Power distribution cabinet safety monitoring system based on remote monitoring

Abstract

The invention discloses a power distribution cabinet safety monitoring system based on remote monitoring, which relates to the technical field of power distribution cabinet monitoring and comprises a data acquisition module, a safety range acquisition module, an abnormal data transmission number generation module, a verification module and a data recovery module. Original abnormal data is hidden in a transmission number through a gradient value, whether the data is tampered or not in the transmission process can be effectively verified by comparing data symbol groups, the integrity and reliability of the data are ensured, malicious tampering and interception are prevented, the safety of data transmission is guaranteed, and misjudgment and potential safety hazards caused by data errors are avoided; and the verification module performs recovery processing on the data passing the verification to ensure that the data is not tampered in the transmission process and ensure the security and reliability of the data, and even if the data is tampered in the transmission process, the verification module can prevent the tampered data from entering the recovery module, so that misjudgment and potential safety hazards caused by data errors are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution cabinet monitoring, and in particular relates to a power distribution cabinet safety monitoring system based on remote monitoring. Background Art

[0002] Distribution cabinets are key equipment in power systems responsible for distributing, controlling, protecting, and monitoring power. Their operational stability and safety are directly related to the safety of the entire power grid and the normal power consumption of users. Traditional distribution cabinet safety management relies on sensor-based remote monitoring systems. These systems typically set fixed alarm thresholds for various monitoring indicators (such as temperature, voltage, and current).

[0003] However, this method usually uses plain text or simple encoding when transmitting abnormal data, and the data can be easily intercepted, tampered with or maliciously interfered with by hackers during the transmission process. Since the security of data transmission in the distribution cabinet is crucial during the security monitoring process, unsafe transmission methods will bring huge hidden dangers to the stable operation of the power grid. Based on this, a distribution cabinet security monitoring system based on remote monitoring is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a distribution cabinet security monitoring system based on remote monitoring, which solves the technical problem that when the distribution cabinet transmits abnormal data during the security monitoring process, it usually adopts plain text or simple encoding transmission, and the data is easily intercepted, tampered or maliciously interfered by hackers during the transmission process.

[0005] A power distribution cabinet safety monitoring system based on remote monitoring, comprising:

[0006] The data acquisition module collects and records historical data on the temperature, voltage, and current of the power distribution cabinet;

[0007] The safety range acquisition module obtains the safety range corresponding to each type of data based on the historical data of various types of data in the distribution cabinet within the preset time period t. The value of t is 1, 2, ..., a, where a refers to the total number of days of the preset time period t, that is, a = 60;

[0008] The abnormal data transmission number generation module represents different types of data through different type symbols ◇, ○ and □, obtains the real-time values of various types of data in the distribution cabinet in real time, compares and analyzes the real-time values of various types of data in the distribution cabinet with the safety ranges corresponding to each type of data, determines the abnormal distribution cabinet, compares and analyzes the real-time values of various types of data in the abnormal distribution cabinet with the lower and upper limits of the safety ranges of each type of data, obtains the gradient values corresponding to each type of data, combines the assignment rules between different types of symbols ◇, ○ and □ to obtain the symbol assignments corresponding to the three different types of symbols, and generates the abnormal data transmission number corresponding to the abnormal distribution cabinet;

[0009] The verification module, when receiving the abnormal data transmission number of the abnormal distribution cabinet, obtains the data symbol group and compares it with the verification symbol group ◇○□. If they are consistent, the verification is passed and the abnormal data transmission number is input to the data recovery module. Otherwise, no processing is performed;

[0010] The data recovery module combines the symbol assignments corresponding to different types of symbols with the numerical conversion rules to recover the gradient values corresponding to each type of data.

