Intelligent water tank water quality monitoring method and system

Abstract

The invention relates to the technical field of intelligent water tank water quality monitoring, and provides an intelligent water tank water quality monitoring method and system. The method comprises the following steps: acquiring monitoring data of each water tank and preset standard data corresponding to each water tank in real time to judge whether the water quality of each water tank is abnormal or not: if so, substituting the monitoring data and the preset standard data into a water quality analysis and evaluation model, and outputting a water quality analysis result; according to a temporary treatment scheme in the water quality analysis result, executing the temporary treatment scheme on the water tank, and obtaining treated water quality data of the treated water tank; substituting the treated water quality data and preset standard data into the water quality analysis and evaluation model again to obtain a temporary treatment result; and feeding back the water quality analysis result and the temporary processing result of the water tank to a user side corresponding to the water tank, and selectively cutting off water supply of the water tank. The method can ensure that a user obtains a water quality problem processing result at the first time, and improves the understanding degree of the user on the water quality problem and the credibility of the user on water use safety.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring of intelligent water tanks, and specifically provides a method and system for monitoring water quality of intelligent water tanks. Background Art

[0002] With the rapid development of my country's economy, the number of high-rise buildings has gradually increased, and the water supply method for high-rise buildings is generally secondary water supply from water tanks; for large, medium and small buildings, the secondary water supply method from water tanks can effectively provide normal water supply, but the water quality in the water tanks is easily subject to secondary pollution.

[0003] In the prior art, water tank water quality is generally monitored by periodic water quality sampling, and the monitoring system generally manages multiple water tanks in the entire area at the same time. Water users cannot promptly learn about the water supply situation and water quality during water use, resulting in a long time for the water tank water to be detected after being contaminated, which reduces the user's trust in the water quality and even affects the user's life safety.

[0004] Accordingly, the art needs a new intelligent water tank water quality monitoring solution to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present invention is proposed to provide an intelligent water tank water quality monitoring method and system to solve or at least partially solve the technical problem that the water tank water quality monitoring method in the prior art generally manages multiple water tanks in the entire area at the same time, and water users cannot promptly know the water supply status and water quality during the water use process.

[0006] In a first aspect, the present invention provides a method for monitoring water quality in an intelligent water tank, the method being applied to an intelligent water tank water quality monitoring system, the system comprising monitoring devices respectively installed in different water tanks, the method comprising the following steps: Obtain the monitoring data of each water tank and the preset standard data corresponding to each water tank in real time; Based on the monitoring data of each water tank and the preset standard data corresponding to each water tank, determine whether the water quality of each water tank is abnormal: If it is determined that the water quality of the water tank is abnormal, the monitoring data of the water tank and the preset standard data corresponding to the water tank are substituted into the water quality analysis and evaluation model, so that the abnormality type and abnormality level corresponding to the water tank are determined according to the monitoring data of the water tank, and the temporary treatment plan corresponding to the water tank is selectively obtained according to the abnormality type and abnormality level corresponding to the water tank, so as to output the water quality analysis result corresponding to the water tank, wherein the water quality analysis result includes the abnormality type and abnormality level corresponding to the water tank, and the temporary treatment plan corresponding to the water tank obtained selectively; If there is a temporary treatment plan in the water quality analysis result corresponding to the water tank, the temporary treatment plan is executed on the water tank, and the treated water quality data of the water tank after treatment is obtained; Re-substituting the processed water quality data and the preset standard data corresponding to the water tank into the water quality analysis and evaluation model to obtain a temporary processing result of the water tank; The water quality analysis result and the temporary treatment result of the water tank are fed back to the user end corresponding to the water tank, and based on the temporary treatment result of the water tank, the water supply of the water tank is selectively cut off.

[0007] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, the monitoring data of the water tank includes monitoring values ​​corresponding to a plurality of different data types, the preset standard data of the water tank includes standard parameter value ranges corresponding to a plurality of different data types, and the monitoring data corresponds to the data type of the preset standard data one by one. The determination of whether the water quality of each water tank is abnormal based on the monitoring data of each water tank and the preset standard data corresponding to each water tank includes: If the monitoring data of any data type of the monitoring data of the water tank is not within the standard parameter value range of the data type in the preset standard data of the water tank, it is determined that the water quality of the water tank is abnormal; Otherwise, it is determined that there is no abnormality in the water quality of the water tank.

[0008] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, the method further trains the water quality analysis and evaluation model through the following steps: Taking each water tank as a training sample, obtaining multiple groups of training sample sets corresponding to each training sample, wherein the training sample sets include monitoring data corresponding to the training sample and preset standard data; According to the multiple groups of training sample sets corresponding to each training sample, determining the recognition anomaly type and recognition anomaly level corresponding to each group of training sample sets; Based on the identification anomaly type and identification anomaly level corresponding to each training sample set, determine whether each training sample set is successfully identified: If it is determined that the identification is successful, then based on the abnormality type and abnormality level corresponding to the training sample set, multiple groups of adjustment schemes corresponding to the training sample set are selectively determined, wherein each group of adjustment schemes includes at least one adjustment type and an adjustment parameter corresponding to each adjustment type; Based on the multiple groups of adjustment schemes corresponding to the training sample set, selecting a group of adjustment schemes as a temporary processing scheme corresponding to the training sample set; Simulating based on the temporary processing scheme corresponding to each group of training sample sets to obtain simulation data corresponding to each group of training sample sets; Based on the simulation data and preset standard data corresponding to each group of training sample sets, determine the processing result and processing evaluation score of the temporary processing solution corresponding to each group of training sample sets, wherein the processing result is processing success or processing failure; Based on the processing results of the temporary processing solutions corresponding to each group of training sample sets, the training results of the processing training in the training of the water quality analysis and evaluation model are obtained.

