An information-based data management system and method for intelligent operation and maintenance equipment boxes

Abstract

The present invention discloses an information-based data management system and method for an intelligent operation and maintenance equipment box, which relates to the technical field of information-based data management, and includes: a temperature monitoring and acquisition frequency regulation module, a gas concentration vector correction module, a leakage point analysis module, a strain gauge data analysis module, a loosening fault discrimination module, and a maintenance point screening and maintenance record module. Set the initial acquisition frequency, monitor the temperature of the equipment box in real time, adjust the acquisition frequency according to the real-time temperature situation, obtain the corrected gas concentration vector, establish a monitoring gas concentration prediction model, calculate the predicted gas concentration, obtain the concentration prediction error value, analyze the leakage point position coordinates and the initial concentration, collect the strain values of the strain gauges in the equipment box at the real-time acquisition frequency, analyze the overall loosening condition of the equipment box, determine whether there is a loosening fault in the equipment box, analyze the most suitable maintenance point. The present invention establishes a concentration prediction model, masters the gas diffusion path and the real-time concentration, and promotes the improvement of the overall operation and maintenance level.

Description

Technical Field

[0001] The present invention relates to the technical field of informatization data management, and specifically to an informatization data management system and method for intelligent operation and maintenance equipment boxes. Background Art

[0002] With the development of technology, intelligent operation and maintenance equipment boxes have been widely deployed in key fields such as industry, energy, and communication. However, traditional operation and maintenance have obvious deficiencies in the temperature monitoring link. In the past, the temperature data of equipment boxes was collected at fixed frequencies at fixed times and locations, which lacked flexibility. Once the temperature inside the equipment box rises abnormally due to reasons such as sudden load changes and heat dissipation blockages, conventional regular collection is very likely to miss the critical nodes of rapid temperature changes and fail to detect high-temperature hidden dangers in a timely manner.

[0003] The gas condition of the environment where the equipment box is located is also crucial for its safe operation. Previous monitoring methods only stayed at the level of simply measuring gas concentration and did not consider the two key dynamic factors of wind direction and wind speed. For example, if a gas leak occurs near the equipment box in a chemical industrial park, due to the unclear diffusion direction of the gas under the push of the wind direction and the real-time change of the concentration under the action of the wind speed, it is difficult for the staff to accurately judge the scope of the leakage area, evacuate the surrounding personnel in a timely manner, and quickly locate the leakage point, which increases the difficulty of subsequent environmental restoration. During the journey of the maintenance personnel to the fault point, it is difficult to avoid the safety risks brought by being too close to the leakage point. Even if the location of the leakage point is located, due to the extremely low physical structure stability efficiency of the manual inspection equipment, it is impossible to continuously obtain the strain gauge data in real time to judge the loosening situation, and it is difficult to detect the long-term accumulated minor loosening. There is a lack of a scientific system for comprehensively evaluating the maintenance points in the maintenance link, and it is impossible to quickly make a choice based on factors such as the number of maintenance personnel and the distance between the maintenance point and the fault point. Moreover, the maintenance process record is missing, which is not conducive to reviewing experience and investigating the root causes of similar faults, and hinders the improvement of the operation and maintenance level. Summary of the Invention

[0004] The purpose of the present invention is to provide an informatization data management system and method for intelligent operation and maintenance equipment boxes to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: An informatization data management method for intelligent operation and maintenance equipment boxes includes the following steps:

[0006] S1. Set the initial collection frequency, monitor the temperature of the equipment box in real time, and adjust the collection frequency according to the real-time temperature situation;

[0007] S2. Perform vector correction on the gas concentration according to the wind direction and wind speed to obtain the corrected gas concentration vector;

[0008] S3. Establish a monitoring gas concentration prediction model, calculate the predicted gas concentration, obtain the concentration prediction error value, and analyze the leakage point position coordinates and the initial concentration;

[0009] S4. Collect the strain values of the strain gauges in the equipment box in real time, and analyze the loosening condition of the overall equipment box;

[0010] S5. According to the comparison result of the continuously collected overall loosening fault coefficient of the equipment box and the threshold value, determine whether there is a loosening fault in the equipment box;

[0011] S6. Analyze the positional relationship among the leakage point, the equipment box, and the maintenance point, consider the real-time number of maintenance personnel at the maintenance point, select the maintenance point to repair the equipment box with a loosening fault, record the maintenance operations in real time during the maintenance, and upload the maintenance operations to the database after the maintenance is completed.