[0011] As a further solution of the present invention, the specific method of obtaining the safety range corresponding to each type of data is as follows:

[0012] The daily calculated data corresponding to each type of data within the preset time t are marked as Akt respectively. The mean Apk corresponding to the daily calculated data Akt of each type of data within the preset time T is obtained. The standard deviation σk corresponding to each type of data is calculated according to the daily calculated data Akt of each type of data within the preset time t. Then, the difference between the mean Apk and the standard deviation σk corresponding to each type of data is used as the lower limit Apk-σk of the class data safety range. The sum of the mean Apk and the standard deviation σk corresponding to each type of data is used as the upper limit Apk+σk of the class data safety range, thereby obtaining the safety range Qk[Apk-σk, Apk+σk] corresponding to each type of data, where k refers to different types of data in the multi-source data, and the values of k are 1, 2 and 3, k=1: temperature data; k=2: voltage data; k=3: current data.

[0013] As a further solution of the present invention: the specific method of determining an abnormal distribution cabinet is:

[0014] The real-time values of various types of data in the distribution cabinet are marked as Dk, and the real-time values Dk of various types of data in the distribution cabinet are compared with the safety ranges corresponding to each type of data to judge the abnormal data. When abnormal data exists, the distribution cabinet is judged to be an abnormal distribution cabinet, otherwise, no processing is performed.

[0015] As a further solution of the present invention: the specific method of determining abnormal data is:

[0016] The real-time values of each type of data are compared with the corresponding safety ranges of each type of data, and the data types whose real-time values are not within the corresponding safety range are marked as abnormal data. Otherwise, no processing is performed.

[0017] As a further solution of the present invention, the specific method of obtaining the gradient values corresponding to each type of data is:

[0018] When the distribution cabinet is judged as an abnormal distribution cabinet, the real-time value corresponding to the abnormal data is obtained. When the real-time value corresponding to the abnormal data is less than the lower limit of the corresponding type of safety range, the absolute value of the difference between the real-time value and the lower limit is marked as D - As the gradient value of the corresponding type, when the real-time value corresponding to the abnormal data is greater than the upper limit of the corresponding type safety range, the absolute value of the difference between the real-time value and the upper limit is marked as D + As the gradient value of the corresponding type, for non-abnormal data, 0 is used as the gradient value R1, R2 and R3 of the corresponding type. At the same time, the gradient value corresponding to each type of data is combined with the type symbol of the corresponding data type through ".

[0019] As a further solution of the present invention, the specific method of representing different types of data by different type symbols ◇, ○ and □ is as follows:

[0020] Temperature data is represented by the type symbol ◇, voltage data is represented by the type symbol ○, and current data is represented by the type symbol □.

[0021] As a further solution of the present invention, the specific method of obtaining the symbol assignments corresponding to the three different types of symbols by combining the assignment rules between the different types of symbols ◇, ○ and □ is as follows:

[0022] The assignment rules are: ○+◇=2, ◇+□=3 and ○+◇+□=23. It can be seen that ○ is greater than ◇, ◇ is greater than □, and the value of □ is the smallest. Moreover, ◇ is 3 greater than □, and ○ is 2 greater than ◇, so ○ is 5 greater than □. Then ○+◇+□=23 is equal to □+5+□+3+□=23, which means 3□+8=23. Calculation shows 3□=15, so the value of □ is 5, the value of ○ is 10, and the value of ◇ is 8. The symbol assignments E1, E2 and E3 corresponding to the three different types of symbols ◇, ○ and □ are 8, 10 and 5 respectively.

[0023] As a further solution of the present invention: the specific method of generating the abnormal data transmission number corresponding to the abnormal distribution cabinet is:

[0024] Transmit ◇″R1+E1 / ○″R2+E2 / □″R3+E3 as the abnormal data transmission number ◇″U1 / ○″U2 / □″U3 of the abnormal distribution cabinet.

[0025] As a further solution of the present invention: the specific method of comparing the data symbol group with the verification symbol group ◇○□ is:

[0026] When the abnormal data transmission number of the abnormal distribution cabinet is tampered with or intercepted during the transmission process, the type symbol □ is transformed into The data symbol group is ◇○ When the abnormal data transmission number of the abnormal distribution cabinet has not been tampered with or intercepted during the transmission process, the data symbol group is ◇○□. If the data symbol group is consistent with the verification symbol group, the verification is passed, and the abnormal data transmission number is input to the data recovery module. Otherwise, no processing is performed.

