Water pump operation control method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a water pump operation control method and device, electronic equipment and storage medium. The method comprises: obtaining operation state data of a plurality of water pumps in a pump group and pipe network pressure detection data of the pump group, the water pumps including water pumps in operation and standby backup pumps; generating a running time database based on the operation state data to obtain running time data corresponding to each water pump; comparing the pipe network pressure detection data with a preset pressure threshold in real time; when the pipe network pressure detection data is lower than the preset pressure threshold, generating a starting sequence of the standby backup pumps according to a preset starting number and the running time database; and starting the standby backup pumps in sequence according to the starting sequence. The method is used to realize orderly starting and balanced rotation of the standby backup pumps when the pipe network pressure is abnormal, avoid pressure impact caused by synchronous starting, and reduce equipment operation and maintenance cost.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and in particular to a method, device, electronic equipment and storage medium for controlling the operation of a water pump. Background Technology

[0002] The polysilicon reduction furnace is the core reaction device in polysilicon production. Its furnace cylinder uses a circulating water system to continuously remove the heat released by the chemical reaction, maintaining a stable furnace temperature. The operational stability of the circulating water system directly affects the safe production and product quality of the reduction furnace. When the circulating water pump malfunctions or the pipeline pressure drops due to equipment failure, load fluctuations, or other reasons, the furnace cylinder temperature will rise abnormally. In severe cases, this can lead to unplanned shutdowns, resulting in the scrapping of a single batch of products and irreversible thermal shock damage to the furnace cylinder itself. Therefore, the industry commonly employs a protection mechanism that automatically starts the backup pump when the pipeline pressure is low. By monitoring the pipeline pressure in real time, the backup pump is automatically activated when the pressure falls below a set threshold to maintain system operation.

[0003] The standby pump start-up control of existing water pump sets generally adopts a pressure interlock mechanism, using the pipeline pressure threshold as the start-up trigger condition. When the pipeline pressure is lower than the set threshold, the standby pump is started to supplement the output. Operators need to pre-set the interlock parameters during normal system operation and manually disconnect the interlock signals of some standby pumps after abnormal conditions occur to adjust the number of standby pumps in operation.

[0004] The existing technology causes all standby pumps to start simultaneously after the low pressure interlock is triggered. A large amount of water flows into the pipeline network in a short period of time, causing a large instantaneous fluctuation in the pipeline network pressure, which disrupts the hydraulic balance of the system and is very likely to trigger a secondary interlock shutdown, which in turn exacerbates the instability of the system. Summary of the Invention

[0005] This application provides a water pump operation control method, device, electronic equipment, and storage medium to achieve the effect of suppressing pipeline pressure fluctuations and improving stability.

[0006] In a first aspect, embodiments of this application provide a water pump operation control method, including:

[0007] The system acquires operating status data of multiple pumps within the pump set and pipeline pressure detection data of the pump set, including pumps in operation and standby pumps.

[0008] Based on the operating status data, the running time data corresponding to each water pump is obtained to generate a running time database.

[0009] The pipeline pressure detection data is compared with a preset pressure threshold in real time;

[0010] When the pipeline pressure detection data is lower than the preset pressure threshold, a standby backup pump start sequence is generated based on the preset start quantity and the runtime database.

[0011] The standby pumps are started one by one according to the start-up sequence.

[0012] In one possible implementation, a startup sequence for the standby backup pump is generated based on a preset startup quantity and the runtime database, including:

[0013] Extract runtime data corresponding to multiple standby backup pumps from the runtime database;

[0014] The standby pumps are sorted in ascending order of runtime data to generate the startup sequence.

[0015] In one possible implementation, generating a startup sequence for the standby backup pump based on a preset startup quantity and the runtime database further includes:

[0016] Obtain the preset wear coefficient weight corresponding to at least one type of pump component of the standby backup pump;

[0017] The comprehensive wear value corresponding to the standby pump is calculated by combining the runtime data with the corresponding preset wear coefficient weight.

[0018] The standby pumps are sorted from low to high based on their comprehensive wear values ​​to generate the startup sequence.

[0019] In one possible implementation, the comprehensive wear value corresponding to the standby backup pump is calculated by combining the runtime data with a corresponding preset wear coefficient weight, including:

[0020] Based on the runtime data and the preset wear coefficient weights corresponding to the various types of water pump components, the wear data corresponding to each type of water pump component is calculated.

[0021] The wear data of various pump components are weighted and summed to obtain the comprehensive wear value of the standby pump.

[0022] In one possible implementation, generating a startup sequence for the standby backup pump based on a preset startup quantity and the runtime database further includes:

[0023] Obtain various operating parameter data of the multiple water pumps;

[0024] A water pump status identifier is generated based on the aforementioned multiple types of operating parameter data;

[0025] The priority of the start-up sequence is adjusted according to the pump status identifier, and the pumps with abnormal status are given lower priority.

[0026] In one possible implementation, generating a pump status identifier based on the multiple types of operating parameter data includes:

[0027] The various types of operational parameter data are input into the pre-trained long short-term memory network model;

[0028] The abnormal state probability data of the water pump is obtained by processing the pre-trained long short-term memory network model.

