Cable storage environment monitoring system based on artificial intelligence

Abstract

The invention relates to a cable storage environment monitoring system based on artificial intelligence, which quantifies the relationship between the indoor environment condition and the environment condition of each interlayer by monitoring the indoor environment condition and the environment condition of each interlayer of a cable storage place, and ensures that the temperature and the humidity of a key interlayer are kept in a reasonable interval by controlling the indoor environment condition. The problem that environment monitoring is inaccurate due to the fact that differences of different interlayers of cable storage places are ignored in the prior art is solved. In addition, according to the environmental condition data of each interlayer in the historical period, the key interlayer different from other interlayers is judged, so that the environmental monitoring efficiency is improved. In addition, by constructing a relation model between the indoor environment conditions and the key interlayer environment conditions, a theoretical basis is provided for processing the abnormal conditions of the key interlayer.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental monitoring and relates to an artificial intelligence-based cable storage environment monitoring system. Background Art

[0002] In today's society, electricity as a basic energy source supports the normal operation of all walks of life, and cables as the key carrier of power transmission, its quality and performance guarantee are crucial. Whether it is urban power grid construction, power transmission in industrial production, or power support for large-scale infrastructure projects, a large number of cables need to be stored and properly stored. The quality of the cable storage environment directly affects the service life, performance stability and safety of the cable during subsequent use. Once the cable has problems such as insulation performance degradation, skin aging, corrosion, etc. due to storage environment problems, it will not only cause huge economic losses, but may also cause power failures and endanger public safety.

[0003] At present, the environmental monitoring of cable storage places is mainly carried out by deploying several sensors to monitor the indoor temperature and humidity. However, people usually use multiple interlayers to store cables, and the environmental conditions (such as temperature and humidity) of each interlayer are often different, which may cause some interlayers to be in an overheated or overhumid state, accelerating the aging and damage of the cable insulation layer, and increasing the potential risk of cable failure. Therefore, it is necessary to adopt more accurate and comprehensive monitoring methods to ensure that the temperature and humidity of each interlayer are kept in a reasonable range, so as to ensure the stability of the cable storage environment, extend the service life of the cable, and reduce the risk of power system failure caused by cable problems. Summary of the invention

[0004] The present invention provides an artificial intelligence-based cable storage environment monitoring system, which aims to quantify the relationship between the indoor environmental conditions and the environmental conditions of each mezzanine by monitoring the environmental conditions indoors and in each mezzanine of the cable storage place, and to ensure that the temperature and humidity of the key mezzanine are maintained in a reasonable range by controlling the indoor environmental conditions, thereby improving the efficiency of cable storage environment monitoring.

[0005] The purpose of the present invention can be achieved through the following technical solutions: The present application provides an artificial intelligence-based cable storage environment monitoring system, comprising an indoor monitoring module, a mezzanine monitoring module, an abnormality analysis module and a control and regulation module, wherein the indoor monitoring module, the mezzanine monitoring module, the abnormality analysis module and the control and regulation module are communicatively connected, wherein: The indoor monitoring module is used to monitor the indoor environmental conditions of the cable storage place; The interlayer monitoring module is used to monitor the environmental conditions of each interlayer in the cable storage place; The abnormality analysis module is used to analyze abnormal conditions of key interlayer environmental conditions; The control and adjustment module is used to control the indoor environmental conditions according to the relationship between the indoor environmental conditions and the key interlayer environmental conditions to handle the abnormal conditions of the key interlayer environmental conditions.

[0006] Furthermore, the environmental conditions include temperature, humidity, light intensity, air circulation speed and air particle concentration.

[0007] Furthermore, the key interlayer is an interlayer whose environmental conditions are significantly different from those of other interlayers in historical periods.

[0008] Furthermore, the interlayers with significantly different environmental conditions from other interlayers during the historical period are confirmed by the following steps: Collect samples of datasets of environmental conditions at various interlayers during historical periods; Obtain the similarity matrix of each interlayer environmental condition; Calculate the average distance between a certain interlayer and other interlayers through the similarity matrix; The average distance threshold is set, and interlayers with a distance greater than the threshold are identified as critical interlayers.

