Intelligent cloud platform for water feeding pump set of water pump house

Abstract

The invention discloses an intelligent cloud platform for a water supply pump group of a water pump house, relates to the technical field of water pump house monitoring, and solves the technical problems that the power of a water pump cannot be intelligently adjusted according to actual working conditions and the operation data of the water pump cannot be deeply analyzed to optimize a management strategy. By means of the water pump information collection and state intelligent monitoring module, water pump basic information is collected in real time and accurately compared with preset normal information, the working state is immediately discriminated, fault hidden dangers are rapidly perceived, the abnormal state analysis module is combined with historical data to deeply analyze abnormal parameters, fault causes are accurately determined, a foundation is built for maintenance, and the maintenance efficiency is improved. The fault risk analysis module calculates abnormal water pump risk values according to factors such as times and time in historical abnormal data and analyzes the trend of the abnormal water pump risk values, dangerous or normal monitoring signals are pre-sent according to a set threshold value, an operator is assisted to carry out measures such as preventive maintenance or strategy adjustment in advance, the fault probability is powerfully reduced, and operation of a water supply system is stabilized.

Description

Technical Field

[0001] The present invention relates to the technical field of water pump room monitoring, and in particular to an intelligent cloud platform for water supply pump groups in a water pump room. Background Art

[0002] In many fields such as modern industry and construction, the water supply pump group in the pump room plays a vital role, which is related to the stability and reliability of water supply and the rational use of energy.

[0003] However, some existing pump room management models have a series of technical difficulties, such as untimely and inaccurate monitoring of the working status of the pump, inability to effectively predict the risk of failure, inability to intelligently adjust the pump power according to actual working conditions, and lack of in-depth analysis of pump operation data to optimize management strategies. These problems make it difficult to meet the needs of modern efficient, intelligent and energy-saving pump room management. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an intelligent cloud platform for water supply pump groups in a pump room, which solves the problems of not being able to intelligently adjust the water pump power according to actual working conditions and the lack of in-depth analysis of water pump operation data to optimize management strategies.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent cloud platform for water supply pump groups in a water pump room, comprising: The water pump information acquisition module is used to obtain the basic information of the water pump group, and the basic information includes the working voltage, working current and working power corresponding to the water pump, and transmits the obtained basic information to the state intelligent monitoring module; The state intelligent monitoring module is used to analyze the working state of the water pump according to the basic information of the water pump group, judge the working state by comparing the basic information with the normal working information, generate the working state analysis result, and transmit the working state analysis result at the same time; The abnormal state analysis module is used to analyze the acquired abnormal working signal, analyze the basic information corresponding to the abnormal water pump, combine the historical data analysis and determine the fault cause of the abnormal water pump, generate fault cause information, and transmit the fault cause information to the fault risk analysis module; The fault risk analysis module is used to predict the risk of abnormal water pumps according to the acquired fault cause information, obtain the number of abnormalities and time in the historical data, calculate the risk value of the abnormal water pump according to the acquired parameters, and analyze the change of the risk value within the time period to generate risk prediction information, and then transmit the risk prediction information to the management information output module; The normal state analysis module is used to analyze the acquired normal working signal, analyze the working power of the normal water pump, screen the water pump to be adjusted based on the working power, adjust the power of all normal water pumps, generate water pump management information, and transmit the water pump management information to the management information output module; The management information output module is used to display the acquired water pump management information and risk warning information to the corresponding operators.

[0006] As a further solution of the present invention, the specific manner in which the intelligent state monitoring module analyzes the working state of the water pump is: Obtain all the water pumps in the water pump room and label them as i, where i=1, 2, ..., j, where j represents the number of water pumps. Then obtain the basic information of the water pumps, and compare the obtained basic information with the normal working information. If the basic information of the water pumps is different from the normal working information, the corresponding water pumps are recorded as abnormal water pumps and an abnormal working signal is generated. Otherwise, if the basic information of the water pumps is the same as the normal working information, the corresponding water pumps are recorded as normal water pumps and a normal working signal is generated.