[0012] Further, in step S1, set the initial acquisition frequency f0 and the equipment high-temperature alarm threshold T0. The acquisition frequency represents the number of times of collecting data in one acquisition cycle, and the acquisition cycle is set by the equipment box. The temperature of the equipment box is monitored in real time as T. When T < T0, it is determined that the equipment is in a normal working state, and the acquisition frequency remains unchanged at f0; when T ≥ T0, a temperature anomaly alarm is triggered, and the acquisition frequency is dynamically adjusted to the real-time acquisition frequency f:

[0013] ;

[0014] where k is the adjustment coefficient of the acquisition frequency, and the maximum allowable acquisition frequency of the equipment box is f max , when it is calculated that f ≥ f max , the acquisition frequency is adjusted to f max .

[0015] Further, in step S2, collect the gas concentration, wind direction, and wind speed of the monitoring gas at A locations. Among them, the gas concentration of the monitoring gas collected at the a-th location is B a , the wind direction is the vector U a , the wind speed magnitude is u a , establish a plane rectangular coordinate system, and perform vector correction on the gas concentration according to the wind direction and wind speed to obtain the corrected gas concentration vector C at the a-th location: a :

[0016] ;

[0017] where the direction of the corrected gas concentration vector C a represents the diffusion direction of the monitoring gas, and the magnitude of the modulus of the corrected gas concentration vector C a represents the gas concentration of the monitoring gas;

[0018] The monitored gas is a gas with a certain degree of danger generated during the operation of the equipment. For example, in the power equipment box, due to the heating, discharging, etc. of electrical components, harmful gases such as ozone may be generated; if the equipment box is applied to a chemical scenario, toxic and harmful gases volatilized and generated by chemical raw materials during the reaction, and the specific monitored gas is set by the system.

[0019] From the perspective of the data collection link, the gas concentration B is collected at A locations a , the wind direction vector U a and the wind speed magnitude u a , which builds a comprehensive information basis for subsequent accurate analysis. The aggregation of data from different locations can reflect the overall situation of the gas environment.

[0020] A plane rectangular coordinate system is established to closely associate the wind direction, wind speed and gas concentration data. Taking the power equipment box as an example, harmful gases such as ozone may damage the line insulation due to their chemical activity and cause short circuits. Accurately monitoring their concentration and diffusion direction can intervene in advance and prevent problems before they occur.

[0021] In the chemical scenario, although the types of toxic and harmful gases depend on the system, after vector correction, C a accurately presents its dynamic distribution, enabling the staff to quickly judge the leakage range. Ensuring the normal operation of the equipment, avoiding shutdowns and production halts caused by gas problems, and greatly improving the safety, reliability and efficiency of intelligent operation and maintenance.

[0022] Furthermore, in step S3, analyze the diffusion situation of the monitored gas at the a-th location with coordinates (x a , y a ), and establish a prediction model for the monitored gas concentration:

[0023] ;

[0024] where represents the predicted gas concentration of the monitored gas at the point (x a , y a ), C0 is the initial concentration of the monitored gas leakage point to be calculated, D represents the known diffusion coefficient, r represents the distance between the position of the a-th location with coordinates (x a , y a ) and the position of the gas leakage point with coordinates (x0, y0), and the concentration prediction error value e a is obtained. , and establish an objective function:

[0025] ;

[0026] Where S is the sum of squares of the prediction error values of the concentrations at A locations. Take the partial derivative of S with respect to x0, ∂S / ∂x0, the partial derivative of S with respect to y0, ∂S / ∂y0, and the partial derivative of S with respect to C0. Additionally, considering , a system of equations is obtained by combining them:

[0027] ;

[0028] Using the substitution and elimination method, the coordinates (x0, y0) of the leakage point and the initial concentration C0 of the leakage point are obtained;

[0029] By establishing a professional gas concentration prediction model, the gas diffusion trend can be accurately simulated. Based on this, the staff can anticipate in advance the range that the gas may affect and quickly know the leakage area when the leakage just occurs.

[0030] Relying on mathematical derivation and calculation, from constructing the objective function to solving the system of equations by combination, the position of the leakage point and the initial concentration are finally determined. This provides a definite direction for quickly repairing the leakage problem, shortens the time for troubleshooting, enables the equipment box to return to the normal operating environment faster, reduces the losses caused by production suspension and shutdown due to gas leakage, improves the overall operation and maintenance efficiency, and ensures the continuous stability of the environment where the equipment box is located.