[0009] Furthermore, the average distance is calculated as follows: , In the formula, D is For the i The average distance between each interlayer and other interlayers; D ij For the i The interlayer and j The distance between the interlayers; n For the i The number of other interlayers in the interlayer.

[0010] Further, the analysis of abnormal conditions of key interlayer environmental conditions includes the following steps: Standard setting: Refer to industry standards, technical parameters and experience to determine the normal range and threshold of environmental condition data; Real-time comparison: compare the real-time monitoring data of key interlayers with the set range and thresholds to detect abnormalities in real time; Trend analysis: Use the MK test method to analyze the change trend of key interlayer real-time data and judge abnormalities in advance; Graded warning: The impact of different environmental conditions on cables is graded and different levels of warnings are sent to operation and maintenance personnel.

[0011] Furthermore, the relationship between the indoor environmental conditions and the key interlayer environmental conditions is confirmed by the following steps: Pearson correlation coefficients were calculated between indoor environmental conditions and key mezzanine environmental condition variables; For variables whose Pearson correlation coefficients were greater than or equal to the preset correlation threshold, a multivariate linear regression model was constructed; For variables whose Pearson correlation coefficient is less than the preset correlation threshold, a BP artificial neural network model is constructed.

[0012] Furthermore, the multiple linear regression model and the BP artificial neural network model both take the indoor environmental conditions as dependent variables and several key interlayer environmental conditions as independent variables.

[0013] Further, the controlling of the indoor environmental conditions to handle the abnormal conditions of the key interlayer environmental conditions comprises the following steps: Abnormal situation assessment: Based on the abnormal analysis of the key mezzanine environment, the specific environmental conditions where the abnormality occurs are identified; Correlation analysis: Find the indoor environmental variables related to the key interlayer abnormal parameters according to the established model, and use the model to determine the indoor environmental conditions that play a key role in the key interlayer abnormal environmental parameters; Formulate control strategies: Set specific control targets for indoor environmental conditions based on key interlayer anomaly assessment and correlation analysis results; Implement control operations: operate indoor environment adjustment equipment according to the selected control target to achieve the corresponding indoor environment conditions; Effect evaluation and adjustment: Compare the key interlayer environmental data before and after regulation to determine whether the regulation measures have achieved expectations and check whether the abnormal parameters have returned to normal; if not, adjust the regulation strategy or revise the model until the key interlayer returns to normal.

[0014] Beneficial effects of the present invention: By monitoring the environmental conditions of the cable storage room and each interlayer, quantifying the relationship between the indoor environmental conditions and the environmental conditions of each interlayer, and controlling the indoor environmental conditions to ensure that the temperature and humidity of the key interlayer remain in a reasonable range, the present invention solves the problem that the prior art ignores the differences between different interlayers in the cable storage place, resulting in inaccurate environmental monitoring.

[0015] The present invention also determines the key interlayer that is different from other interlayers through the environmental condition data of each interlayer in the historical period, thereby improving the efficiency of environmental monitoring.

[0016] In addition, by constructing a relationship model between indoor environmental conditions and key interlayer environmental conditions, a theoretical basis is provided for dealing with abnormal conditions in key interlayers. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1The present invention is a structural diagram of an artificial intelligence-based cable storage environment monitoring system.

[0019] Figure 2 The flowchart of the key interlayer confirmation in one embodiment of the present invention.

[0020] Figure 3 The flowchart for confirming the relationship between indoor environmental conditions and key mezzanine environmental conditions in one embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0022] See also Figure 1-Figure 3 The present application provides an artificial intelligence-based cable storage environment monitoring system, including an indoor monitoring module, a mezzanine monitoring module, an abnormality analysis module and a control and regulation module, wherein the indoor monitoring module, the mezzanine monitoring module, the abnormality analysis module and the control and regulation module are communicatively connected, wherein: The indoor monitoring module is used to monitor the indoor environmental conditions of the cable storage place; The interlayer monitoring module is used to monitor the environmental conditions of each interlayer in the cable storage place; Furthermore, the environmental conditions include temperature, humidity, light intensity, air circulation speed and air particle concentration.

