Power cable axial core sensor-based pressure monitoring method and system

Abstract

The application discloses a kind of pressure monitoring method and system based on power cable axis center sensor, belong to data transmission technical field, the method includes steps S1: sensor module gathers cable traction force data and own position data of measurement time point, via relay module is sent to monitoring module, and monitoring module is based on traction force data Generation adjustment data, adjustment data includes adjustment value and adjustment time point;Step S2: cloud storage module obtains data sequence and carries out verification, if verification passes, execute step S3;Step S3: adjustment data is analyzed, if analysis result does not satisfy preset rule, extract adjustment time point, define as abnormal time point;Step S4: marking module obtains after abnormal time point, and the closest measurement time point of abnormal time point, based on measurement time point obtains corresponding position data and labels indicating label.It can be remotely monitored that manager carries out cable laying process by the application.

Description

Technical Field

[0001] This invention belongs to the field of data transmission technology, specifically relating to a pressure monitoring method and system based on a power transmission cable axis sensor. Background Technology

[0002] High-voltage cable reeling and laying devices are crucial equipment widely used in the power industry, primarily for cable reeling and laying during loading, unloading, and installation. During cable laying, the cable's end is connected to the reeling and laying device via a rope, and a motor is controlled to rotate it, thus laying the cable. Therefore, controlling the rope's traction force is a key aspect of ensuring cable laying quality. Currently, with the rapid development of computer and control technology, the control technology of high-voltage cable reeling and laying devices has greatly improved. By placing sensors at the cable's head, the traction force during cable laying can be monitored in real time, enabling high-voltage cable reeling and laying devices to achieve higher precision and lower error rates in control operations. Furthermore, high-voltage cable reeling and laying can be automated through computer-aided operation. However, current technologies generally only transmit traction force data to the local reeling and laying device; managers not at the construction site cannot obtain this data, thus preventing them from remotely monitoring the cable laying process. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a pressure monitoring method, system, and storage medium based on a power transmission cable axis sensor, thereby resolving the issue of the inability to remotely monitor the cable laying process in the prior art.

[0004] To achieve the aforementioned objectives, this invention proposes a pressure monitoring method based on a power transmission cable axis sensor, comprising:

[0005] Step S1: The sensor module collects cable traction force data and its own position data at the measurement time point, and defines the traction force data, the position data, and the measurement time point as a data sequence. The sensor module sends the data sequence to the relay module, and then the relay module sends it to the monitoring module. The monitoring module generates adjustment data based on the traction force data. The adjustment data includes adjustment values ​​and adjustment time points. The cable winding and unwinding force is adjusted based on the adjustment data.

[0006] Step S2: The cloud storage module obtains the data sequence from the monitoring module and verifies the data sequence. If the verification is successful, proceed to step S3; otherwise, generate a first warning and send it to the mobile terminal.

[0007] Step S3: The cloud storage module analyzes the adjustment data in the data sequence. If the analysis result indicates that the extension force adjustment does not meet the preset rules, the cloud storage module generates a second warning and sends it to the mobile terminal. At the same time, it extracts the adjustment time point in the adjustment data and defines it as an abnormal time point, and continues to execute step S4.

[0008] Step S4: The marking module obtains the measurement time point that is after the abnormal time point and closest to the abnormal time point, obtains the corresponding location data based on the measurement time point, and marks it with an indicator label.

[0009] Furthermore, in step S1, the sensor module sending the data sequence to the relay module includes the following steps:

[0010] The sensor module is pre-set with transmission rules, which include transmission interval, moving speed and transmission distance. The sensor module calculates its real-time speed and the actual distance between itself and the relay module based on the location data, and matches the corresponding transmission interval in the transmission rules based on the real-time speed and the actual distance.

