system for an outdoor advertising machine. Background Art

[0002] In order to maintain normal operation, outdoor advertising machines usually use a cooling system to control the temperature during operation. The Kalman filter algorithm uses the internal temperature data of the advertising machine as the state transition model quantity and the power supply power as the observation quantity. The state transfer matrix in the algorithm can eliminate the observation error in the temperature monitoring process, thereby improving the response of the cooling system. Therefore, using the Kalman filter algorithm to process the state data of the advertising machine can improve the response of the cooling system to the operating state and effectively compensate for the error caused by the delay in the cooling control process.

[0003] However, the state transfer matrix in the Kalman filter algorithm is obtained based on a simplified model or general assumption in the initial state. The algorithm assumes that the temperature change and power response of the system are linear. But in fact, there are many heat sources inside the advertising machine, such as electronic circuit heat source, screen heat source, etc., which leads to a certain time lag between temperature change and heat dissipation control. Due to the existence of these additional heat inputs, the obtained state transfer matrix has obvious errors, and the resulting control results have errors. Summary of the invention

[0004] In order to solve the technical problem that the state transfer matrix has errors in the process of eliminating the observation error in the temperature monitoring process of the outdoor advertising machine by the Kalman filter algorithm, which leads to poor compensation effect of the temperature monitoring data and ultimately unreasonable heat dissipation control strategy, the purpose of the present invention is to provide an intelligent heat dissipation method for outdoor advertising machines, and the technical scheme adopted is as follows:

[0005] The present invention proposes an intelligent heat dissipation method for an outdoor advertising machine, the method comprising:

[0006] Obtaining a state curve of the outdoor advertising machine during operation within a monitoring period, wherein the state curve includes a power supply monitoring curve, a heat dissipation system power curve, various heat source temperature monitoring curves, and an internal temperature monitoring curve of the outdoor advertising machine; taking a data point with a downward trend in the power supply monitoring curve as a first analysis point; screening the maximum value points on the various heat source temperature monitoring curves to obtain a second analysis point;

[0007] Obtaining a first correction value of a state transfer matrix in a Kalman filter algorithm according to a difference in a data change trend on the internal temperature monitoring curve at the first analysis point and a corresponding time point;

[0008] For the heat source temperature monitoring curve of each heat source, the possibility of each heat source being the main heat source is obtained according to the difference between the data on the internal temperature monitoring curve at the second analysis point and the corresponding time point, and the difference in the change trend between the heat source temperature monitoring curve and the internal temperature monitoring curve;

[0009] Screening out the main heat source according to the possibility; obtaining a response delay point of the second analysis point on the power curve of the heat dissipation system for each of the second analysis points of the main heat source; obtaining a response delay of the main heat source according to the possibility of the main heat source and the time interval between the second analysis point and the response delay point;

[0010] A final correction amount of the state transfer matrix is ​​obtained according to the difference between the first correction value and the response delay, the state transfer matrix in the Kalman filter algorithm is adjusted according to the final correction amount, and the temperature monitoring data inside the advertising machine is compensated according to the Kalman filter algorithm, and the temperature state of the advertising machine is judged according to the compensated temperature data and a corresponding heat dissipation strategy is executed.

[0011] Furthermore, the method for obtaining the first analysis point includes:

[0012] The tangent slope of each data point in the power monitoring curve is obtained, and the point where the tangent slope is negative is used as the first analysis point.

[0013] Furthermore, the method for acquiring the second analysis point includes:

[0014] For each heat source temperature monitoring curve, the maximum points are sorted according to the temperature value to obtain a temperature sorting sequence; in the temperature sorting sequence, the position with the largest difference between adjacent elements is selected as the splitting point to obtain two subsequences, and the maximum point in the subsequence with the largest temperature value is used as the second analysis point.

[0015] Furthermore, the method for obtaining the first correction value includes:

[0016] Obtaining an average value of the difference in the tangent slope on the internal temperature monitoring curve at all first analysis points and the corresponding time point, and recording it as the first data change trend difference;

[0017] Obtaining the difference between the average tangent slope of all data points in the power monitoring curve of the power supply and the average tangent slope of all data points on the internal temperature monitoring curve, and recording it as the second data change trend difference;

[0018] The first data change trend difference is multiplied by the data change trend difference to obtain the first correction value.