[0023] In this embodiment, in the cable storage environment, environmental conditions such as temperature, humidity, light intensity, air circulation speed and air particle concentration actually affect the performance and life of the cable.

[0024] In terms of temperature, high temperature will accelerate the aging of cable insulation materials, reduce insulation resistance, and shorten the service life of cables; low temperature will reduce cable flexibility and increase the risk of mechanical damage. Therefore, accurate temperature monitoring and timely regulation are critical to maintaining stable cable performance.

[0025] Under humidity conditions, high humidity makes it easy for moisture to penetrate the cable, reduce insulation performance, and may also cause corrosion of metal parts. Real-time monitoring of humidity and timely dehumidification when humidity exceeds the standard can effectively avoid cable failure due to moisture.

[0026] In terms of light intensity, long-term light exposure, especially ultraviolet light, will cause the cable outer sheath to age, become brittle, and crack, reducing its protection and waterproof performance. Monitoring light intensity and taking shading measures accordingly can slow down the aging process of the outer sheath.

[0027] The air circulation speed is related to the heat exchange and harmful gas diffusion of the cable storage environment. Appropriate air circulation can take away the heat in time, prevent local overheating, and disperse harmful gases at the same time. Monitor the air circulation speed and increase ventilation when the speed is too slow to improve heat dissipation and gas diffusion.

[0028] In terms of air particle concentration, dust and other particles attached to the cable surface will absorb moisture and accelerate corrosion, affect heat dissipation, and even cause electrical failures. Monitoring the concentration of air particles and taking measures such as filtering and cleaning when the concentration exceeds the standard can reduce the adverse effects of particles on the cable.

[0029] In general, environmental conditions such as temperature, humidity, light intensity, air circulation speed and air particle concentration are interrelated and work together to affect the cable storage environment. Comprehensive and accurate monitoring of these environmental conditions and timely regulation based on the monitoring results are of great significance to ensuring cable performance, extending service life and ensuring stable operation of the power system. In actual cable storage management, a complete monitoring system should be established, and these environmental factors should be taken seriously to create a suitable storage environment for cables.

[0030] The abnormality analysis module is used to analyze abnormal conditions of key interlayer environmental conditions; Furthermore, the key interlayer is an interlayer whose environmental conditions are significantly different from those of other interlayers in historical periods.

[0031] In this embodiment, the key interlayer has a special definition, that is, the interlayer whose environmental conditions are significantly different from other interlayers in the historical period. This concept is based on the in-depth analysis of the long-term environmental monitoring data of each interlayer in the cable storage site.

[0032] From the perspective of environmental factors, the key interlayer behaves differently from other interlayers in terms of temperature, humidity, light intensity, air circulation speed, and air particle concentration. For example, in terms of humidity, the humidity of other interlayers is stable at 40% - 60%, while the humidity of the key interlayer is always above 70%. This difference is not a random fluctuation, but a clear and persistent feature based on historical data.

[0033] Paying attention to the key interlayer is of great significance to ensure the safe storage of cables. Due to the special environmental conditions, the risks faced by cables are more unique and serious. For example, if the key interlayer is in a high temperature environment for a long time, it will accelerate the aging of the cable insulation layer, reduce the insulation performance, greatly shorten the service life of the cable, and increase the probability of failure. Once the cable in the key interlayer fails, it may affect the stability and reliability of the entire power transmission system.

[0034] By focusing on monitoring the key interlayer, potential problems caused by special environments can be discovered in a timely manner. With the help of the data collected by the monitoring system, the trend of changes in the key interlayer environment can be predicted in advance and targeted measures can be taken. For example, for key interlayers with high humidity, investment in dehumidification equipment can be increased; for key interlayers with abnormal temperatures, the ventilation and heat dissipation system can be optimized. This can not only ensure the safety of cables in key interlayers, but also provide a reference for environmental regulation of other interlayers, improve the scientificity and effectiveness of overall cable storage environment monitoring, and ensure the stable operation of the power system.

