Hydropower station early warning method based on multi-channel interaction and electronic equipment

Abstract

The invention provides a hydropower station early warning method based on multi-channel interaction and electronic equipment. The method comprises the following steps: acquiring operation data of mechanical equipment, historical maintenance records and management data of workers, and classifying the operation data, the historical maintenance records and the management data to form a data set; analyzing a fault risk coefficient of each mechanical device according to the operation data set; analyzing the fault response speed and the maintenance processing speed of each mechanical device to generate a response data set corresponding to each mechanical device based on a monitoring period with a fixed time length in combination with the historical data set; the mechanical equipment is managed in a gridding mode, and the association level between production lines is evaluated according to the time domain; and performing statistics on first contact list information, second contact list information and third contact list information corresponding to each production line according to the association level between the personnel data set and the production lines, and performing fault hidden danger early warning based on the first to third contact list information. According to the invention, the fault identification accuracy can be improved, and the efficient cooperation capability is improved.

Description

Technical Field

[0001] The present application relates to the technical field of hydropower station monitoring and management, and in particular to a hydropower station early warning (such as oncall early warning) method and electronic equipment based on multi-channel interaction. Background Art

[0002] The OnCall early warning method involves hydropower stations building a comprehensive system that integrates telephone and SMS alarms, query functions, scheduled data transmission, and manual data transmission. This system utilizes advanced computer monitoring technology to collect real-time operational data from hydropower station equipment. Once the monitored data reaches a preset alarm condition, an alarm signal is immediately triggered, and the equipment alarm information is rapidly transmitted to relevant operators through communication channels such as phone and SMS. The OnCall early warning method demonstrates significant characteristics such as timeliness, reliability, flexibility, and traceability. Timeliness is reflected in the ability to quickly and accurately transmit alarm information to responsible personnel, regardless of time and location, ensuring that effective response measures are taken in a very short time, thereby effectively reducing accident losses.

[0003] However, the oncall warning method for hydropower stations in related technologies has problems such as low fault identification accuracy and low warning response achievement rate. Summary of the Invention

[0004] The embodiments of the present application provide a hydropower station early warning method and electronic equipment based on multi-channel interaction, which can solve the problems of low fault identification accuracy and low early warning response achievement rate existing in traditional hydropower station oncall early warning methods.

[0005] According to a first aspect of an embodiment of the present application, a hydropower station early warning method based on multi-channel interaction is provided, comprising:

[0006] Obtain the operating data of all mechanical equipment in the hydropower station, the historical maintenance record data of all mechanical equipment, and the management data of all staff, and classify and process the operating data, historical maintenance record data, and management data to obtain the operating data set, historical data set, and personnel data set;

[0007] Based on the operating data set, the failure risk of each mechanical equipment in different production lines is analyzed to generate corresponding failure risk coefficients; wherein the mechanical equipment in each production line includes at least turbines, generators, and transformers;

[0008] Based on a fixed-length monitoring cycle combined with historical data sets, the fault response speed and maintenance processing speed of each mechanical device are analyzed to generate a corresponding response data group for each mechanical device;

[0009] Grid management of mechanical equipment based on operational data sets, historical data sets, personnel data sets, failure risk factors, and response data groups, and evaluation of the correlation level between production lines based on the time domain;

[0010] According to the correlation level between the personnel data set and the production line, the first contact list information, the second contact list information and the third contact list information corresponding to each production line are counted;

[0011] Based on the operating data of the first mechanical equipment in the hydropower station, combined with at least one of the first contact list information, the second contact list information and the third contact list information, a fault hidden danger warning is performed; the first mechanical equipment is any one or more mechanical equipment among all the mechanical equipment in the hydropower station.

[0012] According to a second aspect of an embodiment of the present application, a hydropower station early warning device based on multi-channel interaction is provided, comprising:

[0013] The classification processing module is used to obtain the operating data of all mechanical equipment in the hydropower station, the historical maintenance record data of all mechanical equipment, and the management data of all staff, and classify the operating data, historical maintenance record data, and management data to obtain the operating data set, historical data set, and personnel data set;

[0014] A first generation module is configured to analyze the failure risk of each mechanical device in different production lines based on the operating data set to generate a corresponding failure risk coefficient; wherein the mechanical devices in each production line include at least a turbine, a generator, and a transformer;

[0015] The second generation module is used to analyze the fault response speed and maintenance processing speed of each mechanical device based on a fixed-length monitoring cycle combined with a historical data set to generate a response data group corresponding to each mechanical device;

[0016] An evaluation module is used to manage mechanical equipment in a grid-based manner based on operational data sets, historical data sets, personnel data sets, failure risk factors, and response data groups, and to evaluate the correlation level between production lines in the time domain;

[0017] A statistics module, configured to collect statistics on the first contact list information, the second contact list information, and the third contact list information corresponding to each production line according to the association level between the personnel data set and the production line;

[0018] The early warning module is used to provide a fault hidden danger early warning based on the operating data of the first mechanical equipment in the hydropower station, combined with at least one of the first contact list information, the second contact list information and the third contact list information; the first mechanical equipment is any one or more mechanical equipment among all the mechanical equipment in the hydropower station.

[0019] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including:

[0020] at least one processor;

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0023] According to a fourth aspect of an embodiment of the present application, a storage medium is provided, wherein the storage medium stores instructions. When the instructions are executed on an electronic device, the electronic device executes the method described in the first aspect above.

[0024] According to a fifth aspect of an embodiment of the present application, a program product is provided, comprising at least one of a program and an instruction, wherein when the at least one of the program and the instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0025] According to the technical solution of the present application, data is integrated and classified into operation data sets, historical data sets and personnel data sets. The failure risk of each mechanical equipment in different production lines is analyzed based on the operation data sets to generate a corresponding failure risk coefficient. Quantifying the risk is conducive to locating specific types of faulty equipment. Targeted analysis of the equipment status, failure patterns and personnel responsibilities of each mechanical equipment in different production lines can identify failure risks in advance. Preventive maintenance reduces the occurrence rate of failures and improves the accuracy of fault identification. In addition, by setting a monitoring cycle with a fixed length and combining it with historical data sets, the fault response speed and maintenance processing speed of each mechanical equipment are analyzed, and the corresponding response data group is generated to provide targeted optimization basis for the fault response process. Mechanical equipment is managed in a grid manner, and the correlation level between production lines is evaluated according to the time domain. According to the correlation level between the personnel data set and the production line, the first contact list information, second contact list information and third contact list information corresponding to each production line are counted, and then a fixed range of fault risk thresholds are set to determine whether there are hidden fault hazards in the mechanical equipment in the production line, and the corresponding alarm signal is output. It can be seen that when a fault is about to occur or has already occurred, the hierarchical contact list and time domain correlation analysis can be used to ensure efficient coordination of human resources, improve the speed and accuracy of emergency response, thereby improving the early warning response achievement rate and having strong efficient coordination capabilities.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flow chart of a hydropower station early warning method based on multi-channel interaction provided in an embodiment of the present application;

[0029] Figure 2 A block diagram of a hydropower station early warning device based on multi-channel interaction provided in an embodiment of the present application;

[0030] Figure 3 This is a block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0032] It should be noted that the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0033] It is worth noting that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0034] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0035] The following describes a hydropower station early warning method and electronic device based on multi-channel interaction according to an embodiment of the present application with reference to the accompanying drawings.

[0036] Figure 1 This is a flow chart of a hydropower station early warning method based on multi-channel interaction provided in an embodiment of the present application. Figure 1 As shown, the hydropower station early warning method based on multi-channel interaction may include but is not limited to the following steps.

