Method for reducing heating of distribution transformer fusion terminal module

Abstract

The invention relates to a method for reducing heating of a distribution transformer fusion terminal module, and the method comprises the steps: detecting whether there is an irrelevant APP except a power distribution room monitoring APP in an upper computer terminal, and shutting down the irrelevant APP; processing according to the information of different power distribution room monitoring APPs by using a hash algorithm to generate a unique key value; constructing an index based on the unique key value, and establishing a shared memory based on the index; and completing data interaction between the power distribution room monitoring APPs according to the shared memory. According to the invention, the APP irrelevant to the monitoring of the power distribution room is shut down, and the shared memory is constructed, so that the occupation of a CPU and the memory can be effectively reduced, and the burden of the system is reduced. Therefore, the overheating problem of the equipment is reduced, and the service life of the distribution transformer fusion terminal is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution room safety, and in particular to a method for reducing the heat generation of a distribution transformer fusion terminal module. Background Art

[0002] The distribution transformer fusion terminal can realize real-time scientific monitoring of distribution transformers. Based on the collected real-time and historical operation data, it can understand the flow of low-voltage energy more comprehensively and timely, and realize a panoramic perception of the low-voltage distribution network. However, the current distribution transformer fusion terminal is pre-installed with multiple APPs when it leaves the factory, such as data center APP, Internet of Things APP, and procurement APP, which leads to the following problems:

[0003] Reduced software stability: Due to the excessive number of deployed apps, the mutual impact between software may lead to reduced system stability, which in turn affects the operating efficiency of the entire terminal.

[0004] Uneven resource usage: Some apps may occupy too much CPU resources, causing the module to overheat, increasing the risk of device failure and reducing the stability of the terminal.

[0005] Increased maintenance complexity: The management and maintenance of multiple apps increases the workload of operation and maintenance personnel and reduces operation and maintenance efficiency. Summary of the invention

[0006] To solve the above problems, an object of an embodiment of the present invention is to provide a method for reducing the heating of a distribution transformer fusion terminal module.

[0007] A method for reducing heating of a distribution transformer fusion terminal module, comprising:

[0008] Step 1: Check whether there are any irrelevant APPs other than the power distribution room monitoring APP in the host computer terminal, and shut down the irrelevant APPs;

[0009] Step 2: Use the hash algorithm to process the information of different power distribution room monitoring apps to generate a unique key value;

[0010] Step 3: Build an index based on the unique key value and create a shared memory based on the index;

[0011] Step 4: Complete data interaction between the power distribution room monitoring apps based on shared memory.

[0012] Preferably, the step 4: completing data interaction between the power distribution room monitoring APPs according to the shared memory includes:

[0013] When the distribution room monitoring APP collects the target data, it finds the index from the shared memory according to the corresponding key value and writes the target data;

[0014] Preferably, the step 4: completing data interaction between the power distribution room monitoring APPs according to the shared memory, further includes:

[0015] According to the information of the target distribution room monitoring APP, the index of the corresponding key value is found through the hash algorithm, and then the data is read from the shared memory according to the index.

[0016] Preferably, a hash function is used to calculate the hash value corresponding to the key value, and the key value node is placed in the corresponding hash bucket; when retrieving the key value, a matching calculation is performed in the corresponding hash bucket to determine whether there is an identical hash value in the hash bucket. When the hash values ​​match, the information values ​​of the distribution room monitoring APP are compared to see if they match. When the information values ​​match, the key value retrieval process is completed.

[0017] Preferably, the step 1: detecting whether there are any irrelevant APPs other than the power distribution room monitoring APP in the host computer terminal, and shutting down the irrelevant APPs, includes:

[0018] Step 1.1: Get the working panel image of the host terminal;

[0019] Step 1.2: gray-scale the working panel image to obtain a gray-scale working panel image;

[0020] Step 1.3: performing denoising processing on the grayscale working panel image to obtain a denoised working panel image;

[0021] Step 1.4: Annotate each denoised work panel image to indicate whether the irrelevant APP is running;

[0022] Step 1.5: Input the labeled work panel image as a sample into the neural network for training to obtain an illegal APP operation recognition model;

[0023] Step 1.6: Use the illegal APP operation identification model to detect whether there are irrelevant APPs running in the host computer terminal, and shut down the irrelevant APPs that are running.

[0024] Preferably, in step 1.2, the grayscale processing formula is:

[0025] T(i,j)=0.299×R(i,j)+0.587×G(i,j)+0.114×B(i,j);

[0026] Wherein, T(i,j) represents the gray value of the grayscale working panel image at (i,j), R(i,j) represents the pixel value of the red channel of the original working panel image at (i,j), G(i,j) represents the pixel value of the green channel of the original working panel image at (i,j), and B(i,j) represents the pixel value of the blue channel of the original working panel image at (i,j).