[0019] Furthermore, the method for obtaining the possibility includes:

[0020] For the heat source temperature monitoring curve of each heat source, the average value of the difference between the data on the internal temperature monitoring curve at all second analysis points and the corresponding time point is obtained, and recorded as the local characteristic data difference; the variance difference between the heat source temperature monitoring curve and the internal temperature monitoring curve is taken as the change trend difference; the product of the local characteristic data difference and the change trend difference is negatively correlated to obtain the possibility.

[0021] Furthermore, the screening method of the main heat source includes:

[0022] The possibilities are sorted according to the numerical values ​​to obtain a possibility sorting sequence; in the possibility sorting sequence, the position with the largest difference between adjacent elements is selected as the split point to obtain two subsequences, and the heat source corresponding to the possibility in the subsequence with the largest possibility is used as the main heat source.

[0023] Furthermore, the method for obtaining the response delay point includes:

[0024] According to the screening method of the second analysis point, a third analysis point on the power curve of the heat dissipation system is obtained;

[0025] For each of the second analysis points of the main heat source, on the heat dissipation system power curve, the first third analysis point after the time point corresponding to the second analysis point is used as the response delay point.

[0026] Furthermore, the method for obtaining the response delay includes:

[0027] The average time interval between all the second analysis points and the response delay point is obtained, and the product of the average time interval and the possibility of the main heat source is used as the response delay of the main heat source.

[0028] Furthermore, the method for obtaining the final correction amount includes:

[0029] The average response delay of all main heat sources is obtained, and the difference between the first correction value and the average response delay is used as the final correction value.

[0030] The present invention proposes an intelligent heat dissipation system for an outdoor advertising machine, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the intelligent heat dissipation method for an outdoor advertising machine.

[0031] The present invention has the following beneficial effects:

[0032] The embodiment of the present invention takes into account that the power data in the Kalman filter algorithm is an observed quantity and the internal temperature data is a state quantity. For the measurement of heat dissipation error, the stage of temperature increase cannot accurately express the damping effect of the advertising machine heat dissipation system on the internal temperature. Therefore, it is necessary to evaluate the state transfer quantity from the stage of observed quantity reduction, so the first correction value is obtained based on the first analysis point. Further considering that there are many heat sources in the advertising machine, and the methods of heat transfer of different heat sources are inconsistent, resulting in a certain delay in the perception of heat from different sources to the heat dissipation system. In order to eliminate the error of multiple heat sources to the state quantity, the difference between the temperature monitoring curves of various heat sources and the internal temperature monitoring curve is further used to characterize the degree of heat provided by the heat source to the advertising machine, and the possibility of the main heat source is obtained and the main heat source is screened out. Then, the response delay of the heat dissipation system to the main heat source is analyzed, and the extreme point with obvious changes on the heat source temperature monitoring curve of the main heat source is used as the second analysis point for analysis, and the response delay point on the power monitoring curve is found, and the response delay is obtained. The response delay can be used to further correct the first correction value to obtain the final correction amount, that is, the greater the delay, the more the value of the state transfer matrix needs to be reduced to reflect a slower response; the less synchronized the response of the internal temperature and power is, the greater the temperature interference from other heat sources is, and the more the value of the state transfer matrix needs to be increased to improve the response sensitivity. Then, the state transfer matrix is ​​adjusted according to the final correction amount, and the internal temperature monitoring data is compensated using the Kalman filter algorithm to obtain an accurate heat dissipation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A flow chart of an intelligent heat dissipation method for an outdoor advertising machine provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of an intelligent heat dissipation method and system for outdoor advertising machines proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0037] The specific scheme of an intelligent heat dissipation method and system for an outdoor advertising machine provided by the present invention is described in detail below with reference to the accompanying drawings.

[0038] The embodiment of the present invention aims to analyze the status data monitored in real time by the outdoor advertising machine, and then determine the accurate state transfer matrix in the Kalman filter algorithm. The advertising machine targeted by the embodiment of the present invention needs to be installed with a temperature sensor, and the model may be the temperature sensor DS18B20. At the same time, temperature sensors also need to be installed for various heat source positions of the advertising machine, such as the screen position, the internal circuit position and other heat source positions. For example, the screen position can collect temperature data through the built-in thermistor in the screen. A power sensor needs to be installed in the power line of the outdoor advertising machine to obtain the power data of the advertising machine during operation. After obtaining the monitoring data, it can be transmitted to the computing cloud through standard interfaces such as I2C, SPI, and UART. In the computing cloud, edge computing devices are used to process data in real time, and the heat dissipation strategy is determined based on the processed data and transmitted to the advertising machine for execution.

