Equipment sequence control state detection method and device, equipment, storage medium and product

Abstract

The invention relates to an equipment sequence control state detection method and device, equipment, a storage medium and a product. The method comprises the steps that sequence control influence indexes of to-be-detected equipment under the working weather condition are acquired, and the sequence control influence indexes are used for representing the influence degree of one-key sequence control operation on the to-be-detected equipment under the working weather condition; under the condition that it is detected that the sequence control influence index is abnormal, sharpening processing is conducted on a sequence control reference image of the to-be-detected equipment, and a sequence control clear image of the to-be-detected equipment is obtained; performing sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the to-be-detected equipment; and performing sequence control state detection on the to-be-detected equipment according to a state comparison result between the sequence control reference state and the actual sequence control state of the to-be-detected equipment. By adopting the method, the detection effect of equipment sequence control state detection is improved.

Description

Technical Field

[0001] The present application relates to the technical field of substations, and in particular to a method and apparatus for detecting a sequential control state of equipment, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the development of science and technology, the substation field has also ushered in technological innovation. Deploying a one-button sequential control system in a substation is an important measure to improve the operating efficiency and safety of the substation. The one-button sequential control system enables operation and maintenance personnel to complete the status conversion of electrical equipment with one click in the control room.

[0003] At present, the one-button sequential control system usually adopts a double confirmation mechanism to detect the sequential control status of the equipment. For example, the position telesignal is used as the first judgment basis and the video image is used as the second judgment basis, and the sequential control operation is performed when the first judgment basis and the second judgment basis are consistent. However, since the weather conditions in the working environment of the electrical equipment are uncontrollable, the second judgment basis cannot reflect the actual matching of the sequential control status reference requirements of the electrical equipment, which makes it easy for the sequential control status of the equipment to be inaccurately detected or the detection takes too long. Therefore, the current detection effect of the sequential control status of the equipment is poor. Summary of the invention

[0004] Based on this, it is necessary to provide a device sequential control state detection method, device, computer equipment, computer-readable storage medium and computer program product to improve the detection effect of device sequential control state detection in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting a sequential control state of a device, comprising:

[0006] Obtaining a sequential control influence index of the device to be detected under the operating weather conditions, wherein the sequential control influence index is used to characterize the degree of influence of the operating weather conditions on the one-key sequential control operation of the device to be detected;

[0007] When the sequence control influencing index is detected to be abnormal, a sequence control reference image of the device to be detected is processed to be clear, so as to obtain a sequence control clear image of the device to be detected;

[0008] Performing a sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected;

[0009] According to the state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected, the sequence control state of the device to be detected is detected.

[0010] In one embodiment, the step of obtaining the sequential control impact index of the equipment to be detected under the operating weather conditions includes:

[0011] Detecting the operating weather conditions of the equipment to be detected according to the meteorological parameters of the environment in which the equipment to be detected is located;

[0012] When it is detected that the operating weather condition of the equipment to be detected is an abnormal weather condition, obtaining environmental parameters under the abnormal operating weather condition;

[0013] According to the environmental parameters, the sequential control impact index of the equipment to be detected under abnormal operating weather conditions is queried.

[0014] In one embodiment, the step of performing a clear processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected includes:

[0015] Locating a fuzzy reference area in the sequence control reference image, wherein the fuzzy reference area refers to a fuzzy image area carrying sequence control state reference information of the device to be detected;

[0016] The fuzzy reference area is preprocessed, and a sequence control reference image obtained by preprocessing the fuzzy reference area is used as the sequence control clear image.

[0017] In one of the embodiments, locating the blurred reference area in the sequential control reference image includes:

[0018] Obtaining the density of white pixels in the sequential control reference image;

[0019] If the white pixel density is greater than or equal to a preset white pixel density threshold, the entire image area of ​​the sequence control reference image is used as the fuzzy reference area;

[0020] If the white pixel density is less than the preset white pixel density threshold, the fuzzy reference area is determined according to the white pixel features of the sequence control reference image.

[0021] In one embodiment, the preprocessing of the blurred reference area includes:

[0022] Obtaining the abnormal weather condition type to which the abnormal weather condition belongs;

[0023] According to the type of abnormal weather condition, matching a corresponding image denoising algorithm for the sequential control reference image;

[0024] The blurred reference area is preprocessed according to the image denoising algorithm.

[0025] In one of the embodiments, when the sequence control influencing index is detected to be abnormal, before the sequence control reference image of the device to be detected is cleared to obtain the sequence control clear image of the device to be detected, the method further includes:

[0026] Detection step: detecting the magnitude relationship between the sequence control influence index and a preset sequence control influence index threshold;

[0027] In the case where it is detected that the size relationship is that the sequence control influence index is greater than or equal to the preset sequence control influence index threshold, determining that the sequence control influence index is abnormal;

[0028] When it is detected that the size relationship is that the sequence control impact indicator is less than the preset sequence control impact indicator threshold, the indicator prompt information corresponding to the sequence control impact indicator is displayed, and the detection step is returned to be executed until it is detected that the size relationship is that the sequence control impact indicator is greater than or equal to the preset sequence control impact indicator threshold.