[0027] Preferably, the step 1.3: performing denoising on the grayscale working panel image to obtain a denoised working panel image comprises:

[0028] Step 1.3.1: Use wavelet transform to decompose the grayscale working panel image into wavelet coefficients of different scales;

[0029] Step 1.3.2: Calculate the threshold based on the decomposition scale of the image;

[0030] Step 1.3.3: construct a denoising function according to the threshold;

[0031] Step 1.3.4: Process the wavelet coefficients using the denoising function to obtain the wavelet coefficients with noise removed;

[0032] Step 1.3.5: Reconstruct the wavelet coefficients from which the noise is removed to obtain a denoised working panel image.

[0033] Preferably, in step 1.3.2, the threshold calculation formula is:

[0034]

[0035] Among them, T represents the threshold, σ represents the standard deviation of Gaussian noise, M represents the length of the image, N represents the width of the image, and j represents the decomposition scale of the image.

[0036] Preferably, the denoising function is:

[0037]

[0038] in, represents the wavelet coefficients to remove noise, w j,k represents the kth wavelet coefficient under the jth decomposition scale, α represents the first adjustable coefficient, β represents the second adjustable coefficient, and σ represents the standard deviation of Gaussian noise.

[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for reducing the heating of a distribution transformer fusion terminal module are implemented.

[0040] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0041] The present invention relates to a method for reducing the heating of a distribution transformer fusion terminal module. Compared with the prior art, the present invention can effectively reduce the CPU and memory usage by shutting down apps that are not related to the monitoring of the power distribution room, thereby reducing the system burden. This helps to reduce the overheating problem of the equipment and extend the service life of the distribution transformer fusion terminal.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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.

[0044] Figure 1 A schematic diagram of a method for reducing heating of a distribution transformer fusion terminal module provided by the present invention;

[0045] Figure 2 A schematic diagram of CPU utilization efficiency in the prior art provided by the present invention;

[0046] Figure 3 A schematic diagram of CPU usage efficiency in this application provided by the present invention. DETAILED DESCRIPTION

[0047] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0048] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] See also Figure 1 , a method for reducing heating of a distribution transformer fusion terminal module, comprising:

[0051] Step 1: Check whether there are any irrelevant APPs other than the power distribution room monitoring APP in the host computer terminal, and shut down the irrelevant APPs;

[0052] Step 2: Use the hash algorithm to process the information of different power distribution room monitoring apps to generate a unique key value;

[0053] Step 3: Build an index based on the unique key value and create a shared memory based on the index;

[0054] Step 4: Complete data interaction between the power distribution room monitoring apps based on shared memory.

[0055] Further, the step 4 comprises:

[0056] When the distribution room monitoring APP collects the target data, it finds the index from the shared memory according to the corresponding key value and writes the target data;

[0057] According to the information of the target distribution room monitoring APP, the index of the corresponding key value is found through the hash algorithm, and then the data is read from the shared memory according to the index.

[0058] In actual applications, the distribution room monitoring APP generates a unique key value through a hash algorithm based on the collected information such as device name, device number, device address, link, etc., and then indexes it based on the key value to open up shared memory for the device.

[0059] After the power distribution room monitoring APP collects real-time data, it generates a key value based on the hash algorithm, then finds the index from the memory and writes the data. Reading data is also based on the device name, device number, address, link and other field information, and then finds the key value index through the hash algorithm, and then reads the data from the memory.

[0060] The hash function calculates the hash value corresponding to the key value, and puts the key value node into the corresponding hash bucket according to the preset rules. When searching, first calculate which bucket the key value is in based on the key value, then perform a matching calculation to compare the hash value. At the same time, in order to prevent collisions, when the hash value matches, the value of the keyword itself will be compared again to ensure that the value corresponding to the key value can still be accurately found when the hash value collides. The hash index table is used to store address offsets. When accessing records, the corresponding memory address offset can be found according to the key value to access data efficiently.

[0061] The access speed of the recorded memory address is the key to improving efficiency. The record key value is the object attribute descriptor OAD (object attribute descriptor), which is the unique identifier of the data. The keyword of the record stored in the hash table is OAD, so it is necessary to find the corresponding memory address offset in the hash table according to OAD to achieve direct positioning of the memory. Different address allocation and access methods are used for structured data and unstructured data respectively. Structured data uses static linked list index mapping, and unstructured data uses the method of adding record information headers.

[0062] For structured data, since the length of each record is the same, the interval of the starting address of each record is fixed. The index of the static linked list can meet this feature. By mapping the position index assigned when inserting the static linked list to the table of the real-time library, the memory address allocation and access function can be met. The index of the static linked list corresponding to the record is stored in the hash table. When accessing, the index value corresponding to the record is found from the hash table, so that the memory address is directly calculated based on the index multiplied by the length of each record.