[0039] See also Figure 1 , which shows a flow chart of an intelligent heat dissipation method for an outdoor advertising machine provided by an embodiment of the present invention, the method comprising:

[0040] Step S1: obtaining a state curve of the outdoor advertising machine during operation within a monitoring period, the state curve including a power supply monitoring curve, various heat source temperature monitoring curves, and an internal temperature monitoring curve of the outdoor advertising machine; taking a data point with a downward trend in the power supply monitoring curve as a first analysis point; screening the maximum value points on various heat source temperature monitoring curves to obtain a second analysis point.

[0041] In the embodiment of the present invention, 20 minutes is set as the length of the monitoring time period, and the monitoring cycle can be further preset. The state curve within a monitoring time period is collected every other monitoring cycle, and analyzed to determine the heat dissipation strategy. It is ensured that the advertising machine can continue to operate in a normal state. The monitoring cycle can be set to one hour, and the sampling frequency within the monitoring time period can be set to collect once per second.

[0042] Because the present invention aims to analyze the error between the observed quantity and the state quantity in the Kalman filter algorithm, the observed quantity is power data, the state quantity is internal temperature data, and the temperature generated by each heat source is one of the reasons for the state transfer matrix error, so firstly obtain the power supply power monitoring curve, each heat source temperature monitoring curve, and internal temperature monitoring curve within the monitoring time period. The heat dissipation system power curve is used to evaluate the response delay of the heat dissipation system in the subsequent process.

[0043] It should be noted that in order to facilitate subsequent data processing, the data values ​​in the obtained data curves are dimensionless, that is, the influence of the dimension is not considered in the subsequent calculation process, and the data can also be normalized to further facilitate data calculation.

[0044] Generally speaking, the greater the load during the operation of the advertising machine, the higher the temperature will be, that is, the change in power and internal temperature tends to change linearly. However, due to the influence of heat dissipation error, the temperature rise stage cannot accurately express the damping effect of the advertising machine's heat dissipation system on the internal temperature. Therefore, it is necessary to evaluate the internal temperature state transition quantity from the observation quantity reduction, that is, the power reduction stage. Therefore, the data point with a downward trend in the power monitoring curve is first used as the first analysis point.

[0045] Further considering that the maximum point on the curve represents an obvious growth trend at that location, in order to further analyze the impact of various heat sources on the advertising machine, it is necessary to screen the maximum points on the temperature monitoring curves of various heat sources to obtain the second analysis point. That is, the screened second analysis point is the point with a more obvious change trend among the maximum points.

[0046] Preferably, in one embodiment of the present invention, the method for acquiring the first analysis point includes:

[0047] Obtain the tangent slope of each data point in the power monitoring curve. The tangent slope characterizes the change trend at the data point. If the tangent slope is positive, it means that the position is increasing, if it is negative, it means that the position is decreasing, and if it is 0, it means that the data at the position has no change. Therefore, the point with a negative tangent slope is used as the first analysis point.

[0048] Preferably, in one embodiment of the present invention, the method for acquiring the second analysis point includes:

[0049] For each heat source temperature monitoring curve, the maximum points are sorted according to the temperature value to obtain a temperature sorting sequence. In one embodiment of the present invention, the sorting order can be sorted in ascending order of data. In the temperature sorting sequence, the position with the largest difference between adjacent elements is selected as the split point to obtain two subsequences, and the maximum point in the subsequence with the largest temperature value is used as the second analysis point. That is, the maximum point in the subsequence with the largest temperature value is considered to be a data point with a drastic and obvious change.

[0050] In other embodiments of the present invention, the temperature values ​​of the maximum points on the heat source temperature monitoring curve may be segmented using the Otsu threshold to divide the maximum points into two categories, and the category with the larger temperature value is selected as the second analysis point category.

[0051] Step S2: Obtain a first correction value of the state transfer matrix in the Kalman filter algorithm according to the difference in the data change trend on the internal temperature monitoring curve at the first analysis point and the corresponding time point.