[0011] The sensor module integrates the traction force data, the position data, and the measurement time point into a corresponding data packet, generates a corresponding verification packet based on the data packet, splits the data packet into multiple sub-data packets of the same type and numbers them, and arranges the sub-data packets of different types alternately to generate a first sequence. In the first sequence, the interval between the sub-data packets of the same type is greater than or equal to the transmission interval. The verification packet is inserted into the first sequence at equal intervals to generate a second sequence. The transmission module sends the second sequence as the data sequence to the relay module.

[0012] Furthermore, in step S2, the cloud storage module verifies the data sequence by including the following steps:

[0013] The relay module uses a first encryption program to convert the data sequence into first data and generates second data for verifying the first data. The relay module sends the data sequence to the monitoring module and sends the first data and the second data to the cloud storage module.

[0014] The cloud storage module calculates the network fluctuation value between itself and the relay module within the detection time, and calculates the prediction error rate for transmitting the first data based on the network fluctuation value. If the prediction error rate is greater than a critical threshold, the first data is verified based on the second data. Otherwise, the cloud storage module uses its own stored second encryption program and combines it with the first data to obtain a verification sequence. If the verification sequence is the same as the data sequence, the verification is successful.

[0015] Furthermore, the prediction error rate is calculated based on the following steps:

[0016] After setting a first duration and a second duration, when the cumulative transmission duration exceeds the first duration, the network fluctuation value is updated based on the first formula every time the second duration is reached. The first formula is: ,in, This refers to the network fluctuation value as described in the last update. Let be the length of the second data sent within the m-th second duration. If an error occurs within the second duration, then... The value is 1 if it is not 1, otherwise it is 0.

[0017] The prediction error rate is calculated based on the second formula. The second formula is: , where LB is the length of the first data to be sent.

[0018] Furthermore, the critical threshold is determined based on the following steps:

[0019] Based on the lengths of the first and second data to be sent, calculate the third duration during which the parsing of the first data fails, and the fourth duration for restoring the first data based on the second data. Then, determine the critical threshold TH corresponding to the first data based on a third formula, whereby: ,in, and These are the third duration and the fourth duration, respectively.

[0020] Furthermore, in step S2, the second encryption procedure, in conjunction with the first data, obtains the verification sequence by including the following steps:

[0021] The first data includes a first calculated value and a second calculated value. The cloud storage module generates a first random number and sends it to the monitoring module. The monitoring module generates a second random number, and based on the fourth and fifth formulas, generates the first calculated value p and the second calculated value q, and sends them to the cloud storage module. The fourth formula is: The fifth formula is: Where e is a preset base, r and v are the first random number and the second random number, respectively, and g is the data sequence. After receiving the first calculated value and the second calculated value, the cloud storage module solves for the verification sequence Y based on the sixth formula, which is: .

[0022] This invention also provides a pressure monitoring system based on a power transmission cable shaft sensor. This system is used to implement the aforementioned pressure monitoring method based on a power transmission cable shaft sensor. The system includes:

[0023] The sensor module is used to collect cable traction force data and its own position data at the measurement time point, and defines the traction force data, the position data and the measurement time point as a data sequence. The sensor module sends the data sequence to the relay module.

[0024] The relay module receives the data sequence sent by the sensor module and forwards it to the monitoring module;

[0025] The monitoring module generates adjustment data based on the traction force data. The adjustment data includes adjustment values ​​and adjustment time points. The cable winding and unwinding force is adjusted based on the adjustment data.

[0026] The cloud storage module obtains the data sequence from the monitoring module and verifies the data sequence. If the verification is successful, the adjustment data in the data sequence is analyzed. If the analysis result indicates that the extension force adjustment does not meet the preset rules, the cloud storage module generates a second warning and sends it to the mobile terminal. At the same time, the adjustment time point in the adjustment data is extracted and defined as an abnormal time point. Otherwise, a first warning is generated and sent to the mobile terminal.

[0027] The marking module is used to obtain the measurement time point that is after the abnormal time point and closest to the abnormal time point, obtain the corresponding location data based on the measurement time point, and mark it with an indicator label.