[0037] In step 101, the operation data of all mechanical equipment in the hydropower station, the historical maintenance record data of all mechanical equipment and the management data of all staff are obtained, and the operation data, historical maintenance record data and management data are classified and processed to obtain an operation data set, a historical data set and a personnel data set.

[0038] In some embodiments, a centralized management system and database can be connected via a network to obtain the operating data of all mechanical equipment in the hydropower station, the historical maintenance record data of all mechanical equipment, and the management data of all staff, and classify them into operating data sets, historical data sets, and personnel data sets.

[0039] In some embodiments, each piece of machinery in the operational dataset can be tagged with a corresponding production line number. Each production line can consist of turbines, engines, and transformers. This relationship between machinery and production lines can clearly identify the ownership of the equipment, facilitating subsequent grid management and correlation analysis.

[0040] In some embodiments, each mechanical device in the historical data set may be marked with a corresponding fault occurrence time point (such as can be represented by gzd), response time point (such as can be represented by xyd) and maintenance processing time (such as can be represented by wxc).

[0041] In some embodiments, the personnel data set may include but is not limited to the production line number of the mechanical equipment under the jurisdiction of each staff member, and the multi-channel data sources are organized into a structured data set to provide a unified data foundation for subsequent analysis.

[0042] In step 102 , based on the operation data set, the failure risk of each mechanical device in different production lines is analyzed to generate a corresponding failure risk coefficient.

[0043] In some embodiments, the mechanical equipment in each production line may include at least a turbine, a generator, and a transformer. In the embodiments of the present application, for each production line (taking the i-th production line as an example), the i-th production line can be extracted according to the production line number based on the operating data set. The i-th production line may include turbine a, generator b, and transformer c. The operating data of turbine a, generator b, and transformer c are collected. The corresponding failure risk coefficient is determined based on the operating data of turbine a, generator b, and transformer c.

[0044] In some embodiments, the failure risk factor of the hydraulic turbine includes a cavitation risk factor, and the calculation formula of the cavitation risk factor is expressed as follows:

[0045]

[0046] Among them, Ksf arepresents the cavitation risk coefficient of the turbine; BJL represents the standard value for measuring the inlet flow rate; jl represents the inlet flow rate of the turbine, α1 represents the weight for the ratio of the inlet flow rate to the standard value; miny represents the pressure value of the lowest pressure point on the surface of the turbine blade; HP represents the vaporization pressure of the turbine, HP=P×μ×se, μ represents the conversion coefficient for water temperature, P represents the atmospheric pressure of the turbine, sw represents the water temperature inside the turbine, α2 represents the weight for the ratio of the lowest pressure value to the vaporization pressure; zs represents the noise intensity generated by the turbine, BZS represents the standard value for measuring the noise intensity, α3 represents the weight for the ratio of the noise intensity to the standard value; zf represents the vibration amplitude of the turbine, Bzf represents the standard value for measuring the vibration amplitude, α4 represents the weight for the ratio of the vibration amplitude to the standard value; zp represents the vibration frequency of the turbine, α5 represents the weight for the vibration frequency; CU represents the coarse-grainedness of the turbine flow channel coating, represents the conversion factor for particle size, represents the initial roughness of the turbine flow channel coating, BT represents the standard life of the turbine flow channel coating, sc represents the continuous operation time of the turbine, θ represents the conversion coefficient, which is used to convert the ratio of the continuous operation time to the standard life into the aging increment, and α6 represents the weight for the roughness of the flow channel coating; α1, α2, α3, α4, α5 and α6 are all constants, and α1+α2+α3+α4+α5+α6=1.

[0047] Exemplarily, the operation data of the turbine can be obtained based on the operation data set. For example, the operation data may include but is not limited to: the water inlet flow rate of the turbine, the pressure values ​​of multiple monitoring points on the surface of the turbine blades (such as marked as {y1, y2, y3, ..., yx}, y1 to yx represent the pressure values ​​of the first to x-th monitoring points), atmospheric pressure, the water temperature inside the turbine, the noise intensity generated by the turbine, the vibration amplitude of the turbine, the vibration frequency of the turbine, the strength of the turbine flow channel coating, the continuous operation time of the turbine, etc. The vaporization pressure HP of the turbine can be calculated based on the water temperature and atmospheric pressure inside the turbine. The calculation formula can be expressed as follows: HP = P × μ × sw. Based on the strength and continuous operation time of the turbine flow channel coating, the coarse-grainedness CU of the turbine flow channel coating is calculated, and its expression is as follows: The ratio of the water inlet flow rate to its standard value, the ratio of the pressure value at the lowest pressure point on the blade surface to the vaporization pressure, the ratio of the noise intensity to its standard value, the vibration amplitude to its standard value, the vibration frequency, and the roughness of the flow channel coating are weighted and summed to finally obtain the cavitation risk coefficient of the turbine.

[0048] In some embodiments, the fault risk coefficient of the generator may include a grounding risk coefficient, and the calculation formula of the grounding risk coefficient is expressed as follows:

[0049]

[0050] Among them, JD b Indicates the grounding risk factor of the generator; CZ + represents the maximum positive number in the three-phase current difference, β1 represents the weight for the three-phase current difference; maxI represents the instantaneous value of the maximum phase current, DI represents the rated current of the generator, β2 represents the weight for the difference between the maximum phase current and the rated current; GY represents the power factor of the generator, BGY represents the standard value used to measure the power factor, β3 represents the weight for the power factor; ZC represents the double amplitude of the generator bearing seat, BZC represents the standard value used to measure the double amplitude, β4 represents the weight for the double amplitude; NO represents the nitrogen oxide concentration in the environment where the generator is located, BNO represents the standard value used to measure the nitrogen oxide concentration, and β5 represents the weight for the nitrogen oxide concentration; β1, β2, β3, β4 and β5 are all constants, and β1+β2+β3+β4+β5=1.

[0051] Exemplarily, the operating data of the generator can be obtained based on the operating data set. The operating data may include, but is not limited to: the three-phase current of the generator stator (e.g., marked as {IE, IF, IG}, IE represents the instantaneous value of the E-phase current, IF represents the instantaneous value of the F-phase current, and IG represents the instantaneous value of the G-phase current), the rated current of the generator, the power of the generator, the double amplitude of the generator bearing seat, and the nitrogen oxide concentration in the environment where the generator is located. Based on the three-phase current and the rated current of the generator stator, the three-phase current difference of the generator is calculated. Optionally, the calculation formula of the three-phase current difference can be expressed as follows:

[0052]

[0053] Wherein, |IE-IF| represents the absolute difference between the E-phase current and the F-phase current, |IE-IG| represents the absolute difference between the E-phase current and the G-phase current, |IG-IF| represents the absolute difference between the G-phase current and the F-phase current, 0.1 represents a constant, and 0.1×DI represents 10% of the rated current of the generator.

[0054] The maximum integer among the three-phase current differences, the difference between the instantaneous value of the maximum phase current and the rated current, the ratio of the generator power factor to its standard value, the ratio of the double amplitude of the generator bearing seat to its standard value, and the ratio of the nitrogen oxide concentration in the environment where the generator is located to its standard value are weighted and summed to finally obtain the grounding risk coefficient of the generator.