[0063] For unstructured data, since the length of each record is not fixed, if the static linked list mapping method is used, each record is of a uniform length, which will cause a great waste of space when allocating memory. At the same time, the memory allocation for insertion and deletion operations may cause problems such as record overwriting. To solve this problem, the program processing will be too complicated, reducing the execution efficiency of the program. Therefore, a memory management method is designed. The information header of each record is stored before each record, and the data is managed within the pre-allocated table space. Among them, the information header contains the following information: the length of the current data block, whether the current data block is free, the length of the previous data block, etc. When inserting a record, the free memory space will be traversed until a free memory space segment that meets the record length is found, the space will be occupied, and the starting address of the space will be returned. At the same time, the remaining space will be allocated as free memory space, and the data length will be recorded in the information header, and it will be set to occupied state. When deleting a record, first check whether the adjacent memory space is also free. If so, merge the free space first, and then set the corresponding information header to free state.

[0064] The memory offset corresponding to the record is stored in the hash table. When accessing, the address offset corresponding to the record is found from the hash table, and the memory address is directly calculated by adding the starting address to the offset.

[0065] In the embodiment of the present invention, step 1: detecting whether there are any irrelevant APPs other than the power distribution room monitoring APP in the host computer terminal, and shutting down the irrelevant APPs, includes:

[0066] Step 1.1: Get the working panel image of the host terminal;

[0067] Step 1.2: gray-scale the working panel image to obtain a gray-scale working panel image;

[0068] In step 1.2, the grayscale processing formula is:

[0069] T(i,j)=0.299×R(i,j)+0.587×G(i,j)+0.114×B(i,j);

[0070] Wherein, T(i,j) represents the gray value of the grayscale working panel image at (i,j), R(i,j) represents the pixel value of the red channel of the original working panel image at (i,j), G(i,j) represents the pixel value of the green channel of the original working panel image at (i,j), and B(i,j) represents the pixel value of the blue channel of the original working panel image at (i,j).

[0071] Grayscale can reduce the complexity of the image and make subsequent image processing more efficient.

[0072] Step 1.3: performing denoising processing on the grayscale working panel image to obtain a denoised working panel image;

[0073] Furthermore, step 1.3 includes:

[0074] Step 1.3.1: Use wavelet transform to decompose the grayscale working panel image into wavelet coefficients of different scales;

[0075] Step 1.3.2: Calculate the threshold based on the decomposition scale of the image;

[0076] In step 1.3.2, the threshold calculation formula is:

[0077]

[0078] Among them, T represents the threshold, σ represents the standard deviation of Gaussian noise, M represents the length of the image, N represents the width of the image, and j represents the decomposition scale of the image.

[0079] Step 1.3.3: construct a denoising function according to the threshold;

[0080] Wherein, the denoising function is:

[0081]

[0082] in, represents the wavelet coefficients to remove noise, w j,k represents the kth wavelet coefficient under the jth decomposition scale, α represents the first adjustable coefficient, β represents the second adjustable coefficient, and σ represents the standard deviation of Gaussian noise.

[0083] Step 1.3.4: Process the wavelet coefficients using the denoising function to obtain the wavelet coefficients with noise removed;

[0084] Step 1.3.5: Reconstruct the wavelet coefficients from which the noise is removed to obtain a denoised working panel image.

[0085] After grayscale and denoising, images are usually clearer and cleaner, suitable for further analysis and recognition. This is very important for the training and inference of machine learning models, because higher quality data can improve the training effect of the model.

[0086] Step 1.4: Annotate each denoised work panel image to indicate whether the irrelevant APP is running;

[0087] Step 1.5: Input the labeled work panel image as a sample into the neural network for training to obtain an illegal APP operation recognition model;

[0088] Step 1.6: Use the illegal APP operation identification model to detect whether there are irrelevant APPs running in the host computer terminal, and shut down the irrelevant APPs that are running.

[0089] By using a neural network (CNN), the present invention can automatically extract complex features from screen images without the need to manually design feature extraction algorithms. This automatic extraction function enables the model to capture subtle differences in the image, thereby improving the accuracy of irrelevant app recognition, please refer to Figure 2-3 The CPU utilization efficiency in the present invention is much lower than that in the prior art.

[0090] It should be noted that the present invention can also adopt a solution of placing the communication module externally: the external communication module and the distribution transformer fusion terminal are connected for communication via an Ethernet port, the original communication module is disabled and removed, the heat dissipation space in the cavity of the distribution transformer fusion terminal is expanded, the problem of insufficient heat dissipation in the closed cavity is avoided, and communication freeze caused by overheating of the communication module is avoided.

[0091] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0092] The present invention relates to a method for reducing the heating of a distribution transformer fusion terminal module. Compared with the prior art, the present invention can effectively reduce the CPU and memory usage by shutting down apps that are not related to distribution room monitoring and building shared memory, thereby reducing the system burden. This helps to reduce the overheating problem of the equipment and extend the service life of the distribution transformer fusion terminal.