[0031] Furthermore, in step S4, the strain values of N strain gauges in the equipment box are collected at the real-time acquisition frequency f, and the mean value E of the strain value of the nth strain gauge is calculated:

[0032] ;

[0033] Where V represents the number of periods for collecting the strain values of the strain gauges, E n_t represents the strain value of the ith strain gauge collected at the tth time. Calculate the standard deviation e of the strain value of the nth strain gauge, and set the upper threshold of the strain value of the nth strain gauge as E max_n =E + 3e, and the lower threshold of the strain value of the nth strain gauge as E min_n =E - 3e. For the strain value collected at the tth time, calculate the loosening fault coefficient F e_t of the nth strain gauge:

[0034] ;

[0035] Calculate the overall loosening fault coefficient F t of the equipment box:

[0036] ;

[0037] Collect strain gauge data in real time. Compared with the traditional method that relies on manual regular inspections, this method can sensitively capture even the most subtle structural changes. Calculate relevant values through scientific calculations, and then set a reasonable warning range to accurately lock potential loosening hazards.

[0038] Quantify the loosening condition of the equipment box into specific fault coefficients, which is different from the ambiguous judgment method. Once these coefficients show abnormalities, a quick response can be made, allocate maintenance resources in advance, reinforce and repair in time, effectively prevent small problems from deteriorating into major faults, ensure the continuous and stable operation of the equipment, and prevent production interruptions caused by sudden damage.

[0039] Maintain its structure stably in the long term, keep it in good operating condition all the time, comprehensively improve the intelligent level and reliability of operation and maintenance, and provide solid support for the efficient operation of all walks of life.

[0040] Furthermore, in step S5, set the loosening fault threshold F min , when it continuously g max times of collecting that the overall loosening fault coefficient of the equipment box is higher than the loosening fault threshold, it is judged that the equipment box has a loosening fault; otherwise, it is judged that the equipment box is operating normally, where g max is the maximum tolerance times set by the system for the loosening fault coefficient being higher than the loosening fault threshold.

[0041] Furthermore, in step S6, collect the real-time number of maintenance personnel at I maintenance points, collect the route distances from I maintenance points to the equipment box with loosening faults, and calculate the maintenance priority coefficient J i :

[0042] ;

[0043] where P i represents the real-time number of maintenance personnel at the i-th maintenance point, Q i represents the route distance from the i-th maintenance point to the equipment box with loosening faults, R iDenote the minimum distance between the route from the \(i\)-th maintenance point to the equipment box and the gas leakage point. Dispatch maintenance personnel from the maintenance point with the highest maintenance priority coefficient to check and repair the equipment box. When the maintenance point is determined, monitor the real-time distance between the maintenance personnel and the equipment box, estimate the time required for the maintenance personnel to reach the equipment box. During the process of the maintenance personnel repairing the equipment box, record the maintenance process in real time through the equipment box and upload it to the database, and try to avoid the leakage point. On the one hand, it is for safety considerations. On the other hand, if the leakage point is under construction and repair, it is not conducive to the maintenance personnel to reach the position of the equipment box. The analysis method for the minimum distance between the route from the maintenance point to the equipment box and the gas leakage point: First, discretize the route, that is, approximate the route as a broken line composed of many small line segments connected in sequence. The more the number of small line segments, the higher the approximation degree. First calculate the distance from the point to these approximate line segments, and then find the minimum distance among them, and use this as the approximate minimum distance between the point and the original curve. As the degree of discretization continues to increase, the approximation degree approaches the real minimum distance situation.

[0044] An information-based data management system for intelligent operation and maintenance equipment boxes, the system includes: a temperature monitoring and acquisition frequency regulation module, a gas concentration vector correction module, a leakage point analysis module, a strain gauge data analysis module, a loosening fault discrimination module, and a maintenance point screening and maintenance record module;

[0045] The temperature monitoring and acquisition frequency regulation module sets the initial acquisition frequency, monitors the temperature of the equipment box in real time, and adjusts the acquisition frequency according to the real-time temperature situation;

[0046] The gas concentration vector correction module performs vector correction on the gas concentration according to the wind direction and wind speed to obtain the corrected gas concentration vector;

[0047] The leakage point analysis module establishes a monitoring gas concentration prediction model, calculates the predicted gas concentration, obtains the concentration prediction error value, and analyzes the leakage point position coordinates and the initial concentration;

[0048] The strain gauge data analysis module collects the strain values of the strain gauges in the equipment box at the real-time acquisition frequency and analyzes the overall loosening condition of the equipment box;

[0049] The loosening fault discrimination module judges whether there is a loosening fault in the equipment box according to the comparison result of the continuously collected overall loosening fault coefficient of the equipment box and the threshold value;

[0050] The maintenance point screening and maintenance record module analyzes the most suitable maintenance point for repairing the equipment box with loosening faults, and records and uploads the maintenance process to the database in real time during maintenance.