[0055] In some embodiments, the fault risk coefficient of the transformer may include a discharge risk coefficient, and the calculation formula of the discharge risk coefficient is expressed as follows:

[0056]

[0057] Among them, Fdf c Indicates the discharge risk factor; Wbd c represents the fluctuation rate of transformer surface temperature, σ1 represents the weight for the fluctuation rate; Zpc c represents the total deviation of the transformer air gap magnetic field flux, σ2 represents the weight for the total deviation; th represents the iron content in the transformer insulating oil, BTH represents the standard value used to measure the iron content, σ3 represents the weight for the ratio of the iron content to the standard value; kl represents the particle size of the transformer insulating oil, BKL represents the standard value used to measure the particle size, σ4 represents the weight for the ratio of the particle size to the standard value; Hn represents the hydrogen concentration in the transformer environment, BHN represents the standard value used to measure the hydrogen concentration, σ5 represents the weight for the ratio of the hydrogen concentration to the standard value, σ1, σ2, σ2, σ4 and σ5 are all constants, and σ1+σ2+σ3+σ4+σ5=1.

[0058] Exemplarily, based on the operating data set, the operating data of the transformer is obtained. The operating data may include, but is not limited to: temperature values ​​at multiple monitoring points on the transformer surface (e.g., labeled {w1, w2, w3, ..., wp}, where w1 to wp represent the temperature values ​​of the first to p-th monitoring points), magnetic flux at multiple monitoring points in the transformer air gap magnetic field (e.g., labeled {t1, t2, t3, ..., t1}, where t1 to t1 represent the magnetic flux of the first to l-th monitoring points), the iron content in the transformer insulating oil, the particle size of the transformer insulating oil, the hydrogen concentration in the environment where the transformer is located, etc. The fluctuation rate of the transformer surface temperature is calculated based on the temperature values ​​at the multiple monitoring points on the transformer surface. Optionally, the calculation formula for the fluctuation rate of the transformer surface temperature can be expressed as follows:

[0059]

[0060] in, represents the average temperature of multiple monitoring points on the transformer surface, wr represents the temperature value of the rth monitoring point, Indicates that the fluctuation rate Wbd of the transformer surface temperature is calculated according to the standard deviation formula c .

[0061] The total deviation of the transformer air gap magnetic flux is calculated based on the magnetic flux of multiple monitoring points of the transformer air gap magnetic field. Optionally, the calculation formula of the total deviation of the transformer air gap magnetic flux can be expressed as follows:

[0062]

[0063] in, It represents the average magnetic flux of multiple monitoring points of the transformer air gap magnetic field, tm represents the magnetic flux of the mth monitoring point, It means that the sum of the absolute difference between the magnetic flux of each monitoring point and the average value is calculated in sequence, which is the total deviation Zpc of the magnetic flux of the transformer air gap magnetic field. c .

[0064] The fluctuation rate of the transformer surface temperature, the total deviation of the transformer air gap magnetic field flux, the ratio of the iron content in the transformer insulating oil to its standard value, the ratio of the particle size of the transformer insulating oil to its standard value, and the ratio of the hydrogen concentration in the transformer environment to its standard value are weighted and summed to finally obtain the transformer discharge risk coefficient.

[0065] In step 103 , based on a fixed-length monitoring cycle combined with a historical data set, the fault response speed and maintenance processing speed of each mechanical device are analyzed to generate a response data set corresponding to each mechanical device.

[0066] In some embodiments, for each mechanical equipment, based on the historical data set, the historical maintenance records of the mechanical equipment from the first to the Cth time during the monitoring period are obtained; based on the historical maintenance records of the mechanical equipment from the first to the Cth time, the average duration of the mechanical equipment failure response and the average duration of the maintenance processing time during the monitoring period are counted; based on the average duration of the mechanical equipment failure response and the average duration of the maintenance processing time during the monitoring period, a response data group of the mechanical equipment is generated.

[0067] For example, taking mechanical equipment v as an example, the historical maintenance records of mechanical equipment v from the first to the Cth time within the monitoring period Q can be collected based on the historical data set. The historical maintenance records may include the response time point, the fault occurrence time point, the fault response duration, the maintenance processing duration, etc. Based on the historical maintenance records of mechanical equipment v from the first to the Cth time, the average duration of the fault response and the average duration of the maintenance processing of mechanical equipment v within the monitoring period are collected, and the average duration of the fault response and the average duration of the maintenance processing of mechanical equipment v within the monitoring period are used as the response data group of mechanical equipment v. For example, the response data group of the mechanical equipment can be expressed as follows:

[0068]

[0069] Among them, xyd u Indicates the response time point in the u-th historical maintenance record of the mechanical equipment, gzd u Indicates the time point of the failure in the u-th historical maintenance record of the mechanical equipment, xyd u -gzd u Indicates the fault response time in the u-th historical maintenance record of the mechanical equipment. Indicates the average time it takes for mechanical equipment to respond to failures during the monitoring period, wxc u Indicates the maintenance processing time in the u-th historical maintenance record of the mechanical equipment. Indicates the average maintenance time of mechanical equipment during the monitoring period, Xysj v Indicates the response data group corresponding to the mechanical equipment.

[0070] In step 104 , mechanical equipment is managed in a grid manner based on the operation data set, the historical data set, the personnel data set, the failure risk coefficient, and the response data group, and the correlation level between production lines is evaluated in the time domain.

[0071] In some embodiments, mechanical equipment can be managed in a grid-like manner according to the production line number. A single grid can contain the operating data, historical maintenance records, staff management data of the corresponding jurisdiction, fault risk coefficients (such as cavitation risk coefficients, grounding risk coefficients, discharge risk coefficients, etc.) and response data groups of all mechanical equipment in a single production line. Each grid can be an independent data packet. Multiple time domains (such as 24 time domains) are preset, and grids are arranged based on the multiple time domains and in combination with the operating time points of all mechanical equipment in a single production line. A single time domain contains several grids. By establishing an arrangement mechanism, the scope of fault propagation can be clarified, and automatic grid sorting according to the production plan is achieved. Grids i and k corresponding to the i-th production line and the k-th production line are extracted, and the correlation level between the i-th production line and the k-th production line is determined. In this way, chain failures can be effectively avoided through risk isolation.

[0072] Exemplarily, the correlation level between the i-th production line and the k-th production line can be determined by the time domain relationship to which the grid i and the grid k respectively belong. Optionally, the optional implementation method of the above-mentioned determination of the correlation level between the i-th production line and the k-th production line is as follows: if the grid i and the grid k are contained in the same time domain, the correlation level between the i-th production line and the k-th production line can be level one, and the fault range of the i-th production line will have a serious impact on the mechanical equipment in the k-th production line; if the time domain corresponding to the grid i is adjacent to the time domain corresponding to the grid k, the correlation level between the i-th production line and the k-th production line can be level two, and the fault range of the i-th production line will have a slight impact on the mechanical equipment in the k-th production line; if the time domain corresponding to the grid i is not adjacent to the time domain corresponding to the grid k, the correlation level between the i-th production line and the k-th production line can be level three, and the fault range of the i-th production line will not affect the mechanical equipment in the k-th production line.

[0073] In step 105 , first contact list information, second contact list information, and third contact list information corresponding to each production line are counted according to the association level between the personnel dataset and the production line.

[0074] In some embodiments, for each production line, taking the i-th production line as an example, data on the personnel responsible for all machinery and equipment in the i-th production line can be collected based on the personnel data set to form a first contact list. Data on the personnel responsible for production lines in the same time domain as the i-th production line can be collected to form a second contact list. Data on the personnel responsible for production lines in adjacent time domains can be collected to form a third contact list. This ensures the accuracy and hierarchy of fault notifications.

[0075] In step 106, a fault potential warning is performed based on the operating data of the first mechanical equipment in the hydropower station and in combination with at least one of the first contact list information, the second contact list information, and the third contact list information.