[0029] The status identifier of the corresponding water pump is generated based on the abnormal status probability data and the preset probability threshold.

[0030] In one possible implementation, it also includes:

[0031] Obtain current production load parameters;

[0032] Based on the preset correspondence between production load and start-up quantity, determine the start-up quantity that matches the current production load parameter, and update the preset start-up quantity.

[0033] Secondly, embodiments of this application provide a water pump operation control device, comprising:

[0034] The acquisition module is used to acquire the operating status data of multiple water pumps in the pump group and the pipeline pressure detection data of the pump group, wherein the water pumps include running water pumps and standby standby pumps.

[0035] The generation module is used to generate a runtime database by obtaining runtime data corresponding to each water pump based on the running status data;

[0036] The comparison module is used to compare the pipeline pressure detection data with a preset pressure threshold in real time;

[0037] The analysis module is used to generate a start-up sequence for standby pumps based on a preset start-up number and the runtime database when the pipeline pressure detection data is lower than the preset pressure threshold.

[0038] The output module is used to start the standby backup pumps one by one according to the start sequence.

[0039] In one possible implementation, the analysis module is specifically used for:

[0040] Extract runtime data corresponding to multiple standby backup pumps from the runtime database;

[0041] The standby pumps are sorted in ascending order of runtime data to generate the startup sequence.

[0042] In one possible implementation, the analysis module is further configured to:

[0043] Obtain the preset wear coefficient weight corresponding to at least one type of pump component of the standby backup pump;

[0044] The comprehensive wear value corresponding to the standby pump is calculated by combining the runtime data with the corresponding preset wear coefficient weight.

[0045] The standby pumps are sorted from low to high based on their comprehensive wear values ​​to generate the startup sequence.

[0046] In one possible implementation, the analysis module is further configured to:

[0047] Based on the runtime data and the preset wear coefficient weights corresponding to the various types of water pump components, the wear data corresponding to each type of water pump component is calculated.

[0048] The wear data of various pump components are weighted and summed to obtain the comprehensive wear value of the standby pump.

[0049] In one possible implementation, the analysis module is further configured to:

[0050] Obtain various operating parameter data of the multiple water pumps;

[0051] A water pump status identifier is generated based on the aforementioned multiple types of operating parameter data;

[0052] The priority of the start-up sequence is adjusted according to the pump status identifier, and the pumps with abnormal status are given lower priority.

[0053] In one possible implementation, the analysis module is further configured to:

[0054] The various types of operational parameter data are input into the pre-trained long short-term memory network model;

[0055] The abnormal state probability data of the water pump is obtained by processing the pre-trained long short-term memory network model.

[0056] The status identifier of the corresponding water pump is generated based on the abnormal status probability data and the preset probability threshold.

[0057] In one possible implementation, the analysis module is further configured to:

[0058] Obtain current production load parameters;

[0059] Based on the preset correspondence between production load and start-up quantity, determine the start-up quantity that matches the current production load parameter, and update the preset start-up quantity.

[0060] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0061] The memory stores computer-executed instructions;

[0062] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0063] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0064] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0065] The pump operation control method, device, electronic equipment, and storage medium provided in this application acquire real-time operating status data of multiple pumps in the pump group and corresponding pipeline pressure detection data, continuously accumulate the running time of each pump and generate a running time database. When the pipeline pressure is lower than a preset threshold and a standby pump is triggered to start, a startup sequence of standby standby pumps is generated by combining the preset startup number with the running time database. Then, the standby standby pumps are started one by one according to the startup sequence. On the one hand, by starting one pump at a time, the instantaneous and large changes in pipeline flow caused by the simultaneous startup of multiple pumps are avoided, and the drastic fluctuations in pipeline pressure are effectively suppressed. On the other hand, by determining the startup order based on the accumulated running time, pumps with shorter running times are put into operation first, balancing the usage frequency of each pump in the pump group, thereby improving the stability of pipeline operation and extending the overall service life of the pump group. Attached Figure Description

[0066] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0067] Figure 1 A schematic diagram illustrating the pump operation control scenario provided in this application;

[0068] Figure 2 A flowchart illustrating the pump operation control method provided in this application;

[0069] Figure 3 A schematic diagram of the pump operation control device provided in this application;

[0070] Figure 4 A schematic diagram of the structure of the electronic device provided in this application.

[0071] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0072] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0073] In the existing technology, the pressure data of the pipeline network is collected in real time by the pressure detection device. When the pressure is lower than the preset threshold, the interlocking logic module sends a start command to all standby pumps in a synchronous manner. There are technical problems such as the simultaneous start of all standby pumps causing the pipeline network pressure to fluctuate greatly and disrupting the hydraulic balance of the system. There are also technical problems such as the lack of a pump set cumulative running time statistics mechanism, which leads to significant differences in the wear degree of each pump and shortens the overall life of the equipment.