[0051] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: On the one hand, by setting an initial acquisition frequency and dynamically adjusting it according to the real-time temperature of the equipment box, temperature changes can be accurately captured. When the temperature of the equipment box rises due to unexpected situations, the system can respond quickly, promptly increase the acquisition frequency, detect potential high-temperature hazards in the first place, trigger an alarm, greatly reduce the risk of equipment failures caused by overheating, and ensure production continuity;

[0052] On the one hand, comprehensively considering the vector correction of wind direction and wind speed on gas concentration, a concentration prediction model is established, which can accurately master the gas diffusion path and real-time concentration. Once there is a gas leak around the equipment box in a chemical industrial park, the leak point can be quickly located and the leakage area can be accurately demarcated.

[0053] On the other hand, the strain gauge data is collected in real time to continuously monitor the looseness of the equipment, discover potential structural problems in advance, and prevent problems before they occur; a repair point screening system is constructed, and the priority coefficient is calculated based on factors such as maintenance personnel and distance, avoiding the safety risks brought to maintenance personnel by the leak point, quickly determining the best repair point, which not only improves the repair efficiency but also provides strong support for subsequent operation and maintenance optimization, promoting the improvement of the overall operation and maintenance level. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0055] Figure 1 is a structural diagram of an information data management system for an intelligent operation and maintenance equipment box of the present invention;

[0056] Figure 2 is a flowchart of an information data management method for an intelligent operation and maintenance equipment box of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Please refer to Figure 1 and Figure 2 , the present invention provides a technical solution: an information data management method for an intelligent operation and maintenance equipment box, including the following steps:

[0059] S1. Set an initial acquisition frequency, monitor the temperature of the equipment box in real time, and adjust the acquisition frequency according to the real-time temperature situation;

[0060] S2. Vectorially correct the gas concentration according to the wind direction and wind speed to obtain a corrected gas concentration vector;

[0061] S3. Establish a monitoring gas concentration prediction model, calculate the predicted gas concentration, obtain a concentration prediction error value, and analyze the leakage point position coordinates and the initial concentration;

[0062] S4. Real-time collect the strain values of the strain gauges in the equipment box and analyze the loosening condition of the overall equipment box;

[0063] S5. According to the comparison result of the continuously collected overall loosening fault coefficient of the equipment box and the threshold value, judge whether there is a loosening fault in the equipment box;

[0064] S6. Analyze the positional relationship among the leakage point, the equipment box and the maintenance point, consider the number of real-time maintenance personnel at the maintenance point, select the maintenance point for the equipment box with loosening faults to go for maintenance, record the maintenance operations in real time during maintenance and upload the maintenance operations to the database after the maintenance is completed. In step S1, set an initial acquisition frequency f0 and an equipment high-temperature alarm threshold T0. The acquisition frequency represents the number of times of collecting data in one acquisition cycle, and the acquisition cycle is set by the equipment box. Real-time monitor the temperature of the equipment box as T. When T < T0, it is judged that the equipment is in normal working state and the acquisition frequency remains unchanged at f0; when T ≥ T0, trigger a temperature anomaly alarm and dynamically adjust the acquisition frequency to a real-time acquisition frequency f:

[0065] ;

[0066] where k is the adjustment coefficient of the acquisition frequency, and the maximum rated acquisition frequency of the equipment box is f max , when it is calculated that f ≥ f max , the acquisition frequency is adjusted to f max ;

[0067] In step S2, collect the gas concentration, wind direction and wind speed of the monitored gas at A locations. Among them, the gas concentration of the monitored gas collected at the a-th location is B a , the wind direction is the vector U a , the wind speed magnitude is u a , establish a plane rectangular coordinate system, vectorially correct the gas concentration according to the wind direction and wind speed, and obtain the corrected gas concentration vector at the a-th location as C a :

[0068] ;

[0069] where the direction of the corrected gas concentration vector C a represents the diffusion direction of the monitored gas, and the magnitude of the modulus of the corrected gas concentration vector C a represents the gas concentration of the monitored gas;

[0070] The monitored gas is a gas with a certain degree of danger generated during the operation of the equipment. For example, in the electrical equipment box, due to the heating and discharging of electrical components, harmful gases such as ozone may be generated; if the equipment box is applied to a chemical scenario, toxic and harmful gases volatilized and reacted from chemical raw materials, and the specific monitored gas is set by the system.