[0029] In a second aspect, the present application also provides a method and apparatus for detecting a sequential control state of a device, comprising:

[0030] An acquisition module, used to acquire a sequential control influence index of the device to be detected under the operating weather conditions, wherein the sequential control influence index is used to characterize the influence of the operating weather conditions on the one-key sequential control operation of the device to be detected;

[0031] A processing module, configured to, when detecting that the sequence control influencing index is abnormal, perform a clear processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected;

[0032] A first detection module is used to perform a sequence control reference state detection on the sequence control clear image to obtain the sequence control reference state of the device to be detected;

[0033] The second detection module is used to perform sequence control state detection on the device to be detected according to a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

[0034] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] Obtain a sequence control influence index of the device to be detected under the operating weather conditions, wherein the sequence control influence index is used to characterize the degree of influence of the operating weather conditions on the one-key sequence control operation of the device to be detected; when it is detected that the sequence control influence index is abnormal, perform clarity processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected; perform sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected; and perform sequence control state detection on the device to be detected based on a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0037] Obtain a sequence control influence index of the device to be detected under the operating weather conditions, wherein the sequence control influence index is used to characterize the degree of influence of the operating weather conditions on the one-key sequence control operation of the device to be detected; when it is detected that the sequence control influence index is abnormal, perform clarity processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected; perform sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected; and perform sequence control state detection on the device to be detected based on a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

[0038] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0039] Obtain a sequence control influence index of the device to be detected under the operating weather conditions, wherein the sequence control influence index is used to characterize the degree of influence of the operating weather conditions on the one-key sequence control operation of the device to be detected; when it is detected that the sequence control influence index is abnormal, perform clarity processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected; perform sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected; and perform sequence control state detection on the device to be detected based on a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

[0040] The above-mentioned equipment sequence control state detection method, device, computer device, computer-readable storage medium and computer program product first obtain the sequence control influence index of the equipment to be detected under the operating weather conditions. Since the sequence control influence index is used to characterize the influence of the operating weather conditions on the one-key sequence control operation of the equipment to be detected, the sequence control influence index can quantify the influence of the current operating weather conditions of the equipment to be detected on the one-key sequence control operation. Then, in the case of abnormal sequence control influence index, the sequence control reference image of the equipment to be detected is clarified to obtain a sequence control clear image of the equipment to be detected, and then the sequence control reference state of the equipment to be detected is obtained based on the sequence control clear image detection. Finally, the sequence control state of the equipment to be detected is detected through the state comparison result between the sequence control reference state and the actual sequence control state of the equipment to be detected. Since in the case of abnormal sequence control influence index, it indicates that the operating weather conditions of the equipment to be detected are abnormal, it indicates that the operating weather conditions of the equipment to be detected are abnormal. The weather conditions have an impact on its one-key sequential control operation, and the clear sequential control image obtained after clarifying the sequential control reference image of the equipment to be detected can remove the interference of the operating weather conditions on the second judgment basis, thereby ensuring the reliability of the sequential control reference state of the equipment to be detected. Finally, the sequential control state of the equipment to be detected can be objectively detected through the state comparison result between the sequential control reference state and the actual sequential control state of the equipment to be detected, instead of directly using the sequential control reference image as the second judgment basis when the operating weather conditions will affect the one-key operation of the equipment to be detected. Therefore, the technical defect that the weather conditions in the working environment of the electrical equipment are uncontrollable, which leads to the second judgment basis being unable to reflect the actual matching of the sequential control state reference requirements of the electrical equipment, and thus making it easy for the equipment sequential control state detection to be inaccurate or the detection time to be too long, is overcome. Therefore, the detection effect of the equipment sequential control state detection is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of a method for detecting a sequential control state of a device in one embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of a method for detecting a sequential control state of a device in another embodiment;

[0044] Figure 3 A schematic diagram of a flow chart of a method for detecting a sequential control state of a device under rainy conditions in an embodiment of the method;

[0045] Figure 4 It is a structural block diagram of a device sequential control state detection device in one embodiment;

[0046] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] First of all, it should be understood that in the process of equipment switching operation, in order to accurately detect the changes in the sequential control state of the electrical equipment, two indications of different principles or sources are usually changed accordingly to achieve double confirmation. For example, in substations, position telesignaling is usually used as the first criterion, and the camera's recognition of the opening and closing position indication is used as the second criterion for double confirmation. However, the reliability of the second criterion is easily affected by weather conditions. For example, in the process of one-button sequential control operation, the collected sequential control reference image is often unable to accurately identify the switch and knife switch opening and closing indications due to rain obstruction, thereby affecting the subsequent double confirmation, and ultimately resulting in the sequential control operation efficiency being affected or the sequential control safety being difficult to ensure on rainy days. Therefore, there is an urgent need for a device sequential control state detection method that can improve the detection effect of equipment sequential control state detection.

[0049] In one embodiment, Figure 1As shown, a device sequence control state detection method is provided. This embodiment takes the method applied to a terminal as an example. The terminal includes, but is not limited to, a personal computer, a laptop computer, a smart phone, and a tablet computer. The terminal includes an acquisition module, a processing module, a first detection module, and a second detection module. The acquisition module is used to acquire a sequence control influence index of the device to be detected under the operating weather conditions. The sequence control influence index is used to characterize the degree of influence of the operating weather conditions on the one-key sequence control operation of the device to be detected. The processing module is used to, when the sequence control influence index is detected to be abnormal, perform a clear processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected. The first detection module is used to perform a sequence control reference state detection on the sequence control cleaning image to obtain the sequence control reference state of the device to be detected. The second detection module is used to perform a sequence control state detection on the device to be detected according to a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected. In this embodiment, through information exchange among the acquisition module, the processing module, the first detection module, and the second detection module, in the process of detecting the sequence control state of the device to be detected, the sequence control influence index is first used to quantitatively evaluate the sequence control state of the device to be detected. The influence of the operating weather conditions on the one-key sequential control operation of the equipment to be detected is determined, and when the sequential control influence index is detected to be abnormal, that is, when the operating weather conditions have a great influence on the one-key sequential control operation of the equipment to be detected, the sequential control reference image is clarified, and the sequential control reference state of the equipment to be detected is detected based on the clear sequential control image obtained by the clarifying process, and finally, the sequential control state of the equipment to be detected is detected according to the state comparison result between the sequential control reference state and the actual sequential control state of the equipment to be detected. Since the clear sequential control image can remove the interference of the operating weather conditions on the second judgment basis, the reliability of the sequential control reference state of the equipment to be detected is guaranteed, that is, the technical defect that the second judgment basis cannot reflect the actual matching of the sequential control state reference demand of the electrical equipment due to the uncontrollable weather conditions in the working environment of the electrical equipment is overcome, which makes it easy to have inaccurate detection of the equipment sequential control state or detection time is too long, etc., so the detection effect of the equipment sequential control state detection is improved. It can be understood that the method can also be applied to the server, and can also be applied to the system including the terminal and the server, and is realized through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 202 to 208. Among them:

[0050] Step 202 , obtaining a sequence control influence index of the device to be detected under the operating weather conditions, wherein the sequence control influence index is used to characterize the influence of the operating weather conditions on the one-key sequence control operation of the device to be detected.