[0076] In some embodiments, the first mechanical equipment may be any one or more mechanical equipment among all mechanical equipment in the hydropower station. For example, the first mechanical equipment may be any mechanical equipment in any production line. In some embodiments, a failure risk coefficient corresponding to the first mechanical equipment may be determined based on operating data of the first mechanical equipment in the hydropower station. Based on the failure risk coefficient and a failure risk threshold of the first mechanical equipment, it may be determined that the first mechanical equipment has a potential failure risk, and an early warning may be issued based on at least one of the first contact list information, the second contact list information, and the third contact list information.

[0077] For example, if the first mechanical device is a turbine, the fault risk coefficient corresponding to the first mechanical device may be a cavitation risk coefficient, and the operating data of the first mechanical device may include but is not limited to: the water inlet flow of the turbine, the pressure values ​​of multiple monitoring points on the surface of the turbine blades (such as marked as {y1, y2, y3, ..., yx}, y1 to yx represent the pressure values ​​of the first to x-th monitoring points), atmospheric pressure, water temperature inside the turbine, noise intensity generated by the turbine, vibration amplitude of the turbine, vibration frequency of the turbine, strength of the turbine flow channel coating, continuous operation time of the turbine, etc. Based on the operating data of the first mechanical device, the cavitation risk coefficient of the first mechanical device is calculated using the above formula (1), and the cavitation risk coefficient is compared with the fault risk threshold (such as the cavitation threshold). If the cavitation risk coefficient exceeds the cavitation threshold, it is determined that the first mechanical device has a fault hidden danger.

[0078] For example, if the first mechanical device is a generator, the fault risk coefficient corresponding to the first mechanical device may be a grounding risk coefficient, and the operating data of the first mechanical device may include but is not limited to: the three-phase current of the generator stator (e.g., marked as {IE, IF, IG}, IE represents the instantaneous value of the E-phase current, IF represents the instantaneous value of the F-phase current, and IG represents the instantaneous value of the G-phase current), the rated current of the generator, the power of the generator, the double amplitude of the generator bearing seat, and the nitrogen oxide concentration in the environment where the generator is located. Based on the operating data of the first mechanical device, the grounding risk coefficient of the first mechanical device is calculated using the above formula (2), and the grounding risk coefficient is compared with the fault risk threshold (e.g., the grounding threshold). If the grounding risk coefficient exceeds the grounding threshold, it is determined that the first mechanical device has a fault risk.

[0079] For example, if the first mechanical device is a transformer, the fault risk coefficient corresponding to the first mechanical device may be a discharge risk coefficient, and the operating data of the first mechanical device may include but is not limited to: the temperature values ​​of multiple monitoring points on the transformer surface (such as marked as {w1, w2, w3, ..., wp}, w1 to wp represent the temperature values ​​of the first to p-th monitoring points), the magnetic flux of multiple monitoring points of the transformer air gap magnetic field (such as marked as {t1, t2, t3, ..., tl}, t1 to tl represent the magnetic flux of the first to l-th monitoring points), the iron content in the transformer insulating oil, the particle size of the transformer insulating oil, the hydrogen concentration of the transformer environment, etc. Based on the operating data of the first mechanical device, the discharge risk coefficient of the first mechanical device is calculated using the above formula (3), and the discharge risk coefficient is compared with the fault risk threshold (such as the discharge threshold). If the discharge risk coefficient exceeds the discharge threshold, it is determined that the first mechanical device has a fault hidden danger.

[0080] For example, if it is determined that a first mechanical device has a potential fault, the production line to which the first mechanical device belongs (i.e., the production line to which the first mechanical device belongs) can be determined, and an alarm signal (e.g., an OnCall alarm signal) can be immediately output to the staff in the first contact list information corresponding to the production line. For example, an alarm signal (e.g., a text message, system notification, phone call, etc.) can be immediately sent to the terminal held by the staff in the first contact list information corresponding to the production line. If the response time of the staff in the first contact list information exceeds the average response time of mechanical device faults in the response data group corresponding to the first mechanical device, an alarm signal (e.g., an OnCall alarm signal) can be immediately output to the staff in the second contact list information corresponding to the production line. For example, an alarm signal (e.g., a text message, system notification, phone call, etc.) can be immediately sent to the terminal held by the staff in the second contact list information corresponding to the production line. Similarly, if the response time of the staff in the second contact list information exceeds the average response time of mechanical device faults in the response data group corresponding to the first mechanical device, an alarm signal can be immediately output to the staff in the third contact list information corresponding to the production line. This can avoid false alarms or missed alarms and ensure the timeliness of fault handling.

[0081] In the above embodiment, data is integrated and classified into an operation data set, a historical data set and a personnel data set. The failure risk of each mechanical equipment in different production lines is analyzed based on the operation data set to generate a corresponding failure risk coefficient. Quantifying the risk is conducive to locating specific types of faulty equipment. Targeted analysis of the equipment status, failure patterns and personnel responsibilities of each mechanical equipment in different production lines can identify failure risks in advance. Preventive maintenance reduces the occurrence rate of failures and improves the accuracy of fault identification. In addition, by setting a monitoring cycle with a fixed length and combining it with historical data sets, the fault response speed and maintenance processing speed of each mechanical equipment are analyzed, and the corresponding response data group is generated to provide targeted optimization basis for the fault response process. Mechanical equipment is managed in a grid manner, and the correlation level between production lines is evaluated according to the time domain. According to the correlation level between the personnel data set and the production line, the first contact list information, second contact list information and third contact list information corresponding to each production line are counted, and then a fixed range of fault risk thresholds are set to determine whether there are hidden fault hazards in the mechanical equipment in the production line, and the corresponding alarm signal is output. It can be seen that when a fault is about to occur or has already occurred, the hierarchical contact list and time domain correlation analysis can be used to ensure efficient coordination of human resources, improve the speed and accuracy of emergency response, thereby improving the early warning response achievement rate and having strong efficient coordination capabilities.

[0082] The calculation processes of the cavitation risk coefficient, the grounding risk coefficient, and the discharge risk coefficient will be described below in conjunction with Examples 1 to 3.

[0083] Example 1

[0084] Please refer to Table 1 for experimental data on the cavitation risk coefficient. This embodiment is based on the explanation of the above method embodiment. Specifically, the calculation process of the cavitation risk coefficient Ksf may include the following steps S11 to S15:

[0085] In step S11, based on the operating data set, the i-th production line is extracted according to the production line number. The i-th production line includes turbine a, generator b and transformer c, and the operating data of turbine a, generator b and transformer c are counted. Multiple different types of mechanical equipment are connected in series with the production line. While clarifying the correlation, it also ensures that subsequent analysis focuses on the same production unit, which facilitates fault tracing and collaborative maintenance.

[0086] In step S12, based on the operating data set, the water inlet flow rate of turbine a is marked as jl, the pressure values ​​of multiple monitoring points on the surface of the turbine a blade are marked as {y1, y2, y3, ..., yx}, the pressure monitoring points are evenly distributed on the surface of the turbine a blade, y1 to yx represent the pressure values ​​of the first to x-th monitoring points, the atmospheric pressure of turbine a is marked as P, the water temperature inside turbine a is marked as sw, the noise intensity generated by turbine a is marked as zs, the vibration amplitude of turbine a is marked as zf, the vibration frequency of turbine a is marked as zp, the particle size of the flow channel coating of turbine a is marked as ld, and the continuous operation time of turbine a is marked as sc.

[0087] In step S13, the vaporization pressure HP of turbine a is calculated, and its expression is as follows:

[0088] HP=P×μ×sw

[0089] In the formula, μ represents the conversion coefficient for water temperature, P×μ×sw represents the vaporization pressure of turbine a at the current water temperature, and clarifies the physical conditions for cavitation. When the minimum pressure on the turbine blades is lower than the vaporization pressure at the current water temperature, cavitation is likely to occur.