[0027] As a further solution of the present invention, the specific method for recovering and obtaining the gradient values corresponding to each type of data is as follows:

[0028] According to the numerical conversion rule: (◇″U1-E1 / ○″U2-E2 / □″U3-E3)=(◇″R1 / ○″R2 / □″R3), the gradient values R1, R2 and R3 corresponding to each type of data are obtained, and the gradient values corresponding to each type of data are restored and obtained.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (1) The present invention encrypts the gradient value and the preset symbol assignment, effectively hiding the original abnormal data. Even if the data packet is intercepted during transmission, the attacker cannot directly interpret its true meaning, greatly enhancing the transmission security of critical infrastructure monitoring data.

[0031] (2) The present invention uses a unique symbol assignment and encryption algorithm to hide the original abnormal data in the transmission number through the gradient value. Even if the data is intercepted, the attacker cannot easily decipher its true meaning, which greatly improves the security of critical power data transmission;

[0032] (3) The present invention can effectively verify whether the data has been tampered with during transmission by comparing data symbol groups, ensuring the integrity and reliability of the data, preventing malicious tampering and interception, ensuring the security of data transmission, and avoiding misjudgments and security risks caused by data errors;

[0033] (4) The present invention ensures that the data has not been tampered with during the transmission process by only recovering the data that has passed the verification, thereby ensuring the security and reliability of the data. Even if the data is tampered with during the transmission process, the verification module will prevent the tampered data from entering the recovery module, thereby avoiding the influence of the erroneous data on the subsequent analysis and processing. The data that has passed the verification is restored through the symbol assignment rules and gradient value recovery, and the original state of the data can be accurately restored. There is no need to transmit the original value, and the data is restored only through the gradient number, which improves bandwidth utilization and transmission security, ensures the integrity and availability of the data, and improves the stability and robustness of the entire monitoring system. It can work normally even in a complex network environment. The restored data can accurately reflect the actual operating status of the distribution cabinet, provide accurate data support for subsequent analysis and processing, and help to timely discover and deal with potential safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the system framework structure of the present invention;

[0035] Figure 2 Schematic diagram of the framework structure of the verification module of the present invention. DETAILED DESCRIPTION

[0036] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Example 1: Please refer to Figure 1 ,This application provides a distribution cabinet safety monitoring system based on remote monitoring, including;

[0038] The data acquisition module collects and records the multimodal historical data of the power distribution cabinet. Multimodal data refers to various types of data such as temperature, voltage, and current.

[0039] The safety range acquisition module obtains the safety range corresponding to each type of data of the power grid smart terminal based on the multimodal historical data of the distribution cabinet within the preset time length t. The specific method is as follows:

[0040] The preset duration t is calculated from the current time and counted back 60 days. The data on the day of acquisition is not included. That is, the preset duration t is equal to 60 days.

[0041] Obtain the average value of each type of data corresponding to each day of the distribution cabinet within the preset time t as the daily calculation data corresponding to each type of data on that day. Based on the daily calculation data corresponding to each type of data within the preset time t, calculate the safety range corresponding to each type of data of the power grid smart terminal. The specific calculation and analysis method is as follows:

[0042] The daily calculated data corresponding to each type of data within the preset time t are marked as Akt, where k refers to different types of data in the multi-source data, and also serves as the category number corresponding to each type of data. t refers to different days, and k takes values of 1, 2, and 3. k=1: temperature data; k=2: voltage data; k=3: current data. A1t represents the daily calculated temperature data of the tth day, A2t represents the daily calculated voltage data of the tth day, and A3t represents the daily calculated current data of the tth day. Where t takes values of 1, 2, ..., a, and a refers to the total number of days in the preset time t, that is, a=60;

[0043] According to the daily calculated data Akt corresponding to each type of data within the preset time t, the average value Apk corresponding to each type of daily calculated data Akt is obtained. According to the daily calculated data Akt corresponding to each type of data within the preset time T and the average value Apk, the safety range Qk corresponding to each type of data of the power grid smart terminal is calculated. The specific calculation method is:

[0044] First, through the formula: According to the daily calculated data Akt corresponding to each type of data within the preset time t, the standard deviation σk corresponding to each type of data is calculated. Then, the difference between the mean Apk and the standard deviation σk corresponding to each type of data is used as the lower limit Apk-σk of the class data safety range. The sum of the mean Apk and the standard deviation σk corresponding to each type of data is used as the upper limit Apk+σk of the class data safety range, thereby obtaining the safety range Qk[Apk-σk, Apk+σk] corresponding to each type of data.