[0007] As a further solution of the present invention, the specific manner in which the abnormal state analysis module analyzes the abnormal working signal is: Obtain all abnormal water pumps and label them as a, where a=1, 2, ..., n, where n represents the number of abnormal water pumps, and obtain the abnormal parameters corresponding to the abnormal water pumps, then obtain historical data, and filter the historical data based on the abnormal parameters to obtain data of the same type, and obtain historical parameters corresponding to the data of the same type, then analyze the fault causes corresponding to the historical parameters, and perform secondary classification of the historical parameters based on the fault causes to obtain fault data of the same type, and establish corresponding parameter matching intervals; The abnormal parameter Ya of the abnormal water pump a is obtained, and the change value of the abnormal parameter within the time period t is obtained and recorded as Ba. Then, the abnormal feature is generated according to the abnormal parameter Ya and the change value Ba and recorded as [Ya, Ba]. Similarly, the parameter matching interval is analyzed and the corresponding parameter matching feature is obtained; At the same time, the parameter matching features are matched with the abnormal features.

[0008] As a further solution of the present invention, the specific manner in which the abnormal state analysis module matches the parameter matching feature with the abnormal feature is: Obtain parameter matching features that match the abnormal features, obtain the corresponding fault causes, and generate fault cause information.

[0009] As a further solution of the present invention, the specific method in which the fault risk analysis module performs risk prediction on the abnormal water pump according to the fault cause information is as follows: Obtain the fault cause information of the abnormal water pump, and at the same time obtain the historical data corresponding to the abnormal water pump, and segment the historical data with time T as the period, then obtain the number of faults in the historical data in the time period corresponding to the time period T, record it as C, then obtain the fault time corresponding to the number of faults C, and obtain the corresponding time value according to the fault time, record it as T1, and substitute the obtained parameters into the formula The corresponding fault frequency P is calculated. Similarly, the fault frequency P of the historical data in all time periods is obtained and recorded as Pk, where k=1, 2, ..., u, where u represents the number of different time periods; Then, the fault level value of the fault number C corresponding to the historical data in different time periods is obtained, and different fault values ​​are assigned according to the fault level. The fault mean of all fault numbers C is calculated, and the obtained fault mean is summed with the fault frequency to obtain the risk value. At the same time, the change of risk value of the historical data in different time periods is analyzed.

[0010] As a further solution of the present invention, the specific method in which the fault risk analysis module analyzes the change of risk values ​​of historical data obtained in different time periods is: If the risk value changes in a stable situation, a normal monitoring signal is generated. Conversely, if the risk value changes in an unstable situation, a danger warning signal is generated.

[0011] As a further solution of the present invention, the specific manner in which the normal state analysis module analyzes the acquired normal working signal is: Get all normal water pumps and label them as b, where b=1, 2, ..., m, where m represents the number of normal water pumps. At the same time, get the working power corresponding to the normal water pump b and label it as Gb. Compare the working power Gb with the warning value Gy. Mark the normal water pump whose working power Gb is less than the warning value Gy as the water pump to be adjusted, and mark the normal water pump whose working power Gb is greater than the warning value Gy as the overloaded water pump. Get the number of overloaded water pumps. If there is only one overloaded water pump, directly adjust the working power of the overloaded water pump based on the warning value, and generate water pump management information at the same time. If there are multiple overloaded water pumps, get all the overloaded water pumps and label them as o, where o=1, 2, …, x, where x represents the number of overloaded water pumps. At the same time, calculate the power difference between the overloaded water pump working power Go and the warning value Gy. Similarly, calculate the power difference of all overloaded water pumps and calculate the sum of the power differences as the total power difference. Similarly, calculate the total power difference of all pumps to be adjusted, and compare and analyze the sizes of the two total power differences.