[0051] It should be noted that the equipment to be tested refers to electrical equipment waiting for sequential control status detection, which may be circuit breakers, disconnectors, and grounding switches, etc. The operating weather conditions refer to the weather conditions of the environment in which the equipment to be tested is located, which may be foggy, rainy, snowy, or sunny days, etc. It can be understood that the operating weather conditions are the weather conditions of the local area where the equipment to be tested is located. The equipment sequential control status detection method can be applied to power grid controlled substations. At present, power grid controlled substations usually use cameras to assist in equipment sequential control operations. However, under abnormal operating weather conditions, the sequential control reference image captured by the camera will be blocked by weather interference objects such as water droplets or snowflakes. For example, rain on a rainy day will block the captured image and make it impossible to distinguish the switch and knife switch opening and closing indications, thereby affecting the one-button sequential control operation of the equipment to be tested.

[0052] It should be noted that the sequential control influence index is used to characterize the influence of the operating weather conditions on the one-key sequential control operation of the equipment to be detected, that is, to quantitatively evaluate the influence of the operating weather conditions on the one-key sequential control operation of the equipment to be detected. It can be understood that the sequential control influence index can be a model evaluation value of the operation reliability evaluation model, which can be a percentage value, such as 70%, 80% or 90%. The larger the percentage value, the higher the reliability of the operation reliability evaluation model, and the smaller the influence of the operating weather conditions on the one-key sequential control operation. The reason is that the second judgment basis can be truly reflected through the rainy day sequential control operation process. Therefore, the sequential control influence index of the equipment to be detected under the operating weather conditions can be extracted by establishing a mapping relationship between the environmental parameters under the operating weather conditions and the sequential control influence index, wherein the environmental parameters under the operating weather conditions can be detected by micrometeorological sensors. For example, in an implementable manner, assuming that the operating weather condition is a rainy day, the rainfall in the environment where the equipment to be detected is located can be collected by a rain collector, and the sequential control influence index of the equipment to be detected under the operating weather state is queried based on the rainfall.

[0053] As an example, step 202 includes: performing weather condition type detection on the operating weather conditions of the equipment to be detected, collecting environmental parameters corresponding to the weather condition type, and using the environmental parameters as indexes to query and obtain sequential control impact indicators of the equipment to be detected under the operating weather conditions.

[0054] Step 204 , when it is detected that the sequence control influencing index is abnormal, the sequence control reference image of the device to be detected is cleared to obtain a sequence control clear image of the device to be detected.

[0055] It should be noted that only under abnormal weather conditions will the one-key sequential control operation of the equipment to be detected be affected by the operating weather conditions of the equipment to be detected. Therefore, it is necessary to detect whether the operating weather conditions of the equipment to be detected are abnormal through the sequential control influence index. It can be understood that when the sequential control influence index of the equipment to be detected is abnormal, it is characterized that the operating weather conditions of the equipment to be detected are abnormal operating weather conditions, that is, the sequential control reference state of the equipment to be detected will be affected by weather interference at this time, and then a preset sequential control influence index threshold can be set to quantitatively evaluate whether the operating weather conditions of the equipment to be detected are abnormal operating weather conditions.

[0056] It should be noted that the sequence control reference image refers to the image used as the second judgment basis for the one-key sequence control operation of the device to be detected. It can be specifically acquired through the image acquisition device deployed by the device sequence control status detection system. The clarity processing method can specifically include image enhancement, image sharpening, and image denoising. It can be understood that the clarity between the sequence control reference image and the sequence control clear image is relative, that is, the clarity of the sequence control clear image is higher than that of the sequence control reference image.

[0057] As an example, step 204 includes: when it is detected that the sequence control influence index is greater than a preset sequence control influence index threshold, determining that the sequence control influence index is abnormal, and performing image enhancement on all areas of the sequence control reference image of the device to be detected to obtain a clear sequence control image of the device to be detected.

[0058] Step 206 , performing a sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected.

[0059] It should be noted that both the clear sequence control image and the reference sequence control image carry reference sequence control status information. The reference sequence control status information may specifically be switch status, knife switch position, protection action and other status indications, etc. By extracting features related to the reference sequence control status information in the clear sequence control image, the detection of the reference sequence control status of the device to be detected can be completed based on the above features.

[0060] As an example, step 206 includes: extracting sequence control reference state features in the sequence control clear image, and detecting the sequence control reference state of the device to be detected based on the feature similarity between the sequence control reference state features and the standard reference state features, wherein the standard reference state features are used to characterize the standard reference state of the device to be detected.

[0061] Step 208 , performing sequence control state detection on the device to be detected according to the state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

[0062] As an example, step 208 includes, when the sequence control reference state and the actual sequence control state of the device to be detected are consistent, determining that the sequence control state of the device to be detected is consistent; when the sequence control reference state and the actual sequence control state of the device to be detected are inconsistent, determining that the sequence control state of the device to be detected is inconsistent.