[0090] In step S14, the roughness CU of the turbine a flow channel coating is calculated, and its expression is as follows:

[0091]

[0092] In the formula, represents the conversion factor for particle size, represents the initial roughness of the turbine a flow channel coating, BT represents the standard life of the turbine a flow channel coating, and θ represents the conversion coefficient, which is used to convert the ratio of the continuous operation time to the standard life into the aging increment to dynamically evaluate the degradation trend of the coating performance. The more severe the degradation, the more bubbles there are inside the turbine.

[0093] In step S15, the cavitation risk factor Ksf of turbine a is calculated. a , which is expressed as follows:

[0094]

[0095] In the formula, BJL represents the standard value for measuring the water inlet flow rate, α1 represents the weight for the ratio of the water inlet flow rate to the standard value, miny represents the pressure value of the lowest pressure point on the surface of turbine blade a, α2 represents the weight for the ratio of the lowest pressure value to the vaporization pressure, BZS represents the standard value for measuring the noise intensity, α3 represents the weight for the ratio of the noise intensity to the standard value, Bzf represents the standard value for measuring the vibration amplitude, α4 represents the weight for the ratio of the vibration amplitude to the standard value, α5 represents the weight for the vibration frequency, α6 represents the weight for the roughness of the flow channel coating, α1, α2, α3, α4, α5 and α6 are all constants, and α1+α2+α3+α4+α5+α6=1. It indicates that the cavitation risk coefficient of turbine a is calculated according to the weights of α1, α2, α3, α4, α5 and α6, balancing the contribution of various factors to the cavitation risk and avoiding a single indicator dominating the risk judgment.

[0096] Table 1 Experimental data of cavitation risk coefficient

[0097]

[0098] For example, the weight ratios of the three experiments in Table 1 are consistent, such as α1 = 0.1, α2 = 0.15, α3 = 0.1, α4 = 0.15, α5 = 0.25, and α6 = 0.25. The standard values ​​of the three experiments are also consistent, BJL = 1000m 3 / s, HP=112.5kPa, BZS=100dB, Bzf=0.6mm; the cavitation threshold KSY is set to 0-2. After judgment, the cavitation risk coefficient Ksf of Experiment 2 and Experiment 3 exceeds the cavitation threshold KSY, indicating that the turbines on the production line corresponding to Experiment 2 and the turbines on the production line corresponding to Experiment 3 have cavitation fault risks, and an OnCall alarm signal should be output immediately to the staff in the first contact list information.

[0099] In the above embodiment, cavitation may occur during turbine operation due to factors such as changes in water pressure. Cavitation occurs when bubbles form in low-pressure areas of liquid. These bubbles rapidly collapse in high-pressure areas, generating a powerful impact force that can corrode and damage components such as turbine blades. This phenomenon is known as cavitation erosion. Cavitation erosion not only reduces turbine efficiency but also shortens turbine service life. In severe cases, it can cause cracks or even breakage in the blades. By quantifying the combined impact of the external environment and equipment aging, the accuracy of risk prediction can be improved.

[0100] Example 2

[0101] Please refer to Table 2 for experimental data on grounding risk coefficient. This embodiment is based on the above method embodiment for explanation. Specifically, the grounding risk coefficient Jdf calculation process may include the following steps S21 to S23:

[0102] In step S21, based on the operating data set, the three-phase current of the stator of generator b is marked as {IE, IF, IG}, where IE represents the instantaneous value of the E-phase current, IF represents the instantaneous value of the F-phase current, and IG represents the instantaneous value of the G-phase current. The rated current of generator b is marked as DI, the power factor of generator b is marked as GY, the double amplitude of the bearing seat of generator b is marked as ZC, and the nitrogen oxide concentration of the environment in which generator b is located is marked as NO.

[0103] In step S22, the three-phase current difference CZ of generator b is calculated, and its expression is as follows:

[0104]

[0105] In the formula, |IE-IF| represents the absolute difference between the E-phase current and the F-phase current, |IE-IG| represents the absolute difference between the E-phase current and the G-phase current, |IG-IF| represents the absolute difference between the G-phase current and the F-phase current, 0.1 represents a constant, and 0.1×DI represents 10% of the rated current of generator b. This effectively eliminates the impact of differences in generator size or model, making the results more comparable.

[0106] In step S23, the ground fault risk factor Jdf of generator b is calculated. b , which is expressed as follows:

[0107]

[0108] In the formula, CZ + Indicates the maximum positive number in the three-phase current difference. If there is no positive number in the three-phase current difference, CZ +The default value is 0.1, β1 represents the weight for the three-phase current difference, maxI represents the instantaneous value of the maximum phase current, β2 represents the weight for the difference between the maximum phase current and the rated current, BGY represents the standard value for measuring the power factor, β3 represents the weight for the power factor, BZC represents the standard value for measuring the double amplitude, β4 represents the weight for the double amplitude, BNO represents the standard value for measuring the nitrogen oxide concentration, β5 represents the weight for the nitrogen oxide concentration, β1, β2, β3, β4 and β5 are all constants, and β1+β2+β3+β4+β5=1, It indicates that the grounding risk coefficient of generator b is calculated based on the weights β1, β2, β3, β4, and β5. By adjusting the weights, the priority requirements of different scenarios can be flexibly adapted, thereby improving the adaptability of the algorithm model.

[0109] Table 2 Experimental data of grounding risk coefficient

[0110]

[0111]

[0112] For example, the weight ratios of the three experiments in Table 2 are consistent, β1 = 0.2, β2 = 0.3, β3 = 0.1, β4 = 0.25, β5 = 0.15, and the standard values ​​of the three experiments are also consistent, DI = 450A, BGY = 0.8, BZC = 0.05mm, BNO = 5mg / m 3 The grounding threshold JDY is set to 0 to 13. After judgment, the grounding risk coefficient Jdf of experiment F exceeds the grounding threshold JDY, indicating that the generator of the production line corresponding to experiment F has a grounding fault hazard, and an OnCall alarm signal should be immediately output to the staff in the first contact list information.

[0113] In the above embodiment, since the three-phase current of the generator is a power supply system composed of three AC power sources with the same frequency, equal amplitude, and phases that are 120° apart, a current difference exceeding 10% of the rated current indicates that the three-phase load of the generator is severely unbalanced. This imbalance can cause additional losses and heat generation in the generator, increase motor vibration and noise, and potentially affect the voltage stability of the power grid. In addition, when the maximum current of a single phase exceeds the specified value, it indicates that the winding of that phase is experiencing current beyond its design range. Prolonged overload operation not only causes increased winding temperature but can also cause serious faults such as insulation breakdown. Insulating materials, such as insulating paper and insulating varnish, often contain nitrogen. When insulating materials burn or decompose at high temperatures, the nitrogen in them is released in the form of nitrogen oxides (NOx). This method focuses on the potential impact of environmental corrosion on the insulation performance of equipment, overcomes the limitations of traditional methods that only focus on electrical parameters, and achieves high-precision calculation of the grounding risk factor. It combines scientificity, flexibility, and practicality, providing a reliable basis for the safe operation and maintenance of generators and risk prevention and control.

[0114] Example 3

[0115] Please refer to Table 3 for experimental data on discharge risk coefficient. This embodiment is based on the explanation of the above method embodiment. Specifically, the discharge risk coefficient Fdf calculation process may include the following steps S31 to S34:

[0116] In step S31, based on the operating data set, the temperature values ​​of multiple monitoring points on the surface of transformer C are marked as {w1, w2, w3, ..., wp}, where w1 to wp represent the temperature values ​​of the first to p-th monitoring points, the magnetic flux of multiple monitoring points in the air gap magnetic field of transformer C is marked as {t1, t2, t3, ..., tl}, where t1 to tl represent the magnetic flux of the first to l-th monitoring points, the iron content in the insulating oil of transformer C is marked as th, the particle size of the insulating oil of transformer C is marked as kl, and the hydrogen concentration of the environment in which transformer C is located is marked as Hn.