[0028] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0029] This invention places a sensor module at the cable head, allowing for the collection of cable traction force data during cable laying. This data is then sent to a monitoring module and a cloud storage module. Upon receiving the traction force data, the monitoring module automatically adjusts the cable reel-in / out device, ensuring that the cable traction force remains within a preset range even as it increases or decreases, thus preventing cable damage. The cloud storage module stores the traction force data in the cloud, facilitating remote monitoring of the construction site via mobile terminals. Furthermore, the cloud storage module can analyze adjustments to pinpoint locations of abnormal traction force adjustments during cable laying, thereby enabling comprehensive monitoring of the cable laying process.

[0030] The cloud storage module in this invention verifies the traction force data when receiving it to determine whether the data has been modified while passing through the monitoring module. Furthermore, the sensor module first sends the data to the relay module, which then transmits it outwards. This further reduces the power consumption of the sensor module and extends its service life. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the steps of pressure monitoring based on a power transmission cable axis sensor according to the present invention.

[0032] Figure 2 This is a structural diagram of a pressure monitoring system based on a power transmission cable axis sensor according to the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0034] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0035] like Figure 1 As shown, the pressure monitoring method based on a power transmission cable axis sensor includes:

[0036] Step S1: The sensor module collects cable traction force data and its own position data at the measurement time point. The traction force data, position data, and measurement time point are defined as a data sequence. The sensor module sends the data sequence to the relay module, which then sends it to the monitoring module. The monitoring module generates adjustment data based on the traction force data. The adjustment data includes the adjustment value and the adjustment time point. The cable winding and unwinding force is adjusted based on the adjustment data.

[0037] Specifically, the sensor module includes an MS series pressure sensor installed at the cable head, an antenna for transmitting and receiving data, and a GPS locator for positioning itself. The pressure sensor collects cable traction force data every 1 second and obtains the current time as the measurement time point, which is in 24-hour format. Then, the GPS locator determines its current position. For ease of description, the traction force data, position data, and measurement time point are collectively referred to as the data sequence. After collecting the data sequence, the sensor module transmits it to a relay module via the antenna, and then the relay module transmits it to the monitoring module. The reason for transmitting to the relay module is that the relay module can be located at the center of the cable laying path, and the distance between the relay module and the sensor module is relatively short. Therefore, the sensor module can use less power to transmit the data sequence to the relay module, and the relay module then uses more power to transmit it outwards, thus extending the lifespan of the sensor module. Furthermore, since there are complex data calculations involved later, adding a calculation module within the sensor module would increase its size; therefore, a relay module is used in this embodiment.

[0038] The monitoring module is installed in the cable reel-in / deel-out device. The module contains adjustment rules, such as limits on the traction force range. When the received traction force data exceeds the limit, the monitoring module generates adjustment data to reduce the traction force of the cable reel-in / deel-out device; conversely, if the traction force is too low, the module automatically increases it. The monitoring module also records the corresponding adjustment time points in a 24-hour format. Notably, the cable reel-in / deel-out device in this embodiment also includes a manual adjustment mode. In this mode, construction personnel can manually adjust the traction force of the cable reel-in / deel-out device according to actual conditions.

[0039] Step S2: The cloud storage module obtains the data sequence from the monitoring module and verifies the data sequence. If the verification is successful, proceed to step S3; otherwise, generate the first warning and send it to the mobile terminal.

[0040] The data sequence is sent to the cloud storage module, so the manager can access the cloud storage module with their own mobile terminal or personal computer to remotely view the data during the cable laying and winding process. Here, the cloud storage module verifies the data sequence from the monitoring module to determine whether the data included has been manually modified. If the verification passes, it means that it has not been modified. If it fails, a first warning is generated and sent to a pre-designated mobile terminal to remind the manager that there may be data tampering at the construction site.

[0041] Step S3: The cloud storage module analyzes the adjustment data in the data sequence. If the analysis result indicates that the extension and retraction force adjustment does not meet the preset rules, the cloud storage module generates a second warning and sends it to the mobile terminal. At the same time, it extracts the adjustment time point in the adjustment data and defines it as an abnormal time point, and continues to execute step S4.