[0063] In the above-mentioned device sequence control state detection method, firstly, based on the mapping relationship between the environmental parameters of the device to be detected under the operating weather conditions and the preset sequence control influence indicators, the sequence control influence indicators of the device to be detected under the operating weather conditions are queried, and then when the sequence control influence indicator is greater than the preset sequence control influence indicator threshold, that is, the operating weather condition of the device to be detected is an abnormal operating weather condition, the sequence control reference image of the device to be detected is clarified to obtain a sequence control clear image of the device to be detected, and finally, based on the state comparison result between the sequence control reference state fed back by the sequence control clear image and the actual sequence control state of the device to be detected, the sequence control state detection is performed on the device to be detected. When the sequence control influence indicator is abnormal, it means that the operating weather condition of the device to be detected has an impact on its one-key sequence control operation, and the device to be detected has a sequence control state detection result. The clear control image obtained after the prepared control reference image is clarified can remove the interference of the operating weather conditions on the second judgment basis, thereby ensuring the reliability of the control reference state of the equipment to be detected, and finally through the state comparison result between the control reference state and the actual control state of the equipment to be detected, the control state of the equipment to be detected can be objectively detected, instead of directly using the control reference image as the second judgment basis when the operating weather conditions will affect the one-key operation of the equipment to be detected. Therefore, the technical defect that the weather conditions in the working environment of the electrical equipment are uncontrollable, which leads to the second judgment basis being unable to reflect the actual matching of the control state reference requirements of the electrical equipment, and thus making it easy to have inaccurate detection of the equipment control state or detection taking too long, is overcome. Therefore, the detection effect of the equipment control state detection is improved.

[0064] In one embodiment, Figure 2 As shown, the sequential control impact indicators of the equipment to be tested under the operating weather conditions are obtained, including:

[0065] Step 302, detecting the operating weather conditions of the equipment to be detected according to the meteorological parameters of the environment in which the equipment to be detected is located.

[0066] It should be noted that when detecting the operating weather conditions of the equipment to be tested, a comprehensive judgment can be made based on the meteorological parameters collected by the micrometeorological sensors. The meteorological parameters may specifically include temperature, humidity, wind speed, precipitation, etc. Based on the meteorological parameters of the equipment to be tested in the environment in which it is located, the actual operating weather conditions of the equipment to be tested can be accurately detected.

[0067] As an example, step 302 includes: acquiring meteorological parameters of the environment in which the equipment to be detected is located through a micro-meteorological sensor, inputting the meteorological parameters into a preset weather condition assessment model, and obtaining the operating weather conditions in which the equipment to be detected is located.

[0068] Step 304, when it is detected that the operating weather condition of the equipment to be detected is an abnormal weather condition, obtaining environmental parameters under the abnormal operating weather condition;

[0069] It should be noted that, for example, in one feasible method, when the operating weather conditions of the equipment to be detected reach rainy conditions, it is characterized that the operating weather conditions of the equipment to be detected are abnormal weather conditions. It can be understood that when the operating weather conditions are under normal weather conditions, the conventional double confirmation mechanism can be used to complete the sequential control state detection of the equipment to be detected. When the operating weather conditions are under abnormal weather conditions, it is necessary to clarify the sequential control reference image. Among them, the preset weather condition assessment model will synchronously output the type label of the operating working condition when assessing the operating weather conditions of the equipment to be detected, and then it can be determined through the type label whether the operating weather condition of the equipment to be detected is an abnormal operating weather condition.

[0070] As an example, step 304 includes: when it is detected that the operating weather condition of the equipment to be detected is identified as an abnormal operating weather condition, obtaining environmental parameters under the abnormal operating weather condition.

[0071] Step 306, querying the sequential control impact index of the equipment to be tested under abnormal operating weather conditions according to the environmental parameters.

[0072] It should be noted that in the process of obtaining the sequential control influence indicators, a mapping relationship between the environmental parameters and the preset influence indicators can be constructed. For example, in one feasible method, assuming that the operating weather condition is the abnormal operating weather condition "rainy day" and the environmental parameter is the rainfall, the sequential control influence index can be set to 76% when the rainfall is 10 mm, and to 78% when the rainfall is 12 mm.

[0073] As an example, step 306 includes: using environmental parameters as indexes, querying sequential control impact indicators of the equipment to be detected under abnormal operating weather conditions.

[0074] In this embodiment, in the process of acquiring the sequence control influence index, the operating weather conditions of the equipment to be detected are first detected according to the meteorological parameters, and when the operating weather conditions of the equipment to be detected are abnormal weather conditions, the environmental parameters under the abnormal operating weather conditions are obtained, and finally, through the mapping relationship between the environmental parameters and the preset sequence control influence indicators, the sequence control influence indicators of the equipment to be detected under the abnormal operating weather conditions are queried, and then only when the operating weather conditions are abnormal weather conditions, the sequence control reference image of the equipment to be detected is clarified to perform sequence control state detection on the equipment to be detected, instead of clarifying the sequence control reference image of the equipment to be detected in all cases. Therefore, the foundation is laid for improving the detection flexibility of the equipment sequence control state detection.

[0075] In one embodiment, the sequential control reference image of the device to be detected is processed to be clear to obtain a sequential control clear image of the device to be detected, including:

[0076] A fuzzy reference area is located in a sequence control reference image, wherein the fuzzy reference area refers to a fuzzy image area that carries sequence control state reference information of the device to be detected; the fuzzy reference area is preprocessed, and the sequence control reference image obtained by preprocessing the fuzzy reference area is used as a sequence control clear image.

[0077] It should be noted that the fuzzy reference area refers to a fuzzy image area that carries the sequence control state reference information of the device to be detected. It can be understood that when it is determined that the sequence control reference image of the device to be detected needs to be clarified, the fuzzy reference area that needs to be clarified can be accurately located, wherein the positioning of the fuzzy reference area in the sequence control reference image can be based on the extracted sequence control state reference features. For example, in one feasible method, all image areas identified by the sequence control state reference features are used as fuzzy reference areas. After ensuring that the preprocessing of the fuzzy reference area is completed, a clear sequence control image of the device to be detected is obtained.