[0009] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, the water quality analysis and evaluation model stores a preset water tank history database and a preset abnormal parameter comparison table, and the method of using each water tank as a training sample and obtaining multiple sets of training sample sets corresponding to each training sample includes: Taking each water tank as a training sample, based on the preset abnormal parameter comparison table, randomly setting multiple groups of data parameters of each training sample as multiple groups of monitoring data corresponding to each training sample, and marking each group of monitoring data based on the abnormal type and abnormal level corresponding to the multiple groups of data parameters corresponding to each training sample; Based on the preset water tank history database, randomly selecting multiple groups of historical preset standard data for each training sample as multiple groups of preset standard data corresponding to each training sample; Randomly pairing multiple groups of monitoring data corresponding to each training sample with multiple groups of preset standard data corresponding to each training sample to obtain multiple groups of training sample sets corresponding to each training sample; Based on the labeling of the monitoring data in each training sample set, each training sample set is labeled.

[0010] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, judging whether each set of training sample sets is successfully identified based on the identified abnormality type and the identified abnormality level corresponding to each set of training sample sets includes: If the marking of the training sample set is the same as the identification anomaly type and the identification anomaly level corresponding to the training sample set, it is determined that the training sample set is successfully identified; Otherwise, it is determined that the training sample set recognition fails.

[0011] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, the selectively determining multiple groups of adjustment solutions corresponding to the training sample set based on the abnormality type and abnormality level corresponding to the training sample set includes: Based on the preset water tank history database, obtaining water quality treatment capacity data corresponding to the training samples of the training sample set; Based on the abnormal type and abnormal level corresponding to the training sample set, determine whether the water quality treatment capacity data corresponding to the training sample meets the requirements: If it is determined that the water quality treatment capacity data corresponding to the training sample meets the requirements, then based on the abnormality type and abnormality level corresponding to the training sample set, multiple groups of adjustment schemes corresponding to the training sample set are determined; Otherwise, it is determined that the training samples corresponding to the training sample set need to be manually cleaned.

[0012] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, the adjustment type in the adjustment scheme corresponds to the data type one by one, and the multiple groups of adjustment schemes corresponding to the training sample set are selected as the temporary processing scheme corresponding to the training sample set, including: Based on the multiple groups of adjustment schemes corresponding to the training sample set and the water quality treatment capacity data corresponding to the training samples of the training sample set, obtaining the adjustment score corresponding to each group of adjustment schemes of the training sample set; Based on the adjustment score corresponding to each group of adjustment solutions of the training sample set, a group of adjustment solutions with the highest adjustment score is selected as the temporary processing solution corresponding to the training sample set.

[0013] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, after judging whether each set of training sample sets is successfully identified based on the identified abnormality type and the identified abnormality level corresponding to each set of training sample sets, the method further includes: Based on the recognition results of each group of training sample sets, the training results of the recognition training in the training of the water quality analysis and evaluation model are obtained.

[0014] In a technical solution of the above-mentioned intelligent water tank water quality monitoring method, after judging whether the processing training in the training of the water quality analysis and evaluation model is successful based on the processing evaluation score of the temporary processing solution corresponding to each group of training sample sets, the method further includes: Based on the training results of the processing training in the training of the water quality analysis and evaluation model and the training results of the recognition training in the training of the water quality analysis and evaluation model, the comprehensive training success rate, the recognition training success rate and the processing training success rate of the training of the water quality analysis and evaluation model are obtained; Based on the fact that the comprehensive training success rate, the recognition training success rate and the processing training success rate of the training of the water quality analysis and evaluation model are all higher than the preset respective success rate thresholds, it is determined that the training of the water quality analysis and evaluation model is successful; Otherwise, it is determined that the training of the water quality analysis and evaluation model has failed, and the water quality analysis and evaluation model needs to be retrained.

[0015] In a second aspect, the present invention provides an intelligent water tank water quality monitoring system, the system comprising a water quality analysis and evaluation model and a control device, the system further comprising monitoring devices respectively installed in different water tanks and processing devices respectively connected to different water tanks, the water quality analysis and evaluation model stores a preset water tank history database and a preset abnormal parameter comparison table, the monitoring device is used to obtain monitoring data of the water tank, and the processing device is used to perform water quality treatment on the water tank according to a temporary treatment plan corresponding to the water tank; the control device comprises a processor and a memory, the memory is suitable for storing a plurality of program codes, the program codes are suitable for being loaded and run by the processor to execute the intelligent water tank water quality monitoring method described in any one of the technical solutions of the above-mentioned intelligent water tank water quality monitoring method; The control device also exchanges information with multiple user terminals to feed back the water quality analysis results and temporary processing results of the water tank to the user terminals.

[0016] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects: By acquiring the monitoring data of each water tank in real time and the preset standard data, it is judged whether there is any abnormality in the water quality of each water tank, thereby realizing the preliminary judgment and real-time monitoring of the water quality of the water tank. When there is an abnormality, the acquired data is substituted into the water quality analysis and evaluation model to obtain the water quality analysis result of the water tank, that is, the abnormal type, abnormal level and selective temporary treatment plan of the water tank, thereby realizing timely obtaining the temporary treatment plan corresponding to the water tank, improving the automatic processing of the abnormal water quality of the water tank, and then combining the treated water quality data obtained after executing the temporary treatment plan on the water tank, and re-analyzing the water quality of the water tank through the water quality analysis and evaluation model, the temporary treatment result of the water tank is obtained, which ensures the water quality of the water tank. The water quality treatment results can be obtained in time, thereby realizing intelligent treatment of the water quality of the water tank, and the temporary treatment results and water quality analysis results are fed back to the user end, so that the user can know the water quality of the water tank in time, improve the user's trust in the water quality of the water tank, and thus improve the user's water experience. The temporary treatment result of the water tank is used to decide whether to cut off the water supply of the water tank, so as to avoid the user using water that does not meet the water quality standards, thereby ensuring the user's life safety, and avoiding the water tank water quality monitoring method in the prior art, which generally manages multiple water tanks in the entire area at the same time, and the water user cannot timely know the water supply situation and water quality during the water use process. Technical problems.