[0074] The pump operation control method provided in this application acquires the operating status data of pumps in operation and standby pumps in the pump group, as well as pipeline pressure detection data. Based on the accumulated operating status data, a running time database is generated. When the pipeline pressure is lower than a preset threshold, a start sequence for standby pumps is generated according to the preset number of start-ups and the running time database, and the standby pumps are started one by one. The method of prioritizing the start of pumps with shorter accumulated running time replaces the original full-scale synchronous start mode, thus solving the problem of pipeline pressure shock caused by the synchronous start of standby pumps.

[0075] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0076] Figure 1 This is a schematic diagram illustrating the application scenario of the water pump operation control provided in this application, such as... Figure 1 As shown, it includes: a water pump assembly 101, a pressure acquisition unit 102, and a control processing unit 103.

[0077] Among them, the water pump set 101 is used to perform fluid transportation operations in the pipeline system, and includes equipment with two types of operating states: running water pump and standby standby pump.

[0078] The pressure acquisition unit 102 is used to acquire pressure detection data of the corresponding pipeline network of the water pump group in real time and send the pressure detection data to the control processing unit.

[0079] The control processing unit 103 is used to receive the operating status data of the water pump set and the pipeline pressure detection data, accumulate the running time data of each water pump based on the operating status data and generate a running time database, compare the pipeline pressure detection data with the preset pressure threshold, and generate the standby backup pump start sequence by combining the preset start number and the running time database when the pressure is lower than the threshold. The standby backup pump start command is output to the water pump set in sequence according to the start sequence, and the standby backup pump is started one by one.

[0080] Figure 2 A flowchart illustrating the pump operation control method provided in this application is shown below. Figure 2 As shown, the method includes:

[0081] S201. Obtain the operating status data of multiple pumps in the pump group and the pipeline pressure detection data of the pump group. The pumps include pumps in operation and standby pumps.

[0082] Specifically, a pump set refers to a combination of multiple water pumps configured in parallel to work together to complete the fluid transport function; a standby pump refers to a water pump in the pump set that is in a stopped and ready-to-go state but can be put into operation as needed to supplement the transport capacity. Operating status data is collected by status acquisition devices deployed on the water pump itself, and pipeline pressure detection data is collected by pressure acquisition devices deployed on the pipeline trunk line. Both types of data are simultaneously uploaded to the control terminal for reception and storage.

[0083] The acquisition of operational status data provides a time start and end basis for the cumulative operation time, and the acquisition of pipeline pressure detection data provides real-time input for the judgment of pressure anomalies. This realizes a comprehensive perception of the pump unit's operational status and pipeline pressure status, and provides an accurate data foundation for operation time statistics and pressure judgment.

[0084] S202. Based on the operating status data, obtain the running time data corresponding to each water pump and generate a running time database.

[0085] Specifically, a runtime database refers to a structured data storage unit used to store the cumulative running time information of water pumps within a pump set. It involves performing cumulative calculations over time on continuously collected operating status data, identifying the time intervals in which water pumps are in operation, and accumulating the duration to obtain the cumulative runtime data for the corresponding water pump. The runtime data from multiple water pumps are then aggregated to form a structured runtime database.

[0086] By continuously monitoring changes in the operating status data of each water pump, the start timestamp is recorded when a pump transitions from standby mode to operating mode, and the end timestamp is recorded when a pump transitions back to standby mode. The duration of this period is calculated and added to the pump's cumulative operating time record. The longer the cumulative operating time of a water pump, the greater the wear and tear on its mechanical components. By establishing a correlation between the operating time of each water pump and the degree of wear, quantitative data is provided for the balanced rotation of standby pumps.

[0087] S203. Compare the pipeline pressure detection data with the preset pressure threshold in real time.

[0088] Specifically, the preset pressure threshold is a pre-defined critical value used to determine whether the pipeline pressure meets operational requirements. Real-time collected pipeline pressure monitoring data is periodically compared with the pre-stored pressure threshold to determine the current pressure range. When the pressure drops below the safety threshold, it indicates insufficient water supply capacity from the operating pumps or an abnormality in the pipeline, requiring immediate replenishment of water supply.

[0089] By comparing data in real time, the system ensures timely pressure status assessment and can trigger subsequent control actions as soon as insufficient pipeline pressure is detected, thus preventing a continuous drop in pressure from affecting system operation.

[0090] S204. When the pipeline pressure detection data is lower than the preset pressure threshold, a standby backup pump start sequence is generated based on the preset start quantity and runtime database.

[0091] Specifically, when the pressure comparison unit determines that the pipeline pressure detection data is lower than the preset pressure threshold, the standby pump startup process is triggered. The preset startup quantity refers to the number of standby pumps that need to be started after the interlock is triggered, pre-set according to the production load conditions. The startup sequence is a sequential startup order list of standby pumps formed according to a specific sorting rule. The sorting rule is based on the cumulative runtime of each standby pump in the runtime database, determining the startup priority in ascending order of shortest runtime, with standby pumps having shorter cumulative runtimes appearing earlier in the sequence.