[0042] The preset rules here also include the same traction range limit as in the monitoring module, and the preset rules also include adjustment trend rules. For example, if the actual traction force of the cable is too large, and the construction personnel then manually increase the traction force of the take-up and release device, the adjustment trend rule will not be met. In this case, a second warning will be generated and sent to the pre-designated mobile terminal to remind the manager that there may be behavior that damages the cable at the construction site.

[0043] Step S4: The marking module obtains the measurement time point that is after the abnormal time point and closest to the abnormal time point, obtains the corresponding location data based on the measurement time point, and marks it with an indicator label.

[0044] Specifically, assuming the abnormal time point is 15:00:00, and the closest measurement time point after that time point is 15:00:01, then the location data at 15:00:01 is obtained, and an indicator label is marked on this location data. The significance of marking the indicator label here is that since there is abnormal traction on the cable head near the location, there may be a foreign object at this location, or the bending angle is too large. At this time, the construction personnel need to confirm the problem at this location. By determining the problem, it can help the construction personnel to judge whether this abnormal traction will cause damage to the cable.

[0045] This invention places a sensor module at the head of the cable, allowing for the collection of cable traction force data during cable laying. This data is then sent to a monitoring module and a cloud storage module. Upon receiving the traction force data, the monitoring module automatically adjusts the cable reel-in / out device to ensure the cable traction force remains within a preset range, preventing cable damage. The cloud storage module stores the traction force data in the cloud, facilitating remote monitoring of the construction site via mobile terminals. Furthermore, the cloud storage module can automatically analyze adjustments, pinpointing any abnormal traction force adjustments during cable laying, thus enabling comprehensive monitoring of the cable laying process.

[0046] The cloud storage module in this invention verifies the traction force data when receiving it to determine whether the data has been modified while passing through the monitoring module. Furthermore, the sensor module first sends the data to the relay module, which then transmits it outwards, thereby reducing the power consumption of the sensor module and extending its service life.

[0047] Of particular note is that this invention enables managers who are not physically present to view cable traction data, thereby achieving remote monitoring of the cable laying process.

[0048] As is well known, cables are laid underground and are constantly moving, resulting in harsh laying environments. In addition, the transmission power of sensor modules is relatively low. When sensor modules communicate with relay modules, data loss is inevitable. Current technologies generally add error correction codes to the data to be transmitted to correct errors in the data. However, in extremely harsh communication environments, continuous data loss may occur. Therefore, a more reliable data transmission method is needed to ensure that data can be reliably transmitted to the receiving end.

[0049] Therefore, in step S1, the sensor module sending the data sequence to the relay module includes the following steps:

[0050] Transmission rules are pre-set within the sensor module, including transmission interval, moving speed, and transmission distance. The sensor module calculates its real-time speed and the actual distance between itself and the relay module based on the location data, and matches the corresponding transmission interval in the transmission rules based on the real-time speed and the actual distance.

[0051] Specifically, transmission rules are first formulated based on historical data loss patterns. These rules include base values ​​and incremental values. For example, the faster the cable moves, the higher the packet loss rate; or the farther the sensor module is from the repeater module, the higher the packet loss rate. Therefore, the sensor module first determines its own speed based on location data. If it moves 0.3m between two measurement points, its speed is 0.3m / s. At this point, the distance to the repeater module is 22m. Here, the base value is set to a speed between 0m / s and 0.2m / s. For distances between 19m and 21m, three transmission intervals are used. The increment is set as follows: for every 0.2m / s increase in the cable's moving speed, one transmission interval is added. For example, the transmission interval increases by 1 for speeds between 0.2m / s and 0.4m / s. For every 2m the sensor module moves away from the repeater module, one transmission interval is added. For example, the transmission interval increases by 1 for distances between 21m and 23m. Therefore, the transmission interval is 3 + 2 = 5, because one transmission interval is added for both speed and distance.