[0071] From the perspective of the data acquisition link, the gas concentration B is collected at A locations a , the wind direction vector U a and the wind speed magnitude u a , which constructs a comprehensive information basis for subsequent accurate analysis. The aggregation of data from different locations can reflect the overall situation of the gas environment.

[0072] Establish a plane rectangular coordinate system to closely associate the wind direction, wind speed and gas concentration data. Taking the electrical equipment box as an example, harmful gases such as ozone may damage the line insulation due to chemical activity and cause short circuits. Accurately monitoring its concentration and diffusion direction can intervene in advance and prevent problems before they occur.

[0073] In the chemical scenario, although the types of toxic and harmful gases depend on the system, after vector correction, C a accurately presents its dynamic distribution, enabling the staff to quickly judge the leakage range. Ensuring the normal operation of the equipment, avoiding shutdowns and production halts caused by gas problems, and greatly improving the safety, reliability and efficiency of intelligent operation and maintenance.

[0074] In step S3, analyze the diffusion situation of the monitored gas at the a-th location with coordinates (x a , y a ), and establish a prediction model for the monitored gas concentration:

[0075] ;

[0076] Where represents the predicted gas concentration of the monitored gas at the point (x a , y a ), C0 is the initial concentration of the monitored gas leakage point to be calculated, D represents the known diffusion coefficient, and the diffusion coefficients of common gases can be obtained through the network. r represents the distance between the position of the a-th location with coordinates (x a , y a ) and the gas leakage point position with coordinates (x0, y0), and obtain the concentration prediction error value at the a-th location , and establish an objective function:

[0077] ;

[0078] Where S is the sum of the squares of the concentration prediction error values at A locations. Take the partial derivative of S with respect to x0, ∂S / ∂x0, take the partial derivative of S with respect to y0, ∂S / ∂y0, and take the partial derivative of S with respect to C0. Additionally, considering that , a system of equations is obtained by combining them:

[0079] ;

[0080] Using the substitution and elimination method, the coordinates (x0, y0) of the leakage point and the initial concentration C0 of the leakage point are obtained;

[0081] By establishing a professional gas concentration prediction model, the gas diffusion trend can be accurately simulated. With this, the staff can predict in advance the range that the gas may affect and quickly know the leakage area when the leakage just occurs.

[0082] Relying on mathematical derivation and calculation, from constructing the objective function to solving the system of equations by combining them, the position of the leakage point and the initial concentration are finally determined. This provides a definite direction for quickly repairing the leakage problem, shortens the time-consuming for troubleshooting, enables the equipment box to return to the normal operating environment faster, reduces the losses caused by production suspension and shutdown due to gas leakage, improves the overall operation and maintenance efficiency, and ensures the continuous stability of the environment where the equipment box is located.

[0083] In step S4, the strain values of N strain gauges in the equipment box are collected at the real-time acquisition frequency f, and the mean value E of the strain value of the nth strain gauge is calculated:

[0084] ;

[0085] Where V represents the number of periods for collecting the strain values of the strain gauges, and E n_t represents the strain value of the ith strain gauge collected at the tth time. Calculate the standard deviation e of the strain value of the nth strain gauge, and set the upper threshold of the strain value of the nth strain gauge to E max_n =E + 3e, and the lower threshold of the strain value of the nth strain gauge to E min_n =E - 3e. For the strain value collected at the tth time, calculate the loosening fault coefficient F e_t of the nth strain gauge:

[0086] ;

[0087] Calculate the overall loosening fault coefficient F t of the equipment box:

[0088] ;

[0089] Collect strain gauge data in real time. Compared with the traditional method that relies on manual regular inspections, this method can keenly capture even the most subtle structural changes. Through scientific calculations, relevant values are obtained, and then a reasonable warning range is set to accurately lock in potential loosening hazards.