[0012] As a further solution of the present invention, the specific manner in which the normal state analysis module compares and analyzes the magnitude of the two total power differences is: If the total power difference of the overloaded water pumps is greater than the total power difference of the water pumps to be adjusted, a new water pump needs to be added and water pump management information is generated at the same time. If the total power difference of the overloaded water pumps is less than the total power difference of the water pumps to be adjusted, the total power difference of the overloaded water pumps is evenly divided according to the number of water pumps to be adjusted, and the power of the water pumps to be adjusted is adjusted, and water pump management information is generated at the same time.

[0013] Beneficial Effects The present invention provides an intelligent cloud platform for water pump groups in a water pump room. Compared with the prior art, it has the following beneficial effects: The present invention uses the water pump information collection and status intelligent monitoring module to collect basic water pump information in real time and accurately compare it with the preset normal information, instantly identify the working status, and quickly detect potential faults. Once an abnormal water pump is identified, the abnormal status analysis module will combine historical data to deeply analyze the abnormal parameters, accurately determine the cause of the fault, lay a solid foundation for maintenance, significantly improve the efficiency of fault diagnosis, and reduce downtime losses caused by time-consuming troubleshooting.

[0014] The fault risk analysis module calculates the abnormal water pump risk value and analyzes its trend based on the number, time and other factors in the historical abnormal data, and sends out danger or normal monitoring signals in advance according to the set threshold, helping operators to implement preventive maintenance or strategy adjustment measures in advance, effectively reducing the probability of failure, stabilizing the operation of the water supply system, and weakening the economic and social impact caused by sudden failures.

[0015] In normal state, the normal state analysis module screens out the pumps to be adjusted and overloaded according to the comparison between real-time power and warning value. In the case of multiple such pumps, the power difference calculation and comparison are carried out to scientifically decide whether to add pumps or adjust power according to the results. This intelligent power adjustment mechanism ensures efficient operation of pumps, avoids energy waste, improves energy utilization of pump rooms, helps to extend pump life, reduce maintenance costs, and achieve intelligent and precise operation of pump room management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] For example, see Figure 1 The present application provides an intelligent cloud platform for water pump room water supply pump group, including: water pump information collection module, state intelligent monitoring module, abnormal state analysis module, normal state analysis module, fault risk analysis module and management information output module. Figure 1 It can be known that the above functional modules are electrically connected in a unidirectional manner.

[0019] The water pump information acquisition module is used to obtain the basic information of the water pump group, and the basic information includes the working voltage, working current and working power corresponding to the water pump, and transmits the obtained basic information to the status intelligent monitoring module.

[0020] The intelligent status monitoring module is used to analyze the working status of the water pump according to the basic information of the water pump group, judge the working status by comparing the basic information with the normal working information, generate the working status analysis results, and transmit the working status analysis results at the same time.

[0021] All water pumps in the water pump room are obtained and labeled as i, and i=1, 2, ..., j, where j represents the number of water pumps. Then the basic information of the water pump is obtained, and the obtained basic information is compared with the normal working information. The data corresponding to the normal working information here is set by the operator, such as the voltage value, current value and working power value corresponding to the normal working. If the basic information of the water pump is different from the normal working information, it means that the working state of the water pump is abnormal, and the corresponding water pump is recorded as an abnormal water pump, and a working abnormality signal is generated. On the contrary, if the basic information of the water pump is the same as the normal working information, it means that the working state of the water pump is normal, and the corresponding water pump is recorded as a normal water pump, and a normal working signal is generated; For example, the voltage range of a certain model of water pump when working normally is set to 380V±10V, the current range is 10A±1A, and the working power is 5kW+0.5kW. The voltage of one water pump is 360V and the current of the other water pump is 8A. The corresponding water pump is marked as an abnormal water pump. On the contrary, if the voltage of the water pump is 381V, the water pump is marked as a normal water pump.