[0117] In step S32, the fluctuation rate Wbd of the transformer c surface temperature is calculated. c , which is expressed as follows:

[0118]

[0119] In the formula, represents the average temperature of multiple monitoring points on the surface of transformer c, wr represents the temperature value of the rth monitoring point, It means that the fluctuation rate of the transformer C surface temperature is calculated according to the standard deviation formula, which reflects the discrete degree of temperature distribution and can effectively identify local overheating or heat dissipation abnormalities.

[0120] In step S33, the total deviation Zpc of the air gap magnetic field flux of transformer c is calculated. c , which is expressed as follows:

[0121]

[0122] In the formula, It represents the average magnetic flux of multiple monitoring points of the transformer c air gap magnetic field, tm represents the magnetic flux of the mth monitoring point, It means that the sum of the absolute differences between the magnetic flux at each monitoring point and the average value is calculated in sequence, which is the total deviation of the magnetic flux in the transformer C air gap field. The sum of the absolute differences is used to measure the magnetic field uniformity. It is sensitive to potential faults such as local magnetic saturation or winding deformation, and facilitates the early detection of electromagnetic imbalance problems.

[0123] In step S34, the discharge risk factor Fdf of the transformer c is calculated. c , which is expressed as follows:

[0124]

[0125] In the formula, σ1 represents the weight for volatility, σ2 represents the weight for total deviation, BTH represents the standard value for measuring iron content, σ3 represents the weight for the ratio of iron content to the standard value, BKL represents the standard value for measuring particle size, σ4 represents the weight for the ratio of particle size to the standard value, BHN represents the standard value for measuring hydrogen concentration, σ5 represents the weight for the ratio of hydrogen concentration to the standard value, σ1, σ2, σ3, σ4 and σ5 are all constants, and σ1+σ2+σ3+σ4+σ5=1. It indicates that the discharge risk coefficient of transformer c is calculated according to the weights of σ1, σ2, σ3, σ4 and σ5. By adjusting the weights to adapt to different scenario requirements, the flexibility and adaptability of the model are enhanced.

[0126] Table 3 Discharge risk coefficient experimental data

[0127]

[0128] For example, the weight ratios of the three experiments in Table 3 are consistent, σ1 = 0.2, σ2 = 0.2, σ3 = 0.3, σ4 = 0.1, σ5 = 0.2, and the standard values ​​of the three experiments are also consistent, BTH = 2.5 mg / kg, BKL = 10 mg / m 3 , BHN=50mg / m 3The discharge threshold FDY is set to 0-0.9. After judgment, the discharge risk coefficient Fdf of experiments U and V exceeds the discharge threshold FDY, indicating that the transformers of the production line corresponding to experiment U and the transformers of the production line corresponding to experiment V have discharge fault risks. An OnCall alarm signal should be output immediately to the staff in the first contact list information.

[0129] In the above embodiment, the iron content and particle size directly reflect the aging and contamination level of the transformer insulating oil. In addition, the introduction of hydrogen concentration as an environmental indicator can provide early warning of gas accumulation caused by insulating oil cracking or partial discharge, which makes up for the traditional early warning method's neglect of the impact of gas. Taking into account thermal, electrical, material and environmental factors, the automated assessment significantly improves the accuracy and foresight of risk prediction, providing reliable technical support for condition-based maintenance and life management of transformers.

[0130] Figure 2 This is a block diagram of a hydropower station early warning device based on multi-channel interaction provided by an embodiment of the present application. Figure 2 As shown, the hydropower station early warning device based on multi-channel interaction may include a classification processing module 201 , a first generation module 202 , a second generation module 203 , an evaluation module 204 , a statistics module 205 and an early warning module 206 .

[0131] Among them, the classification processing module 201 is used to obtain the operating data of all mechanical equipment in the hydropower station, the historical maintenance record data of all mechanical equipment and the management data of all staff, and classify the operating data, historical maintenance record data and management data to obtain the operating data set, historical data set and personnel data set.

[0132] The first generating module 202 is configured to analyze the failure risk of each mechanical device in different production lines based on the operating data set to generate a corresponding failure risk coefficient; wherein the mechanical devices in each production line include at least a turbine, a generator, and a transformer.

[0133] The second generating module 203 is configured to analyze the fault response speed and maintenance processing speed of each mechanical device based on a fixed-length monitoring cycle combined with a historical data set to generate a response data group corresponding to each mechanical device.

[0134] The evaluation module 204 is used to manage mechanical equipment in a grid manner based on the operation data set, historical data set, personnel data set, failure risk coefficient and response data group, and evaluate the correlation level between production lines in the time domain.

[0135] The statistics module 205 is used to count the first contact list information, the second contact list information and the third contact list information corresponding to each production line according to the association level between the personnel data set and the production line.

[0136] The early warning module 206 is used to provide a fault hidden danger early warning based on the operating data of the first mechanical equipment in the hydropower station, combined with at least one of the first contact list information, the second contact list information and the third contact list information; the first mechanical equipment is any one or more mechanical equipment among all the mechanical equipment in the hydropower station.

[0137] In some embodiments, the early warning module 206 is used to: determine the failure risk coefficient corresponding to the first mechanical equipment based on the operating data of the first mechanical equipment in the hydropower station; determine that the first mechanical equipment has a potential failure based on the failure risk coefficient and the failure risk threshold of the first mechanical equipment, and issue an early warning based on at least one of the first contact list information, the second contact list information and the third contact list information.

[0138] In some embodiments, the failure risk factor of the hydraulic turbine includes a cavitation risk factor, and the calculation formula of the cavitation risk factor is expressed as follows:

[0139]

[0140] Among them, Ksf a represents the cavitation risk coefficient of the turbine; BJL represents the standard value for measuring the inlet flow rate; jl represents the inlet flow rate of the turbine, α1 represents the weight for the ratio of the inlet flow rate to the standard value; miny represents the pressure value of the lowest pressure point on the surface of the turbine blade; HP represents the vaporization pressure of the turbine, HP=P×μ×se, μ represents the conversion coefficient for water temperature, P represents the atmospheric pressure of the turbine, sw represents the water temperature inside the turbine, α2 represents the weight for the ratio of the lowest pressure value to the vaporization pressure; zs represents the noise intensity generated by the turbine, BZS represents the standard value for measuring the noise intensity, α3 represents the weight for the ratio of the noise intensity to the standard value; zf represents the vibration amplitude of the turbine, Bzf represents the standard value for measuring the vibration amplitude, α4 represents the weight for the ratio of the vibration amplitude to the standard value; zp represents the vibration frequency of the turbine, α5 represents the weight for the vibration frequency; CU represents the coarse-grainedness of the turbine flow channel coating, represents the conversion factor for particle size, represents the initial roughness of the turbine flow channel coating, BT represents the standard life of the turbine flow channel coating, sc represents the continuous operation time of the turbine, θ represents the conversion coefficient, which is used to convert the ratio of the continuous operation time to the standard life into the aging increment, and α6 represents the weight for the roughness of the flow channel coating; α1, α2, α3, α4, α5 and α6 are all constants, and α1+α2+α3+α4+α5+α6=1.