[0078] As an example, feature extraction is performed on the sequence control reference image to obtain the sequence control state reference feature, and the image area marked by the sequence control state reference feature is used as the fuzzy reference area, wherein the fuzzy reference area refers to the fuzzy image area that carries the sequence control state reference information of the device to be detected; the fuzzy reference area is globally enhanced, and the sequence control reference image obtained by globally enhancing the fuzzy reference area is used as the sequence control clear image.

[0079] In this embodiment, firstly, based on the sequence control state reference feature of the sequence control reference image, the fuzzy reference area is located in the sequence control reference image, and then the fuzzy reference area is globally enhanced, and the sequence control reference image completed by the global enhanced fuzzy reference area is used as the sequence control clear image, that is, the fuzzy reference area is accurately located in the sequence control reference image, and the fuzzy reference area is preprocessed in a targeted manner, thereby reducing the amount of preprocessing of the image area of ​​the sequence control reference image in the process of clarifying the sequence control reference image of the device to be detected. Therefore, on the premise of laying the foundation for improving the detection effect of the sequence control state detection of the device, it also lays the foundation for improving the detection efficiency of the sequence control state detection of the device.

[0080] In one embodiment, locating a blurred reference area in a sequential control reference image includes:

[0081] Obtain the white pixel density in the sequence control reference image; if the white pixel density is greater than or equal to the preset white pixel density threshold, the entire image area of ​​the sequence control reference image is used as the blurred reference area; if the white pixel density is less than the preset white pixel density threshold, determine the blurred reference area based on the white pixel characteristics of the sequence control reference image.

[0082] It should be noted that different abnormal operating weather conditions have different degrees of interference with the sequence control reference image. Therefore, different fuzzy reference areas need to be preprocessed under different abnormal operating weather conditions. Then, the fuzzy reference area of ​​the sequence control reference image can be accurately located through the relevant parameters of the white pixels in the sequence control reference image. For example, the sequence control reference image taken on a rainy day shows white transparent water droplets, the sequence control reference image taken on a foggy day shows white line-like occlusions, and the sequence control reference image taken on a snowy day shows white opaque dot-like occlusions. Since the interference of foggy days on the sequence control reference image is evenly distributed in the entire image, while the interference of rainy and snowy days on the sequence control reference image is targeted at local areas of the image, it is necessary to locate different fuzzy reference areas according to the different degrees of influence of the equipment to be tested under different abnormal weather conditions.

[0083] As an example, white pixels are extracted from a sequence control reference image, and the white pixel density of the sequence control reference image is determined based on the ratio between the area occupied by the white pixels and the image area of ​​the sequence control reference image; if the white pixel density is greater than or equal to a preset white pixel density threshold, the entire image area of ​​the sequence control reference image is used as a fuzzy reference area; if the white pixel density is less than the preset white pixel density threshold, the area marked with white pixel features of the sequence control reference image is used as a fuzzy reference area.

[0084] In this embodiment, the degree of interference of abnormal operating weather conditions on the sequential control reference image of the detection equipment is distinguished by the size relationship between the white pixel density and the preset white pixel density threshold. When the interference degree is large, the entire image area is used as the fuzzy reference area. When the interference degree is small, the fuzzy reference area is locally located. The purpose of accurately positioning the fuzzy reference area can be achieved, thereby improving the positioning accuracy of the fuzzy reference area.

[0085] In one embodiment, the fuzzy reference area is preprocessed, including:

[0086] The abnormal weather condition type to which the abnormal weather condition belongs is obtained; according to the abnormal weather condition type, a corresponding image denoising algorithm is matched to the sequential control reference image; according to the image denoising algorithm, the fuzzy reference area is preprocessed.

[0087] It should be noted that under different abnormal weather conditions, different image denoising algorithms are used to preprocess the blurred reference area. For example, on rainy days, a rain removal algorithm is used to preprocess the blurred reference area, and on snowy days, a snow removal algorithm is used to preprocess the blurred reference area. Therefore, it is necessary to complete the matching of the image denoising algorithm based on the type of abnormal weather condition during the execution of the sequential control operation process.

[0088] As an example, the abnormal weather condition type to which the abnormal weather condition belongs is obtained; the image denoising algorithm of the sequential control reference image is queried using the abnormal weather condition type as an index; and the fuzzy reference area is preprocessed according to the image denoising algorithm.

[0089] In an implementable manner, the image denoising algorithm may specifically be an image de-raining algorithm. According to the image de-raining algorithm, the specific steps of pre-processing the blurred reference area may be as follows:

[0090] First, according to the idea of ​​multi-stage step-by-step restoration, the sequential reference image is derained and restored in three stages, wherein the image deraining algorithm is implemented based on a preset image deraining model, which is a three-stage MPRNet network structure. In the first stage, four first sequential reference sub-image blocks at the first resolution after width-half splitting are used as network input, and in the second stage, two second sequential reference sub-images at the second resolution after height-half splitting are used as input, and in the third stage, the sequential reference image at the third resolution is used as input, wherein the first resolution is smaller than the second resolution, the second resolution is smaller than the third resolution, and the third resolution is the original resolution. It can be understood that in the above three-stage image preprocessing process, the first two stages adopt an encoder-decoder self-network of a u-net architecture embedded based on a channel attention mechanism, and gradually expand the receptive field through multiple downsampling, thereby widely learning contextual information, while the third stage adopts a convolution stacking paradigm original resolution sub-network embedded based on a channel attention mechanism to learn the fine texture required in the output image without changing the image resolution.