[0035] Furthermore, the interlayers with significantly different environmental conditions from other interlayers during the historical period are confirmed by the following steps: (1) Collecting samples of environmental condition datasets of each mezzanine in historical periods: In order to fully understand the environmental conditions of each mezzanine, it is necessary to collect environmental condition data of each mezzanine over a period of time (i.e., historical period). These data cover multiple aspects such as temperature, humidity, light intensity, air circulation speed, and air particle concentration. By arranging corresponding sensors in each mezzanine, these environmental parameters are continuously recorded to form a rich dataset sample. The more comprehensive the collected data and the longer the time span, the more accurately it can reflect the true characteristics and changing laws of the environmental conditions of each mezzanine, providing a reliable basis for subsequent analysis. For example, long-term monitoring can reveal the changing trends of the environmental conditions of certain mezzanines in specific seasons or time periods, which is crucial for determining whether they are critical mezzanines.

[0036] (2) Obtaining the similarity matrix of the environmental conditions of each interlayer: After obtaining the data set samples of the environmental conditions of each interlayer, it is necessary to calculate the similarity of the environmental conditions between each interlayer, and then form a similarity matrix. There are various methods for calculating similarity, and common ones include algorithms based on Euclidean distance and cosine similarity. Taking Euclidean distance as an example, it measures the similarity by calculating the straight-line distance between two interlayers in the multidimensional environmental parameter space. The closer the distance, the higher the similarity. The similarity between each two interlayers is calculated and arranged in a certain order to form a similarity matrix. This matrix shows the similarity relationship between the environmental conditions of each interlayer in an intuitive form. The elements on the diagonal usually represent the similarity between the interlayer itself, with a value of 1 (completely similar), while other elements reflect the similarity between different interlayers. Through the similarity matrix, the relative relationship between the environmental conditions of each interlayer can be quickly understood, providing a data basis for the subsequent analysis of the differences between a certain interlayer and other interlayers.

[0037] (3) Calculate the average distance between a certain interlayer and other interlayers through the similarity matrix: Based on the obtained similarity matrix, take a certain interlayer as the benchmark and calculate its distance from all other interlayers. The distance here corresponds to the similarity. The higher the similarity, the smaller the distance; conversely, the lower the similarity, the larger the distance. Add the distance between this interlayer and each other interlayer, and then divide it by the total number of interlayers minus 1 (because the distance between itself is not calculated), and you will get the average distance between this interlayer and other interlayers. This average distance comprehensively reflects the overall difference in the environmental conditions of this interlayer and other interlayers. The larger the average distance, the more different the environmental conditions of this interlayer are from those of other interlayers, and the more likely it is to be a key interlayer.

[0038] (4) Set an average distance threshold and identify interlayers greater than the threshold as critical interlayers: In order to identify which interlayers are critical from the numerous interlayers, a reasonable average distance threshold needs to be set. The setting of this threshold is usually determined based on experience, industry standards, and research on the impact on cable performance. If the average distance between a certain interlayer and other interlayers is greater than the set threshold, it means that the environmental conditions of the interlayer are significantly different from those of other interlayers, and it meets the definition of a critical interlayer, so it is identified as a critical interlayer. Reasonable setting of the threshold is crucial. If the threshold is set too high, some critical interlayers that have obvious differences but not to an extremely high degree may be missed; if the threshold is set too low, some interlayers with less significant differences may be misjudged as critical interlayers, increasing unnecessary monitoring costs and workload. By setting the threshold scientifically and reasonably and judging the critical interlayer accordingly, we can accurately focus on the special interlayers that have a significant impact on the cable storage environment, providing precise targets for targeted monitoring and regulation.

[0039] Furthermore, the average distance is calculated as follows: , In the formula, D is For the i The average distance between each interlayer and other interlayers; D ij For the i The interlayer and j The distance between the interlayers; n For the i The number of other interlayers in the interlayer.