[0090] Quantify the loosening condition of the equipment box into specific fault coefficients, different from the ambiguous judgment method. Once these coefficients show abnormalities, a rapid response can be made, repair resources can be allocated in advance, and timely reinforcement and repair can be carried out to effectively prevent small problems from deteriorating into major faults, ensure the continuous and stable operation of the equipment, and prevent production interruptions caused by sudden damage.

[0091] Maintain its structure stably in the long term, keep it in good operating condition all the time, comprehensively improve the intelligent level and reliability of operation and maintenance, and provide a solid support for the efficient operation of all walks of life.

[0092] In step S5, set the loosening fault threshold F min , when it appears that the overall loosening fault coefficient of the equipment box is higher than the loosening fault threshold for g max consecutive times, it is judged that the equipment box has a loosening fault; otherwise, it is judged that the equipment box is operating normally, where g max is the maximum tolerance times set by the system for the loosening fault coefficient to be higher than the loosening fault threshold.

[0093] In step S6, collect the real-time number of maintenance personnel at I maintenance points, collect the route distances from the I maintenance points to the equipment box with loosening faults, and calculate the maintenance priority coefficient J i :

[0094] ;

[0095] where P i represents the real-time number of maintenance personnel at the i-th maintenance point, Q i represents the route distance from the i-th maintenance point to the equipment box with loosening faults, and R iDenote the minimum distance between the route from the \(i\)-th maintenance point to the equipment box and the gas leakage point. Dispatch maintenance personnel from the maintenance point with the highest maintenance priority coefficient to check and repair the equipment box. When the maintenance point is determined, monitor the real-time distance between the maintenance personnel and the equipment box, estimate the time for the maintenance personnel to reach the equipment box. During the process of the maintenance personnel repairing the equipment box, record the maintenance process in real time through the equipment box and upload it to the database, and try to bypass the leakage point. On the one hand, it is for safety considerations. On the other hand, if the leakage point is under construction and repair, it is not conducive to the maintenance personnel to reach the location of the equipment box. The analysis method of the minimum distance between the route from the maintenance point to the equipment box and the gas leakage point: First, discretize the route, that is, approximate the route as a broken line composed of many small line segments connected in sequence. The more the number of small line segments, the higher the approximation degree. First, calculate the distance from the point to these approximate line segments, and then find the minimum distance among them, and use this as the approximate minimum distance between the point and the original curve. As the degree of discretization continuously increases, the approximation degree approaches the real minimum distance situation.

[0096] An information-based data management system for intelligent operation and maintenance equipment boxes, the system includes: a temperature monitoring and acquisition frequency regulation module, a gas concentration vector correction module, a leakage point analysis module, a strain gauge data analysis module, a loosening fault discrimination module, and a maintenance point screening and maintenance record module;

[0097] The temperature monitoring and acquisition frequency regulation module sets the initial acquisition frequency, monitors the temperature of the equipment box in real time, and adjusts the acquisition frequency according to the real-time temperature situation;

[0098] The gas concentration vector correction module performs vector correction on the gas concentration according to the wind direction and wind speed to obtain the corrected gas concentration vector;

[0099] The leakage point analysis module establishes a monitoring gas concentration prediction model, calculates the predicted gas concentration, obtains the concentration prediction error value, and analyzes the leakage point position coordinates and the initial concentration;

[0100] The strain gauge data analysis module collects the strain values of the strain gauges in the equipment box at the real-time acquisition frequency and analyzes the overall loosening condition of the equipment box;

[0101] The loosening fault discrimination module judges whether there is a loosening fault in the equipment box according to the comparison result of the overall loosening fault coefficient of the equipment box continuously collected and the threshold value;

[0102] The maintenance point screening and maintenance record module analyzes the most suitable maintenance point for repairing the equipment box with loosening faults, and records and uploads the maintenance process to the database in real time during the maintenance.

[0103] Example 1: Take the intelligent operation and maintenance equipment box for storing raw materials of hazardous chemicals in a certain chemical industrial park as an example to fully demonstrate the practical application value of this information-based data management method.