[0017] In the technical solution of the present invention, in the model training, a water tank is used as a training sample, and multiple groups of training sample sets corresponding to each training sample are obtained. According to these training sample sets, the identification abnormality type and the identification abnormality level corresponding to each training sample set are determined to obtain the identification result of each training sample set, and then the training result of the identification training of each training sample set is obtained, so as to realize the identification training of the abnormality type and abnormality level and its training result, and when the identification is successful, continue to perform subsequent processing training to improve the training efficiency of the model training, and selectively determine multiple groups of corresponding adjustment schemes according to the abnormality type and abnormality level of the training sample set, and select the most suitable group of adjustment schemes as the temporary processing scheme of the training sample set, so as to improve the matching degree between the temporary processing scheme and the training sample, and then obtain the simulation data by simulating the temporary processing scheme, and then determine the temporary processing result and processing evaluation score of each training sample set in combination with the preset standard data to judge whether the processing training in the model training is successful, so that the trained model can technically and accurately identify the abnormal situation of water quality, and derive the corresponding temporary processing scheme according to the abnormal situation, thereby improving the intelligence of the water quality analysis and evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which: Figure 1 It is a schematic flow chart of the main steps of a method for monitoring water quality in an intelligent water tank according to an embodiment of the present invention; Figure 2 is a schematic flow chart of the main steps of training a water quality analysis and evaluation model according to an embodiment of the present invention; Figure 3 It is a main structural block diagram of an intelligent water tank water quality monitoring system according to an embodiment of the present invention.

[0019] List of reference numerals: 300: Intelligent water tank water quality monitoring system; 301: Control device; 3011: Processor; 3012: Memory; 3013: Program code; 302: Water quality analysis and evaluation model; 3021: Water tank history database; 3022: Abnormal parameter comparison table; 303: Monitoring device; 304: Processing device. DETAILED DESCRIPTION

[0020] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0021] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0022] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for monitoring water quality in an intelligent water tank according to an embodiment of the present invention. Figure 1 As shown, the intelligent water tank water quality monitoring method in the embodiment of the present invention is applied to an intelligent water tank water quality monitoring system, the system includes monitoring devices respectively installed in different water tanks, and the method mainly includes the following steps S101-step S103.

[0023] Step S101: acquiring monitoring data of each water tank and preset standard data corresponding to each water tank in real time; Step S102: Based on the monitoring data of each water tank and the preset standard data corresponding to each water tank, determine whether the water quality of each water tank is abnormal: Specifically, in some embodiments, the monitoring data of the water tank includes monitoring values ​​corresponding to a plurality of different data types, the preset standard data of the water tank includes standard parameter value ranges corresponding to a plurality of different data types, and the monitoring data corresponds to the data type of the preset standard data one-to-one, and judging whether the water quality of each water tank is abnormal based on the monitoring data of each water tank and the preset standard data corresponding to each water tank includes: If the monitoring data of any data type of the monitoring data of the water tank is not within the standard parameter value range of the data type in the preset standard data of the water tank, it is determined that the water quality of the water tank is abnormal; Otherwise, it is determined that there is no abnormality in the water quality of the water tank.

[0024] Step S103: If it is determined that the water quality of the water tank is abnormal, the monitoring data of the water tank and the preset standard data corresponding to the water tank are substituted into the water quality analysis and evaluation model, so that the abnormality type and abnormality level corresponding to the water tank are determined according to the monitoring data of the water tank, and the temporary treatment plan corresponding to the water tank is selectively obtained according to the abnormality type and abnormality level corresponding to the water tank, so as to output the water quality analysis result corresponding to the water tank, wherein the water quality analysis result includes the abnormality type and abnormality level corresponding to the water tank, and the temporary treatment plan corresponding to the water tank obtained selectively; Specifically, in some embodiments, the method further includes: If it is determined that the water quality of the water tank is not abnormal, then obtaining environmental data of the water tank, wherein the environmental data includes current environmental data and historical environmental data; Determining an estimated probability that water quality in the water tank is contaminated based on the environmental data of the water tank; Based on the estimated probability of water pollution in the water tank, an early warning signal is selectively sent to the user to effectively prevent water pollution, thereby improving the user's trust in water safety.

[0025] like Figure 2 As shown, further, in some embodiments, the monitoring data of the water tank and the preset standard data corresponding to the water tank are substituted into the water quality analysis and evaluation model, so that the abnormal type and abnormal level corresponding to the water tank are determined according to the monitoring data of the water tank, and the temporary treatment plan corresponding to the water tank is selectively obtained according to the abnormal type and abnormal level corresponding to the water tank, so as to output the water quality analysis result corresponding to the water tank. Before that, the method further trains the water quality analysis and evaluation model through the following steps: Taking each water tank as a training sample, obtaining multiple groups of training sample sets corresponding to each training sample, wherein the training sample sets include monitoring data corresponding to the training sample and preset standard data; According to the multiple groups of training sample sets corresponding to each training sample, determining the recognition anomaly type and recognition anomaly level corresponding to each group of training sample sets; Based on the identification anomaly type and identification anomaly level corresponding to each training sample set, determine whether each training sample set is successfully identified: If it is determined that the identification is successful, then based on the abnormality type and abnormality level corresponding to the training sample set, multiple groups of adjustment schemes corresponding to the training sample set are selectively determined, wherein each group of adjustment schemes includes at least one adjustment type and an adjustment parameter corresponding to each adjustment type; Based on the multiple groups of adjustment solutions corresponding to the training sample set, select a group of adjustment solutions as a temporary processing solution corresponding to the training sample set; Simulating based on the temporary processing scheme corresponding to each group of training sample sets to obtain simulation data corresponding to each group of training sample sets; Based on the simulation data and preset standard data corresponding to each group of training sample sets, determine the processing result and processing evaluation score of the temporary processing solution corresponding to each group of training sample sets, wherein the processing result is processing success or processing failure; Based on the processing results of the temporary processing solutions corresponding to each group of training sample sets, the training results of the processing training in the training of the water quality analysis and evaluation model are obtained.