[0040] Further, the analysis of abnormal conditions of key interlayer environmental conditions includes the following steps: Standard setting: This step aims to establish a benchmark for determining whether the environmental conditions of the key interlayer are abnormal. Industry standards are specifications summarized by the entire industry after long-term practice and research. They reflect generally applicable environmental requirements and set the basic framework for the cable storage environment. Technical parameters come from the cable manufacturer's detailed description of the performance of specific cable products, which clarifies the environmental range in which the cable is suitable for operation or storage in the best state. Experience is accumulated from various situations encountered in the past and the corresponding processing results during the actual cable storage and maintenance process. By combining these three aspects of information, the normal range of various environmental condition data of the key interlayer and the threshold for triggering abnormal judgment can be accurately determined. For example, referring to the industry standard, it is known that the storage temperature of a certain type of cable should be between 5℃ and 40℃. Combined with the cable technical parameters and the actual operation and maintenance, it is found that the cable ages faster when the temperature exceeds 35℃, so the normal range is set to 5℃-35℃, and 35℃ is used as the abnormal threshold of temperature.

[0041] Real-time comparison: In the process of key interlayer environmental monitoring, the real-time monitoring data is the direct basis for judging whether the current environment is abnormal. By comparing these real-time data with the pre-set normal range and thresholds one by one, data points that exceed the normal range can be quickly found, thereby capturing abnormal environmental conditions in real time. This real-time comparison is like an accurate "monitor" that continuously checks the environmental data of the key interlayer. For example, when the data sent back by the humidity sensor in real time shows that the humidity is 80%, and the pre-set normal range of humidity is 30% - 70%, the real-time comparison can immediately determine that the humidity is abnormal at this time, providing key information for timely response measures.

[0042] Trend analysis: In addition to paying attention to whether the real-time data exceeds the threshold, the changing trend of the data is equally important for detecting potential anomalies in advance. The MK test (Mann-Kendall test) is a non-parametric statistical test method that can effectively analyze the trend of time series data. It is not limited by the data distribution form and is applicable to various types of environmental data. By performing the MK test on the real-time data of the key interlayer, it can be found whether the data is in a stable state, gradually rising or falling, or there is a sudden change. For example, when analyzing temperature data, if the MK test results show that the temperature data shows a significant upward trend, even if the current temperature is still within the normal range, it indicates that the threshold may be exceeded in the future, and abnormal conditions may occur. This ability to judge abnormalities in advance enables operation and maintenance personnel to take corresponding preventive measures before potential problems develop into serious failures, effectively ensuring the safe operation of the cable.

[0043] Gradual warning: Different abnormal environmental conditions have different degrees of impact on cable performance and life. The graded warning step is to classify abnormal conditions into different levels based on this difference, so as to convey information to operation and maintenance personnel in a more targeted manner. For example, a slight temperature abnormality may only slightly accelerate the aging of the cable, but will not have a serious impact on the normal operation of the cable in the short term, so it can be classified as a lower level; while a severe humidity abnormality may cause a sharp drop in the insulation performance of the cable, and there is a risk of causing serious faults such as short circuits, so it is classified as a higher level. According to different levels, corresponding warning information is sent to the operation and maintenance personnel, so that the operation and maintenance personnel can quickly understand the severity of the abnormal situation, reasonably arrange resources and take corresponding treatment measures. High-level warnings will prompt the operation and maintenance personnel to take immediate action, while lower-level warnings can be arranged for inspection and processing at an appropriate time, thereby improving the efficiency and accuracy of operation and maintenance work.

[0044] The control and adjustment module is used to control the indoor environmental conditions according to the relationship between the indoor environmental conditions and the key interlayer environmental conditions to handle the abnormal conditions of the key interlayer environmental conditions.