[0141] In some embodiments, the fault risk coefficient of the generator includes a grounding risk coefficient, and the calculation formula of the grounding risk coefficient is expressed as follows:

[0142]

[0143] Among them, JD b Indicates the grounding risk factor of the generator; CA + represents the maximum positive number in the three-phase current difference, β1 represents the weight for the three-phase current difference; maxI represents the instantaneous value of the maximum phase current, DI represents the rated current of the generator, β2 represents the weight for the difference between the maximum phase current and the rated current; GY represents the power factor of the generator, BGY represents the standard value used to measure the power factor, β3 represents the weight for the power factor; ZC represents the double amplitude of the generator bearing seat, BZC represents the standard value used to measure the double amplitude, β4 represents the weight for the double amplitude; NO represents the nitrogen oxide concentration in the environment where the generator is located, BNO represents the standard value used to measure the nitrogen oxide concentration, and β5 represents the weight for the nitrogen oxide concentration; β1, β2, β3, β4 and β5 are all constants, and β1+β2+β3+β4+β5=1.

[0144] In some embodiments, the fault risk coefficient of the transformer includes a discharge risk coefficient, and the calculation formula of the discharge risk coefficient is expressed as follows:

[0145]

[0146] Among them, Fdf c Indicates the discharge risk factor; Wbd c represents the fluctuation rate of transformer surface temperature, σ1 represents the weight for the fluctuation rate; Zpc c represents the total deviation of the transformer air gap magnetic field flux, σ2 represents the weight for the total deviation; th represents the iron content in the transformer insulating oil, BTH represents the standard value used to measure the iron content, and σ3 represents the weight for the ratio of the iron content to the standard value; kl represents the particle size of the transformer insulating oil, BKL represents the standard value used to measure the particle size, and σ4 represents the weight for the ratio of the particle size to the standard value; Hn represents the hydrogen concentration in the transformer environment, BHN represents the standard value used to measure the hydrogen concentration, and σ5 represents the weight for the ratio of the hydrogen concentration to the standard value. σ1, σ2, σ3, σ4, and σ5 are all constants, and σ1+σ2+σ3+σ4+σ5=1.

[0147] In some embodiments, the second generation module 203 is used to: for each mechanical equipment, obtain the historical maintenance records of the mechanical equipment from the first to the Cth time within the monitoring period based on the historical data set; based on the historical maintenance records of the mechanical equipment from the first to the Cth time, calculate the average duration of the mechanical equipment failure response and the average duration of the maintenance processing time within the monitoring period; based on the average duration of the mechanical equipment failure response and the average duration of the maintenance processing time within the monitoring period, generate a response data group of the mechanical equipment.

[0148] In some embodiments, the response data set of the mechanical device is represented as follows:

[0149]

[0150] Among them, xyd u Indicates the response time point in the u-th historical maintenance record of the mechanical equipment, gzd u Indicates the time point of the failure in the u-th historical maintenance record of the mechanical equipment, xyd u -gzd u Indicates the fault response time in the u-th historical maintenance record of the mechanical equipment. Indicates the average time it takes for mechanical equipment to respond to failures during the monitoring period, wxc u Indicates the maintenance processing time in the u-th historical maintenance record of the mechanical equipment. Indicates the average maintenance time of mechanical equipment during the monitoring period, Xysj v Indicates the response data group corresponding to the mechanical equipment.

[0151] In some embodiments, the evaluation module 204 is used to: manage mechanical equipment in a grid-like manner according to the production line number, where a single grid contains the operating data, historical maintenance records, staff management data of the corresponding jurisdiction, fault risk coefficients, and response data groups of all mechanical equipment in a single production line; arrange the grids based on a plurality of preset time domains and in combination with the operating time points of all mechanical equipment in a single production line, where a single time domain contains a plurality of grids; extract the grid i and grid k corresponding to the i-th production line and the k-th production line, and determine the correlation level between the i-th production line and the k-th production line, including: if the grid i and the grid k are contained in the same time domain, then determine the i-th production line. The association level between the production line and the k-th production line is level one, in which the fault range of the i-th production line will have a serious impact on the mechanical equipment in the k-th production line; if the time domain corresponding to the grid i is adjacent to the time domain corresponding to the grid k, then the association level between the i-th production line and the k-th production line is determined to be level two, in which the fault range of the i-th production line will have a slight impact on the mechanical equipment in the k-th production line; if the time domain corresponding to the grid i is not adjacent to the time domain corresponding to the grid k, then the association level between the i-th production line and the k-th production line is determined to be level three, in which the fault range of the i-th production line will not affect the mechanical equipment in the k-th production line.

[0152] In some embodiments, the statistical module 205 is used to: based on the personnel data set, count the staff data corresponding to the jurisdiction of all mechanical equipment in the i-th production line to form a first contact list information, count the staff data of the jurisdiction of the production line belonging to the same time domain as the i-th production line to form a second contact list information, and count the staff data of the jurisdiction of the production line in the adjacent time domain to the i-th production line to form a third contact list information.

[0153] It should be noted that the above explanation of the embodiment of the hydropower station early warning method based on multi-channel interaction is also applicable to the hydropower station early warning device based on multi-channel interaction in this embodiment, and will not be repeated here.

[0154] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0155] like Figure 3, is a block diagram of an electronic device according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0156] like Figure 3 As shown, the electronic device includes: one or more processors 301, a memory 302, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, a multi-processor system). Figure 3 A processor 301 is taken as an example.

[0157] Memory 302 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor, causing the at least one processor to execute the hydropower station early warning method based on multi-channel interaction provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to execute the hydropower station early warning method based on multi-channel interaction provided in this application.

[0158] The memory 302 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions / modules corresponding to the hydropower station early warning method based on multi-channel interaction in the embodiment of the present application (for example, the attached Figure 2 The processor 301 executes the non-transient software programs, instructions, and modules stored in the memory 302 to execute various functional applications and data processing of the server, thereby implementing the hydropower station early warning method based on multi-channel interaction in the above method embodiment.

[0159] The memory 302 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 302 may optionally include a memory remotely located relative to the processor 301, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0160] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 3 The bus connection is taken as an example.

[0161] The input device 303 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device, such as input devices such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, a pointer, one or more mouse buttons, a trackball, and a joystick. The output device 304 may include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0162] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0163] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0164] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0165] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0166] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0167] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0168] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation method of this application, but the protection scope of this application is not limited to this. Any technical personnel familiar with this technical field, within the technical scope disclosed in this application, can make equivalent replacements or changes based on the technical solution and inventive concept of this application, which should be covered by the protection scope of this application.

[0169] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A hydropower station early warning method based on multi-channel interaction, characterized in that: include: Obtaining operation data of all mechanical equipment in the hydropower station, historical maintenance record data of all mechanical equipment, and management data of all staff members, and classifying and processing the operation data, the historical maintenance record data, and the management data to obtain an operation data set, a historical data set, and a personnel data set; Analyzing the failure risk of each mechanical device in different production lines based on the operating data set to generate a corresponding failure risk coefficient; wherein the mechanical devices in each production line include at least a turbine, a generator, and a transformer; Analyzing the fault response speed and maintenance processing speed of each mechanical device based on a fixed-length monitoring cycle combined with the historical data set to generate a response data group corresponding to each mechanical device; Managing mechanical equipment in a grid manner based on the operation data set, the historical data set, the personnel data set, the fault risk coefficient, and the response data group, and evaluating the correlation level between production lines in a time domain; According to the association level between the personnel data set and the production line, counting the first contact list information, the second contact list information and the third contact list information corresponding to each production line; Based on the operating data of the first mechanical equipment in the hydropower station, combined with at least one of the first contact list information, the second contact list information and the third contact list information, a fault hidden danger warning is performed; the first mechanical equipment is any one or more mechanical equipment among all the mechanical equipment in the hydropower station.