[0026] Specifically, in some embodiments, the water quality analysis and evaluation model stores a preset water tank history database and a preset abnormal parameter comparison table, and the method of using each water tank as a training sample and obtaining multiple sets of training sample sets corresponding to each training sample includes: Taking each water tank as a training sample, based on the preset abnormal parameter comparison table, randomly setting multiple groups of data parameters of each training sample as multiple groups of monitoring data corresponding to each training sample, and marking each group of monitoring data based on the abnormal type and abnormal level corresponding to the multiple groups of data parameters corresponding to each training sample; Based on the preset water tank history database, randomly selecting multiple groups of historical preset standard data for each training sample as multiple groups of preset standard data corresponding to each training sample; Randomly pairing multiple groups of monitoring data corresponding to each training sample with multiple groups of preset standard data corresponding to each training sample to obtain multiple groups of training sample sets corresponding to each training sample; Based on the labeling of the monitoring data in each training sample set, each training sample set is labeled.

[0027] Specifically, in some embodiments, the water quality analysis and evaluation model stores a preset water tank history database and a preset abnormal parameter comparison table, the water tank history database stores the numbers of each water tank, the historical preset standard data corresponding to each water tank, and the water quality treatment capacity data corresponding to each water tank, and the abnormal parameter comparison table includes multiple different abnormal types, multiple abnormal levels corresponding to each abnormal type, parameter ranges corresponding to each abnormal level of each abnormal type, and parameter types of the parameter ranges.

[0028] Specifically, in some embodiments, judging whether each set of training sample sets is successfully identified based on the identified anomaly type and the identified anomaly level corresponding to each set of training sample sets includes: If the marking of the training sample set is the same as the identification anomaly type and the identification anomaly level corresponding to the training sample set, it is determined that the training sample set is successfully identified; Otherwise, it is determined that the training sample set recognition fails.

[0029] Specifically, in some embodiments, selectively determining a plurality of groups of adjustment schemes corresponding to the training sample set based on the abnormality type and abnormality level corresponding to the training sample set includes: Based on the preset water tank history database, obtaining water quality treatment capacity data corresponding to the training samples of the training sample set; Based on the abnormal type and abnormal level corresponding to the training sample set, determine whether the water quality treatment capacity data corresponding to the training sample meets the requirements: If it is determined that the water quality treatment capacity data corresponding to the training sample meets the requirements, then based on the abnormality type and abnormality level corresponding to the training sample set, multiple groups of adjustment schemes corresponding to the training sample set are determined; Otherwise, it is determined that the training samples corresponding to the training sample set need to be manually cleaned.

[0030] Specifically, in some embodiments, the method further includes: If the water quality analysis and evaluation model output determines that the water tank requires manual cleaning, the water quality analysis results of the water tank and the situation that the water tank requires manual cleaning will be fed back to the user end corresponding to the water tank, and the water supply to the water tank will be cut off immediately to prevent the user from using water that does not meet the water quality standards, thereby ensuring the user's life safety.

[0031] Specifically, in some embodiments, based on the abnormality type and abnormality level corresponding to the training sample set, determining whether the water quality treatment capacity data corresponding to the training sample meets the requirements includes: Based on the water quality treatment capacity data corresponding to the training samples of each training sample set and the monitoring data in each training sample set, the optimal treatment data for water quality treatment of each training sample set is obtained; If the optimal treatment data for water quality treatment of the training sample set is higher than the preset standard data in the training sample set, it is determined that the water quality treatment capacity data corresponding to the training sample meets the requirements; Otherwise, it is determined that the water quality treatment capacity data corresponding to the training sample does not meet the requirements.

[0032] Specifically, the water quality treatment capacity data includes treatment coefficient values ​​corresponding to different data types; the optimal treatment data for water quality treatment of each training sample set is obtained by the following formula: The data value of any data type in the optimal treatment data for water quality treatment in the training sample set = the monitoring value of the data type in the monitoring data in the training sample set * (1-the treatment coefficient value corresponding to the data type in the water quality treatment capacity data of the training sample).

[0033] Specifically, in some embodiments, the adjustment types in the adjustment scheme correspond to the data types one by one, and the selecting a group of adjustment schemes as the temporary processing scheme corresponding to the training sample set based on the multiple groups of adjustment schemes corresponding to the training sample set includes: Based on the multiple groups of adjustment schemes corresponding to the training sample set and the water quality treatment capacity data corresponding to the training samples of the training sample set, obtaining the adjustment score corresponding to each group of adjustment schemes of the training sample set; Based on the adjustment score corresponding to each group of adjustment solutions of the training sample set, a group of adjustment solutions with the highest adjustment score is selected as the temporary processing solution corresponding to the training sample set.