[0092] After the interlock is triggered, the sorting unit extracts the water pumps currently in standby mode and their cumulative runtime from the runtime database, sorts them from shortest to longest cumulative runtime, and selects the first preset number of water pumps to start from the sorting results to form a startup sequence.

[0093] Pumps with shorter cumulative operating time have relatively lower mechanical wear. Prioritizing the startup of these pumps can help balance the wear of each pump and avoid the instantaneous flow impact on the pipeline caused by the simultaneous startup of all standby pumps. This solves the problem of sudden and large fluctuations in pipeline pressure caused by simultaneous startup and achieves balanced rotation of standby pumps, making the wear of each pump more consistent.

[0094] S205. Start the standby pumps one by one according to the startup sequence.

[0095] Specifically, following the order indicated by the startup sequence, startup control commands are sent to the corresponding standby pumps one by one, switching the pumps from standby mode to operating mode. The next pump is started only after the previous one has started and is running stably. The startup interval can be configured according to the system's hydraulic response characteristics to ensure that the network pressure stabilizes after the previous pump starts before starting the next one, avoiding pressure disturbances caused by overlapping startups.

[0096] The sequential startup of pumps gradually increases the circulating water flow in the pipeline network, and the pipeline network pressure rises steadily. This avoids the instantaneous large flow impact caused by the simultaneous startup of multiple pumps, and enables the smooth and orderly commissioning of standby pumps. The fluctuation range of pipeline network pressure is effectively controlled, and the operational stability of the pipeline network system is significantly improved.

[0097] The pump operation control method provided in this application acquires the operating status data of pumps in operation and standby pumps in the pump group, as well as pipeline pressure detection data. Based on the operating status data, it accumulates the running time of each pump and generates a running time database. When the pipeline pressure is lower than a preset pressure threshold, it generates a standby pump start sequence according to the preset number of starts and the accumulated running time in the running time database, sorted from shortest to longest. The standby pumps are started one by one in sequence, realizing the on-demand orderly start and balanced rotation of standby pumps. This solves the technical problems of pipeline pressure shock caused by synchronous start of standby pumps and uneven pump wear caused by lack of running time monitoring in the prior art. It achieves the effects of improving the operational stability of the circulating water system, extending the overall service life of the pump group, and reducing operation and maintenance costs.

[0098] This embodiment, based on the above embodiments, provides a detailed description of the water pump operation control method, which includes:

[0099] a1: Extract runtime data corresponding to multiple standby pumps from the runtime database.

[0100] Specifically, from the generated runtime database, records corresponding to standby pumps currently in standby mode are selected, and the cumulative runtime value of each standby standby pump is extracted. Runtime can quantify the mechanical wear of the pumps. By selecting the runtime data of standby standby pumps from the runtime database, the sorting range is limited to currently startable pumps, eliminating interference from pumps already in operation. This ensures that the data objects in the sorting operation accurately correspond to the schedulable standby pump resources, providing effective input data for generating the startup sequence.

[0101] a2: Sort the standby pumps in ascending order of runtime length data to generate a startup sequence.

[0102] Specifically, the extracted runtime data is used as the sorting basis to arrange the standby pumps in ascending order, with pumps having shorter runtime values ​​ranking higher, ultimately forming a structured startup sequence. Using runtime as the sorting basis enables simple and quantifiable balanced rotation control, ensuring that the operating opportunities of each pump correspond inversely to its historical operating time. This avoids scaling or jamming malfunctions caused by prolonged standby, while also preventing excessive wear from frequent start-stop operations.

[0103] This invention extracts runtime data of standby pumps from a runtime database, generates a startup sequence based on the runtime data in ascending order, and uses the cumulative runtime as the sorting dimension to quantify the startup order of the standby pumps, thereby balancing the wear of each pump in the pump group and avoiding startup failures of pumps that have been idle for a long time.

[0104] This embodiment, based on the above embodiments, provides a detailed description of the water pump operation control method, which includes:

[0105] b1, obtain the preset wear coefficient weight corresponding to at least one type of pump component of the standby pump.

[0106] Specifically, the preset wear coefficient weight is a pre-set quantitative weight parameter that reflects the wear rate of different pump components and their impact on the overall machine. It can be set in advance by engineers based on the pump model, component material, and historical operation and maintenance data. Components can include core vulnerable parts such as impellers, bearings, and seals, and the weight values ​​can be pre-set based on the wear characteristics and replacement costs of the components.

[0107] A water pump consists of multiple functional components. Due to differences in the force applied, the medium in contact with the pump, and the mode of motion, each component experiences varying degrees of wear over the same operating time. By introducing weighted wear coefficients for each component, the overall wear of the water pump is decomposed into the independent wear contribution of each key component. This provides a differentiated weighting basis for comprehensive wear quantification assessment, enabling component-level differentiated characterization of the pump's wear state and providing a parameter basis for the accurate calculation of comprehensive wear values.

[0108] b2 calculates the comprehensive wear value of the standby pump by combining runtime data with the corresponding preset wear coefficient weight.