[0091] Furthermore, the first and second stages use an encoding-decoding network structure similar to U-Net, which is a fully convolutional neural network for image segmentation without a fully connected layer. Specifically, it consists of a contraction path encoder for downsampling feature extraction, an expansion path decoder for upsampling image restoration, and a jump connection path for transmitting feature information of each scale in the encoder to the decoder. It can be understood that in the first and second stages, as the number of upsampling times increases, the number of channels of the convolution sum gradually decreases, and is ultimately mapped to the number of categories required by the visual task, thereby completing the output of image segmentation. Through the above encoding-decoding network structure, not only can high-level semantic information be extracted and image detail information be gradually restored, but also the encoder's Multi-scale image features are passed to the decoder without reservation, minimizing information loss in the process of restoring the sequential control reference image to the sequential control clear image; in the third stage, a feature extraction sub-network based on the convolutional cascade paradigm is adopted, in which the feature extraction sub-network is cascaded by one original resolution sub-module, and then the final cascade module outputs the residual image through a convolutional layer. Finally, the short-circuit connection of the residual network adds the sequential control reference image and the output residual image pixel by pixel to obtain the final restored sequential control clear image. It can be understood that since the original resolution self-network will not perform any downsampling operations, using this network architecture in the later process of fusing the residual image and the sequential control reference image can produce high-resolution features with rich spatial information.

[0092] Among them, the preset image region model involves a first sub-neural network for the first and second stages and a second sub-neural network for the third stage. The first sub-neural network and the second sub-neural network are both embedded with a channel attention mechanism sub-network module. The channel attention mechanism sub-network module consists of an initial convolution layer, a global average pooling, an attention perception network module, and a residual connection module. First, the initial convolution layer extracts the shallow features of the original input feature map, and the GAP layer (global average pooling) collects the global spatial information of the output shallow feature map and flattens it into a one-dimensional tensor on the channel dimension. Then, the attention network module in the sub-network is used to control the shallow features in the reference image. The raindrop features are focused, the raindrop information in the reference image is implicitly learned, and the attention weight tensor of the raindrop features is output through the activation function; then, the attention tensor on the channel dimension is multiplied pixel by pixel with the shallow feature map, and the attention feature map after feature focusing is output; finally, the element-by-element addition operation adds the attention features to the original input feature map, and the feature information is passed to the next stage for subsequent processing; since this module has the advantage of channel feature focusing, embedding this module in the two sub-networks of the preset image area model can not only effectively reduce feature redundancy, but also pass the focused features to the next module, enriching the expression of subsequent features.

[0093] In the process of rain removal based on the preset image deraining model, in order to take into account the fine spatial details of the image and the multi-scale characteristics of the image, the preset image deraining model fully utilizes the respective advantages of the two network design paradigms and the attention mechanism, combines and embeds multiple single-stage network architectures, and constructs a multi-stage rain image restoration framework. That is, according to their respective advantages, the preset image deraining model adopts an encoder-decoder network in the early and middle stages of the first and second stages, and adds a channel attention submodule to the encoder-decoder network and skip layer connection at each scale, and in the decoder module, a bilinear upsampling operation is used to replace The transposed convolution operation of the original U-Net effectively solves the problem of chessboard artifact interference caused by the transposed convolution to the output image. In the last stage of the third stage, the single-scale stacked convolution network is also embedded with a channel attention module. At the same time, in order to distinguish the existing image deraining model from directly predicting the restored image at each stage and only adding the preprocessing method of the supervisory signal in the later stage, the preset image deraining model introduces a supervisory attention module, which not only adds the supervisory signal between each stage, but also embeds the attention mechanism sub-network module. Among them, the information transmission channel of the channel attention mechanism sub-network module is divided into two channels: the upper channel and the lower channel. First, in this stage, the rain feature map output by the U-Net sub-network decoder is passed through the lower 1*1 convolution layer to generate a residual image with 3 channels. The residual image is added pixel by pixel to the rainy day image to output a rough restored rainy day image. The rough restored image is passed through an attention convolutional network module to generate pixel-by-pixel attention mask features. These features are pixel-by-pixel multiplied with the recalibrated output features of the upper 1*1 convolution to generate attention-guided features. The output features are added to the original features through constant mapping to generate enhanced features after attention focusing, which are passed to the next stage for further processing. The multi-stage supervised attention sub-network is integrated. It combines the advantages of multi-stage supervised learning, attention mechanism and multi-stage features. By introducing supervision signals between each stage, the image restoration process can be optimized on a step-by-step basis to obtain more accurate restoration results. At the same time, the clever embedding of the attention mechanism can help the model effectively suppress unimportant features in the current stage and only allow useful information to propagate to the next stage, reducing the interference of invalid features on the final output. In addition, by embedding the SAM module in each stage of the model, the model can better integrate the information of the previous stage and recalibrate the features through the attention mechanism, thereby improving the overall deraining performance of the model.

[0094] In this embodiment, the image denoising algorithm of the sequential control reference image is matched by the type of abnormal weather condition to which the abnormal weather condition belongs, and finally the fuzzy reference area is preprocessed based on the matched image denoising algorithm. That is, the corresponding image denoising algorithm can be set to perform interference removal processing according to the actual working environment of the equipment to be detected, thereby improving the preprocessing accuracy of the fuzzy reference area.

[0095] In one embodiment, when an abnormality in the sequence control influencing index is detected, before a sequence control reference image of the device to be detected is cleared to obtain a sequence control clear image of the device to be detected, the device sequence control state detection method further includes:

[0096] Detection steps: detect the size relationship between the sequence control influence indicator and the preset sequence control influence indicator threshold; when it is detected that the size relationship is that the sequence control influence indicator is greater than or equal to the preset sequence control influence indicator threshold, determine that the sequence control influence indicator is abnormal; when it is detected that the size relationship is that the sequence control influence indicator is less than the preset sequence control influence indicator threshold, display the indicator prompt information corresponding to the sequence control influence indicator, and return to execute the detection steps until it is detected that the size relationship is that the sequence control influence indicator is greater than or equal to the preset sequence control influence indicator threshold.