[0052] The internal temperature data is a state quantity, and the power data is an observed quantity. If the power supply is on a downward trend, and the temperature change trend inside the advertising machine does not correspond to it, it means that the advertising machine may be affected by heat accumulation or the presence of more heat from other heat sources, resulting in more additional heat in addition to the heat generated by the electronic components of the advertising machine itself. At this time, it means that the heat dissipation system needs a faster sensitivity to solve the situation of more additional heat source input, that is, the state transfer matrix should be increased. Because the first analysis point is the position of the downward trend on the power supply power monitoring curve, the embodiment of the present invention obtains the first correction value of the state transfer matrix in the Kalman filter algorithm based on the difference in the data change trend on the internal temperature monitoring curve at the first analysis point and the corresponding time point. That is, the greater the difference in the change trend, the greater the first correction value, which means that the data in the state transfer matrix should be increased.

[0053] Preferably, in one embodiment of the present invention, not only the difference in the change trend at the position of the first analysis point is taken into consideration, but also the change trend of the overall data is introduced. Then, the method for obtaining the first correction value includes:

[0054] Because there are multiple first analysis points, each analysis point can correspond to a data change trend difference, so the average value of the difference in the tangent slope of the internal temperature monitoring curve at all first analysis points and the corresponding time point is obtained, which is recorded as the first data change trend difference. The larger the difference in the first data change trend, the greater the difference between the change trend on the internal temperature monitoring curve and the power data under the downward trend, and the more likely there is a heat dissipation error.

[0055] The difference between the average tangent slope of all data points in the power monitoring curve and the average tangent slope of all data points on the internal temperature monitoring curve is obtained and recorded as the second data change trend difference. That is, the second data change trend difference represents the overall data change trend between the data curves of the two dimensions.

[0056] The first data change trend difference is multiplied by the data change trend difference to obtain a first correction value. The greater the difference between the two change data trends, the more likely the advertising machine is to be affected by the extra heat, and the greater the first correction value should be.

[0057] As an example, in one embodiment of the present invention, the formula for obtaining the first correction value is expressed as:

[0058] ; Where E represents the first correction value, m represents the number of the first analysis points, Indicates the tangent slope corresponding to the nth first analysis point on the power monitoring curve. It represents the tangent slope of the internal temperature monitoring curve at the time point corresponding to the nth first analysis point. is the average tangent slope of all data points on the power monitoring curve, It is the average tangent slope of all data points of the internal temperature monitoring curve.

[0059] Step S3: For the heat source temperature monitoring curve of each heat source, the possibility of each heat source being the main heat source is obtained based on the difference in data on the internal temperature monitoring curve at the second analysis point and the corresponding time point, as well as the difference in change trends between the heat source temperature monitoring curve and the internal temperature monitoring curve.

[0060] There are many sources of heat generated inside the advertising machine, and different heat sources transfer heat in different ways. For example, the heat generated by the power supply is transferred through the line, and the heat generated by the screen is transferred through radiation. Different heat transfer methods cause different delays in the cooling system's perception of it. For the errors caused by the delay, the errors caused by different heat inputs on the cooling system regulation are different. Some heat sources have little or no effect on the cooling system, so when performing data analysis, it is necessary to screen out these heat sources and analyze the heat sources with the main influence.

[0061] For the heat source temperature monitoring curve of each heat source, the second analysis point represents the point with obvious changes on the curve. The position of the second analysis point represents that the heat source has produced a more drastic instantaneous change. Therefore, the difference between the temperature of the heat source and the temperature data at the corresponding time point on the internal temperature monitoring curve can be analyzed based on the second analysis point. The larger the difference, the weaker the influence of the heat source on the overall temperature, and it is not the main heat source. Similarly, the difference in the change trend between the heat source temperature monitoring curve and the internal temperature monitoring curve can be further analyzed. The larger the difference in the change trend, the more dissimilar the data changes of the two curves, which means that the heat source will not affect the overall temperature, and it is less likely to be the main heat source. Therefore, based on the two differences, the possibility of each heat source being the main heat source can be obtained.

[0062] Preferably, in one embodiment of the present invention, the method for obtaining the possibility includes:

[0063] For the heat source temperature monitoring curve of each heat source, the average value of the difference between all the second analysis points and the data on the internal temperature monitoring curve at the corresponding time point is obtained, which is recorded as the local characteristic data difference. The second analysis point is the data point where the local position on the heat source temperature monitoring curve changes dramatically. The greater the difference between the second analysis point and the data at the corresponding position on the internal temperature monitoring curve, the more it indicates that the dramatic change in the temperature of the heat source will not affect the change in the overall temperature inside the advertising machine. Then, all the second analysis points are counted to obtain the local characteristic data difference. The greater the difference in the local characteristic data, the greater the difference in the temperature data of the two dimensions at the local position, and the lower the possibility that the heat source is the main heat source.