[0109] Specifically, the comprehensive wear value refers to the overall wear quantification index obtained by combining the wear amounts calculated separately for each component of the water pump according to preset rules. The calculation process involves multiplying the running time data of the standby pump with the preset wear coefficient weights corresponding to each pump component, and then combining the calculation results of each component to obtain the comprehensive wear value characterizing the overall wear state of the water pump. The higher the comprehensive wear value, the higher the comprehensive wear degree of the water pump under the current cumulative running time.

[0110] By reading the cumulative running time data of each standby pump, retrieving the preset wear coefficient weights corresponding to each component of the pump, multiplying the cumulative running time by the wear coefficient weights of each component respectively, and then summing the product results of all components, the comprehensive wear value of the pump is obtained. This expands the wear assessment from a single time dimension to a multi-component comprehensive dimension, improving the accuracy of the quantitative assessment of wear status and enabling the ranking results to reflect the actual wear differences of each pump.

[0111] b3. Based on the comprehensive wear value, the standby pumps are sorted in ascending order to generate a start-up sequence.

[0112] Specifically, the overall wear values ​​of the standby pumps are sorted in ascending order, with pumps having lower overall wear values ​​placed at the beginning of the sequence, corresponding to higher startup priority. After sorting, a startup sequence is generated based on the ascending order. The order of the pumps in the startup sequence corresponds to their startup order. Pumps with the same overall wear value can have their startup order determined based on runtime data or other preset secondary rules.

[0113] The overall wear value is positively correlated with the overall wear degree of the water pump. The lower the value, the lighter the overall wear degree of the key components of the water pump, the higher the reliability and remaining service life. Giving priority to water pumps with lighter wear is conducive to the overall wear balance of the pump group and reduces the probability of early failure of the water pump due to excessive wear of local components.

[0114] In some alternative implementations, b2 above includes:

[0115] b21, based on the runtime data and the preset wear coefficient weights corresponding to various water pump components, calculate the wear data corresponding to each type of water pump component.

[0116] Specifically, for each standby pump, the cumulative runtime data is multiplied by the preset wear coefficient weight corresponding to that component to calculate the wear amount data for that single component. The wear amount data refers to the quantified value of the accumulated wear degree of a single pump component within a specific runtime, which is determined by the product of the runtime and the wear coefficient weight.

[0117] The wear rate of different components is characterized by their respective wear coefficient weights. Calculating them separately can independently present the different wear levels accumulated by each component under the same operating time, providing sub-item data for subsequent synthesis of comprehensive wear values, and realizing the component decomposition and quantification of the water pump wear state.

[0118] b22 performs weighted summation on the wear data of various pump components to obtain the comprehensive wear value of the standby pump.

[0119] Specifically, the wear data calculated from each component are summed up to obtain the comprehensive wear value for the standby pump. The value is positively correlated with the overall wear degree of the pump.

[0120] This invention improves the accuracy of pump set wear balance control by obtaining the preset wear coefficient weights corresponding to at least one type of pump component of the standby pump, calculating the wear amount data of each component using the running time data and the preset wear coefficient weights, and then weighting and summing them to obtain a comprehensive wear value. A start-up sequence is generated based on the comprehensive wear value in ascending order, thereby improving the accuracy of pump set wear balance control.

[0121] This embodiment, based on the above embodiments, provides a detailed description of the water pump operation control method, which includes:

[0122] c1 is used to obtain various operating parameter data of multiple water pumps.

[0123] Specifically, multi-parameter sensors installed on each water pump synchronously collect various operational parameter data during pump operation. Operational parameter data refers to a multi-dimensional set of parameters reflecting the physical state of the pump during operation, including vibration data and current data. Vibration data is collected by vibration sensors installed on the pump bearing housings to characterize the mechanical state of the pump's rotating components. Current data is collected by current transformers installed in the pump's power supply circuit to characterize the load state of the pump's drive motor. The data acquisition process is synchronized with the pump's operating status, and the data acquisition cycle can be configured according to actual needs.

[0124] Multi-parameter sensors acquire vibration and current signals from each water pump at a fixed acquisition frequency. The original signals are filtered and amplified by a signal conditioning circuit, and then converted from analog to digital to obtain digitized operating parameter data. The amplitude and frequency characteristics of the vibration signal can reflect mechanical faults such as bearing wear and rotor imbalance, while the fluctuation characteristics of the current signal can reflect abnormal motor load. The collaborative analysis of multiple types of operating parameter data provides a multi-dimensional information basis for judging the condition of the water pump.

[0125] c2 generates pump status identifiers based on multiple types of operating parameter data.

[0126] Specifically, pump status identifiers refer to the marking information used to distinguish the current health status of a pump, including at least two categories: normal status identifiers and abnormal status identifiers. A normal status identifier indicates that the pump's current operating parameters are within the allowable range and it can participate in startup scheduling normally. An abnormal status identifier indicates that the pump's current operating parameters deviate from the normal range, posing a potential risk of failure, and it should not be prioritized for startup. The status judgment logic compares multiple types of operating parameter data with their corresponding normal parameter ranges. When any type of operating parameter data exceeds its normal parameter range, an abnormal status identifier is generated.