[0097] It should be noted that the terminal for controlling the sequential control status detection of the equipment may specifically be a monitoring terminal. To achieve accurate detection of the sequential control status of the equipment, the sequential control reference image of the equipment to be detected may be clarified only when the operating conditions of the equipment to be detected have affected the one-key sequential control operation of the equipment to be detected. When the one-key sequential control operation of the equipment to be detected is not affected, a prompt is given through the indicator prompt information displayed on the monitoring terminal. For example, the indicator prompt information may specifically be an evaluation value output by a pop-up warning.

[0098] As an example, the detection steps are: detecting the size relationship between the sequence control influence indicator and a preset sequence control influence indicator threshold; when it is detected that the size relationship is that the sequence control influence indicator is greater than or equal to the sequence control influence indicator threshold, determining that the sequence control influence indicator is abnormal; when it is detected that the size relationship is that the sequence control influence indicator is less than the preset sequence control influence indicator threshold, displaying the indicator prompt information corresponding to the sequence control influence indicator, and returning to execute the detection steps until it is detected that the size relationship is that the sequence control influence indicator is greater than or equal to the sequence control influence indicator threshold.

[0099] In this embodiment, before the sequence control reference image of the equipment to be detected is clarified, the corresponding equipment sequence control status detection process is executed according to the size relationship between the sequence control influence index and the preset sequence control influence index threshold, so that when the working weather conditions have a small impact on the one-key sequence control operation of the equipment to be detected, the staff is prompted through indicator prompt information, thereby strengthening the human-computer interaction in the equipment sequence control status detection process, and thus laying a foundation for improving the detection flexibility of the equipment sequence control status detection.

[0100] In one feasible method, assuming that the working weather condition is rainy, you can refer to Figure 3 , Figure 3 To represent the flow diagram of equipment sequential control status detection under rainy conditions, first, the operating weather conditions of the equipment to be detected are detected through the parameters collected by the rainfall collection device, and when it is detected that the operating weather conditions of the equipment to be detected reach the rainy day conditions, that is, when it is detected that the operating weather conditions of the equipment to be detected are abnormal operating weather conditions, the operation reliability evaluation model is used to evaluate whether the sequential control influence index is abnormal, and then when the sequential control influence index is lower than 60%, a pop-up warning output evaluation value is output, and the operator continues to wait after confirmation, and when the model evaluation value is higher than 60%, it indicates that the sequential control status of the equipment detected after the rainy day sequential control operation process is reliable, the rainy day sequential control operation process is called, and when the sequential control influence index is higher than 60%, the sequential control image acquisition device is used to collect the equipment to be detected. A sequential control reference image is obtained, and after locating the blurred reference area of ​​the sequential control reference image, the sequential control reference image is preprocessed by a rain removal algorithm to obtain a sequential control clear image, and then the image recognition algorithm is used to perform a reference state detection on the sequential control clear image to obtain the sequential control reference state of the device to be detected, and finally the actual sequential control state of the device to be detected is obtained through the device control state information reported by the power monitoring system, and then the sequential control state detection result of the device to be detected is obtained by comparing the consistency between the sequential control reference state and the actual sequential control state, that is, a double confirmation result is obtained. In addition, when it is determined that the operating weather conditions do not meet the rainy day conditions, the conventional sequential control process is used for execution without the need for operation reliability evaluation and rain removal. Finally, the device sequential control state detection method of this embodiment can complete the accurate detection of the sequential control state of the device to be detected.

[0101] When the sequence control influencing index is abnormal, it means that the operating weather conditions of the equipment to be detected have an impact on its one-key sequence control operation. The clear sequence control image obtained after clarifying the sequence control reference image of the equipment to be detected can remove the interference of the operating weather conditions on the second judgment basis, thereby ensuring the reliability of the sequence control reference state of the equipment to be detected. Finally, the sequence control state of the equipment to be detected can be objectively detected through the state comparison result between the sequence control reference state and the actual sequence control state of the equipment to be detected, instead of directly using the sequence control reference image as the second judgment basis when the operating weather conditions will affect the one-key operation of the equipment to be detected. Therefore, the technical defect that the weather conditions of the working environment of the electrical equipment are uncontrollable, which leads to the second judgment basis being unable to reflect the actual matching of the sequence control state reference requirements of the electrical equipment, and thus making it easy for the equipment sequence control state detection to be inaccurate or the detection time to be too long, is overcome. Therefore, the detection effect of the equipment sequence control state detection is improved.

[0102] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0103] Based on the same inventive concept, the embodiment of the present application also provides a device sequential control state detection device for implementing the device sequential control state detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more device sequential control state detection device embodiments provided below can refer to the limitations of the device sequential control state detection method above, and will not be repeated here.

[0104] In an exemplary embodiment, Figure 4 As shown, a device sequential control state detection device is provided, including: an acquisition module 401, a processing module 402, a first detection module 403 and a second detection module 404, wherein:

[0105] The acquisition module 401 is used to acquire a sequence control influence index of the device to be detected under the operating weather conditions, wherein the sequence control influence index is used to characterize the influence degree of the operating weather conditions on the one-key sequence control operation of the device to be detected;

[0106] The processing module 402 is used to perform a clearing process on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected when the sequence control influencing index is detected to be abnormal;

[0107] A first detection module 403 is used to perform a sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected;

[0108] The second detection module 404 is used to perform sequence control state detection on the device to be detected according to a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

[0109] In one embodiment, the acquisition module 401 is further used for:

[0110] According to the meteorological parameters of the environment in which the equipment to be detected is located, the operating weather conditions of the equipment to be detected are detected; when it is detected that the operating weather conditions of the equipment to be detected are abnormal weather conditions, the environmental parameters under the abnormal operating weather conditions are obtained; according to the environmental parameters, the sequential control influence indicators of the equipment to be detected under the abnormal operating weather conditions are queried.