[0045] Furthermore, the relationship between the indoor environmental conditions and the key interlayer environmental conditions is confirmed by the following steps: Calculate the Pearson correlation coefficient: To clarify the degree of linear correlation between the variables of indoor and key interlayer environmental conditions, for each set of corresponding variables, such as the temperature and humidity of the indoor and key interlayer, a specific algorithm is used to comprehensively consider the change trend and discrete conditions of the two sets of data, and the Pearson correlation coefficient is calculated. The coefficient ranges from -1 to 1, which measures the strength and direction of the linear correlation between variables. The closer the value is to 1 or -1, the stronger the linear relationship is; the closer it is to 0, the weaker the linear relationship is.

[0046] Construct a multivariate linear regression model (for highly correlated variables): Preset the correlation threshold. When the Pearson correlation coefficient is greater than or equal to the threshold, it indicates that there is a strong linear relationship between the corresponding environmental variables of the indoor and key interlayers. At this time, a certain environmental variable of the key interlayer is set as the result to be predicted, and the indoor environmental variables related to it are used as influencing factors. By analyzing the collected data, a set of coefficients and constants are determined, and a model that can describe the relationship between the two in a linear way is constructed, so that the key interlayer environmental variables can be predicted based on the indoor environmental variables.

[0047] Constructing BP artificial neural network model (for low correlation variables): If the Pearson correlation coefficient is less than the preset threshold, it means that the linear relationship between the indoor and key interlayer environmental variables is weak, and there may be a complex nonlinear relationship. At this time, the BP artificial neural network model is used to input the low-correlated indoor environmental variables into the input layer, and the key interlayer environmental variables are output as the output layer. The network contains one or more hidden layers. After multiple trainings, the data is processed from the input layer to the output layer through the hidden layer. The error is compared between the prediction and the actual value, and then the back propagation is used to adjust the connection weights of the neurons in each layer to reduce the error, so that the model can learn the complex nonlinear relationship between the variables.

[0048] Furthermore, the multiple linear regression model and the BP artificial neural network model both take the indoor environmental conditions as dependent variables and several key interlayer environmental conditions as independent variables.

[0049] In this embodiment, when studying the relationship between indoor environmental conditions and key interlayer environmental conditions, the constructed multiple linear regression model and BP artificial neural network model both assume that the key interlayer environmental conditions are independent variables, which are causes, and the indoor environmental conditions are dependent variables, which are effects.

[0050] From the perspective of the multivariate linear regression model, it is based on the assumption that the key interlayer environmental conditions will have a linear causal effect on the indoor environmental conditions. In actual operation, a large amount of data such as temperature, humidity, and air particle concentration of the indoor environment, as well as the corresponding data such as temperature, humidity, and air circulation speed of the key interlayer environment are collected. By analyzing these data, the model attempts to determine a set of coefficients and constants so that the independent variable of the key interlayer environmental conditions is multiplied and accumulated with the corresponding coefficients, and then the constant term is added to predict the dependent variable of the indoor environmental conditions as accurately as possible. For example, if the temperature of the key interlayer increases, the model will adjust the coefficients to reflect the trend of its influence on the result variable of indoor temperature, thereby revealing the linear causal relationship between the two and providing a basis for regulating the indoor environment by changing the key interlayer environment.

[0051] The BP artificial neural network model is also based on the causal logic that the key interlayer environmental conditions affect the indoor environmental conditions. This model has an input layer, a hidden layer, and an output layer. The key interlayer environmental condition data enters from the input layer, and the hidden layer neurons perform complex nonlinear processing and conversion on it. After layer-by-layer operations, the predicted value of the indoor environmental conditions is output at the output layer. During the training process, the model continuously adjusts the connection weights between neurons based on the error between the predicted value and the actual indoor environmental condition value with the help of the back propagation algorithm. Through repeated training with a large amount of data, the BP neural network can learn how the key interlayer environmental conditions complexly affect the indoor environmental conditions, that is, clarify the nonlinear causal relationship between the two, and more accurately predict the indoor environmental conditions based on the key interlayer environmental conditions, providing support for precise control of the indoor environment.