[0022] Then the abnormal working signal is transmitted to the abnormal state analysis module, and the normal working signal is transmitted to the normal state analysis module.

[0023] The abnormal state analysis module is used to analyze the acquired abnormal working signals, analyze the basic information corresponding to the abnormal water pump, combine historical data analysis and determine the fault cause of the abnormal water pump, generate fault cause information, and transmit the fault cause information to the fault risk analysis module.

[0024] Obtain all abnormal water pumps and label them as a, and a=1, 2, ..., n, where n represents the number of abnormal water pumps. At the same time, obtain the abnormal parameters corresponding to the abnormal water pumps, and the abnormal parameters here are obtained by comparing the normal working information. Then obtain the historical data, and filter the historical data based on the abnormal parameters to obtain the same type of data. For example, if the abnormal data is voltage abnormality, select the working abnormal data caused by voltage abnormality in the historical data, and obtain the historical parameters corresponding to the same type of data. Then analyze the fault causes corresponding to the historical parameters, and perform secondary classification on the historical parameters based on the fault causes to obtain the same type of fault data. At the same time, establish the corresponding parameter matching interval, and the parameter matching interval here is obtained by integrating the historical parameters corresponding to the same type of fault data. For example, when analyzing the voltage abnormality caused by the reduction of the internal winding insulation of the motor, it is found that the voltage value when the fault occurs in the historical data is mostly between 390V and 410V, so this voltage range is set as the parameter matching interval corresponding to the fault cause; The abnormal parameter Ya of the abnormal water pump a is obtained, and Y here is a general term for the types of abnormal parameters, and Y can be expressed as a voltage value, a current value, or a power value, etc. For example, if a certain abnormal water pump Y=3, its abnormal performance is abnormal voltage. After detection, the voltage value of the water pump Y3=410V (the normal voltage range is 380V±10V), and the change value of the abnormal parameter within the time period t is obtained and recorded as Ba, and the value of the time period t is set by the operator. For example, according to the time period t set by the operator (assuming t = 10 minutes), within these 10 minutes, the water pump voltage rises from 405V to 410V, then the voltage change value B3=5V, and then the abnormal feature is generated according to the abnormal parameter Ya and the change value Ba and recorded as [Ya, Ba]. Further substitute the specific numerical value to represent Y3=[410V, 5V]. Similarly, the parameter matching interval is analyzed and the corresponding parameter matching feature is obtained; Then, the obtained abnormal features are matched with the parameter matching features to obtain the parameter matching features that match the abnormal features, and the corresponding fault causes are obtained, and the fault cause information is generated. For example, for faults such as voltage abnormality caused by a slight short circuit in the motor winding, the parameter matching interval is analyzed by historical data to be between 400V-420V and the voltage rise within 10 minutes is between 3V-8V, then the corresponding parameter matching feature can be expressed as [400V-420V, 3V-8V].

[0025] Fault risk analysis module, this module is used to predict the risk of abnormal water pumps based on the acquired fault cause information, by obtaining the number and time of abnormalities in historical data, and calculating the risk value of the abnormal water pump based on the acquired parameters, and analyzing the changes in the risk value within the time period, to generate risk prediction information, and then transmit the risk prediction information to the management information output module.