2. The method according to claim 1, wherein The performing of a fault hidden danger warning based on the operation data of the first mechanical equipment in the hydropower station in combination with at least one of the first contact list information, the second contact list information, and the third contact list information includes: determining a failure risk coefficient corresponding to a first mechanical device in the hydropower station based on operating data of the first mechanical device; Based on the failure risk coefficient and failure risk threshold of the first mechanical equipment, it is determined that the first mechanical equipment has a potential failure, and an early warning is issued based on at least one of the first contact list information, the second contact list information, and the third contact list information.

3. The method according to claim 1 or 2, wherein: The fault risk coefficient of the turbine includes a cavitation risk coefficient, and the calculation formula of the cavitation risk coefficient is as follows: Among them, Ksf a represents the cavitation risk coefficient of the turbine; BJL represents the standard value for measuring the inlet flow rate; jl represents the inlet flow rate of the turbine, α1 represents the weight for the ratio of the inlet flow rate to the standard value; miny represents the pressure value of the lowest pressure point on the surface of the turbine blade; HP represents the vaporization pressure of the turbine, HP=P×μ×sw, μ represents the conversion coefficient for water temperature, P represents the atmospheric pressure of the turbine, sw represents the water temperature inside the turbine, α2 represents the weight for the ratio of the lowest pressure value to the vaporization pressure; zs represents the noise intensity generated by the turbine, BZS represents the standard value for measuring the noise intensity, α3 represents the weight for the ratio of the noise intensity to the standard value; zf represents the vibration amplitude of the turbine, Bzf represents the standard value for measuring the vibration amplitude, α4 represents the weight for the ratio of the vibration amplitude to the standard value; zp represents the vibration frequency of the turbine, α5 represents the weight for the vibration frequency; CU represents the coarse-grainedness of the turbine flow channel coating, represents the conversion factor for particle size, represents the initial roughness of the turbine flow channel coating, BT represents the standard life of the turbine flow channel coating, sc represents the continuous operation time of the turbine, θ represents the conversion coefficient, which is used to convert the ratio of the continuous operation time to the standard life into the aging increment, and α6 represents the weight for the roughness of the flow channel coating; α1, α2, α3, α4, α5 and α6 are all constants, and α1+α2+α3+α4+α5+α6=1.

4. The method according to claim 1 or 2, wherein: The fault risk coefficient of the generator includes a grounding risk coefficient, and the calculation formula of the grounding risk coefficient is as follows: Among them, JD b Indicates the grounding risk factor of the generator; CZ + represents the maximum positive number in the three-phase current difference, β1 represents the weight for the three-phase current difference; maxI represents the instantaneous value of the maximum phase current, DI represents the rated current of the generator, β2 represents the weight for the difference between the maximum phase current and the rated current; GY represents the power factor of the generator, BGY represents the standard value for measuring the power factor, β3 represents the weight for the power factor; ZC represents the double amplitude of the generator bearing seat, BZC represents the standard value for measuring the double amplitude, β4 represents the weight for the double amplitude; NO represents the nitrogen oxide concentration in the environment where the generator is located, BNO represents the standard value for measuring the nitrogen oxide concentration, β5 represents the weight for the nitrogen oxide concentration; β1, β2, β3, β4 and β5 are all constants, and β1+β2+β3+β4+β5=1.

5. The method according to claim 1 or 2, wherein: The fault risk coefficient of the transformer includes a discharge risk coefficient, and the calculation formula of the discharge risk coefficient is as follows: Among them, Fdf c Indicates the discharge risk factor; Wbd c represents the fluctuation rate of the transformer surface temperature, σ1 represents the weight for the fluctuation rate; Zpc c represents the total deviation of the air gap magnetic field flux of the transformer, σ2 represents the weight for the total deviation; th represents the iron content in the transformer insulating oil, BTH represents the standard value for measuring the iron content, σ3 represents the weight for the ratio of the iron content to the standard value; kl represents the particle size of the transformer insulating oil, BKL represents the standard value for measuring the particle size, σ4 represents the weight for the ratio of the particle size to the standard value; Hn represents the hydrogen concentration in the environment where the transformer is located, BHN represents the standard value for measuring the hydrogen concentration, σ5 represents the weight for the ratio of the hydrogen concentration to the standard value, σ1, σ2, σ3, σ4 and σ5 are all constants, and σ1+σ2+σ2+σ4+σ5=1.

6. The method according to claim 1, wherein The fixed-duration monitoring cycle is combined with the historical data set to analyze the fault response speed and maintenance processing speed of each mechanical device to generate a response data group corresponding to each mechanical device, including: For each mechanical equipment, obtaining historical maintenance records of the mechanical equipment from the first to the Cth time during the monitoring period according to the historical data set; Based on the historical maintenance records of the mechanical equipment from the first to the Cth time, calculate the average duration of the mechanical equipment failure response and the average duration of the maintenance processing during the monitoring period; A response data set of the mechanical equipment is generated based on the average duration of the mechanical equipment failure response and the average duration of the maintenance processing within the monitoring period.

7. The method according to claim 6, wherein The response data set of the mechanical equipment is shown as follows: Among them, xyd u Indicates the response time point in the u-th historical maintenance record of the mechanical equipment, gzd u Indicates the time point when the failure occurred in the u-th historical maintenance record of the mechanical equipment, xyd u -gzd u Indicates the fault response time in the u-th historical maintenance record of the mechanical equipment, Indicates the average duration of the mechanical equipment failure response during the monitoring period, wxc u Indicates the maintenance processing time in the u-th historical maintenance record of the mechanical equipment, Indicates the average maintenance time of the mechanical equipment during the monitoring period, Xysj v Indicates the response data group corresponding to the mechanical equipment.

8. The method according to claim 1, wherein The grid management of mechanical equipment based on the operation data set, the historical data set, the personnel data set, the fault risk coefficient, and the response data group, and evaluating the correlation level between production lines in the time domain, includes: Mechanical equipment is managed in a grid-like manner according to production line numbers. A single grid contains the operating data, historical maintenance records, staff management data for the corresponding jurisdiction, fault risk factors, and response data groups for all mechanical equipment in a single production line. Arranging grids based on a plurality of preset time domains and in combination with the operating time points of all mechanical equipment in the single production line, wherein a single time domain includes a plurality of grids; Extracting grids i and grids k corresponding to the i-th production line and the k-th production line, and determining the correlation level between the i-th production line and the k-th production line, including: if the grid i and the grid k are included in the same time domain, determining that the correlation level between the i-th production line and the k-th production line is level one, wherein the fault range of the i-th production line will have a serious impact on the mechanical equipment in the k-th production line; if the time domain corresponding to the grid i is adjacent to the time domain corresponding to the grid k, determining that the correlation level between the i-th production line and the k-th production line is level two, wherein the fault range of the i-th production line will have a slight impact on the mechanical equipment in the k-th production line; if the time domain corresponding to the grid i is not adjacent to the time domain corresponding to the grid k, determining that the correlation level between the i-th production line and the k-th production line is level three, wherein the fault range of the i-th production line will not affect the mechanical equipment in the k-th production line.

9. The method according to claim 1, wherein The step of collecting the first contact list information, the second contact list information, and the third contact list information corresponding to each production line according to the association level between the personnel data set and the production line includes: According to the personnel data set, the data of the staff in the jurisdiction corresponding to all the mechanical equipment in the i-th production line are counted to form the first contact list information, the data of the staff in the jurisdiction of the production lines belonging to the same time domain as the i-th production line are counted to form the second contact list information, and the data of the staff in the jurisdiction of the production lines in the adjacent time domain to the i-th production line are counted to form the third contact list information.

10. An electronic device, characterized in that: include: at least one processor; a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

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