[0034] Specifically, in some embodiments, obtaining the adjustment score corresponding to each group of adjustment schemes of the training sample set based on the multiple groups of adjustment schemes corresponding to the training sample set and the water quality treatment capacity data corresponding to the training samples of the training sample set includes: Performing simulation based on multiple groups of adjustment schemes corresponding to the training sample set to obtain simulation data corresponding to each group of adjustment schemes, wherein the simulation data includes simulation values ​​of different data types; Based on the simulation data corresponding to each group of adjustment schemes and the water quality treatment capacity data corresponding to the training samples of the training sample set, the adjustment score corresponding to each group of adjustment schemes of the training sample set is obtained.

[0035] Specifically, the water quality treatment capacity data also includes weights corresponding to different data types; the adjustment score corresponding to each group of adjustment schemes in the training sample set is obtained by the following formula: , Wherein, P is the adjustment score corresponding to the adjustment plan; is the monitoring value of the i-th data type of the monitoring data corresponding to the training sample set; is the simulated value of the i-th data type in the simulated data corresponding to the adjustment scheme; is the treatment coefficient value corresponding to the i-th data type in the water quality treatment capacity data of the training sample; is the weight corresponding to the i-th data type, and the weights corresponding to all data types The sum is 1.

[0036] Specifically, in some embodiments, determining the processing result and processing evaluation score of the temporary processing solution corresponding to each group of training sample sets based on the simulation data and preset standard data corresponding to each group of training sample sets includes: Determine the processing evaluation score of the temporary processing solution corresponding to each group of training sample sets based on the simulation data corresponding to each group of training sample sets and the preset standard data; Based on the processing evaluation score of the temporary processing solution corresponding to each group of training sample sets, the processing result of the temporary processing solution corresponding to each group of training sample sets is determined.

[0037] Specifically, the simulation data includes simulation values ​​of different data types. If the simulation value corresponding to the data type in the simulation data corresponding to the training sample set is within the standard value range corresponding to the data type in the preset standard data of the training sample, the processing evaluation score corresponding to the data type in the processing evaluation score corresponding to the training sample set is recorded as "1"; otherwise, the processing evaluation score corresponding to the data type in the processing evaluation score corresponding to the training sample set is recorded as "0"; Based on the processing evaluation scores of all data types in the processing evaluation scores corresponding to the training sample set, the processing evaluation score of the temporary processing solution corresponding to the training sample set is obtained.

[0038] Specifically, the processing evaluation score of the temporary processing solution corresponding to each group of training sample sets is obtained by the following formula: , Among them, Q is the processing evaluation score of the temporary processing solution corresponding to the training sample set; is the processing evaluation score of the i-th data type in the processing evaluation score corresponding to the training sample set; is the weight corresponding to the i-th data type, and the weights corresponding to all data types The sum is 1.

[0039] Specifically, in some embodiments, determining the processing result of the temporary processing solution corresponding to each group of training sample sets based on the processing evaluation score of the temporary processing solution corresponding to each group of training sample sets includes: If the processing evaluation score of the temporary processing solution corresponding to the training sample set exceeds a preset temporary processing threshold, then the processing result of the temporary processing solution corresponding to the training sample set is judged to be a successful processing; Otherwise, it is determined that the processing result of the temporary processing solution corresponding to the training sample set is a processing failure.

[0040] Specifically, the preset temporary processing threshold may be 0.95 or 0.9. The setting of the preset temporary processing threshold here is only an exemplary description. In actual testing, those skilled in the art may set it according to actual needs, which will not be elaborated here.

[0041] Specifically, in some embodiments, after determining whether each set of training sample sets is successfully identified based on the identified anomaly type and the identified anomaly level corresponding to each set of training sample sets, the method further includes: Based on the recognition results of each group of training sample sets, the training results of the recognition training in the training of the water quality analysis and evaluation model are obtained.

[0042] Specifically, in some embodiments, the obtaining of the training results of the recognition training in the training of the water quality analysis and evaluation model based on the recognition results of each group of training sample sets includes: Based on the recognition results of each training sample set, determine the recognition success rate of all training sample sets; If the recognition success rate of all training sample sets is higher than a preset recognition success rate threshold, the training result of the recognition training in the training of the water quality analysis and evaluation model is determined to be successful training; Otherwise, it is determined that the training result of the recognition training in the training of the water quality analysis and evaluation model is training failure.

[0043] Specifically, the preset recognition success rate threshold may be 95% or 98%. The setting of the preset recognition success rate threshold here is only an exemplary description. In actual testing, those skilled in the art may set it according to actual needs, which will not be elaborated here.

[0044] Specifically, in some embodiments, the processing result of the temporary processing scheme corresponding to each set of training sample sets to obtain the training result of the processing training in the training of the water quality analysis and evaluation model includes: If the processing result of the temporary processing scheme corresponding to the training sample set is successful processing, then the training result of the processing training in the training of the water quality analysis and evaluation model is successful training; Otherwise, the training result of the processing training in the training of the water quality analysis and evaluation model is training failure.

[0045] Specifically, in some embodiments, after determining whether the processing training in the training of the water quality analysis and evaluation model is successful based on the processing evaluation score of the temporary processing solution corresponding to each group of training sample sets, the method further includes: Based on the training results of the processing training in the training of the water quality analysis and evaluation model and the training results of the recognition training in the training of the water quality analysis and evaluation model, the comprehensive training success rate, the recognition training success rate and the processing training success rate of the training of the water quality analysis and evaluation model are obtained; Based on the fact that the comprehensive training success rate, the recognition training success rate and the processing training success rate of the training of the water quality analysis and evaluation model are all higher than the preset respective success rate thresholds, it is determined that the training of the water quality analysis and evaluation model is successful; Otherwise, it is determined that the training of the water quality analysis and evaluation model has failed, and the water quality analysis and evaluation model needs to be retrained.