[0093] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned distribution room operating condition warning method are implemented. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present invention are the same as the beneficial effects of the method for reducing the heat generation of the distribution transformer fusion terminal module described in the above-mentioned technical solution, which will not be repeated here.

[0094] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technical solution that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for reducing heating of a distribution transformer fusion terminal module, characterized in that: include: Step 1: Check whether there are any irrelevant APPs other than the power distribution room monitoring APP in the host computer terminal, and shut down the irrelevant APPs; Step 2: Use the hash algorithm to process the information of different power distribution room monitoring apps to generate a unique key value; Step 3: Build an index based on the unique key value and create a shared memory based on the index; Step 4: Complete data interaction between the power distribution room monitoring apps based on shared memory.

2. A method for reducing heating of a distribution transformer fusion terminal module according to claim 1, characterized in that: The step 4: completing data interaction between the power distribution room monitoring apps according to the shared memory, includes: When the distribution room monitoring APP collects the target data, it finds the index from the shared memory according to the corresponding key value and writes the target data.

3. A method for reducing heating of a distribution transformer fusion terminal module according to claim 2, characterized in that: The step 4: completing data interaction between the power distribution room monitoring APPs according to the shared memory, further includes: According to the information of the target distribution room monitoring APP, the index of the corresponding key value is found through the hash algorithm, and then the data is read from the shared memory according to the index.

4. A method for reducing heating of a distribution transformer fusion terminal module according to claim 1, characterized in that: The hash function is used to calculate the hash value corresponding to the key value, and the key value node is placed in the corresponding hash bucket; when retrieving the key value, a matching calculation is performed in the corresponding hash bucket to determine whether there is the same hash value in the hash bucket. When the hash values ​​match, the information values ​​of the distribution room monitoring APP are compared to see if they match. When the information values ​​match, the key value retrieval process is completed.

5. The method for reducing heating of a distribution transformer fusion terminal module according to claim 1, characterized in that: The step 1: detecting whether there are any irrelevant APPs other than the power distribution room monitoring APP in the host computer terminal, and shutting down the irrelevant APPs, includes: Step 1.1: Get the working panel image of the host terminal; Step 1.2: graying the working panel image to obtain a grayed working panel image; Step 1.3: performing denoising processing on the grayscale working panel image to obtain a denoised working panel image; Step 1.4: Annotate each denoised work panel image to indicate whether the irrelevant APP is running; Step 1.5: Input the labeled work panel image as a sample into the neural network for training to obtain an illegal APP operation recognition model; Step 1.6: Use the illegal APP operation identification model to detect whether there are irrelevant APPs running in the host computer terminal, and shut down the irrelevant APPs that are running.

6. A method for reducing heating of a distribution transformer fusion terminal module according to claim 5, characterized in that: In step 1.2, the grayscale processing formula is: T(i,j)=0.299×R(i,j)+0.587×G(i,j)+0.114×B(i,j); Wherein, T(i,j) represents the gray value of the grayscale working panel image at (i,j), R(i,j) represents the pixel value of the red channel of the original working panel image at (i,j), G(i,j) represents the pixel value of the green channel of the original working panel image at (i,j), and B(i,j) represents the pixel value of the blue channel of the original working panel image at (i,j).

7. A method for reducing heating of a distribution transformer fusion terminal module according to claim 6, characterized in that: The step 1.3: performing denoising on the grayscale working panel image to obtain a denoised working panel image, comprises: Step 1.3.1: Use wavelet transform to decompose the grayscale working panel image into wavelet coefficients of different scales; Step 1.3.2: Calculate the threshold based on the decomposition scale of the image; Step 1.3.3: construct a denoising function according to the threshold; Step 1.3.4: Process the wavelet coefficients using the denoising function to obtain the wavelet coefficients with noise removed; Step 1.3.5: Reconstruct the wavelet coefficients from which the noise is removed to obtain a denoised working panel image.

8. A method for reducing heating of a distribution transformer fusion terminal module according to claim 7, characterized in that: In step 1.3.2, the threshold calculation formula is: Among them, T represents the threshold, σ represents the standard deviation of Gaussian noise, M represents the length of the image, N represents the width of the image, and j represents the decomposition scale of the image.

9. A method for reducing heating of a distribution transformer fusion terminal module according to claim 8, characterized in that: in, The denoising function is: in, represents the wavelet coefficients to remove noise, w j,k represents the kth wavelet coefficient under the jth decomposition scale, α represents the first adjustable coefficient, β represents the second adjustable coefficient, and σ represents the standard deviation of Gaussian noise.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in a method for reducing heat generation of a distribution transformer fusion terminal module according to any one of claims 1 to 9 are implemented.

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