[0127] Operating parameters characterize the health status of water pumps from different dimensions. Abnormal vibration usually indicates mechanical failure, while abnormal current usually indicates electrical or load failure. By comprehensively judging multiple parameters, the accuracy of status identification can be improved, enabling automatic judgment and marking of water pump health status, and making the sorting take into account both the degree of equipment wear and the real-time health status.

[0128] c3 adjusts the priority of the start-up sequence based on the pump status identifier, and puts the pumps with abnormal status later in the priority.

[0129] Specifically, based on the generated startup sequence, the pump status identifiers corresponding to each pump in the sequence are read, and pumps marked with abnormal status are given priority postponement. Priority postponement means moving the pump's position in the startup sequence towards the end of the sequence, delaying its startup order. After the postponement operation is completed, the positions of other pumps in the startup sequence are readjusted to maintain sequence continuity. If the number of abnormal pumps exceeds the preset startup limit, only pumps in normal status are retained in the sequence, and abnormal pumps are temporarily excluded from the current startup sequence.

[0130] Pumps in abnormal condition have a high probability of starting failure or malfunction during operation. By prioritizing them, the risk of secondary system abnormalities caused by starting failure of abnormal pumps can be reduced. This avoids pumps with potential faults being started first, reduces the probability of starting failure, and improves the reliability of the system under abnormal operating conditions.

[0131] In some alternative implementations, c2 includes:

[0132] c21 inputs various types of runtime parameter data into the pre-trained long short-term memory network model.

[0133] Specifically, the collected operational parameter data of various water pumps are used as input feature vectors and fed into a pre-trained Long Short-Term Memory (LSTM) network model. The pre-trained LSTM network model is an artificial neural network model built using a LSTM architecture and trained offline using historical operational data. It is used to process time-series operational parameter data and output the probability of state anomalies. LSTM is a variant of recurrent neural networks suitable for processing and predicting time-series data, effectively solving the gradient vanishing problem in long-series data processing by introducing a gating mechanism. The input data is organized into time windows, with each time window containing a sequence of operational parameter data from multiple consecutive sampling times.

[0134] By segmenting the various operating parameter data of each water pump according to the preset time window length, input sequence samples are generated. The input sequence samples are then fed into a pre-trained long short-term memory network model loaded with trained model parameters. The model forward calculates the hidden state at each time step and outputs the state anomaly probability data.

[0135] Long Short-Term Memory (LSTM) network models selectively memorize and forget sequence information through gating units. They can learn the temporal differences between normal and abnormal states from the time evolution patterns of historical operational parameter data, extract implicit state features from multiple types of operational parameter data, and provide probabilistic judgment results for state identification generation.

[0136] c22, the abnormal state probability data of the water pump is obtained by processing the pre-trained long short-term memory network model.

[0137] Specifically, the pre-trained Long Short-Term Memory (LSTM) network model processes the input multi-class operational parameter data sequences step-by-step, outputting the pump's current state anomaly probability data at the model's output layer. The state anomaly probability data refers to the continuous numerical value between zero and one output by the model, representing the likelihood that the pump is currently in an abnormal state. The closer the value is to one, the higher the probability of an anomaly.

[0138] c23 generates the corresponding water pump status identifier based on the abnormal status probability data and the preset probability threshold.

[0139] Specifically, the preset probability threshold is a critical probability value used to distinguish between normal and abnormal states. When the probability of an abnormal state is greater than or equal to the preset probability threshold, an abnormal state identifier is generated. When the probability of an abnormal state is less than the preset probability threshold, a normal state identifier is generated.

[0140] By comparing the abnormal state probability data with a preset probability threshold, and selecting to generate a normal state identifier or an abnormal state identifier based on the comparison result, the probabilistic model output is transformed into a binary state identifier that can be directly used for priority adjustment, providing state input for the priority adjustment of the start sequence.

[0141] This invention acquires various operating parameter data of multiple water pumps, generates water pump status identifiers based on these data, and postpones the start-up priority of abnormal water pumps according to the status identifiers. It also incorporates equipment health status information into the standby pump start-up sequencing decision, thereby preventing potentially faulty water pumps from being started first based on wear balancing, reducing the risk of start-up failure and the probability of secondary anomalies, and improving the reliability of pump unit operation.

[0142] This embodiment, based on the above embodiments, provides a detailed description of the water pump operation control method, which includes:

[0143] d1, retrieves the current production load parameters.

[0144] Specifically, production load parameters refer to quantitative parameters reflecting the actual conveying demand intensity of the current fluid conveying system. They are quantitative indicators characterizing the current production operation intensity of the polysilicon reduction furnace and are determined comprehensively by process parameters such as feed flow rate, reaction temperature setpoint, and number of operating furnace cylinders. The current production load parameters are collected from the production process control system to obtain the real-time fluid demand scale of the system, serving as the input basis for adjusting the number of standby pumps to be started.