[0046] Specifically, the preset comprehensive training success rate threshold, recognition training success rate threshold and processing training success rate threshold can all be 95%, and the preset comprehensive training success rate threshold, recognition training success rate threshold and processing training success rate threshold can also be 92%, 95% and 95% respectively. The settings of the preset comprehensive training success rate threshold, recognition training success rate threshold and processing training success rate threshold here are only exemplary descriptions. In actual tests, those skilled in the art can set them according to actual needs, which will not be repeated here.

[0047] In the above embodiment, in the model training, the water tank is used as a training sample, and multiple groups of training sample sets corresponding to each training sample are obtained. According to these training sample sets, the identification abnormality type and the identification abnormality level corresponding to each training sample set are determined to obtain the identification result of each training sample set, and then the training result of the identification training of each training sample set is obtained, so as to realize the identification training of the abnormality type and abnormality level and its training result, and when the identification is successful, the subsequent processing training is continued to be performed to improve the training efficiency of the model training, and according to the abnormality type and abnormality level of the training sample set, multiple groups of corresponding adjustment schemes are selectively determined, and the most suitable group of adjustment schemes are selected as the temporary processing scheme of the training sample set, so as to improve the matching degree between the temporary processing scheme and the training sample, and then the simulation data is obtained by simulating the temporary processing scheme, and then the temporary processing result and the processing evaluation score of each training sample set are determined in combination with the preset standard data to judge whether the processing training in the model training is successful, so that the trained model can technically and accurately identify the abnormal situation of water quality, and derive the corresponding temporary processing scheme according to the abnormal situation, thereby improving the intelligence of the water quality analysis and evaluation model.

[0048] Step S104: if there is a temporary treatment plan in the water quality analysis result corresponding to the water tank, the temporary treatment plan is executed on the water tank, and the treated water quality data of the water tank after treatment is obtained; Step S105: Substituting the processed water quality data and the preset standard data corresponding to the water tank into the water quality analysis and evaluation model again to obtain a temporary processing result of the water tank; Specifically, in some embodiments, the step of resubstituting the processed water quality data and the preset standard data corresponding to the water tank into the water quality analysis and evaluation model to obtain a temporary processing result of the water tank includes: Determining a treatment evaluation score corresponding to a temporary treatment solution for the water tank based on the treated water quality data and preset standard data corresponding to the water tank; If the treatment evaluation score corresponding to the temporary treatment solution of the water tank exceeds the preset temporary treatment threshold, the temporary treatment result of the water tank is determined to be a successful treatment; Otherwise, it is determined that the temporary processing result of the water tank is processing failure.

[0049] Step S106: Feedback the water quality analysis result and the temporary processing result of the water tank to the user end corresponding to the water tank, and selectively cut off the water supply to the water tank based on the temporary processing result of the water tank.

[0050] Specifically, in some embodiments, selectively cutting off the water supply to the water tank based on the temporary processing result of the water tank includes: If the temporary processing result of the water tank is processing failure, the water supply of the water tank is cut off; otherwise, the current water supply status of the water tank is maintained, and based on the feedback result of the user corresponding to the water tank, the water supply of the water tank is selectively cut off.

[0051] Specifically, selectively cutting off the water supply to the water tank based on the feedback result of the user corresponding to the water tank includes: Obtaining feedback results from all users corresponding to the water tank, and determining a decision result on whether the water supply corresponding to the water tank is cut off based on cluster analysis; If the water supply cut-off decision result of the water tank is that the water supply is cut off, the water supply of the water tank is cut off; otherwise, the current water supply state of the water tank is maintained.

[0052] Based on the above steps S101 to S106, the monitoring data of each water tank and the preset standard data are obtained in real time to determine whether the water quality of each water tank is abnormal, thereby realizing the preliminary judgment and real-time monitoring of the water quality of the water tank. When an abnormality exists, the acquired data is substituted into the water quality analysis and evaluation model to obtain the water quality analysis result of the water tank, that is, the abnormal type, abnormal level and selective temporary treatment plan of the water tank, thereby realizing timely obtaining the temporary treatment plan corresponding to the water tank, improving the automatic processing of abnormal water quality conditions of the water tank, and then combining the treated water quality data obtained after executing the temporary treatment plan on the water tank, and re-analyzing the water quality of the water tank through the water quality analysis and evaluation model to obtain the temporary treatment result of the water tank. The result ensures that the treatment results of the water quality in the water tank are known in time, thereby realizing the intelligent treatment of the water quality in the water tank, and feeding back the temporary treatment results and water quality analysis results to the user end, so that the user can know the water quality of the water tank in time, improve the user's trust in the water quality of the water tank, and thus improve the user's water use experience, and then decide whether to cut off the water supply of the water tank based on the temporary treatment result of the water tank, so as to avoid the user using water that does not meet the water quality standards, thereby ensuring the user's life safety, and avoiding the water tank water quality monitoring method in the prior art, which generally manages multiple water tanks in the entire area at the same time, and the water user cannot timely know the water supply situation and water quality during the water use process.

[0053] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order, and they can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0054] Furthermore, the present invention also provides an intelligent water tank water quality monitoring system.

[0055] See attached Figure 3 , Figure 3 FIG. 1 is a main structural block diagram of an intelligent water tank water quality monitoring system according to an embodiment of the present invention. Figure 3As shown, the intelligent water tank water quality monitoring system 300 in the embodiment of the present invention mainly includes a water quality analysis and evaluation model 302 and a control device 301, the system also includes monitoring devices 303 respectively installed in different water tanks and processing devices 304 respectively connected to different water tanks, the water quality analysis and evaluation model 302 stores a preset water tank history database 3021 and a preset abnormal parameter comparison table 3022, the monitoring device 303 is used to obtain monitoring data of the water tank, and the processing device 304 is used to perform water quality treatment on the water tank according to the temporary treatment plan corresponding to the water tank; the control device 301 includes a processor 3011 and a memory 3012, the memory 3012 can be configured to store a program code 3013 for executing the intelligent water tank water quality monitoring method of the above method embodiment, the processor 3011 can be configured to execute the program code 3013 in the memory 3012, the program code 3013 includes but is not limited to the program code 3013 for executing the above intelligent water tank water quality monitoring method. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device 301 can be a control device formed by various electronic devices.