[0026] Obtain the fault cause information of the abnormal water pump, and at the same time obtain the historical data corresponding to the abnormal water pump, and process the historical data in segments with time T as the period, and the value of time T is set by the operator, then obtain the number of faults in the historical data in the time period corresponding to the time period T and record it as C, then obtain the fault time corresponding to the number of faults C, and obtain the corresponding time value according to the fault time and record it as T1, and substitute the obtained parameters into the formula The corresponding fault frequency P is calculated, and the fault frequency P calculated here is the fault frequency within the time period T. Similarly, the fault frequency P of the historical data in all time periods is obtained and recorded as Pk, and k=1, 2, ..., u, where u represents the number of different time periods; Then, the fault level value corresponding to the number of faults C of the historical data in different time periods is obtained, and different fault values ​​are assigned according to the fault level, and the fault mean of all faults C is calculated. The obtained fault mean and the fault frequency are summed to obtain the risk value, and the change of the risk value of the historical data in different time periods is analyzed; For example, the fault level is set to be divided into five levels, level one represents a minor fault, and level five represents a serious fault. If a certain type of fault has occurred 5 times in the past month, its fault level is evaluated as level three. The fault value is determined by assigning a value according to the fault level. Assuming that the level one fault is assigned a value of 1, level two is 3, level three is 5, level four is 7, and level five is 9, then the fault value of the level three fault that occurred 5 times is 5x5=25. Then, calculate the average of the fault values ​​corresponding to all fault times C.

[0027] If the risk value changes in a stable situation, which means that the difference in risk values ​​is within five, a normal monitoring signal is generated. Conversely, if the risk value changes in an unstable situation, which means that the difference in risk values ​​exceeds five and includes five, a danger warning signal is generated.

[0028] Management information output module, which is used to display the acquired risk prediction information to the corresponding operator.

[0029] As the second embodiment of the present invention, this embodiment is implemented on the basis of the first embodiment, and the difference from the first embodiment is as follows: Normal state analysis module, this module is used to analyze the acquired normal working signal, analyze the working power of the normal water pump, and obtain the water pump to be adjusted based on the working power screening, comprehensively adjust the power of all normal water pumps, generate water pump management information, and transmit the water pump management information to the management information output module.

[0030] Obtain all normal water pumps and label them as b, where b = 1, 2, …, m, and m represents the number label of normal water pumps. At the same time, obtain the working power corresponding to the normal water pump b, denoted as Gb, and the working power here is the real-time power. Then compare the working power Gb with the warning value Gy, and the specific value of the warning value is set by the personnel. Mark the normal water pump with Gb less than the warning value Gy as the water pump to be adjusted, and mark the normal water pump with Gb greater than the warning value Gy as the overloaded water pump; In a specific example, assume that the water supply system sets the warning value Gy = 20kW according to past experience and the rated parameters of the equipment. Just like the water pump with the number b = 3 mentioned before, since G3 = 15kW < Gy = 20kW, this water pump is identified as the water pump to be adjusted, which means its working power has not reached the ideal state and its operating parameters may need to be adjusted, such as appropriately increasing the rotational speed to increase the water supply volume and power to make it closer to the efficient operating range. On the contrary, if there is another water pump with the number b = 4 and its working power G4 = 25kW > Gy = 20kW, then this water pump is marked as the overloaded water pump.

[0031] Obtain the number of overloaded water pumps. If the number of overloaded water pumps is one, directly adjust the working power of the overloaded water pump based on the warning value, and at the same time generate water pump management information. If the number of overloaded water pumps is multiple, obtain all the overloaded water pumps and label them as o, where o = 1, 2, …, x, and x represents the number label of overloaded water pumps. At the same time, calculate the power difference between the working power Go of the overloaded water pump and the warning value Gy, and calculate the sum of the power differences of all overloaded water pumps in the same way and denote it as the total power difference. Similarly, calculate the total power difference of all water pumps to be adjusted, and compare and analyze the magnitudes of the two total power differences; For example, there are three overloaded water pumps with the numbers o = 1, o = 2, o = 3, and their working powers are G1 = 33kW, G2 = 36kW, G3 = 34kW respectively, and the warning value Gy = 30kW. Then the power differences are G1 = G1 - Gy, = 33 - 30 = 3kW, G2 = G2 - Gy = 36 - 30 = 6kW, G3 = G3 - Gy = 34 - 30 = 4kW. Then calculate the sum of these power differences, that is, the total power difference G 总 = G1 + G2 + G3 = 3 + 6 + 4 = 13kW.