[0145] By using the current production load parameters to provide a basis for the dynamic adjustment of the number of pumps to be started, the startup of standby pumps is linked to real-time production needs, realizing the linkage between pump control and production process status, and providing real-time parameter input for on-demand adjustment of the number of pumps to be started.

[0146] For example, the production load of the polysilicon reduction furnace determines the heat exchange requirements of the circulating water system. The higher the production load, the more heat is released by the reaction, the higher the requirements for the circulating water flow rate and water supply reliability, and the more backup pumps need to be configured.

[0147] d2, based on the preset correspondence between production load and start-up quantity, determine the start-up quantity that matches the current production load parameters, and update the preset start-up quantity.

[0148] Specifically, the correspondence between production load and the number of standby pumps started refers to a pre-established mapping relationship between different load ranges and the corresponding number of standby pumps started. By matching the acquired current production load parameters with the load ranges in the correspondence, the load range to which the current production load parameters belong is found (e.g., starting 1-2 standby pumps under low load conditions and starting 3-4 standby pumps under high load conditions). The start-up quantity value corresponding to this range is extracted and written to the preset start-up quantity parameter storage location, thus completing the update of the preset start-up quantity.

[0149] There is a positive correlation between production load and the required number of standby pumps. By pre-setting the correspondence, the number of pumps to be started can be automatically adjusted according to changes in production load without manual intervention. Dynamically updating the preset number of pumps to start ensures that the number of standby pumps to be started always matches the current production load, reducing the number of unnecessary standby pumps to be started under low load conditions and ensuring sufficient standby water supply capacity under high load conditions.

[0150] It achieves adaptive matching between the number of start-up pumps and the production load, avoiding hydraulic shock and energy waste caused by an excessive number of standby pumps starting under low load conditions, while ensuring a water supply safety margin under high load conditions.

[0151] This invention obtains the current production load parameters, determines the matching number of startups based on the preset correspondence between production load and startup quantity, and updates the preset startup quantity. This allows the number of standby pumps to be started to be dynamically adjusted with the production load, matching the startup quantity with production demand and improving adaptability and operational economy under different working conditions.

[0152] In an optional implementation, the method further includes: calculating redundancy based on the pump model; and adjusting the startup sequence based on the redundancy.

[0153] Specifically, redundancy = number of spare pumps of the same model × (1 - percentage of currently operating pumps). For example, if there are 4 pumps of a certain model, and 2 of them are currently operating, then the redundancy is 4 × (1 - 2 / 6) = 2.67. If the running time is the same, the pump with higher redundancy will be placed at the beginning of the startup sequence.

[0154] When the number of operating pumps or standby pumps changes, the redundancy is automatically recalculated and the startup sequence is adjusted.

[0155] Pumps of the same model are interchangeable in terms of installation dimensions, hydraulic characteristics, and control interfaces. When there are sufficient backup pumps of a certain model, prioritizing the activation of that model's backup pumps can provide more alternative options of the same model in the event of a sudden failure, thereby improving the system's fault tolerance to cascading failures. For example, if the number of backup pumps of a certain model is scarce, overuse of that model's backup pumps may lead to a depletion of spare parts for that model. If a pump of that model fails, there will be no pump of the same model available for replacement.

[0156] By introducing a redundancy correction dimension based on runtime and wear level ranking, the startup sequence takes into account the balanced configuration of system backup capacity in addition to the wear leveling target. This avoids the risk of spare parts depletion caused by excessive consumption of a single model of backup pump, improves the balance of backup resources of pump sets at the model level, and enhances the system's recovery capability under multiple fault conditions.

[0157] This optional implementation calculates redundancy by obtaining the ratio of the number of spare pumps of the same model to the number of currently operating pumps. The redundancy is used as the basis for adjusting the startup sequence. Based on wear leveling, it achieves optimized allocation of spare resources at the model level, effectively improving the system's fault tolerance and ability to handle multiple faults.

[0158] Figure 3 This is a schematic diagram of the structure of the water pump operation control device provided in this application, as shown below. Figure 3 As shown, the water pump operation control device 30 provided in this embodiment includes:

[0159] The acquisition module 301 is used to acquire the operating status data of multiple water pumps in the pump group and the pipeline pressure detection data of the pump group. The water pumps include running water pumps and standby standby pumps.

[0160] The generation module 302 is used to generate a runtime database by obtaining runtime data corresponding to each water pump based on the running status data;

[0161] The comparison module 303 is used to compare the pipeline pressure detection data with the preset pressure threshold in real time;

[0162] Analysis module 304 is used to generate a standby backup pump start sequence based on a preset start quantity and runtime database when the pipeline pressure detection data is lower than a preset pressure threshold.

[0163] Output module 305 is used to start the standby pumps one by one according to the start sequence.

[0164] In one possible implementation, the analysis module 304 is specifically used for:

[0165] Extract runtime data corresponding to multiple standby backup pumps from the runtime database;

[0166] The standby pumps are sorted in ascending order of runtime duration to generate a startup sequence.