[0056] The control device 301 also exchanges information with multiple user terminals to feed back the water quality analysis results and temporary processing results of the water tank to the user terminals.

[0057] Specifically, in some embodiments, the water tank history database 3021 stores the numbers of each water tank, the historical preset standard data corresponding to each water tank, and the water quality treatment capacity data corresponding to each water tank, and the abnormal parameter comparison table 3022 includes multiple different abnormal types, multiple abnormal levels corresponding to each abnormal type, parameter ranges corresponding to each abnormal level of each abnormal type, and parameter types of the parameter ranges.

[0058] Specifically, in some embodiments, the user terminal can be a mobile phone, a tablet computer, or a laptop, a desktop computer, etc. This is not restrictive, and the technician can set it according to actual usage requirements.

[0059] Specifically, in some embodiments, the control device 301 also exchanges information with multiple user terminals to feed back the water quality analysis results and temporary treatment results of the water tank to the user terminals. The specific communication connection method for the information interaction between the user terminal and the control device 301 is not restrictive, and the technician can set it according to the actual use requirements, as long as the communication connection can be achieved, for example, it can be connected through wifi or Bluetooth, etc. In addition, as a preferred setting method, when the user terminal is a mobile phone or a tablet computer, the technician can set the app to facilitate user operation. Of course, it can also be operated directly through the web page. This is not restrictive, and the technician can set it according to the actual use requirements.

[0060] In one implementation, the description of the specific implementation functions may refer to steps S101 to S106.

[0061] The intelligent water tank water quality monitoring system 300 is used to perform Figure 1 The embodiment of the intelligent water tank water quality monitoring method shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technicians in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the intelligent water tank water quality monitoring system 300 can refer to the contents described in the embodiment of the intelligent water tank water quality monitoring method, which will not be repeated here.

[0062] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor 3011, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes a computer program code 3013, and the computer program code 3013 can be in the form of source code, object code, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code 3013. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electric carrier signals and telecommunication signals.

[0063] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program code 3013 for executing the smart water tank water quality monitoring method of the above method embodiment, and the program code 3013 can be loaded and run by the processor 3011 to implement the above smart water tank water quality monitoring method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0064] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor 3011 itself, or a part of the software, a part of the hardware, or a part of the combination of software and hardware in the processor 3011. Therefore, the number of each module in the figure is only schematic.

[0065] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0066] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for monitoring water quality in an intelligent water tank, characterized in that: The method is applied to an intelligent water tank water quality monitoring system, the system comprising monitoring devices respectively installed in different water tanks, and the method comprises the following steps: Obtain the monitoring data of each water tank and the preset standard data corresponding to each water tank in real time; Based on the monitoring data of each water tank and the preset standard data corresponding to each water tank, determine whether the water quality of each water tank is abnormal: If it is determined that the water quality of the water tank is abnormal, the monitoring data of the water tank and the preset standard data corresponding to the water tank are substituted into the water quality analysis and evaluation model, so that the abnormality type and abnormality level corresponding to the water tank are determined according to the monitoring data of the water tank, and the temporary treatment plan corresponding to the water tank is selectively obtained according to the abnormality type and abnormality level corresponding to the water tank, so as to output the water quality analysis result corresponding to the water tank, wherein the water quality analysis result includes the abnormality type and abnormality level corresponding to the water tank, and the temporary treatment plan corresponding to the water tank obtained selectively; If there is a temporary treatment plan in the water quality analysis result corresponding to the water tank, the temporary treatment plan is executed on the water tank, and the treated water quality data of the water tank after treatment is obtained; Re-substituting the processed water quality data and the preset standard data corresponding to the water tank into the water quality analysis and evaluation model to obtain a temporary processing result of the water tank; The water quality analysis result and the temporary treatment result of the water tank are fed back to the user end corresponding to the water tank, and based on the temporary treatment result of the water tank, the water supply of the water tank is selectively cut off.

2. The intelligent water tank water quality monitoring method according to claim 1, characterized in that: The monitoring data of the water tank includes monitoring values ​​corresponding to a plurality of different data types, the preset standard data of the water tank includes standard parameter value ranges corresponding to a plurality of different data types, and the monitoring data corresponds to the data type of the preset standard data one by one. The determination of whether the water quality of each water tank is abnormal based on the monitoring data of each water tank and the preset standard data corresponding to each water tank includes: If the monitoring data of any data type of the monitoring data of the water tank is not within the standard parameter value range of the data type in the preset standard data of the water tank, it is determined that the water quality of the water tank is abnormal; Otherwise, it is determined that there is no abnormality in the water quality of the water tank.