[0111] In one embodiment, the processing module 402 is further configured to:

[0112] Locate a fuzzy reference area in the sequence control reference image, wherein the fuzzy reference area refers to a fuzzy image area that carries reference information of the sequence control state of the device to be detected; preprocess the fuzzy reference area, and use the sequence control reference image obtained by preprocessing the fuzzy reference area as the sequence control clear image.

[0113] In one embodiment, the processing module 402 is further configured to:

[0114] Obtain the white pixel density in the sequence control reference image; if the white pixel density is greater than or equal to a preset white pixel density threshold, use the entire image area of ​​the sequence control reference image as the blurred reference area; if the white pixel density is less than the preset white pixel density threshold, determine the blurred reference area based on the white pixel characteristics of the sequence control reference image.

[0115] In one embodiment, the processing module 403 is further configured to:

[0116] Obtain the abnormal weather condition type to which the abnormal weather condition belongs; match the corresponding image denoising algorithm for the sequential control reference image according to the abnormal weather condition type; and preprocess the fuzzy reference area according to the image denoising algorithm.

[0117] In one of the embodiments, the device sequential control state detection device is also used for:

[0118] Detection step: detect the size relationship between the sequence control influence indicator and the preset sequence control influence indicator threshold; when it is detected that the size relationship is that the sequence control influence indicator is greater than or equal to the preset sequence control influence indicator threshold, determine that the sequence control influence indicator is abnormal; when it is detected that the size relationship is that the sequence control influence indicator is less than the preset sequence control influence indicator threshold, display the indicator prompt information corresponding to the sequence control influence indicator, and return to execute the detection step until it is detected that the size relationship is that the sequence control influence indicator is greater than or equal to the preset sequence control influence indicator threshold.

[0119] Each module in the above-mentioned device sequential control state detection device can be implemented in whole or in part by software, hardware and their combination. Each of 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 corresponding operations of each of the above modules.

[0120] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for detecting a device sequential control state is implemented. Those skilled in the art can understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0122] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0123] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for detecting a sequential control state of a device, characterized in that: The method comprises: Obtaining a sequential control influence index of the device to be detected under the operating weather conditions, wherein the sequential control influence index is used to characterize the degree of influence of the operating weather conditions on the one-key sequential control operation of the device to be detected; When the sequence control influencing index is detected to be abnormal, a sequence control reference image of the device to be detected is processed to be clear, so as to obtain a sequence control clear image of the device to be detected; Performing a sequence control reference state detection on the sequence control clear image to obtain a sequence control reference state of the device to be detected; According to the state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected, the sequence control state of the device to be detected is detected.

2. The method according to claim 1, characterized in that The step of obtaining the sequential control impact index of the equipment to be tested under the operating weather conditions includes: Detecting the operating weather conditions of the equipment to be detected according to the meteorological parameters of the environment in which the equipment to be detected is located; When it is detected that the operating weather condition of the equipment to be detected is an abnormal weather condition, obtaining environmental parameters under the abnormal operating weather condition; According to the environmental parameters, the sequential control impact index of the equipment to be detected under abnormal operating weather conditions is queried.

3. The method according to claim 2, characterized in that The step of performing a clear processing on the sequence control reference image of the device to be detected to obtain a clear sequence control image of the device to be detected includes: Locating a fuzzy reference area in the sequence control reference image, wherein the fuzzy reference area refers to a fuzzy image area carrying sequence control state reference information of the device to be detected; The fuzzy reference area is preprocessed, and a sequence control reference image obtained by preprocessing the fuzzy reference area is used as the sequence control clear image.

4. The method according to claim 3, characterized in that The locating the blurred reference area in the sequence control reference image comprises: Obtaining the density of white pixels in the sequential control reference image; If the white pixel density is greater than or equal to a preset white pixel density threshold, the entire image area of ​​the sequence control reference image is used as the fuzzy reference area; If the white pixel density is less than the preset white pixel density threshold, the fuzzy reference area is determined according to the white pixel features of the sequence control reference image.

5. The method according to claim 3, characterized in that: The preprocessing of the fuzzy reference area includes: Obtaining the abnormal weather condition type to which the abnormal weather condition belongs; According to the type of abnormal weather condition, matching a corresponding image denoising algorithm for the sequential control reference image; The blurred reference area is preprocessed according to the image denoising algorithm.

6. The method according to claim 1, characterized in that In the case where the sequence control influencing index is detected to be abnormal, before the sequence control reference image of the device to be detected is cleared to obtain the sequence control clear image of the device to be detected, the method further includes: Detection step: detecting the magnitude relationship between the sequence control influence index and a preset sequence control influence index threshold; In the case where it is detected that the size relationship is that the sequence control influence index is greater than or equal to the preset sequence control influence index threshold, determining that the sequence control influence index is abnormal; When it is detected that the size relationship is that the sequence control impact indicator is less than the preset sequence control impact indicator threshold, the indicator prompt information corresponding to the sequence control impact indicator is displayed, and the detection step is returned to be executed until it is detected that the size relationship is that the sequence control impact indicator is greater than or equal to the preset sequence control impact indicator threshold.

7. A device for detecting the sequential control state of equipment, characterized in that: The device comprises: An acquisition module, used to acquire a sequential control influence index of the device to be detected under the operating weather conditions, wherein the sequential control influence index is used to characterize the influence of the operating weather conditions on the one-key sequential control operation of the device to be detected; A processing module, configured to, when detecting that the sequence control influencing index is abnormal, perform a clear processing on the sequence control reference image of the device to be detected to obtain a sequence control clear image of the device to be detected; A first detection module is used to perform a sequence control reference state detection on the sequence control clear image to obtain the sequence control reference state of the device to be detected; The second detection module is used to perform sequence control state detection on the device to be detected according to a state comparison result between the sequence control reference state and the actual sequence control state of the device to be detected.

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

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

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

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