[0052] The sensor module integrates traction force data, position data, and measurement time points into corresponding data packets. Based on the data packets, it generates corresponding verification packets, splits the data packets into multiple sub-data packets of the same type and numbers them, and arranges sub-data packets of different types alternately to generate a first sequence. In the first sequence, the interval between sub-data packets of the same type is greater than or equal to the transmission interval. The verification packets are inserted into the first sequence at equal intervals to generate a second sequence. The transmission module sends the second sequence as a data sequence to the relay module.

[0053] Here, the data packets generated from traction force data, position data, and measurement time points are designated as data packet A, data packet B, and data packet C. Then, check packets a, b, and c are generated for data packets A, B, and C. These check packets can be generated using methods such as LDPC or bit flipping; the specific generation process is existing technology and will not be elaborated here. After generating the data packets, they are further divided into multiple sub-data packets. For example, data packet A is divided into sub-data packets A1, A2, and A3. These sub-data packets are then alternated with other sub-data packets to generate the first sequence. For simplicity, the transmission interval is set to 3. Assuming the first sequence is A1-B1-C1-A2-B2-C2-A3-B3-C3, there are 3 transmission intervals between A1 and A2. Then, check packets are inserted into the first sequence at equal intervals to obtain the second sequence, which is A1-B1-C1-a-A2-B2-C2-b-A3-B3-C3-c. In traditional transmission methods, the data transmission order is A1-A2-A3-B1-B2-B3-C1-C2-C3-abc. If a transmission environment is poor, causing the longer data A1-A2-A3 to be lost, then even with check packet a, the data cannot be recovered. In this case, the relay module will request the sensor module to retransmit the data, affecting the real-time performance of data transmission. However, in this invention, even if A1-B1-C1 is lost at the same location, the data can still be recovered using check packets, thus eliminating the need for retransmission. It is worth noting that if the number of sub-data packets is insufficient to meet the transmission interval, blank data packets are generated to complete the first sequence.

[0054] In step S2 of this embodiment, the cloud storage module verifies the data sequence by including the following steps:

[0055] The relay module uses a first encryption program to convert the data sequence into first data and generates second data for verifying the first data. The relay module sends the data sequence to the monitoring module and sends the first and second data to the cloud storage module.

[0056] Specifically, upon receiving the data sequence, the relay module encrypts it into first data using a first encryption program. Simultaneously, to prevent errors during transmission, it generates second data for verification. The relay module then directly sends the data sequence to the monitoring module, eliminating the need for decryption and thus accelerating data processing. The first and second data are then sent to the cloud storage module, bypassing the monitoring module. This avoids modification of the first and second data by the monitoring module. Furthermore, since the first and second data volumes are larger than the data sequence, and the data sequence passes through the monitoring module before reaching the cloud storage module, the arrival time of both is balanced, aiming for simultaneous arrival for easier processing by the cloud storage module.

[0057] The cloud storage module calculates the network fluctuation value between itself and the relay module within the detection time. Based on the network fluctuation value, it calculates the prediction error rate of the transmitted first data. If the prediction error rate is greater than the critical threshold, the first data is verified based on the second data. Otherwise, the cloud storage module uses its own stored second encryption program and combines it with the first data to obtain a verification sequence. If the verification sequence and the data sequence are the same, the verification is successful.

[0058] Specifically, a larger network fluctuation value indicates a greater likelihood of errors occurring during data transmission between the cloud storage module and the relay module. The prediction error rate of the first data refers to the calculated prediction error rate after its generation. A higher prediction error rate indicates a greater likelihood of errors in the first data reaching the cloud storage module. Therefore, if the prediction error rate exceeds a critical threshold, it indicates a high probability of errors occurring after the first data is sent from the relay module to the cloud storage module, requiring the use of second data for checking and correction. If the prediction error rate is not greater than the critical threshold, the checking process is skipped, thereby increasing data processing speed. After obtaining the first data, the cloud storage module uses a second encryption program to calculate a verification sequence and compares it with the data sequence sent by the monitoring module. If they match, it indicates that the data has not been modified. The specific calculation processes of the first and second encryption programs will be described later.