[0045] By calculating the mean and standard deviation of historical data, a safety range is dynamically defined, which can be adaptively adjusted with seasonal, environmental, and load changes. This greatly reduces false positives and false negatives caused by environmental changes, significantly improving the accuracy and intelligence of monitoring.

[0046] The abnormal data transmission number generation module represents different types of data in multi-source data with different symbols, obtains the real-time values of various types of data in the distribution cabinet in real time, compares and analyzes the real-time values of various types of data in the distribution cabinet with the safety ranges corresponding to each type of data, and determines the abnormal distribution cabinet. Based on the real-time values of various types of data in the abnormal distribution cabinet and the lower and upper limits of the safety ranges of various types of data, the module generates the abnormal data transmission number corresponding to the abnormal distribution cabinet. The specific method is as follows:

[0047] The temperature, voltage and current data in the multi-source data are represented by three different type symbols: ◇, ○ and □. That is, the temperature data is represented by the type symbol ◇, the voltage data is represented by the type symbol ○, and the current data is represented by the type symbol □.

[0048] Compare and analyze the real-time values of various data of the distribution cabinet with the corresponding safety ranges of each type of data. The specific method for judging abnormal distribution cabinets is as follows:

[0049] The real-time values of various types of data in the distribution cabinet are marked as Dk, and the real-time values Dk of various types of data in the distribution cabinet are compared with the safety ranges Qk[Apk-σk, Apk+σk] corresponding to each type of data. When the real-time value of at least one type is not within the safety range of the corresponding type of data, that is, when abnormal data exists, the distribution cabinet is determined to be an abnormal distribution cabinet. Otherwise, no processing is performed. That is, when the real-time values of various types of data in the distribution cabinet are all within the safety range Qk of the corresponding type, no processing is performed.

[0050] Compare the real-time value Dk of each type of data with the corresponding safety range Qk of each type of data, and mark the data type whose real-time value Dk is not within the corresponding safety range Qk as abnormal data;

[0051] When the distribution cabinet is judged as an abnormal distribution cabinet, the real-time value corresponding to the abnormal data of the distribution cabinet is obtained and compared with the lower limit and upper limit of the safety range of the corresponding type. When the real-time value corresponding to the abnormal data is less than the lower limit of the safety range of the corresponding type, the absolute value of the difference between the real-time value and the lower limit is marked as D - As the gradient value of the corresponding type, it is combined with the type symbol of the corresponding data type through ". When the real-time value corresponding to the abnormal data is greater than the upper limit of the corresponding type safety range, the absolute value of the difference between the real-time value and the upper limit is marked as D + As the gradient value of the corresponding type, it is combined with the symbol of the corresponding data type through ", and for non-abnormal data, 0 is used as the gradient value of the corresponding type;

[0052] The various data symbols of the abnormal distribution cabinet and the gradient values of various data are expressed in the form of ◇″R1 / ○″R2 / □″R3, where R1, R2 and R3 represent the gradient values corresponding to the various data respectively;

[0053] By encrypting the gradient values with pre-set symbol assignments, the original abnormal data is effectively hidden. Even if the data packet is intercepted during transmission, attackers cannot directly decipher its true meaning. Furthermore, by verifying specific symbols in the verification module, it can effectively identify whether the data has been tampered with during transmission, greatly enhancing the transmission security of critical infrastructure monitoring data.