[0032] For example, there are two water pumps to be adjusted, and their working powers are G 待1=25kW, G 待2 =23kW, the warning value Gy=30kW, then the power difference is ΔG 待1 =Gy-G 待1 =30-25=5kW, ΔG 待2 =Gy-G 待2 =30-23=7kW, the total power difference of the water pump to be adjusted △G 待总 =ΔG 待1 +ΔG 待2 =5+7=12kW.

[0033] If the total power difference of the overloaded water pump is greater than the total power difference of the water pump to be adjusted, a new water pump needs to be added, and water pump management information is generated at the same time; if the total power difference of the overloaded water pump is less than the total power difference of the water pump to be adjusted, the total power difference of the overloaded water pump is evenly divided according to the number of water pumps to be adjusted, and the power of the water pump to be adjusted is adjusted, and water pump management information is generated at the same time; The generated water pump management information is then transmitted to the management information output module.

[0034] A management information output module is used to display the acquired water pump management information to the corresponding operator.

[0035] As the third embodiment of the present invention, the focus is on combining the implementation processes of the first embodiment and the second embodiment.

[0036] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0037] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent cloud platform for water pump groups in a pump room, characterized in that: include: The water pump information acquisition module is used to obtain the basic information of the water pump group, and the basic information includes the working voltage, working current and working power corresponding to the water pump, and transmits the obtained basic information to the state intelligent monitoring module; The state intelligent monitoring module is used to analyze the working state of the water pump according to the basic information of the water pump group, judge the working state by comparing the basic information with the normal working information, generate the working state analysis result, and transmit the working state analysis result at the same time; The abnormal state analysis module is used to analyze the acquired abnormal working signal, analyze the basic information corresponding to the abnormal water pump, combine the historical data analysis and determine the fault cause of the abnormal water pump, generate fault cause information, and transmit the fault cause information to the fault risk analysis module; The fault risk analysis module is used to predict the risk of abnormal water pumps according to the acquired fault cause information, obtain the number of abnormalities and time in the historical data, calculate the risk value of the abnormal water pump according to the acquired parameters, and analyze the change of the risk value within the time period to generate risk prediction information, and then transmit the risk prediction information to the management information output module; The normal state analysis module is used to analyze the acquired normal working signal, analyze the working power of the normal water pump, screen the water pump to be adjusted based on the working power, adjust the power of all normal water pumps, generate water pump management information, and transmit the water pump management information to the management information output module; The management information output module is used to display the acquired water pump management information and risk warning information to the corresponding operators.

2. According to claim 1, a water pump room water supply pump group intelligent cloud platform is characterized in that: The specific method in which the state intelligent monitoring module analyzes the working state of the water pump is: Obtain all the water pumps in the water pump room and label them as i, where i=1, 2, ..., j, where j represents the number of water pumps. Then obtain the basic information of the water pumps, and compare the obtained basic information with the normal working information. If the basic information of the water pumps is different from the normal working information, the corresponding water pumps are recorded as abnormal water pumps and an abnormal working signal is generated. Otherwise, if the basic information of the water pumps is the same as the normal working information, the corresponding water pumps are recorded as normal water pumps and a normal working signal is generated.

3. According to claim 1, a pump room water supply pump group intelligent cloud platform is characterized in that: The specific method in which the abnormal state analysis module analyzes the abnormal working signal is as follows: Obtain all abnormal water pumps and label them as a, where a=1, 2, ..., n, where n represents the number of abnormal water pumps, and obtain the abnormal parameters corresponding to the abnormal water pumps, then obtain historical data, and filter the historical data based on the abnormal parameters to obtain data of the same type, and obtain historical parameters corresponding to the data of the same type, then analyze the fault causes corresponding to the historical parameters, and perform secondary classification of the historical parameters based on the fault causes to obtain fault data of the same type, and establish corresponding parameter matching intervals; The abnormal parameter Ya of the abnormal water pump a is obtained, and the change value of the abnormal parameter within the time period t is obtained and recorded as Ba. Then, the abnormal feature is generated according to the abnormal parameter Ya and the change value Ba and recorded as [Ya, Ba]. Similarly, the parameter matching interval is analyzed and the corresponding parameter matching feature is obtained; At the same time, the parameter matching features are matched with the abnormal features.