[0059] In this embodiment, the prediction error rate is calculated based on the following steps:

[0060] After setting a first duration and a second duration, once the cumulative transmission duration exceeds the first duration, the current network fluctuation value is updated based on the first formula every time the second duration is reached. The first formula is: ,in, This is the network fluctuation value since the last update. Let be the length of the second data sent within the m-th second time interval. If an error occurs within the second time interval, then... The value is 1, otherwise it is set to 0.

[0061] Specifically, the first duration is set to 10 seconds, and the second duration is set to 5 seconds. After the relay module establishes communication with the cloud storage module, and 10 seconds have passed since the data transmission started, the relay module calculates the network fluctuation value every 5 seconds. The initial network fluctuation value is 0. Assuming n is 5, the fluctuation value is updated for the 5th time. First, the network fluctuation value after the 4th update is obtained, let's say 0.9. Then, the length of the data transmitted within these 5 seconds is obtained, such as 80 bits. It is also checked whether the cloud storage module has performed any corrections after the 5-second data transmission is complete. If corrections are performed, it indicates an error occurred during data transmission. The value is set to 1. The first four transmissions sent a total of 400 bits of data. The calculation is (0.9*400+1*80) / (400+80) = 0.92. Similarly, if there are no errors, the calculation is (0.9*400+0*80) / (400+80) = 0.75. This formula shows that if an error occurs, the calculated network fluctuation value will increase; if no error occurs, the fluctuation value will decrease. Furthermore, if there are multiple error-free transmissions, the fluctuation value will decrease to a very small value. Therefore, compared to the cumulative error rate method, this calculation method better reflects the real-time changes in the network.

[0062] The prediction error rate is calculated based on the second formula. The second formula is: , where LB is the length of the first data to be sent.

[0063] After calculating the current network fluctuation value, the probability of an error occurring after sending the first data can be calculated using the second formula, i.e., the prediction error rate. As can be seen from the second formula, the larger the network fluctuation value, the larger the calculated error rate will be, and the longer the length of the first data will also be, the larger the calculated network error rate will be.

[0064] In this embodiment, the critical threshold is determined based on the following steps:

[0065] Based on the lengths of the first and second data to be sent, calculate the third time interval during which the parsing of the first data fails, and the fourth time interval for restoring the first data based on the second data. Then, determine the critical threshold TH corresponding to the first data using the third formula: ,in, and These are the third and fourth durations, respectively.

[0066] Traditional threshold settings typically use fixed values. However, in this embodiment, the specific value of the critical threshold changes with the first and second data. Specifically, after generating the first and second data, the lengths of the first and second data are first obtained. Then, the third time required to parse the first data to determine if an error has occurred is obtained. Estimating the parsing and restoration time based on the data length is a common technique; for example, the parsing time for each bit is set according to the verification method, and then multiplied by the total time. Similarly, after an error occurs, the fourth time required to restore the first data is calculated, using the same principle as the third time. Finally, the critical threshold is calculated using a third formula. This third formula means that the longer the parsing time, the larger the calculated critical threshold. Therefore, to balance data processing efficiency, the critical threshold increases when the parsing time is long, potentially preventing it from being verified. Similarly, the longer the restoration time, the larger the calculated critical threshold.

[0067] In step S2, the second encryption procedure, in conjunction with the first data, obtains the verification sequence by including the following steps:

[0068] The first data includes a first calculated value and a second calculated value. The cloud storage module generates a first random number and sends it to the monitoring module. The monitoring module generates a second random number, and based on the fourth and fifth formulas, generates a first calculated value p and a second calculated value q, and sends them to the cloud storage module. The fourth formula is: The fifth formula is: Where e is the preset base, r and v are the first and second random numbers respectively, and g is the data sequence. After receiving the first and second calculated values, the cloud storage module solves for the verification sequence Y based on the sixth formula, which is: .