[0064] The variance difference between the heat source temperature monitoring curve and the internal temperature monitoring curve is taken as the trend difference. Similar to the local feature data difference, the greater the trend difference, the more out of sync the data change of the heat source is with the internal temperature monitoring curve, and the lower the possibility that the heat source is the main heat source.

[0065] The product of the local feature data difference and the change trend difference is negatively correlated to obtain the possibility.

[0066] It should be noted that the negative correlation mapping method can be implemented in the form of an inverse number or a function mapping method. In one embodiment of the present invention, the inverse of the product is used as the power of an exponential function with a natural constant as the base, and the result obtained is the negative correlation mapping of the product and the normalized result.

[0067] Step S4: Filter out the main heat sources according to the possibility; for each second analysis point of the main heat source, obtain the response delay point of the second analysis point on the power monitoring curve of the power supply; obtain the response delay of the main heat source according to the possibility of the main heat source and the time interval between the second analysis point and the response delay point.

[0068] The main heat sources can be screened out based on the possibility, that is, the main heat sources are all heat sources with greater possibility.

[0069] In one embodiment of the present invention, a method for screening the main heat source includes: sorting the possibilities according to numerical values ​​to obtain a possibility sorting sequence; in the possibility sorting sequence, selecting the position with the largest difference between adjacent elements as the splitting point to obtain two subsequences, and taking the heat source corresponding to the possibility in the subsequence with the largest possibility as the main heat source.

[0070] Similarly, in other embodiments of the present invention, the threshold segmentation method can also be used to process the possibilities and screen out the main heat sources, which will not be described in detail.

[0071] For the main heat source, the response delay point of each second analysis point can be obtained on the heat dissipation system power curve based on the second analysis point. That is, the response delay point indicates that the heat dissipation system responds to the drastic temperature change at the second analysis point of the main heat source at this point. Therefore, the greater the time interval between the second analysis point and the response delay point, the greater the response delay of the main heat source; further combined with the possibility of the main heat source, the greater the possibility, the greater the impact of the main heat source on the temperature of the advertising machine, and the greater the response delay. Therefore, the response delay of each main heat source can be obtained.

[0072] Preferably, in one embodiment of the present invention, the method for obtaining the response delay point includes:

[0073] According to the screening method of the second analysis point, a third analysis point on the heat dissipation system power curve is obtained. That is, the third analysis point is a maximum point with obvious and drastic changes on the heat dissipation system power curve.

[0074] For each second analysis point of the main heat source, on the heat dissipation system power curve, the first third analysis point after the time point corresponding to the second analysis point is taken as the response delay point. That is, the third analysis point where the power change first occurs after the time point of the second analysis point is considered to be the response measure taken by the heat dissipation system when the temperature of the heat source changes suddenly.

[0075] Preferably, in one embodiment of the present invention, the method for obtaining the response delay includes:

[0076] The average time interval between all second analysis points and the response delay point is obtained, and the product of the average time interval and the possibility of the main heat source is used as the response delay of the main heat source. That is, the larger the average time interval, the greater the possibility of the main heat source, indicating that the response delay of the cooling system to the heat source is greater.

[0077] Step S5: Obtain the final correction amount of the state transfer matrix according to the difference between the first correction value and the response delay, adjust the state transfer matrix in the Kalman filter algorithm according to the final correction amount, compensate the temperature monitoring data inside the advertising machine according to the Kalman filter algorithm, and judge the temperature state of the advertising machine according to the compensated temperature data and execute the corresponding heat dissipation strategy.

[0078] For the cooling system of the advertising machine, the greater the response delay, the higher the response delay of the cooling system to the main heat source, the greater the response error of the cooling system, and the need to adjust the state transfer matrix to respond to the slow release of heat to ensure that the heat from the main heat source can be completely dissipated; the larger the first correction value, the greater the amount of additional heat source, the faster the cooling system response is needed to release the heat quickly in time, and the data in the state transfer matrix should be increased. Therefore, the final correction amount of the state transfer matrix can be obtained based on the difference between the first correction value and the response delay.