[0167] In one possible implementation, the analysis module 304 is further configured to:

[0168] Obtain the preset wear coefficient weights corresponding to at least one type of pump component of the standby backup pump;

[0169] The comprehensive wear value of the standby pump is calculated by combining runtime data with the corresponding preset wear coefficient weights.

[0170] The standby pumps are sorted from low to high based on their comprehensive wear values ​​to generate a startup sequence.

[0171] In one possible implementation, the analysis module 304 is further configured to:

[0172] Based on runtime data and the preset wear coefficient weights corresponding to various water pump components, the wear data corresponding to various water pump components are calculated respectively.

[0173] The wear data of various pump components are weighted and summed to obtain the comprehensive wear value of the standby pump.

[0174] In one possible implementation, the analysis module 304 is further configured to:

[0175] Acquire various operating parameter data from multiple water pumps;

[0176] Pump status identifiers are generated based on multiple types of operating parameter data;

[0177] The priority of the start-up sequence is adjusted according to the pump status identifier, and the pumps with abnormal status are given lower priority.

[0178] In one possible implementation, the analysis module 304 is further configured to:

[0179] Input various types of operational parameter data into a pre-trained long short-term memory network model;

[0180] The abnormal state probability data of the water pump is obtained by processing a pre-trained long short-term memory network model.

[0181] The status identifier of the corresponding water pump is generated based on the abnormal status probability data and the preset probability threshold.

[0182] In one possible implementation, the analysis module 304 is further configured to:

[0183] Obtain current production load parameters;

[0184] Based on the preset correspondence between production load and start-up quantity, determine the start-up quantity that matches the current production load parameters and update the preset start-up quantity.

[0185] The water pump operation control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0186] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0187] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above method.

[0188] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0189] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0190] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0191] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0192] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0193] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0194] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0195] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0196] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0199] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0201] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for controlling the operation of a water pump, characterized in that, include: The system acquires operating status data of multiple pumps within the pump set and pipeline pressure detection data of the pump set, including pumps in operation and standby pumps. Based on the operating status data, the running time data corresponding to each water pump is obtained to generate a running time database. The pipeline pressure detection data is compared with a preset pressure threshold in real time; When the pipeline pressure detection data is lower than the preset pressure threshold, a standby backup pump start sequence is generated based on the preset start quantity and the runtime database. The standby pumps are started one by one according to the start-up sequence.

2. The method according to claim 1, characterized in that, The standby backup pump startup sequence is generated based on the preset startup quantity and the runtime database, including: Extract runtime data corresponding to multiple standby backup pumps from the runtime database; The standby pumps are sorted in ascending order of runtime data to generate the startup sequence.

3. The method according to claim 1, characterized in that, The process of generating a standby backup pump startup sequence based on a preset startup quantity and the runtime database also includes: Obtain the preset wear coefficient weight corresponding to at least one type of pump component of the standby backup pump; The comprehensive wear value corresponding to the standby pump is calculated by combining the runtime data with the corresponding preset wear coefficient weight. The standby pumps are sorted from low to high based on their comprehensive wear values ​​to generate the startup sequence.

4. The method according to claim 3, characterized in that, The comprehensive wear value of the standby pump is calculated by combining the runtime data with the corresponding preset wear coefficient weights, including: Based on the runtime data and the preset wear coefficient weights corresponding to the various types of water pump components, the wear data corresponding to each type of water pump component is calculated. The wear data of various pump components are weighted and summed to obtain the comprehensive wear value of the standby pump.

5. The method according to claim 1, characterized in that, The process of generating a standby backup pump startup sequence based on a preset startup quantity and the runtime database also includes: Obtain various operating parameter data of the multiple water pumps; A water pump status identifier is generated based on the aforementioned multiple types of operating parameter data; The priority of the start-up sequence is adjusted according to the pump status identifier, and the pumps with abnormal status are given lower priority.

6. The method according to claim 5, characterized in that, Based on the aforementioned multiple types of operating parameter data, a water pump status identifier is generated, including: The various types of operational parameter data are input into the pre-trained long short-term memory network model; The abnormal state probability data of the water pump is obtained by processing the pre-trained long short-term memory network model. The status identifier of the corresponding water pump is generated based on the abnormal status probability data and the preset probability threshold.

7. The method according to claim 1, characterized in that, Also includes: Obtain current production load parameters; Based on the preset correspondence between production load and start-up quantity, determine the start-up quantity that matches the current production load parameter, and update the preset start-up quantity.

8. A water pump operation control device, characterized in that, include: The acquisition module is used to acquire the operating status data of multiple water pumps in the pump group and the pipeline pressure detection data of the pump group, wherein the water pumps include running water pumps and standby standby pumps. The generation module is used to generate a runtime database by obtaining runtime data corresponding to each water pump based on the running status data; The comparison module is used to compare the pipeline pressure detection data with a preset pressure threshold in real time; The analysis module is used to generate a start-up sequence for standby pumps based on a preset start-up number and the runtime database when the pipeline pressure detection data is lower than the preset pressure threshold. The output module is used to start the standby backup pumps one by one according to the start sequence.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

Images