[0069] The following is a derivation of the above process: The monitoring module obtains the first and second calculated values ​​based on the first and second random numbers. Then, the second calculated value is sent to the cloud storage module. The cloud storage module uses the first and second calculated values ​​to solve the sixth formula. On the left side of the equals sign in the sixth formula... On the right side of the equals sign, Therefore, for the equality to hold, Y = g. Thus, the verification sequence Y obtained by the cloud storage module should be equal to the data sequence g. Therefore, if the obtained verification sequence is the same as the data sequence, it indicates that the data has not been tampered with. Furthermore, the encryption method of this invention makes the encryption process reversible. When the data sent by the monitoring module is corrupted due to network issues, the data sequence can be obtained by restoring the verification sequence. Compared to traditional irreversible hash encryption methods, this encryption method is more reliable.

[0070] like Figure 2 As shown, the present invention also provides a pressure monitoring system based on a power transmission cable shaft sensor. This system is used to implement the aforementioned pressure monitoring method based on a power transmission cable shaft sensor. The system includes:

[0071] The sensor module is used to collect cable traction force data and its own position data at the measurement time point. The traction force data, position data and measurement time point are defined as a data sequence, and the sensor module sends the data sequence to the relay module.

[0072] The relay module receives the data sequence sent by the sensor module and forwards it to the monitoring module.

[0073] The monitoring module generates adjustment data based on traction force data. The adjustment data includes adjustment values ​​and adjustment time points. The cable winding and unwinding force is adjusted based on the adjustment data.

[0074] The cloud storage module obtains data sequences from the monitoring module and verifies the data sequences. If the verification is successful, it analyzes the adjustment data in the data sequences. If the analysis results indicate that the extension and retraction force adjustment does not meet the preset rules, the cloud storage module generates a second warning and sends it to the mobile terminal. At the same time, it extracts the adjustment time points in the adjustment data and defines them as abnormal time points. Otherwise, it generates a first warning and sends it to the mobile terminal.

[0075] The tagging module is used to obtain the measurement time point that is after the abnormal time point and closest to the abnormal time point, obtain the corresponding location data based on the measurement time point, and mark the corresponding label.

[0076] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0077] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0079] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

[0080] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A pressure monitoring method based on a power transmission cable axis sensor, characterized in that, include: Step S1: The sensor module collects cable traction force data and its own position data at the measurement time point, and defines the traction force data, the position data, and the measurement time point as a data sequence. The sensor module sends the data sequence to the relay module, and then the relay module sends it to the monitoring module. The monitoring module generates adjustment data based on the traction force data. The adjustment data includes adjustment values ​​and adjustment time points. The cable winding and unwinding force is adjusted based on the adjustment data. Step S2: The cloud storage module obtains the data sequence from the monitoring module and verifies the data sequence. If the verification is successful, proceed to step S3; otherwise, generate a first warning and send it to the mobile terminal. Step S3: The cloud storage module analyzes the adjustment data of the data sequence. If the analysis result indicates that the release force adjustment does not meet the preset rules, the cloud storage module generates a second warning and sends it to the mobile terminal. At the same time, it extracts the adjustment time point in the adjustment data and defines it as an abnormal time point, and continues to execute step S4. Step S4: The marking module obtains the measurement time point that is after the abnormal time point and closest to the abnormal time point, obtains the corresponding location data based on the measurement time point, and marks it with an indicator label; In step S2, the cloud storage module verifies the data sequence by including the following steps: The relay module uses a first encryption program to convert the data sequence into first data and generates second data for verifying the first data. The relay module sends the data sequence to the monitoring module and sends the first data and the second data to the cloud storage module. The cloud storage module calculates the network fluctuation value between itself and the relay module during the detection time, and calculates the prediction error rate of transmitting the first data based on the network fluctuation value. If the prediction error rate is greater than a critical threshold, the first data is verified based on the second data; otherwise, the verification is skipped, and the cloud storage module directly uses its own stored second encryption program and combines it with the first data to obtain a verification sequence. If the verification sequence is the same as the data sequence, the verification is successful.