[0052] Further, the controlling of the indoor environmental conditions to handle the abnormal conditions of the key interlayer environmental conditions comprises the following steps: Abnormal situation assessment: This step is the starting point for dealing with abnormal situations in the critical interlayer environment. Through a comprehensive analysis of the critical interlayer environment in the early stage, including comparison of real-time data with preset standards, data trend analysis and other means, we can accurately find out the specific environmental conditions that deviate from the normal range. For example, if the monitoring system shows that the temperature of the critical interlayer exceeds the normal range, or the humidity is at an abnormally low value for a long time, these temperatures, humidities, etc. become locked abnormal environmental conditions. Clarifying the abnormality provides a clear direction for subsequent targeted problem solving. Only by knowing clearly where the problem is can we take the right measures to deal with it.

[0053] Correlation analysis: The previously established model of the relationship between indoor environmental conditions and key interlayer environmental conditions plays a key role at this moment. Based on the locked key interlayer abnormal parameters, such as abnormal temperature parameters, the model is used to find the indoor environmental variables associated with them. The model may show that indoor environmental factors such as indoor ventilation volume and air conditioning set temperature are closely related to the key interlayer temperature. Through in-depth analysis of the model, the indoor environmental conditions that play a decisive role in the key interlayer abnormal environmental parameters are further determined. This step clarifies where to start to change the indoor environment and then affect the key interlayer environment, because only by grasping the key indoor environmental influencing factors can the regulation be more effective.

[0054] Formulate control strategies: Combine the results of the first two steps, namely the evaluation results of abnormal conditions in the key interlayer and the analysis of the correlation between the indoor and outdoor environments, to set specific control targets for indoor environmental conditions. If the temperature of the key interlayer is too high and it is found that the indoor ventilation volume is the key influencing factor, then the control target may be set to increase the indoor ventilation volume to a specific value, or reduce the indoor temperature to a suitable range, in the hope of improving the high temperature anomaly of the key interlayer. Clear and specific control targets provide clear guidance for subsequent control operations, ensuring that the control actions are targeted and operational.

[0055] Implement control operations: According to the determined control objectives, the corresponding indoor environment adjustment equipment is actually operated. If the control objective is to increase the indoor ventilation volume, it is necessary to operate the ventilation equipment, such as turning on more ventilation fans, adjusting the opening of ventilation duct valves, etc., so that the indoor environment reaches the set ventilation volume conditions. This step is to transform the strategy into actual action, directly act on the indoor environment, and indirectly affect the key interlayer environment by changing the indoor environmental conditions, in the hope of solving the abnormal environment problems of the key interlayer. During the operation, it is necessary to ensure the accuracy and safety of equipment operation to ensure the smooth progress of the control work.

[0056] Effect evaluation and adjustment: Compare the environmental data of the key interlayer before and after the implementation of the control measures to determine whether the control measures have achieved the expected effect. Focus on checking whether the parameters that were abnormal before have returned to the normal range, such as whether the temperature of the key interlayer has returned to the normal range and whether the humidity has stabilized at a reasonable level. If the comparison shows that the abnormal parameters have not returned to normal and the control has not met expectations, it is necessary to analyze the reasons in depth. The reason may be that the control strategy itself is unreasonable, such as insufficient control strength or incorrect control direction; it may also be that the previously established model has deviations, resulting in an inaccurate understanding of the relationship between indoor and outdoor environments. In response to these possible reasons, adjust the control strategy accordingly, such as increasing the control strength, changing the control method, or revising the model to re-sort out the correlation between indoor and outdoor environments. Through continuous evaluation and adjustment, the treatment plan is continuously optimized until the environment of the key interlayer returns to normal, ensuring the stability and safety of the cable storage environment.