[0079] Preferably, in one embodiment of the present invention, the average response delay of all major heat sources is obtained, and the difference between the first correction value and the average response delay is used as the final correction amount. It should be noted that in the embodiment of the present invention, in order to ensure the accuracy of data calculation, each type of data needs to be normalized according to the data range of its category. For the final correction amount, if it is less than 0, it means that the value of the state transfer matrix needs to be reduced to slow down the response to reflect the delay of heat dissipation; if it is equal to 0, it means that the state transfer matrix can be kept unchanged; if it is greater than 0, it means that the value in the state transfer matrix needs to be increased, and a more sensitive response is required to solve the temperature change caused by the additional heat increase.

[0080] The embodiment of the present invention can determine the scaling factor based on experiments. When the state transfer matrix needs to be changed, the final correction amount is adjusted to an appropriate range using the scaling factor, and then added to the initial state transfer matrix to obtain the corrected state transfer matrix. The specific method for obtaining the scaling factor can be determined through experiments and will not be limited or elaborated on.

[0081] In Kalman filtering, the state transfer matrix describes how the system state evolves at each time step, and an inaccurate state transfer matrix will cause the erroneous propagation of the state error to the next time step, affecting the calculation of the Kalman gain. The Kalman gain determines the weight distribution between the predicted value and the observed value. An accurate state transfer matrix can ensure the correctness of the prediction error covariance matrix, so that the Kalman gain can more reasonably distribute the information from these two sources. Therefore, after obtaining the corrected state transfer matrix, the prediction error covariance is calculated in combination with the process noise covariance matrix, and then the observation matrix U in the temperature monitoring process and the covariance matrix J in the measurement noise process are obtained, and then the Kalman gain is calculated. This process is a technical means well known to those skilled in the art and will not be repeated here.

[0082] The Kalman gain value obtained according to the Kalman filter algorithm can be used to compensate the temperature monitoring data inside the advertising machine to obtain the compensated temperature data. When the compensated temperature data is within the normal range, it means that the heat dissipation strategy of the heat dissipation system is fine and the current heat dissipation power can be maintained; when the compensated temperature data is close to overheating, it is necessary to increase the heat dissipation power of the heat dissipation system, reduce the screen brightness, slow down the processing speed and other operations to reduce the workload of the equipment; when the compensated temperature data is already overheated, emergency cooling measures need to be implemented; when the compensated temperature data indicates that the advertising machine equipment is in an abnormal state, the advertising machine is promptly controlled to shut down, or the heat dissipation system is controlled to run at full power to avoid further equipment damage or safety risks. It should be noted that the specific temperature data compensation method is a technical means well known to those skilled in the art and will not be described in detail here. Other heat dissipation strategies may also be set in other embodiments of the present invention, which are not limited here.

[0083] In summary, the embodiment of the present invention uses the data point with a downward trend in the power supply power monitoring curve as the first analysis point; screens the maximum value points on the various heat source temperature monitoring curves to obtain the second analysis point; and obtains the first correction value based on the first analysis point. The difference between the various heat source temperature monitoring curves and the internal temperature monitoring curve characterizes the degree of heat provided by the heat source to the advertising machine, obtains the possibility of the main heat source and screens out the main heat source. The extreme point with obvious changes on the heat source temperature monitoring curve of the main heat source is used as the second analysis point for analysis to find the response delay point on the power supply power monitoring curve, and then obtain the response delay. The first correction value can be further corrected using the response delay to obtain the final correction amount. The state transfer matrix is ​​adjusted according to the final correction amount, and the internal temperature monitoring data is compensated using the Kalman filter algorithm to obtain an accurate heat dissipation strategy. The present invention effectively adjusts the state transfer matrix in the Kalman filter algorithm, and then accurately compensates the internal temperature data of the advertising machine to obtain an effective heat dissipation strategy.

[0084] Based on the same inventive concept, the present invention also proposes an intelligent heat dissipation system for outdoor advertising machines, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any step of an intelligent heat dissipation method for outdoor advertising machines.