[0054] The temperature, voltage and current correspond to three different types of symbols ◇, ○ and □, respectively. If the assignment rules are met: ○+◇=2, ◇+□=3 and ○+◇+□=23, then the symbol assignments E1, E2 and E3 corresponding to the three different types of symbols can be obtained.

[0055] Transmit ◇″R1+E1 / ○″R2+E2 / □″R3+E3 as the abnormal data transmission number ◇″U1 / ○″U2 / □″U3 of the abnormal distribution cabinet;

[0056] Through unique symbol assignment and encryption algorithms, the original abnormal data is hidden in the transmission number through gradient values. Even if the data is intercepted, attackers cannot easily crack its true meaning, which greatly improves the data transmission security of critical power facilities.

[0057] Example 2: As Example 2 of the present invention, please refer to Figure 2 , in the specific implementation of this application, compared with embodiment 1, the technical solution of this embodiment differs from embodiment 1 only in that this embodiment further includes a verification module and a data recovery module;

[0058] The verification module, when receiving the abnormal data transmission number ◇″U1 / ○″U2 / □″U3 of the abnormal distribution cabinet, extracts the type symbol of each type of data, obtains the data symbol group, and compares it with the verification symbol group ◇○□. If they are consistent, the verification is passed. Otherwise, no processing is performed. When the abnormal data transmission number of the abnormal distribution cabinet is tampered with and intercepted during the transmission process, the type symbol □ is transformed into The data symbol group is ◇○ When the abnormal data transmission number of the abnormal distribution cabinet is not tampered with or intercepted during the transmission process, the data symbol group is ◇○□;

[0059] When the verification is passed, the abnormal data transmission number ◇″U1 / ○″U2 / □″U3 is transmitted to the data recovery module;

[0060] Verify the received abnormal data transmission number. Extract the data symbol group and compare it with the verification symbol group ◇○□. If the data is tampered with during transmission, the symbol □ will be transformed into The data symbol group becomes ◇○ After verification, the data is transmitted to the data recovery module; by comparing the data symbol group, it can effectively verify whether the data has been tampered with during the transmission process, ensure the integrity and reliability of the data, prevent malicious tampering and interception, ensure the security of data transmission, and avoid misjudgment and security risks caused by data errors.

[0061] The data recovery module, according to the assignment rules satisfied by three different type symbols ◇, ○, and □ (i.e., ○ + ◇ = 2, ◇ + □ = 3, and ○ + ◇ + □ = 23), obtains the symbol assignments E1, E2, and E3 corresponding to the three different type symbols respectively. Then, according to the numerical conversion rule: (◇″U1 - E1 / ○″U2 - E2 / □″U3 - E3) = (◇″R1 / ○″R2 / □″R3), obtains the gradient values R1, R2, and R3 corresponding to various types of data respectively, and performs recovery acquisition on the gradient values corresponding to various types of data;

[0062] The specific method for obtaining the symbol assignments E1, E2, and E3 corresponding to the three different type symbols respectively is as follows:

[0063] From ○ + ◇ = 2, ◇ + □ = 3, and ○ + ◇ + □ = 23, it can be known that ○ is greater than ◇, ◇ is greater than □, the assignment of □ is the smallest, and ◇ is 3 larger than □, and ○ is 2 larger than ◇, then ○ is 5 larger than □. Then ○ + ◇ + □ = 23 is equal to □ + 5 + □ + 3 + □ = 23, and it can be obtained that 3□ + 8 = 23. By calculation, 3□ = 15, then the assignment of □ is 5, the assignment of ○ is 10, and the assignment of ◇ is 8;

[0064] Then the symbol assignments E1, E2, and E3 corresponding to the three different type symbols ◇, ○, and □ are 8, 10, and 5 respectively;

[0065] According to (◇″U1 - E1 / ○″U2 - E2 / ″U3 - E3) = (◇″R1 / ○″R2 / □″R3) to obtain the gradient values R1, R2, and R3 corresponding to various types of data respectively, and perform recovery acquisition on the gradient values corresponding to various types of data;

[0066] Only perform recovery processing on the data that passes the verification to ensure that the data is not tampered with during the transmission process, and guarantee the security and reliability of the data. Even if the data is tampered with during the transmission process, the verification module will prevent the tampered data from entering the recovery module, thereby avoiding the impact of incorrect data on subsequent analysis and processing. For the data that passes the verification, through the symbol assignment rule and gradient value recovery, the original state of the data can be accurately restored, ensuring the integrity and availability of the data, improving the stability and robustness of the entire monitoring system, and enabling it to work normally even in a complex network environment. The recovered data can accurately reflect the actual operating state of the power distribution cabinet, providing accurate data support for subsequent analysis and processing, and helping to detect and handle potential safety hazards in a timely manner.