3. The intelligent water tank water quality monitoring method according to claim 2 is characterized in that: The method also trains the water quality analysis and assessment model by the following steps: Taking each water tank as a training sample, obtaining multiple groups of training sample sets corresponding to each training sample, wherein the training sample sets include monitoring data corresponding to the training sample and preset standard data; According to the multiple groups of training sample sets corresponding to each training sample, determining the recognition anomaly type and recognition anomaly level corresponding to each group of training sample sets; Based on the identification anomaly type and identification anomaly level corresponding to each training sample set, determine whether each training sample set is successfully identified: If it is determined that the identification is successful, then based on the abnormality type and abnormality level corresponding to the training sample set, multiple groups of adjustment schemes corresponding to the training sample set are selectively determined, wherein each group of adjustment schemes includes at least one adjustment type and an adjustment parameter corresponding to each adjustment type; Based on the multiple groups of adjustment schemes corresponding to the training sample set, selecting a group of adjustment schemes as a temporary processing scheme corresponding to the training sample set; Simulating based on the temporary processing scheme corresponding to each group of training sample sets to obtain simulation data corresponding to each group of training sample sets; Based on the simulation data and preset standard data corresponding to each group of training sample sets, determine the processing result and processing evaluation score of the temporary processing solution corresponding to each group of training sample sets, wherein the processing result is processing success or processing failure; Based on the processing results of the temporary processing solutions corresponding to each group of training sample sets, the training results of the processing training in the training of the water quality analysis and evaluation model are obtained.

4. The intelligent water tank water quality monitoring method according to claim 3 is characterized in that: The water quality analysis and evaluation model stores a preset water tank history database and a preset abnormal parameter comparison table. The method of using each water tank as a training sample and obtaining multiple sets of training sample sets corresponding to each training sample includes: Taking each water tank as a training sample, based on the preset abnormal parameter comparison table, randomly setting multiple groups of data parameters of each training sample as multiple groups of monitoring data corresponding to each training sample, and marking each group of monitoring data based on the abnormal type and abnormal level corresponding to the multiple groups of data parameters corresponding to each training sample; Based on the preset water tank history database, randomly selecting multiple groups of historical preset standard data for each training sample as multiple groups of preset standard data corresponding to each training sample; Randomly pairing multiple groups of monitoring data corresponding to each training sample with multiple groups of preset standard data corresponding to each training sample to obtain multiple groups of training sample sets corresponding to each training sample; Based on the labeling of the monitoring data in each training sample set, each training sample set is labeled.

5. The intelligent water tank water quality monitoring method according to claim 4 is characterized in that: The determining whether each set of training sample sets is successfully identified based on the identified anomaly type and the identified anomaly level corresponding to each set of training sample sets includes: If the marking of the training sample set is the same as the identification anomaly type and the identification anomaly level corresponding to the training sample set, it is determined that the training sample set is successfully identified; Otherwise, it is determined that the training sample set recognition fails.

6. The intelligent water tank water quality monitoring method according to claim 5, characterized in that: The selectively determining a plurality of adjustment schemes corresponding to the training sample set based on the abnormality type and abnormality level corresponding to the training sample set comprises: Based on the preset water tank history database, obtaining water quality treatment capacity data corresponding to the training samples of the training sample set; Based on the abnormal type and abnormal level corresponding to the training sample set, determine whether the water quality treatment capacity data corresponding to the training sample meets the requirements: If it is determined that the water quality treatment capacity data corresponding to the training sample meets the requirements, then based on the abnormality type and abnormality level corresponding to the training sample set, multiple groups of adjustment schemes corresponding to the training sample set are determined; Otherwise, it is determined that the training samples corresponding to the training sample set need to be manually cleaned.

7. The intelligent water tank water quality monitoring method according to claim 6, characterized in that: The adjustment types in the adjustment scheme correspond to the data types one by one, and the selecting a group of adjustment schemes as the temporary processing scheme corresponding to the training sample set based on the multiple groups of adjustment schemes corresponding to the training sample set includes: Based on the multiple groups of adjustment schemes corresponding to the training sample set and the water quality treatment capacity data corresponding to the training samples of the training sample set, obtaining the adjustment score corresponding to each group of adjustment schemes of the training sample set; Based on the adjustment score corresponding to each group of adjustment solutions of the training sample set, a group of adjustment solutions with the highest adjustment score is selected as the temporary processing solution corresponding to the training sample set.

8. The intelligent water tank water quality monitoring method according to claim 7, characterized in that: After determining whether each set of training sample sets is successfully identified based on the identified abnormality type and the identified abnormality level corresponding to each set of training sample sets, the method further includes: Based on the recognition results of each group of training sample sets, the training results of the recognition training in the training of the water quality analysis and evaluation model are obtained.

9. The intelligent water tank water quality monitoring method according to claim 8, characterized in that: After judging whether the processing training in the training of the water quality analysis and evaluation model is successful based on the processing evaluation score of the temporary processing scheme corresponding to each group of training sample sets, the method further includes: Based on the training results of the processing training in the training of the water quality analysis and evaluation model and the training results of the recognition training in the training of the water quality analysis and evaluation model, the comprehensive training success rate, the recognition training success rate and the processing training success rate of the training of the water quality analysis and evaluation model are obtained; Based on the fact that the comprehensive training success rate, the recognition training success rate and the processing training success rate of the training of the water quality analysis and evaluation model are all higher than the preset respective success rate thresholds, it is determined that the training of the water quality analysis and evaluation model is successful; Otherwise, it is determined that the training of the water quality analysis and evaluation model has failed, and the water quality analysis and evaluation model needs to be retrained.

10. An intelligent water tank water quality monitoring system, characterized in that: The system includes a water quality analysis and evaluation model and a control device, the system also includes monitoring devices respectively installed in different water tanks and processing devices respectively connected to different water tanks, the water quality analysis and evaluation model stores a preset water tank history database and a preset abnormal parameter comparison table, the monitoring device is used to obtain monitoring data of the water tank, and the processing device is used to perform water quality treatment on the water tank according to a temporary treatment plan corresponding to the water tank; the control device includes a processor and a memory, the memory is suitable for storing a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute the intelligent water tank water quality monitoring method according to any one of claims 1 to 9; The control device also exchanges information with multiple user terminals to feed back the water quality analysis results and temporary processing results of the water tank to the user terminals.

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