[0104] In terms of temperature monitoring, the initial set acquisition period is 1 hour, and it is acquired 6 times per hour. The high-temperature alarm threshold T0 is set at 50 °C. Under normal circumstances, the temperature T of the equipment box is maintained at about 40 °C, and the acquisition frequency runs according to f0. One day, due to poor heat dissipation of the surrounding equipment, the temperature of the equipment box rose rapidly. When T reached 52 °C, a high-temperature alarm was triggered. According to the set adjustment coefficient k (combined with the maximum acquisition frequency of the equipment box, here k is taken as 3, and it can be acquired at most 60 times per hour), the acquisition frequency was dynamically adjusted to real-time acquisition to quickly capture the temperature change. The operation and maintenance personnel received the alarm in time for investigation to avoid danger caused by the deterioration of raw materials due to high temperature.

[0105] In the gas monitoring link, data is collected at 5 key locations around the equipment box. At a certain moment, the concentration B of chemical gas is monitored at point A a rises, and the wind direction vector U a points to the living area of the park, and the wind speed u a is 3 m / s. After vector correction, C a is obtained to accurately judge the gas diffusion direction and real-time concentration. The staff quickly blocked the dangerous area and evacuated the crowd.

[0106] Once a gas leak is suspected, using the concentration prediction model, by calculating the data collected at each point, the coordinates (x0, y0) of the leak point and the initial concentration C0 are successfully located, indicating the direction for emergency repair.

[0107] For strain gauge monitoring, the real-time strain gauge data collected shows that the overall loosening fault coefficient F of the equipment box has been rising continuously for a period of time, exceeding the loosening fault threshold F min , and the continuous acquisition times reach the system-set g max = 5 times. It is determined that there is a loosening hidden danger in the equipment box, and it is strengthened in advance to prevent leakage risks.

[0108] In the maintenance link, there are 3 maintenance points in the park. According to the number of real-time maintenance personnel P i at each maintenance point, the route distance Q i from the maintenance point to the faulty equipment box, and the minimum distance R i from the maintenance point to the gas leak point, the maintenance priority coefficient J i is calculated, the best maintenance point is selected, and the maintenance personnel arrive quickly. The maintenance process is recorded and uploaded in real time to ensure subsequent operation and maintenance review and optimization.

[0109] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. An information data management method for an intelligent operation and maintenance equipment box, characterized in that: The method comprises the following steps: S1. Set the initial acquisition frequency, monitor the equipment box temperature in real time, and adjust the acquisition frequency according to the real-time temperature conditions; S2. Performing vector correction on the gas concentration according to the wind direction and wind speed to obtain a corrected gas concentration vector; S3. Establish a monitoring gas concentration prediction model, calculate the predicted gas concentration, obtain the concentration prediction error value, and analyze the leakage point location coordinates and initial concentration; S4. Real-time collection of strain values ​​of strain gauges in the equipment box to analyze the overall looseness of the equipment box; S5. judging whether the equipment box has a loose fault according to the comparison result between the continuously collected overall loose fault coefficient of the equipment box and the threshold value; S6. Analyze the positional relationship between the leakage point, the equipment box and the maintenance point, consider the number of people in real time at the maintenance point, select the maintenance point where the equipment box with loose fault is to be repaired, record the maintenance operation in real time during the maintenance and upload the maintenance operation to the database after the maintenance is completed; In step S2, the gas concentration, wind direction and wind speed of the monitored gas are collected at A locations, wherein the gas concentration of the monitored gas collected at the ath location is B a , wind direction is vector U a , the wind speed is u a , establish a plane rectangular coordinate system, perform vector correction on the gas concentration according to the wind direction and wind speed, and obtain the corrected gas concentration vector C at the ath location a : ； The corrected gas concentration vector C a The direction of indicates the diffusion direction of the monitored gas, and the corrected gas concentration vector C a The size of the mode indicates the gas concentration of the monitored gas; In step S3, the analysis coordinates are in (x a ,y a ) and establish a monitoring gas concentration prediction model based on the monitoring gas diffusion situation at the a-th location: ； in Indicates that at point (x a ,y a ), C0 is the initial concentration of the monitoring gas leakage point to be calculated, D represents the known diffusion coefficient, and r represents the coordinate (x a ,y a The distance between the position of the ath location and the gas leakage point with coordinates (x0, y0) is used to obtain the concentration prediction error value e at the ath location. a , , establish the objective function: ； Where S is the sum of squares of the predicted concentration errors of A locations. The partial derivative of S with respect to x0 is ∂S / ∂x0, the partial derivative of S with respect to y0 is ∂S / ∂y0, and the partial derivative of S with respect to C0 is ∂S / ∂C0. In addition, considering , combined to get the system of equations: ； Substituting into the elimination method, we can obtain the coordinates of the leakage point (x0, y0) and the initial concentration C0 of the leakage point.