[0067] First, by studying the historical operating data of the distribution cabinet over the past period of time, and using the mean and standard deviation in statistics, a "floating" safety range is dynamically calculated for various indicators such as temperature, voltage, and current. Subsequently, the system monitors the current data in real time. Once any data is found to deviate from this dynamic safety range, it will be immediately judged as abnormal. Then, the system will calculate the "distance" (i.e., gradient value) of this data point from the safety boundary, and encode and encrypt this gradient value with the specific symbol representing the data type and its encrypted value to generate a unique "abnormal data transmission number". After this number is securely sent to the monitoring center, the data recovery module performs reverse decryption operations to accurately restore the degree of abnormality of various indicators and provide decision support for operation and maintenance personnel; avoid tampering of various types of data in the distribution cabinet during transmission under abnormal circumstances, enhance the transmission security of various types of abnormal data in the distribution cabinet under abnormal circumstances, and further improve the data transmission security of distribution cabinet safety monitoring.

[0068] Example 3: As Example 3 of the present invention, when this application is specifically implemented, compared with Example 1 and Example 2, the technical solution of this example is to combine the solutions of the above-mentioned Example 1 and Example 2 for implementation.

[0069] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0070] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A power distribution cabinet safety monitoring system based on remote monitoring, characterized in that: include: The data acquisition module collects and records historical data on the temperature, voltage, and current of the power distribution cabinet; The safety range acquisition module obtains the safety range corresponding to each type of data based on the historical data of various types of data in the distribution cabinet within the preset time period t. The value of t is 1, 2, ..., a, where a refers to the total number of days of the preset time period t, that is, a = 60; The abnormal data transmission number generation module represents different types of data through different type symbols ◇, ○ and □, obtains the real-time values of various types of data in the distribution cabinet in real time, compares and analyzes the real-time values of various types of data in the distribution cabinet with the safety ranges corresponding to each type of data, determines the abnormal distribution cabinet, compares and analyzes the real-time values of various types of data in the abnormal distribution cabinet with the lower and upper limits of the safety ranges of each type of data, obtains the gradient values corresponding to each type of data, combines the assignment rules between different types of symbols ◇, ○ and □ to obtain the symbol assignments corresponding to the three different types of symbols, and generates the abnormal data transmission number corresponding to the abnormal distribution cabinet; The verification module, when receiving the abnormal data transmission number of the abnormal distribution cabinet, obtains the data symbol group and compares it with the verification symbol group ◇○□. If they are consistent, the verification is passed and the abnormal data transmission number is input to the data recovery module. Otherwise, no processing is performed; The data recovery module combines the symbol assignments corresponding to different types of symbols with the numerical conversion rules to recover the gradient values corresponding to each type of data.

2. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 1, characterized in that: The specific methods for obtaining the security range corresponding to each type of data are as follows: The daily calculated data corresponding to each type of data within the preset time t are marked as Akt respectively. The mean Apk corresponding to the daily calculated data Akt of each type of data within the preset time T is obtained. The standard deviation σk corresponding to each type of data is calculated according to the daily calculated data Akt of each type of data within the preset time t. Then, the difference between the mean Apk and the standard deviation σk corresponding to each type of data is used as the lower limit Apk-σk of the class data safety range. The sum of the mean Apk and the standard deviation σk corresponding to each type of data is used as the upper limit Apk+σk of the class data safety range, thereby obtaining the safety range Qk[Apk-σk, Apk+σk] corresponding to each type of data, where k refers to different types of data in the multi-source data, and the values of k are 1, 2 and 3, k=1: temperature data; k=2: voltage data; k=3: current data.

3. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 2, characterized in that: The specific method for determining abnormal distribution cabinets is as follows: The real-time values of various types of data in the distribution cabinet are marked as Dk, and the real-time values Dk of various types of data in the distribution cabinet are compared with the safety ranges corresponding to each type of data to judge the abnormal data. When abnormal data exists, the distribution cabinet is judged to be an abnormal distribution cabinet, otherwise, no processing is performed.

4. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 3, characterized in that: The specific method for judging abnormal data is as follows: The real-time values of each type of data are compared with the corresponding safety ranges of each type of data, and the data types whose real-time values are not within the corresponding safety range are marked as abnormal data. Otherwise, no processing is performed.

5. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 4, characterized in that: The specific method of obtaining the gradient values corresponding to each type of data is: When the distribution cabinet is judged as an abnormal distribution cabinet, the real-time value corresponding to the abnormal data is obtained. When the real-time value corresponding to the abnormal data is less than the lower limit of the corresponding type of safety range, the absolute value of the difference between the real-time value and the lower limit is marked as D - As the gradient value of the corresponding type, when the real-time value corresponding to the abnormal data is greater than the upper limit of the corresponding type safety range, the absolute value of the difference between the real-time value and the upper limit is marked as D + As the gradient value of the corresponding type, for non-abnormal data, 0 is used as the gradient value R1, R2 and R3 of the corresponding type. At the same time, the gradient value corresponding to each type of data is combined with the type symbol of the corresponding data type through ".

6. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 1, characterized in that: The specific way to represent different types of data using different type symbols ◇, ○ and □ is as follows: Temperature data is represented by the type symbol ◇, voltage data is represented by the type symbol ○, and current data is represented by the type symbol □.

7. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 5, characterized in that: The specific way to obtain the symbol assignments corresponding to the three different types of symbols by combining the assignment rules between different types of symbols ◇, ○ and □ is as follows: The assignment rules are: ○+◇=2, ◇+□=3 and ○+◇+□=23. It can be seen that ○ is greater than ◇, ◇ is greater than □, and the value of □ is the smallest. Moreover, ◇ is 3 greater than □, and ○ is 2 greater than ◇, so ○ is 5 greater than □. Then ○+◇+□=23 is equal to □+5+□+3+□=23, which means 3□+8=23. Calculation shows 3□=15, so the value of □ is 5, the value of ○ is 10, and the value of ◇ is 8. The symbol assignments E1, E2 and E3 corresponding to the three different types of symbols ◇, ○ and □ are 8, 10 and 5 respectively.

8. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 7, characterized in that: The specific method of generating the abnormal data transmission number corresponding to the abnormal distribution cabinet is as follows: Transmit ◇″R1+E1 / ○″R2+E2 / □″R3+E3 as the abnormal data transmission number ◇″U1 / ○″U2 / □″U3 of the abnormal distribution cabinet.

9. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 8, characterized in that: The specific method of comparing the data symbol group with the verification symbol group ◇○□ is: When the abnormal data transmission number of the abnormal distribution cabinet is tampered with or intercepted during the transmission process, the type symbol □ is transformed into The data symbol group is When the abnormal data transmission number of the abnormal distribution cabinet has not been tampered with or intercepted during the transmission process, the data symbol group is ◇○□. If the data symbol group is consistent with the verification symbol group, the verification is passed, and the abnormal data transmission number is input to the data recovery module. Otherwise, no processing is performed.

10. A power distribution cabinet safety monitoring system based on remote monitoring according to claim 9, characterized in that: The specific method for recovering the gradient values corresponding to each type of data is as follows: According to the numerical conversion rule: (◇″U1-E1 / ○″U2-E2 / □″U3-E3)=(◇″R1 / ○″R2 / □″R3), the gradient values R1, R2 and R3 corresponding to each type of data are obtained, and the gradient values corresponding to each type of data are restored and obtained.

Images