2. The pressure monitoring method based on a power transmission cable axis sensor according to claim 1, characterized in that, In step S1, the sensor module sending the data sequence to the relay module includes the following steps: The sensor module is pre-set with transmission rules, which include transmission interval, moving speed and transmission distance. The sensor module calculates its real-time speed and the actual distance between itself and the relay module based on the location data, and matches the corresponding transmission interval in the transmission rules based on the real-time speed and the actual distance. The sensor module integrates the traction force data, the position data, and the measurement time point into a corresponding data packet, generates a corresponding verification packet based on the data packet, splits the data packet into multiple sub-data packets of the same type and numbers them, and arranges the sub-data packets of different types alternately to generate a first sequence. In the first sequence, the interval between the sub-data packets of the same type is greater than or equal to the transmission interval. The verification packet is inserted into the first sequence at equal intervals to generate a second sequence. The transmission module sends the second sequence as the data sequence to the relay module.

3. The pressure monitoring method based on a power transmission cable axis sensor according to claim 1, characterized in that, The prediction error rate is calculated based on the following steps: After setting a first duration and a second duration, when the cumulative transmission duration exceeds the first duration, the network fluctuation value is updated based on the first formula every time the second duration is reached. The first formula is: ,in, This refers to the network fluctuation value as described in the last update. Let be the length of the second data sent within the m-th second duration. If an error occurs within the second duration, then... The value is 1 if it is not 1, otherwise it is 0. The prediction error rate is calculated based on the second formula. The second formula is: , where LB is the length of the first data to be sent.

4. The pressure monitoring method based on a power transmission cable axis sensor according to claim 1, characterized in that, The critical threshold is determined based on the following steps: Based on the lengths of the first and second data to be sent, calculate the third duration during which the parsing of the first data fails, and the fourth duration for restoring the first data based on the second data. Then, determine the critical threshold TH corresponding to the first data based on a third formula, whereby: ,in, and These are the third duration and the fourth duration, respectively.

5. The pressure monitoring method based on a power transmission cable axis sensor according to claim 1, characterized in that, In step S2, the second encryption program, in conjunction with the first data, obtains the verification sequence by including the following steps: The first data includes a first calculated value and a second calculated value. The cloud storage module generates a first random number and sends it to the monitoring module. The monitoring module generates a second random number, and based on the fourth and fifth formulas, generates the first calculated value p and the second calculated value q, and sends them to the cloud storage module. The fourth formula is: The fifth formula is: Where e is a preset base, r and v are the first random number and the second random number, respectively, and g is the data sequence. After receiving the first calculated value and the second calculated value, the cloud storage module solves for the verification sequence Y based on the sixth formula, which is: .

6. A pressure monitoring system based on a power transmission cable axis sensor, used to implement the pressure monitoring method based on a power transmission cable axis sensor as described in any one of claims 1-5, characterized in that, include: The sensor module is used to collect cable traction force data and its own position data at the measurement time point, and defines the traction force data, the position data and the measurement time point as a data sequence. The sensor module sends the data sequence to the relay module. The relay module receives the data sequence sent by the sensor module and forwards it to the monitoring module; The monitoring module generates adjustment data based on the traction force data. The adjustment data includes adjustment values ​​and adjustment time points. The cable winding and unwinding force is adjusted based on the adjustment data. The cloud storage module obtains the data sequence from the monitoring module and verifies the data sequence. If the verification is successful, the adjustment data of the data sequence is analyzed. If the analysis result indicates that the extension force adjustment does not meet the preset rules, the cloud storage module generates a second warning and sends it to the mobile terminal. At the same time, the adjustment time point in the adjustment data is extracted and defined as an abnormal time point. Otherwise, a first warning is generated and sent to the mobile terminal. The marking module is used to obtain the measurement time point that is after the abnormal time point and closest to the abnormal time point, obtain the corresponding location data based on the measurement time point, and mark it with an indicator label.

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