[0057] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A cable storage environment monitoring system based on artificial intelligence, characterized in that: It includes an indoor monitoring module, a mezzanine monitoring module, an abnormal analysis module and a control and adjustment module, wherein the indoor monitoring module, the mezzanine monitoring module, the abnormal analysis module and the control and adjustment module are communicatively connected, wherein: The indoor monitoring module is used to monitor the indoor environmental conditions of the cable storage place; The interlayer monitoring module is used to monitor the environmental conditions of each interlayer in the cable storage place; The abnormality analysis module is used to analyze abnormal conditions of key interlayer environmental conditions; The control and adjustment module is used to control the indoor environmental conditions according to the relationship between the indoor environmental conditions and the key interlayer environmental conditions to handle the abnormal conditions of the key interlayer environmental conditions.

2. The cable storage environment monitoring system based on artificial intelligence according to claim 1 is characterized in that: The environmental conditions include temperature, humidity, light intensity, air circulation speed and air particle concentration.

3. The cable storage environment monitoring system based on artificial intelligence according to claim 1 is characterized in that: The key interlayer is an interlayer whose environmental conditions are significantly different from those of other interlayers in historical periods.

4. The cable storage environment monitoring system based on artificial intelligence according to claim 3 is characterized by: Interlayers with significantly different environmental conditions from other interlayers during the historical period are confirmed by the following steps: Collect samples of datasets of environmental conditions at various interlayers during historical periods; Obtain the similarity matrix of each interlayer environmental condition; Calculate the average distance between a certain interlayer and other interlayers through the similarity matrix; The average distance threshold is set, and interlayers with a distance greater than the threshold are identified as critical interlayers.

5. The cable storage environment monitoring system based on artificial intelligence according to claim 4 is characterized in that: The average distance is calculated as follows: ， In the formula, D is For the i The average distance between each interlayer and other interlayers; D ij For the i The interlayer and j The distance between the interlayers; n For the i The number of other interlayers in the interlayer.

6. The cable storage environment monitoring system based on artificial intelligence according to claim 1 is characterized in that: The analysis of abnormal conditions of key interlayer environmental conditions includes the following steps: Standard setting: Refer to industry standards, technical parameters and experience to determine the normal range and threshold of environmental condition data; Real-time comparison: compare the real-time monitoring data of key interlayers with the set range and thresholds to detect abnormalities in real time; Trend analysis: Use the MK test method to analyze the change trend of key interlayer real-time data and judge abnormalities in advance; Graded warning: The impact of different environmental conditions on cables is graded and different levels of warnings are sent to operation and maintenance personnel.

7. The cable storage environment monitoring system based on artificial intelligence according to claim 1 is characterized in that: The relationship between the indoor environmental conditions and the key interlayer environmental conditions is confirmed by the following steps: Pearson correlation coefficients were calculated between indoor environmental conditions and key mezzanine environmental condition variables; For variables whose Pearson correlation coefficients were greater than or equal to the preset correlation threshold, a multivariate linear regression model was constructed; For variables whose Pearson correlation coefficient is less than the preset correlation threshold, a BP artificial neural network model is constructed.

8. The cable storage environment monitoring system based on artificial intelligence according to claim 7 is characterized by: The multiple linear regression model and the BP artificial neural network model both take the indoor environmental conditions as dependent variables and several key interlayer environmental conditions as independent variables.

9. The cable storage environment monitoring system based on artificial intelligence according to claim 8 is characterized by: The method of controlling the indoor environmental conditions to handle abnormal conditions of the key interlayer environmental conditions includes the following steps: Abnormal situation assessment: Based on the abnormal analysis of the key mezzanine environment, the specific environmental conditions where the abnormality occurs are identified; Correlation analysis: Find the indoor environmental variables related to the key interlayer abnormal parameters according to the established model, and use the model to determine the indoor environmental conditions that play a key role in the key interlayer abnormal environmental parameters; Formulate control strategies: Set specific control targets for indoor environmental conditions based on key interlayer anomaly assessment and correlation analysis results; Implement control operations: operate indoor environment adjustment equipment according to the selected control target to achieve the corresponding indoor environment conditions; Effect evaluation and adjustment: Compare the key interlayer environmental data before and after regulation to determine whether the regulation measures have achieved expectations and check whether the abnormal parameters have returned to normal; if not, adjust the regulation strategy or revise the model until the key interlayer returns to normal.

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