4. According to claim 3, a pump room water supply pump group intelligent cloud platform is characterized in that: The specific method in which the abnormal state analysis module matches the parameter matching feature with the abnormal feature is: Obtain parameter matching features that match the abnormal features, obtain the corresponding fault causes, and generate fault cause information.

5. According to claim 1, a pump room water supply pump group intelligent cloud platform is characterized in that: The specific method in which the fault risk analysis module predicts the risk of an abnormal water pump according to the fault cause information is as follows: Obtain the fault cause information of the abnormal water pump, and at the same time obtain the historical data corresponding to the abnormal water pump, and segment the historical data with time T as the period, then obtain the number of faults in the historical data in the time period corresponding to the time period T, record it as C, then obtain the fault time corresponding to the number of faults C, and obtain the corresponding time value according to the fault time, record it as T1, and substitute the obtained parameters into the formula The corresponding fault frequency P is calculated. Similarly, the fault frequency P of the historical data in all time periods is obtained and recorded as Pk, where k=1, 2, ..., u, where u represents the number of different time periods; Then, the fault level value of the fault number C corresponding to the historical data in different time periods is obtained, and different fault values ​​are assigned according to the fault level. The fault mean of all fault numbers C is calculated, and the obtained fault mean is summed with the fault frequency to obtain the risk value. At the same time, the change of risk value of the historical data in different time periods is analyzed.

6. According to claim 5, a pump room water supply pump group intelligent cloud platform is characterized in that: The specific method in which the fault risk analysis module analyzes the change in risk value of historical data obtained in different time periods is as follows: If the risk value changes in a stable situation, a normal monitoring signal is generated. Conversely, if the risk value changes in an unstable situation, a danger warning signal is generated.

7. According to claim 1, a pump room water supply pump group intelligent cloud platform is characterized in that: The specific method in which the normal state analysis module analyzes the acquired normal working signal is as follows: Get all normal water pumps and label them as b, where b=1, 2, ..., m, where m represents the number of normal water pumps. At the same time, get the working power corresponding to the normal water pump b and label it as Gb. Compare the working power Gb with the warning value Gy. Mark the normal water pump whose working power Gb is less than the warning value Gy as the water pump to be adjusted, and mark the normal water pump whose working power Gb is greater than the warning value Gy as the overloaded water pump. Get the number of overloaded water pumps. If there is only one overloaded water pump, directly adjust the working power of the overloaded water pump based on the warning value, and generate water pump management information at the same time. If there are multiple overloaded water pumps, get all the overloaded water pumps and label them as o, where o=1, 2, …, x, where x represents the number of overloaded water pumps. At the same time, calculate the power difference between the overloaded water pump working power Go and the warning value Gy. Similarly, calculate the power difference of all overloaded water pumps and calculate the sum of the power differences as the total power difference. Similarly, calculate the total power difference of all pumps to be adjusted, and compare and analyze the sizes of the two total power differences.

8. The intelligent cloud platform for water pump group in a water pump room according to claim 7 is characterized in that: The specific method of comparing and analyzing the difference between the two total powers by the normal state analysis module is as follows: If the total power difference of the overloaded water pumps is greater than the total power difference of the water pumps to be adjusted, a new water pump needs to be added and water pump management information is generated at the same time. If the total power difference of the overloaded water pumps is less than the total power difference of the water pumps to be adjusted, the total power difference of the overloaded water pumps is evenly divided according to the number of water pumps to be adjusted, and the power of the water pumps to be adjusted is adjusted, and water pump management information is generated at the same time.

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