2. The information data management method for an intelligent operation and maintenance equipment box according to claim 1 is characterized in that: In step S1, the initial acquisition frequency f0 and the equipment high temperature alarm threshold T0 are set. The acquisition frequency represents the number of times data is collected in one acquisition cycle. The acquisition cycle is set by the equipment box. The real-time monitoring temperature of the equipment box is T. When T<T0, the equipment is judged to be in normal working state, and the acquisition frequency remains unchanged at f0; when T≥T0, the abnormal temperature alarm is triggered, and the acquisition frequency is dynamically adjusted to the real-time acquisition frequency f: ； Where k is the adjustment coefficient of the acquisition frequency.

3. The information data management method for an intelligent operation and maintenance equipment box according to claim 2 is characterized in that: In step S4, the strain values ​​of N strain gauges in the equipment box are collected according to the real-time collection frequency f, and the mean value E of the strain value of the nth strain gauge is calculated: ； Where V represents the number of cycles for collecting strain values ​​of the strain gauge, E n_t represents the strain value of the ith strain gauge collected at the tth time, calculates the standard deviation e of the strain value of the nth strain gauge, and sets the upper threshold of the strain value of the nth strain gauge to E max_n =E+3e, the lower limit threshold of the strain value of the nth strain gauge is E min_n =E-3e, for the strain value collected for the tth time, calculate the loosening failure coefficient F of the nth strain gauge e_t : ； Calculate the overall looseness failure coefficient F of the equipment box t : 。 4. The information data management method for an intelligent operation and maintenance equipment box according to claim 3 is characterized in that: In step S5, the loose fault threshold F is set min , when continuous g max When the overall looseness fault coefficient of the equipment box is higher than the looseness fault threshold, it is judged that the equipment box has a loose fault; otherwise, it is judged that the equipment box is operating normally, where g max The maximum tolerable number of times the loose failure coefficient set for the system is higher than the loose failure threshold.

5. The information data management method for intelligent operation and maintenance equipment box according to claim 4 is characterized in that: In step S6, the real-time number of maintenance personnel at I maintenance point is collected, the route distance between I maintenance point and the equipment box with loose fault is collected, and the maintenance priority coefficient J of the i-th maintenance point is calculated. i : ； Where P i represents the real-time maintenance staff of the i-th maintenance point, Q i represents the route distance from the ith maintenance point to the equipment box with loose fault, R i It represents the minimum distance between the route from the i-th maintenance point to the equipment box and the gas leakage point, and dispatches maintenance personnel from the maintenance point with the highest maintenance priority coefficient to inspect and repair the equipment box.

6. The information data management method for intelligent operation and maintenance equipment box according to claim 5 is characterized in that: After the maintenance point is determined, the real-time distance between the maintenance personnel and the equipment box is monitored, and the time it takes for the maintenance personnel to reach the equipment box is estimated. When the maintenance personnel is repairing the equipment box, the maintenance process is recorded in real time through the equipment box and uploaded to the database.

7. An information data management system for an intelligent operation and maintenance equipment box, the system is applied to an information data management method for an intelligent operation and maintenance equipment box according to any one of claims 1 to 6, characterized in that: The system includes: a temperature monitoring and acquisition frequency control module, a gas concentration vector correction module, a leakage point analysis module, a strain gauge data analysis module, a loose fault identification module and a maintenance point screening and maintenance record module; The temperature monitoring and acquisition frequency control module sets the initial acquisition frequency, monitors the equipment box temperature in real time, and adjusts the acquisition frequency according to the real-time temperature conditions; The gas concentration vector correction module performs vector correction on the gas concentration according to the wind direction and wind speed to obtain a corrected gas concentration vector; The leakage point analysis module establishes a monitoring gas concentration prediction model, calculates the predicted gas concentration, obtains the concentration prediction error value, and analyzes the leakage point location coordinates and the initial concentration; The strain gauge data analysis module collects the strain value of the strain gauge in the frequency collection equipment box in real time to analyze the overall looseness of the equipment box; The loose fault identification module determines whether the equipment box has a loose fault according to the comparison result of the overall loose fault coefficient of the equipment box collected continuously and the threshold value; The maintenance point screening and maintenance record module analyzes the most suitable maintenance point for repairing the equipment box with loose faults, records the maintenance operation in real time during maintenance, and uploads the maintenance operation video to the database.

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