[0085] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent heat dissipation method for outdoor advertising machines, characterized in that: The method comprises: Obtaining a state curve of the outdoor advertising machine during operation within a monitoring period, wherein the state curve includes a power supply monitoring curve, a heat dissipation system power curve, various heat source temperature monitoring curves, and an internal temperature monitoring curve of the outdoor advertising machine; taking a data point with a downward trend in the power supply monitoring curve as a first analysis point; screening the maximum value points on the various heat source temperature monitoring curves to obtain a second analysis point; Obtaining a first correction value of a state transfer matrix in a Kalman filter algorithm according to a difference in a data change trend on the internal temperature monitoring curve at the first analysis point and a corresponding time point; For the heat source temperature monitoring curve of each heat source, the possibility of each heat source being the main heat source is obtained according to the difference between the data on the internal temperature monitoring curve at the second analysis point and the corresponding time point, and the difference in the change trend between the heat source temperature monitoring curve and the internal temperature monitoring curve; Screening out the main heat source according to the possibility; obtaining a response delay point of the second analysis point on the power curve of the heat dissipation system for each of the second analysis points of the main heat source; obtaining a response delay of the main heat source according to the possibility of the main heat source and the time interval between the second analysis point and the response delay point; Obtaining a final correction amount of a state transfer matrix according to the difference between the first correction value and the response delay, adjusting the state transfer matrix in the Kalman filter algorithm according to the final correction amount, compensating the temperature monitoring data inside the advertising machine according to the Kalman filter algorithm, and judging the temperature state of the advertising machine according to the compensated temperature data and executing a corresponding heat dissipation strategy; The method for obtaining the first correction value includes: Obtaining an average value of the difference in the tangent slope on the internal temperature monitoring curve at all first analysis points and the corresponding time point, and recording it as the first data change trend difference; Obtaining the difference between the average tangent slope of all data points in the power monitoring curve of the power supply and the average tangent slope of all data points on the internal temperature monitoring curve, and recording it as the second data change trend difference; multiplying the first data change trend difference by the data change trend difference to obtain the first correction value; The method for obtaining the possibility includes: For the heat source temperature monitoring curve of each heat source, the average value of the difference between the data on the internal temperature monitoring curve at all second analysis points and the corresponding time point is obtained, and recorded as the local characteristic data difference; the variance difference between the heat source temperature monitoring curve and the internal temperature monitoring curve is taken as the change trend difference; the product of the local characteristic data difference and the change trend difference is negatively correlated to obtain the possibility.

2. The intelligent heat dissipation method for outdoor advertising machines according to claim 1, characterized in that: The method for obtaining the first analysis point includes: The tangent slope of each data point in the power monitoring curve is obtained, and the point where the tangent slope is negative is used as the first analysis point.

3. The intelligent heat dissipation method for outdoor advertising machines according to claim 1, characterized in that: The method for acquiring the second analysis point includes: For each heat source temperature monitoring curve, the maximum points are sorted according to the temperature value to obtain a temperature sorting sequence; in the temperature sorting sequence, the position with the largest difference between adjacent elements is selected as the splitting point to obtain two subsequences, and the maximum point in the subsequence with the largest temperature value is used as the second analysis point.

4. The intelligent heat dissipation method for outdoor advertising machines according to claim 1, characterized in that: The screening method of the main heat source comprises: The possibilities are sorted according to the numerical values ​​to obtain a possibility sorting sequence; in the possibility sorting sequence, the position with the largest difference between adjacent elements is selected as the split point to obtain two subsequences, and the heat source corresponding to the possibility in the subsequence with the largest possibility is used as the main heat source.

5. The intelligent heat dissipation method for outdoor advertising machines according to claim 3, characterized in that: The method for obtaining the response delay point includes: According to the screening method of the second analysis point, a third analysis point on the power curve of the heat dissipation system is obtained; For each of the second analysis points of the main heat source, on the heat dissipation system power curve, the first third analysis point after the time point corresponding to the second analysis point is used as the response delay point.

6. The intelligent heat dissipation method for outdoor advertising machines according to claim 1, characterized in that: The method for obtaining the response delay includes: The average time interval between all the second analysis points and the response delay point is obtained, and the product of the average time interval and the possibility of the main heat source is used as the response delay of the main heat source.

7. The intelligent heat dissipation method for outdoor advertising machines according to claim 1, characterized in that: The method for obtaining the final correction amount includes: The average response delay of all main heat sources is obtained, and the difference between the first correction value and the average response delay is used as the final correction value.

8. An intelligent cooling system for an outdoor advertising machine, 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 intelligent heat dissipation method for an outdoor advertising machine as described in any one of claims 1 to 7 are implemented.

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